system

Abstract

The system according to this embodiment aims to efficiently improve the UI / UX of an application. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a proposal unit, and an output unit. The reception unit receives the application's URL and source code. The analysis unit analyzes the application based on the URL and source code received by the reception unit. The proposal unit makes improvement suggestions based on the analysis results obtained by the analysis unit. The output unit outputs the improvement suggestions made by the proposal unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that a great deal of resources are required to improve the UI / UX of an application, and it is difficult to improve it efficiently.

[0005] The system according to the embodiment aims to efficiently improve the UI / UX of an application.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a proposal unit, and an output unit. The reception unit receives the application's URL and source code. The analysis unit analyzes the application based on the URL and source code received by the reception unit. The proposal unit makes improvement suggestions based on the analysis results obtained by the analysis unit. The output unit outputs the improvement suggestions made by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently improve the UI / UX of an application. [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 manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

[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 UI / UX improvement system according to an embodiment of the present invention is a system that uses a generative AI to improve the UI / UX of internal and external applications. This UI / UX improvement system is a mechanism in which the application's URL and source code are passed as input to the generative AI, which analyzes the application from a UI / UX perspective and outputs improvement suggestions. For example, if there are many screen transitions, suggestions may include placing buttons on related screens to create links, suggesting color codes to unify the color scheme, and suggesting improvements to the source code of functions with slow response speeds. This mechanism reduces the learning cost for developers regarding UI / UX and the man-hours required for user research, consideration, and verification. Furthermore, it is expected to improve the work efficiency of users and reduce the number of inquiries from users, allowing both users and developers to concentrate more on core tasks and leading to increased productivity. In addition, increased productivity will reduce the man-hours of outsourced and temporary staff, leading to cost reductions. In this way, the UI / UX improvement system can efficiently improve the UI / UX of applications.

[0029] The UI / UX improvement system according to the embodiment comprises a reception unit, an analysis unit, a proposal unit, and an output unit. The reception unit receives the application's URL and source code. For example, the reception unit can receive the URL of a web application and the source code of a mobile application. The reception unit may include AI processing. The analysis unit analyzes the application based on the URL and source code received by the reception unit. For example, the analysis unit can perform static analysis, dynamic analysis, and performance analysis of the code. The analysis unit includes generation AI processing. The proposal unit makes improvement suggestions based on the analysis results obtained by the analysis unit. For example, the proposal unit can make suggestions such as placing buttons on related screens to link them when there are many screen transitions, suggesting color codes to unify the color scheme, and suggesting improvements to the source code of functions with slow response speeds. The proposal unit includes generation AI processing. The output unit outputs the improvement suggestions made by the proposal unit. For example, the output unit can provide the improvement suggestions to the user in the form of a report, dashboard display, notification, etc. The output unit may include AI processing. As a result, the UI / UX improvement system according to this embodiment can efficiently improve the UI / UX of an application.

[0030] The reception desk accepts application URLs and source code. For example, the reception desk can accept URLs for web applications and source code for mobile applications. Specifically, the reception desk receives this information when a user enters a URL through a web interface or uploads source code. The reception desk may also include AI processing. For example, the reception desk automatically verifies the format and content of the received URLs and source code, and preprocesses them to enable appropriate analysis. The AI ​​checks whether the entered URL is valid and whether the source code is in the correct format, and notifies the user if there are any errors. The reception desk can also save the user's input history and provide a function to reuse URLs and source code that have been received in the past. This saves the user the trouble of re-entering information they have already entered. Furthermore, the reception desk can accept multiple applications and projects simultaneously, allowing users to request UI / UX improvements for multiple applications at once. This allows the reception desk to improve user convenience and support an efficient UI / UX improvement process.

[0031] The analysis department analyzes applications based on URLs and source code received by the reception department. For example, the analysis department can perform static analysis, dynamic analysis, and performance analysis of the code. Static analysis detects the structure of the source code, syntax errors, and security risks. Dynamic analysis actually runs the application and evaluates the user interface behavior and performance. Performance analysis measures the application's response time and resource usage to identify bottlenecks. The analysis department also includes generative AI processing. Generative AI analyzes source code and application operation logs and automatically extracts potential problems and areas for improvement. For example, generative AI evaluates the usability and visual consistency of the user interface and generates specific suggestions for improvement. Furthermore, generative AI can provide guidelines for optimal UI / UX design based on past analysis results and best practices. In addition, the analysis department combines multiple analysis methods to perform a comprehensive evaluation and provide data to improve the overall quality of the application. This allows the analysis department to accurately grasp the UI / UX challenges faced by users and provide a foundation for proposing effective improvement measures.

[0032] The proposal department makes improvement suggestions based on the analysis results obtained by the analysis department. For example, the proposal department can suggest placing buttons to link related screens when there are many screen transitions, suggest color codes to unify the color scheme, and suggest improvements to the source code of functions with slow response speeds. The proposal department also includes processing by a generative AI. Based on the analysis results, the generative AI generates specific suggestions regarding user interface optimization and improvement of the user experience. For example, the generative AI analyzes user behavior patterns and suggests prioritizing improvements to the most frequently used functions. The generative AI also provides suggestions for designing visually appealing and user-friendly interfaces based on color theory and design best practices. Furthermore, the proposal department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it can evaluate the results after users implement the suggestions and reflect them in future suggestions to provide more effective improvements. The proposal department can also present multiple improvement options and support users in making the best choice. In this way, the proposal department can provide flexible and effective UI / UX improvement suggestions that meet user needs and support the improvement of application quality.

[0033] The output unit outputs improvement suggestions made by the suggestion unit. For example, the output unit can provide improvement suggestions to the user in the form of reports, dashboard displays, notifications, etc. Specifically, the output unit provides an interface to display the suggested content in a visually easy-to-understand manner, so that the user can easily understand and implement the suggestions. The output unit may also include AI processing. For example, the output unit can automatically select the optimal output format based on the user's past behavior and feedback. The output unit can also support the user in efficiently carrying out improvement work by setting priorities for the suggested content and highlighting important suggestions. Furthermore, the output unit can provide a function to track the implementation status of improvement suggestions and update the progress in real time. This allows the user to grasp the progress of the improvement work at a glance and make adjustments as needed. The output unit can also evaluate the effectiveness of improvement suggestions and provide feedback to the user. For example, it can measure the improvement in application performance and user experience after implementing the improvement suggestions and output the results as a report. This allows the output unit to provide the user with information to confirm the effectiveness of the improvement suggestions and plan the next steps.

[0034] The analysis unit can analyze applications from a UI / UX perspective. For example, the analysis unit can perform usability testing, heuristic evaluation, and collect user feedback. This enables detailed analysis from a UI / UX perspective. Some or all of the above processes in the analysis unit may be performed using generative AI, or they may not. For example, the analysis unit can input UI / UX data of the application into a generative AI, which can then perform analysis based on that data.

[0035] The suggestion unit can propose placing buttons on related screens to link them when there are many screen transitions. For example, the suggestion unit can propose placing shortcut buttons on related screens to simplify operations that users frequently perform. The suggestion unit can also analyze the user's operation flow and propose the optimal screen transitions. This improves the efficiency of screen transitions. Some or all of the above processing in the suggestion unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the suggestion unit can input application screen transition data into a generation AI, and the generation AI can propose the optimal screen transitions based on that data.

[0036] The proposal unit can propose color codes to unify color tones. For example, the proposal unit can analyze the application's color palette and propose color codes based on brand guidelines. Furthermore, the proposal unit can propose color codes that consider contrast ratios to improve user visibility. This improves UI consistency. Some or all of the above processing in the proposal unit may be performed using a generative AI, or not. For example, the proposal unit can input the application's color code data into a generative AI, which can then propose the optimal color codes based on that data.

[0037] The proposal unit can propose improvements to the source code of functions with slow response speeds. For example, the proposal unit can identify performance bottlenecks in the application and propose code optimization, algorithm improvements, and refactoring. It can also make specific suggestions for performance improvement, such as optimizing database queries and utilizing caching. This improves the application's performance. Some or all of the above processing in the proposal unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the proposal unit can input the application's source code data into a generative AI, which can then use that data to make optimal improvement suggestions.

[0038] The output unit can provide improvement suggestions to the user. For example, the output unit can generate improvement suggestions in report format and send them to the user via email. The output unit can also provide improvement suggestions in real time through a dashboard display. Furthermore, the output unit can immediately notify the user of improvement suggestions using a notification function, thereby allowing the user to receive the suggestions. Some or all of the above-described processes in the output unit may be performed using AI or not. For example, the output unit can generate a report based on improvement suggestions generated by a generating AI and provide that report to the user.

[0039] The reception department can analyze past reception history and select the optimal reception method. For example, the reception department can prioritize suggesting reception methods that the user has frequently used in the past. It can also suggest the optimal reception method for a specific time of day based on past reception history. Furthermore, the reception department can select a reception method tailored to the user's preferences based on past reception history. This allows for the selection of the optimal reception method based on past history. Some or all of the above processing in the reception department may be performed using AI, or not. For example, the reception department can input past reception history data into a generating AI, which can then suggest the optimal reception method based on that data.

[0040] The reception unit can filter the user's current projects and areas of interest upon receipt. For example, the reception unit can prioritize receiving URLs and source code related to the user's current projects. It can also filter highly relevant URLs and source code based on the user's areas of interest. Furthermore, the reception unit can select appropriate URLs and source code according to the user's project progress. This allows the reception unit to prioritize receiving information related to the user's projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's project data into a generating AI, which can then perform optimal filtering based on that data.

[0041] The reception unit can prioritize receiving URLs and source code that are highly relevant to the user, taking into account the user's geographical location. For example, if the user is in a specific region, the reception unit can prioritize receiving URLs and source code related to that region. The reception unit can also suggest the most relevant URLs and source code based on the user's current location. Furthermore, the reception unit can filter highly relevant information based on the user's geographical location. This allows the reception unit to receive highly relevant information based on the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input the user's geographical location data into a generating AI, which can then suggest the most relevant URLs and source code based on that data.

[0042] The reception unit can analyze the user's social media activity and accept relevant URLs and source code upon receiving a request. For example, the reception unit can accept relevant URLs and source code based on information shared by the user on social media. It can also suggest URLs and source code related to topics of interest based on the user's social media activity. Furthermore, the reception unit can accept relevant URLs and source code based on information shared by the user's social media followers and friends. This allows the reception unit to receive relevant information based on the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's social media data into a generating AI, which can then suggest the most suitable URLs and source code based on that data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the application. For example, the analysis unit can perform a detailed analysis for high-importance applications and a concise analysis for low-importance applications. Furthermore, the analysis unit can adjust the depth and scope of the analysis according to the importance of the application. This allows for analysis to be performed with an appropriate level of detail according to the importance of the application. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input application importance data into a generative AI, and the generative AI can adjust the level of detail of the analysis based on that data.

[0044] The analysis unit can apply different analysis algorithms depending on the application category during analysis. For example, the analysis unit can apply an analysis algorithm specifically focused on improving operational efficiency to business applications. It can also apply an analysis algorithm that emphasizes user engagement to entertainment applications. Furthermore, it can apply an analysis algorithm designed to maximize learning effectiveness to educational applications. This allows for the application of an appropriate analysis algorithm based on the application category. Some or all of the above-described processes in the analysis unit may be performed using generative AI, or they may be performed without generative AI. For example, the analysis unit can input application category data into a generative AI, which can then apply the most suitable analysis algorithm based on that data.

[0045] The analysis unit can determine the priority of the analysis based on the submission date of the applications. For example, the analysis unit can prioritize the analysis of recently submitted applications. It can also postpone the analysis of older applications. Furthermore, the analysis unit can dynamically adjust the analysis priority according to the submission date. This allows for analysis to be performed with appropriate priorities based on the application submission date. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input application submission date data into a generative AI, which can then determine the priority based on that data.

[0046] The analysis unit can adjust the order of analysis based on the relevance of applications during the analysis process. For example, the analysis unit can prioritize the analysis of applications related to the user's current project. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of applications. In addition, the analysis unit can prioritize the analysis of applications related to the user's area of ​​interest. This allows for analysis to be performed in an appropriate order according to the relevance of applications. Some or all of the above-described processes in the analysis unit may be performed using generative AI, or not. For example, the analysis unit can input application relevance data into a generative AI, which can then determine the optimal analysis order based on that data.

[0047] The proposal unit can adjust the level of detail of its proposals based on the importance of the application. For example, the proposal unit can provide detailed proposals for high-importance applications and concise proposals for low-importance applications. Furthermore, the proposal unit can adjust the depth and scope of its proposals according to the importance of the application. This allows for proposals to be made with an appropriate level of detail for each application. Some or all of the above processing in the proposal unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the proposal unit can input application importance data into a generative AI, which can then adjust the level of detail of its proposals based on that data.

[0048] The proposal unit can apply different proposal algorithms depending on the application category when making a proposal. For example, the proposal unit can apply a proposal algorithm specifically focused on improving operational efficiency to business applications. It can also apply a proposal algorithm that emphasizes user engagement to entertainment applications. Furthermore, it can apply a proposal algorithm designed to maximize learning effectiveness to educational applications. This allows for the application of an appropriate proposal algorithm according to the application category. Some or all of the above processing in the proposal unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the proposal unit can input application category data into a generative AI, which can then apply the optimal proposal algorithm based on that data.

[0049] The proposal department can determine the priority of proposals based on the submission date of the applications. For example, the proposal department can prioritize recently submitted applications. It can also postpone older applications. Furthermore, the proposal department can dynamically adjust the priority of proposals according to the submission date. This allows for proposals to be submitted with appropriate priority according to the application's submission date. Some or all of the above processing in the proposal department may be performed using a generative AI, or not. For example, the proposal department can input application submission date data into a generative AI, which can then determine the priority based on that data.

[0050] The suggestion unit can adjust the order of suggestions based on the relevance of the applications. For example, the suggestion unit can prioritize suggesting applications related to the user's current project. It can also dynamically adjust the order of suggestions based on the relevance of the applications. Furthermore, it can prioritize suggesting applications related to the user's areas of interest. This allows for suggestions to be made in an appropriate order according to the relevance of the applications. Some or all of the above processing in the suggestion unit may be performed using or without a generative AI. For example, the suggestion unit can input application relevance data into a generative AI, which can then determine the optimal suggestion order based on that data.

[0051] The output unit can select the optimal display method by referring to the user's past operation history when outputting. For example, the output unit can prioritize providing display methods that the user has preferred to use in the past. Furthermore, the output unit can select the most efficient display method from the user's past operation history. In addition, the output unit can analyze the user's operation history and propose a highly visible display method. This allows the output unit to provide the optimal display method based on the user's past operation history. Some or all of the above processing in the output unit may be performed using AI, or without AI. For example, the output unit can input user operation history data into a generating AI, which can then propose the optimal display method based on that data.

[0052] The output unit can customize the displayed content based on the user's current projects and areas of interest during output. For example, the output unit can prioritize displaying information related to the project the user is currently working on. It can also display highly relevant information based on the user's areas of interest. Furthermore, the output unit can display appropriate information according to the user's project progress. This allows for the priority display of information related to the user's projects and areas of interest. Some or all of the above processing in the output unit may be performed using AI, or not. For example, the output unit can input the user's project data into a generating AI, which can then propose the most suitable display content based on that data.

[0053] The output unit can select the optimal display method when outputting, taking into account the user's device information. For example, if the user is using a smartphone, the output unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the output unit can provide a display method optimized for a larger screen. Additionally, if the user is using a smartwatch, the output unit can provide a concise and highly visible display method. This allows the output unit to provide the optimal display method based on the user's device information. Some or all of the above processing in the output unit may be performed using AI, or without AI. For example, the output unit can input the user's device information into a generating AI, which can then propose the optimal display method based on that data.

[0054] The output unit can provide multilingual displays according to the user's language settings when outputting. For example, the output unit can automatically set the display language based on the language settings of the user's device. The output unit can also provide a language switching function if the user uses multiple languages. Furthermore, the output unit can provide displays in a specific language if the user selects that language. This allows for multilingual displays based on the user's language settings. Some or all of the above processing in the output unit may be performed using AI or not. For example, the output unit can input the user's language setting data into a generating AI, which can then suggest the optimal display language based on that data.

[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 past reception history and select the optimal reception method. For example, the reception department can prioritize suggesting reception methods that the user has frequently used in the past. It can also suggest the optimal reception method for a specific time of day based on past reception history. Furthermore, the reception department can select a reception method tailored to the user's preferences based on past reception history. This allows for the selection of the optimal reception method based on past history. Some or all of the above processing in the reception department may be performed using AI, or not. For example, the reception department can input past reception history data into a generating AI, which can then suggest the optimal reception method based on that data.

[0057] The reception unit can filter the user's current projects and areas of interest upon receipt. For example, the reception unit can prioritize receiving URLs and source code related to the user's current projects. It can also filter highly relevant URLs and source code based on the user's areas of interest. Furthermore, the reception unit can select appropriate URLs and source code according to the user's project progress. This allows the reception unit to prioritize receiving information related to the user's projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's project data into a generating AI, which can then perform optimal filtering based on that data.

[0058] The reception unit can prioritize receiving URLs and source code that are highly relevant to the user, taking into account the user's geographical location. For example, if the user is in a specific region, the reception unit can prioritize receiving URLs and source code related to that region. The reception unit can also suggest the most relevant URLs and source code based on the user's current location. Furthermore, the reception unit can filter highly relevant information based on the user's geographical location. This allows the reception unit to receive highly relevant information based on the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input the user's geographical location data into a generating AI, which can then suggest the most relevant URLs and source code based on that data.

[0059] The reception unit can analyze the user's social media activity and accept relevant URLs and source code upon receiving a request. For example, the reception unit can accept relevant URLs and source code based on information shared by the user on social media. It can also suggest URLs and source code related to topics of interest based on the user's social media activity. Furthermore, the reception unit can accept relevant URLs and source code based on information shared by the user's social media followers and friends. This allows the reception unit to receive relevant information based on the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's social media data into a generating AI, which can then suggest the most suitable URLs and source code based on that data.

[0060] The analysis unit can adjust the level of detail of the analysis based on the importance of the application. For example, the analysis unit can perform a detailed analysis for high-importance applications and a concise analysis for low-importance applications. Furthermore, the analysis unit can adjust the depth and scope of the analysis according to the importance of the application. This allows for analysis to be performed with an appropriate level of detail according to the importance of the application. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input application importance data into a generative AI, and the generative AI can adjust the level of detail of the analysis based on that data.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The reception unit accepts the application's URL or source code. For example, it can accept the URL of a web application or the source code of a mobile application. The reception unit may also include AI processing. Step 2: The analysis unit analyzes the application based on the URL and source code received by the reception unit. For example, it can perform static analysis, dynamic analysis, and performance analysis of the code. The analysis unit also handles the processing of generated AI. Step 3: The proposal department makes improvement suggestions based on the analysis results obtained by the analysis department. For example, it may suggest placing buttons on related screens to link them when there are many screen transitions, suggest color codes to unify the color scheme, or suggest improvements to the source code of functions with slow response speeds. The proposal department includes processing by the generation AI. Step 4: The output unit outputs the improvement suggestions made by the suggestion unit. For example, the improvement suggestions can be provided to the user in the form of a report, dashboard display, or notification. The output unit may also include AI processing.

[0063] (Example of form 2) The UI / UX improvement system according to an embodiment of the present invention is a system that uses a generative AI to improve the UI / UX of internal and external applications. This UI / UX improvement system is a mechanism in which the application's URL and source code are passed as input to the generative AI, which analyzes the application from a UI / UX perspective and outputs improvement suggestions. For example, if there are many screen transitions, suggestions may include placing buttons on related screens to create links, suggesting color codes to unify the color scheme, and suggesting improvements to the source code of functions with slow response speeds. This mechanism reduces the learning cost for developers regarding UI / UX and the man-hours required for user research, consideration, and verification. Furthermore, it is expected to improve the work efficiency of users and reduce the number of inquiries from users, allowing both users and developers to concentrate more on core tasks and leading to increased productivity. In addition, increased productivity will reduce the man-hours of outsourced and temporary staff, leading to cost reductions. In this way, the UI / UX improvement system can efficiently improve the UI / UX of applications.

[0064] The UI / UX improvement system according to the embodiment comprises a reception unit, an analysis unit, a proposal unit, and an output unit. The reception unit receives the application's URL and source code. For example, the reception unit can receive the URL of a web application and the source code of a mobile application. The reception unit may include AI processing. The analysis unit analyzes the application based on the URL and source code received by the reception unit. For example, the analysis unit can perform static analysis, dynamic analysis, and performance analysis of the code. The analysis unit includes generation AI processing. The proposal unit makes improvement suggestions based on the analysis results obtained by the analysis unit. For example, the proposal unit can make suggestions such as placing buttons on related screens to link them when there are many screen transitions, suggesting color codes to unify the color scheme, and suggesting improvements to the source code of functions with slow response speeds. The proposal unit includes generation AI processing. The output unit outputs the improvement suggestions made by the proposal unit. For example, the output unit can provide the improvement suggestions to the user in the form of a report, dashboard display, notification, etc. The output unit may include AI processing. As a result, the UI / UX improvement system according to this embodiment can efficiently improve the UI / UX of an application.

[0065] The reception desk accepts application URLs and source code. For example, the reception desk can accept URLs for web applications and source code for mobile applications. Specifically, the reception desk receives this information when a user enters a URL through a web interface or uploads source code. The reception desk may also include AI processing. For example, the reception desk automatically verifies the format and content of the received URLs and source code, and preprocesses them to enable appropriate analysis. The AI ​​checks whether the entered URL is valid and whether the source code is in the correct format, and notifies the user if there are any errors. The reception desk can also save the user's input history and provide a function to reuse URLs and source code that have been received in the past. This saves the user the trouble of re-entering information they have already entered. Furthermore, the reception desk can accept multiple applications and projects simultaneously, allowing users to request UI / UX improvements for multiple applications at once. This allows the reception desk to improve user convenience and support an efficient UI / UX improvement process.

[0066] The analysis department analyzes applications based on URLs and source code received by the reception department. For example, the analysis department can perform static analysis, dynamic analysis, and performance analysis of the code. Static analysis detects the structure of the source code, syntax errors, and security risks. Dynamic analysis actually runs the application and evaluates the user interface behavior and performance. Performance analysis measures the application's response time and resource usage to identify bottlenecks. The analysis department also includes generative AI processing. Generative AI analyzes source code and application operation logs and automatically extracts potential problems and areas for improvement. For example, generative AI evaluates the usability and visual consistency of the user interface and generates specific suggestions for improvement. Furthermore, generative AI can provide guidelines for optimal UI / UX design based on past analysis results and best practices. In addition, the analysis department combines multiple analysis methods to perform a comprehensive evaluation and provide data to improve the overall quality of the application. This allows the analysis department to accurately grasp the UI / UX challenges faced by users and provide a foundation for proposing effective improvement measures.

[0067] The proposal department makes improvement suggestions based on the analysis results obtained by the analysis department. For example, the proposal department can suggest placing buttons to link related screens when there are many screen transitions, suggest color codes to unify the color scheme, and suggest improvements to the source code of functions with slow response speeds. The proposal department also includes processing by a generative AI. Based on the analysis results, the generative AI generates specific suggestions regarding user interface optimization and improvement of the user experience. For example, the generative AI analyzes user behavior patterns and suggests prioritizing improvements to the most frequently used functions. The generative AI also provides suggestions for designing visually appealing and user-friendly interfaces based on color theory and design best practices. Furthermore, the proposal department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it can evaluate the results after users implement the suggestions and reflect them in future suggestions to provide more effective improvements. The proposal department can also present multiple improvement options and support users in making the best choice. In this way, the proposal department can provide flexible and effective UI / UX improvement suggestions that meet user needs and support the improvement of application quality.

[0068] The output unit outputs improvement suggestions made by the suggestion unit. For example, the output unit can provide improvement suggestions to the user in the form of reports, dashboard displays, notifications, etc. Specifically, the output unit provides an interface to display the suggested content in a visually easy-to-understand manner, so that the user can easily understand and implement the suggestions. The output unit may also include AI processing. For example, the output unit can automatically select the optimal output format based on the user's past behavior and feedback. The output unit can also support the user in efficiently carrying out improvement work by setting priorities for the suggested content and highlighting important suggestions. Furthermore, the output unit can provide a function to track the implementation status of improvement suggestions and update the progress in real time. This allows the user to grasp the progress of the improvement work at a glance and make adjustments as needed. The output unit can also evaluate the effectiveness of improvement suggestions and provide feedback to the user. For example, it can measure the improvement in application performance and user experience after implementing the improvement suggestions and output the results as a report. This allows the output unit to provide the user with information to confirm the effectiveness of the improvement suggestions and plan the next steps.

[0069] The analysis unit can analyze applications from a UI / UX perspective. For example, the analysis unit can perform usability testing, heuristic evaluation, and collect user feedback. This enables detailed analysis from a UI / UX perspective. Some or all of the above processes in the analysis unit may be performed using generative AI, or they may not. For example, the analysis unit can input UI / UX data of the application into a generative AI, which can then perform analysis based on that data.

[0070] The suggestion unit can propose placing buttons on related screens to link them when there are many screen transitions. For example, the suggestion unit can propose placing shortcut buttons on related screens to simplify operations that users frequently perform. The suggestion unit can also analyze the user's operation flow and propose the optimal screen transitions. This improves the efficiency of screen transitions. Some or all of the above processing in the suggestion unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the suggestion unit can input application screen transition data into a generation AI, and the generation AI can propose the optimal screen transitions based on that data.

[0071] The proposal unit can propose color codes to unify color tones. For example, the proposal unit can analyze the application's color palette and propose color codes based on brand guidelines. Furthermore, the proposal unit can propose color codes that consider contrast ratios to improve user visibility. This improves UI consistency. Some or all of the above processing in the proposal unit may be performed using a generative AI, or not. For example, the proposal unit can input the application's color code data into a generative AI, which can then propose the optimal color codes based on that data.

[0072] The proposal unit can propose improvements to the source code of functions with slow response speeds. For example, the proposal unit can identify performance bottlenecks in the application and propose code optimization, algorithm improvements, and refactoring. It can also make specific suggestions for performance improvement, such as optimizing database queries and utilizing caching. This improves the application's performance. Some or all of the above processing in the proposal unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the proposal unit can input the application's source code data into a generative AI, which can then use that data to make optimal improvement suggestions.

[0073] The output unit can provide improvement suggestions to the user. For example, the output unit can generate improvement suggestions in report format and send them to the user via email. The output unit can also provide improvement suggestions in real time through a dashboard display. Furthermore, the output unit can immediately notify the user of improvement suggestions using a notification function, thereby allowing the user to receive the suggestions. Some or all of the above-described processes in the output unit may be performed using AI or not. For example, the output unit can generate a report based on improvement suggestions generated by a generating AI and provide that report to the user.

[0074] The reception desk can estimate the user's emotions and adjust the timing of URL and source code submission based on the estimated emotions. For example, if the reception desk is stressed, it can delay submission and wait until the user is relaxed. If the user is relaxed, the reception desk can immediately accept the URL or source code. Furthermore, if the user is in a hurry, the reception desk can prioritize accepting the URL or source code. This allows for the acceptance of URLs and source code at an appropriate time according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user facial expression data into a generative AI, which can then estimate emotions based on that data.

[0075] The reception department can analyze past reception history and select the optimal reception method. For example, the reception department can prioritize suggesting reception methods that the user has frequently used in the past. It can also suggest the optimal reception method for a specific time of day based on past reception history. Furthermore, the reception department can select a reception method tailored to the user's preferences based on past reception history. This allows for the selection of the optimal reception method based on past history. Some or all of the above processing in the reception department may be performed using AI, or not. For example, the reception department can input past reception history data into a generating AI, which can then suggest the optimal reception method based on that data.

[0076] The reception unit can filter the user's current projects and areas of interest upon receipt. For example, the reception unit can prioritize receiving URLs and source code related to the user's current projects. It can also filter highly relevant URLs and source code based on the user's areas of interest. Furthermore, the reception unit can select appropriate URLs and source code according to the user's project progress. This allows the reception unit to prioritize receiving information related to the user's projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's project data into a generating AI, which can then perform optimal filtering based on that data.

[0077] The reception desk can estimate the user's emotions and determine the priority of URLs and source code to be received based on the estimated emotions. For example, if the reception desk is stressed, it can postpone less important URLs and source code. If the user is relaxed, it can prioritize receiving more important URLs and source code. Furthermore, if the user is in a hurry, it can prioritize receiving urgent URLs and source code. This allows for the prioritization of URLs and source code according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI, which can then determine priorities based on that data.

[0078] The reception unit can prioritize receiving URLs and source code that are highly relevant to the user, taking into account the user's geographical location. For example, if the user is in a specific region, the reception unit can prioritize receiving URLs and source code related to that region. The reception unit can also suggest the most relevant URLs and source code based on the user's current location. Furthermore, the reception unit can filter highly relevant information based on the user's geographical location. This allows the reception unit to receive highly relevant information based on the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input the user's geographical location data into a generating AI, which can then suggest the most relevant URLs and source code based on that data.

[0079] The reception unit can analyze the user's social media activity and accept relevant URLs and source code upon receiving a request. For example, the reception unit can accept relevant URLs and source code based on information shared by the user on social media. It can also suggest URLs and source code related to topics of interest based on the user's social media activity. Furthermore, the reception unit can accept relevant URLs and source code based on information shared by the user's social media followers and friends. This allows the reception unit to receive relevant information based on the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's social media data into a generating AI, which can then suggest the most suitable URLs and source code based on that data.

[0080] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. Furthermore, if the user is excited, the analysis unit can provide analysis results using visually stimulating graphics. This allows the analysis results to be presented in an appropriate way according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using or without a generative AI. For example, the analysis unit can input user emotion data into a generative AI, and the generative AI can adjust its presentation based on that data.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the application. For example, the analysis unit can perform a detailed analysis for high-importance applications and a concise analysis for low-importance applications. Furthermore, the analysis unit can adjust the depth and scope of the analysis according to the importance of the application. This allows for analysis to be performed with an appropriate level of detail according to the importance of the application. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input application importance data into a generative AI, and the generative AI can adjust the level of detail of the analysis based on that data.

[0082] The analysis unit can apply different analysis algorithms depending on the application category during analysis. For example, the analysis unit can apply an analysis algorithm specifically focused on improving operational efficiency to business applications. It can also apply an analysis algorithm that emphasizes user engagement to entertainment applications. Furthermore, it can apply an analysis algorithm designed to maximize learning effectiveness to educational applications. This allows for the application of an appropriate analysis algorithm based on the application category. Some or all of the above-described processes in the analysis unit may be performed using generative AI, or they may be performed without generative AI. For example, the analysis unit can input application category data into a generative AI, which can then apply the most suitable analysis algorithm based on that data.

[0083] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can provide a longer analysis with detailed explanations. Furthermore, if the user is excited, the analysis unit can provide an analysis with visually stimulating effects. This allows for the provision of analysis results of an appropriate length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using or without a generative AI. For example, the analysis unit can input user emotion data into a generative AI, which can then adjust the length of the analysis based on that data.

[0084] The analysis unit can determine the priority of the analysis based on the submission date of the applications. For example, the analysis unit can prioritize the analysis of recently submitted applications. It can also postpone the analysis of older applications. Furthermore, the analysis unit can dynamically adjust the analysis priority according to the submission date. This allows for analysis to be performed with appropriate priorities based on the application submission date. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input application submission date data into a generative AI, which can then determine the priority based on that data.

[0085] The analysis unit can adjust the order of analysis based on the relevance of applications during the analysis process. For example, the analysis unit can prioritize the analysis of applications related to the user's current project. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of applications. In addition, the analysis unit can prioritize the analysis of applications related to the user's area of ​​interest. This allows for analysis to be performed in an appropriate order according to the relevance of applications. Some or all of the above-described processes in the analysis unit may be performed using generative AI, or not. For example, the analysis unit can input application relevance data into a generative AI, which can then determine the optimal analysis order based on that data.

[0086] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, it can provide concise suggestions that get straight to the point. Furthermore, if the user is excited, it can provide suggestions using visually stimulating graphics. This allows the suggestion unit to provide suggestions in an appropriate way that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using or without a generative AI. For example, the suggestion unit can input user emotion data into a generative AI, which can then adjust its presentation based on that data.

[0087] The proposal unit can adjust the level of detail of its proposals based on the importance of the application. For example, the proposal unit can provide detailed proposals for high-importance applications and concise proposals for low-importance applications. Furthermore, the proposal unit can adjust the depth and scope of its proposals according to the importance of the application. This allows for proposals to be made with an appropriate level of detail for each application. Some or all of the above processing in the proposal unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the proposal unit can input application importance data into a generative AI, which can then adjust the level of detail of its proposals based on that data.

[0088] The proposal unit can apply different proposal algorithms depending on the application category when making a proposal. For example, the proposal unit can apply a proposal algorithm specifically focused on improving operational efficiency to business applications. It can also apply a proposal algorithm that emphasizes user engagement to entertainment applications. Furthermore, it can apply a proposal algorithm designed to maximize learning effectiveness to educational applications. This allows for the application of an appropriate proposal algorithm according to the application category. Some or all of the above processing in the proposal unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the proposal unit can input application category data into a generative AI, which can then apply the optimal proposal algorithm based on that data.

[0089] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can provide short, concise suggestions. If the user is relaxed, it can provide longer suggestions with detailed explanations. Furthermore, if the user is excited, it can provide suggestions with visually stimulating effects. This allows for the provision of suggestions of appropriate length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using or without a generative AI. For example, the suggestion unit can input user emotion data into a generative AI, which can then adjust the length of the suggestions based on that data.

[0090] The proposal department can determine the priority of proposals based on the submission date of the applications. For example, the proposal department can prioritize recently submitted applications. It can also postpone older applications. Furthermore, the proposal department can dynamically adjust the priority of proposals according to the submission date. This allows for proposals to be submitted with appropriate priority according to the application's submission date. Some or all of the above processing in the proposal department may be performed using a generative AI, or not. For example, the proposal department can input application submission date data into a generative AI, which can then determine the priority based on that data.

[0091] The suggestion unit can adjust the order of suggestions based on the relevance of the applications. For example, the suggestion unit can prioritize suggesting applications related to the user's current project. It can also dynamically adjust the order of suggestions based on the relevance of the applications. Furthermore, it can prioritize suggesting applications related to the user's areas of interest. This allows for suggestions to be made in an appropriate order according to the relevance of the applications. Some or all of the above processing in the suggestion unit may be performed using or without a generative AI. For example, the suggestion unit can input application relevance data into a generative AI, which can then determine the optimal suggestion order based on that data.

[0092] The output unit can estimate the user's emotions and adjust the display method of the output based on the estimated user emotions. For example, if the user is tense, the output unit can provide a simple and highly visible display method. If the user is relaxed, the output unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the output unit can provide a concise display method. This allows the output to be displayed in an appropriate way according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the output unit may be performed using AI or not. For example, the output unit can input user emotion data into the generative AI, and the generative AI can adjust the display method based on that data.

[0093] The output unit can select the optimal display method by referring to the user's past operation history when outputting. For example, the output unit can prioritize providing display methods that the user has preferred to use in the past. Furthermore, the output unit can select the most efficient display method from the user's past operation history. In addition, the output unit can analyze the user's operation history and propose a highly visible display method. This allows the output unit to provide the optimal display method based on the user's past operation history. Some or all of the above processing in the output unit may be performed using AI, or without AI. For example, the output unit can input user operation history data into a generating AI, which can then propose the optimal display method based on that data.

[0094] The output unit can customize the displayed content based on the user's current projects and areas of interest during output. For example, the output unit can prioritize displaying information related to the project the user is currently working on. It can also display highly relevant information based on the user's areas of interest. Furthermore, the output unit can display appropriate information according to the user's project progress. This allows for the priority display of information related to the user's projects and areas of interest. Some or all of the above processing in the output unit may be performed using AI, or not. For example, the output unit can input the user's project data into a generating AI, which can then propose the most suitable display content based on that data.

[0095] The output unit can estimate the user's emotions and adjust the output operation procedure based on the estimated user emotions. For example, if the user is tense, the output unit can provide a simple and intuitive operation procedure. If the user is relaxed, the output unit can provide a detailed operation procedure. Furthermore, if the user is in a hurry, the output unit can provide a procedure that allows for quick operation. This allows the output to be provided with an appropriate operation procedure according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the output unit may be performed using AI or not. For example, the output unit can input user emotion data into a generative AI, and the generative AI can adjust the operation procedure based on that data.

[0096] The output unit can select the optimal display method when outputting, taking into account the user's device information. For example, if the user is using a smartphone, the output unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the output unit can provide a display method optimized for a larger screen. Additionally, if the user is using a smartwatch, the output unit can provide a concise and highly visible display method. This allows the output unit to provide the optimal display method based on the user's device information. Some or all of the above processing in the output unit may be performed using AI, or without AI. For example, the output unit can input the user's device information into a generating AI, which can then propose the optimal display method based on that data.

[0097] The output unit can provide multilingual displays according to the user's language settings when outputting. For example, the output unit can automatically set the display language based on the language settings of the user's device. The output unit can also provide a language switching function if the user uses multiple languages. Furthermore, the output unit can provide displays in a specific language if the user selects that language. This allows for multilingual displays based on the user's language settings. Some or all of the above processing in the output unit may be performed using AI or not. For example, the output unit can input the user's language setting data into a generating AI, which can then suggest the optimal display language based on that data.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The reception desk can estimate the user's emotions and adjust the timing of URL and source code submission based on the estimated emotions. For example, if the reception desk is stressed, it can delay submission and wait until the user is relaxed. If the user is relaxed, the reception desk can immediately accept the URL or source code. Furthermore, if the user is in a hurry, the reception desk can prioritize accepting the URL or source code. This allows for the acceptance of URLs and source code at an appropriate time according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user facial expression data into a generative AI, which can then estimate emotions based on that data.

[0100] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. Furthermore, if the user is excited, the analysis unit can provide analysis results using visually stimulating graphics. This allows the analysis results to be presented in an appropriate way according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using or without a generative AI. For example, the analysis unit can input user emotion data into a generative AI, and the generative AI can adjust its presentation based on that data.

[0101] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, it can provide concise suggestions that get straight to the point. Furthermore, if the user is excited, it can provide suggestions using visually stimulating graphics. This allows the suggestion unit to provide suggestions in an appropriate way that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using or without a generative AI. For example, the suggestion unit can input user emotion data into a generative AI, which can then adjust its presentation based on that data.

[0102] The output unit can estimate the user's emotions and adjust the display method of the output based on the estimated user emotions. For example, if the user is tense, the output unit can provide a simple and highly visible display method. If the user is relaxed, the output unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the output unit can provide a concise display method. This allows the output to be displayed in an appropriate way according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the output unit may be performed using AI or not. For example, the output unit can input user emotion data into the generative AI, and the generative AI can adjust the display method based on that data.

[0103] The output unit can estimate the user's emotions and adjust the output operation procedure based on the estimated user emotions. For example, if the user is tense, the output unit can provide a simple and intuitive operation procedure. If the user is relaxed, the output unit can provide a detailed operation procedure. Furthermore, if the user is in a hurry, the output unit can provide a procedure that allows for quick operation. This allows the output to be provided with an appropriate operation procedure according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the output unit may be performed using AI or not. For example, the output unit can input user emotion data into a generative AI, and the generative AI can adjust the operation procedure based on that data.

[0104] The reception department can analyze past reception history and select the optimal reception method. For example, the reception department can prioritize suggesting reception methods that the user has frequently used in the past. It can also suggest the optimal reception method for a specific time of day based on past reception history. Furthermore, the reception department can select a reception method tailored to the user's preferences based on past reception history. This allows for the selection of the optimal reception method based on past history. Some or all of the above processing in the reception department may be performed using AI, or not. For example, the reception department can input past reception history data into a generating AI, which can then suggest the optimal reception method based on that data.

[0105] The reception unit can filter the user's current projects and areas of interest upon receipt. For example, the reception unit can prioritize receiving URLs and source code related to the user's current projects. It can also filter highly relevant URLs and source code based on the user's areas of interest. Furthermore, the reception unit can select appropriate URLs and source code according to the user's project progress. This allows the reception unit to prioritize receiving information related to the user's projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's project data into a generating AI, which can then perform optimal filtering based on that data.

[0106] The reception unit can prioritize receiving URLs and source code that are highly relevant to the user, taking into account the user's geographical location. For example, if the user is in a specific region, the reception unit can prioritize receiving URLs and source code related to that region. The reception unit can also suggest the most relevant URLs and source code based on the user's current location. Furthermore, the reception unit can filter highly relevant information based on the user's geographical location. This allows the reception unit to receive highly relevant information based on the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input the user's geographical location data into a generating AI, which can then suggest the most relevant URLs and source code based on that data.

[0107] The reception unit can analyze the user's social media activity and accept relevant URLs and source code upon receiving a request. For example, the reception unit can accept relevant URLs and source code based on information shared by the user on social media. It can also suggest URLs and source code related to topics of interest based on the user's social media activity. Furthermore, the reception unit can accept relevant URLs and source code based on information shared by the user's social media followers and friends. This allows the reception unit to receive relevant information based on the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's social media data into a generating AI, which can then suggest the most suitable URLs and source code based on that data.

[0108] The analysis unit can adjust the level of detail of the analysis based on the importance of the application. For example, the analysis unit can perform a detailed analysis for high-importance applications and a concise analysis for low-importance applications. Furthermore, the analysis unit can adjust the depth and scope of the analysis according to the importance of the application. This allows for analysis to be performed with an appropriate level of detail according to the importance of the application. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input application importance data into a generative AI, and the generative AI can adjust the level of detail of the analysis based on that data.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The reception unit accepts the application's URL or source code. For example, it can accept the URL of a web application or the source code of a mobile application. The reception unit may also include AI processing. Step 2: The analysis unit analyzes the application based on the URL and source code received by the reception unit. For example, it can perform static analysis, dynamic analysis, and performance analysis of the code. The analysis unit also handles the processing of generated AI. Step 3: The proposal department makes improvement suggestions based on the analysis results obtained by the analysis department. For example, it may suggest placing buttons on related screens to link them when there are many screen transitions, suggest color codes to unify the color scheme, or suggest improvements to the source code of functions with slow response speeds. The proposal department includes processing by the generation AI. Step 4: The output unit outputs the improvement suggestions made by the suggestion unit. For example, the improvement suggestions can be provided to the user in the form of a report, dashboard display, or notification. The output unit may also include AI processing.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and output 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 application's URL and source code. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the application based on the received URL and source code. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes improvement suggestions based on the analysis results. The output unit is implemented by the control unit 46A of the smart device 14 and provides the improvement suggestions to the user. 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.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and output 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 application's URL and source code. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the application based on the received URL and source code. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes improvement suggestions based on the analysis results. The output unit is implemented by the control unit 46A of the smart glasses 214 and provides the improvement suggestions to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and output 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 application's URL and source code. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the application based on the received URL and source code. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes improvement suggestions based on the analysis results. The output unit is implemented by the control unit 46A of the headset terminal 314 and provides the improvement suggestions to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and output 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 application's URL and source code. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the application based on the received URL and source code. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes improvement suggestions based on the analysis results. The output unit is implemented by the control unit 46A of the robot 414 and provides the improvement suggestions to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Note 1) A reception desk that accepts the application's URL and source code, An analysis unit analyzes the application based on the URL and source code received by the reception unit, A proposal unit that makes improvement suggestions based on the analysis results obtained by the aforementioned analysis unit, The system includes an output unit that outputs improvement suggestions made by the proposal unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit is Analyzing an application from a UI / UX perspective The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, When there are many screen transitions, we propose placing buttons on related screens to create links. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We propose color codes to unify the color scheme. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We will propose improvements to the source code for features with slow response times. The system described in Appendix 1, characterized by the features described herein. (Note 6) The output unit is, Providing improvement suggestions to users The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of URL and source code submissions based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze past reception history and select the most suitable reception method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is During registration, filtering is performed 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 URLs and source code to accept 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 During the submission process, the system prioritizes accepting URLs and source code that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is Upon registration, the system analyzes the user's social media activity and accepts relevant URLs and source code. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the application. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, different analysis algorithms are applied depending on the application category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During the analysis, prioritize the analysis based on the application submission date. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of the applications. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, adjust the level of detail in the proposal based on the importance of the application. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the application category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When submitting a proposal, prioritize the proposals based on the submission date. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the applications. The system described in Appendix 1, characterized by the features described herein. (Note 25) The output unit is, It estimates the user's emotions and adjusts how the output is displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The output unit is, When outputting data, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The output unit is, When outputting, the displayed content is customized based on the user's current project and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 28) The output unit is, It estimates the user's emotions and adjusts the output operation procedure based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The output unit is, When outputting, the system selects the optimal display method considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The output unit is, When outputting, the display will support multiple languages ​​according to the user's language settings. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that accepts the application's URL and source code, An analysis unit analyzes the application based on the URL and source code received by the reception unit, A proposal unit that makes improvement suggestions based on the analysis results obtained by the aforementioned analysis unit, The system includes an output unit that outputs improvement suggestions made by the proposal unit. A system characterized by the following features.

2. The aforementioned analysis unit is Analyzing an application from a UI / UX perspective The system according to feature 1.

3. The aforementioned proposal section is, When there are many screen transitions, we propose placing buttons on related screens to create links. The system according to feature 1.

4. The aforementioned proposal section is, We propose color codes to unify the color scheme. The system according to feature 1.

5. The aforementioned proposal section is, We will propose improvements to the source code for features with slow response times. The system according to feature 1.

6. The output unit is, Providing improvement suggestions to users The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of URL and source code submissions based on the estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze past reception history and select the most suitable reception method. The system according to feature 1.

9. The aforementioned reception unit is During registration, filtering is performed based on the user's current projects and areas of interest. The system according to feature 1.

10. The aforementioned reception unit is It estimates the user's emotions and determines the priority of URLs and source code to accept based on the estimated user emotions. The system according to feature 1.

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