System

Abstract

An object of a system according to an embodiment is to efficiently improve the design of a WEB service or an application and realize optimal conversion.SOLUTION: A system includes a design data collection part, a design analysis part, and a high-conversion design proposal part. The design data collection unit collects design data of a web service or an application. The design analysis unit analyzes the design data collected by the design data collection unit. The high conversion design proposal unit automatically proposes a high conversion design on the basis of the design data analyzed by the design analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, improving the design of web services and applications had the problem that the process of achieving optimal conversion required time and effort.

[0005] The system according to the embodiment aims to efficiently improve the design of web services and applications and achieve optimal conversions. [Means for solving the problem]

[0006] The system according to the embodiment includes a design data collection unit, a design analysis unit, and a high-conversion design proposal unit. The design data collection unit collects design data for web services and applications. The design analysis unit analyzes the design data collected by the design data collection unit. The high-conversion design proposal unit automatically proposes high-conversion designs based on the design data analyzed by the design analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently improve the design of web services and applications, and achieve optimal conversions. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The design improvement system according to an embodiment of the present invention collects design data for web services and applications, analyzes it using a generative AI, and automatically proposes high-conversion designs. This significantly reduces the time required for design creation, implementation, and evaluation, and enables operation without A / B testing.

[0029] A design improvement system according to an embodiment includes a design data collection unit, a design analysis unit, and a high-conversion design proposal unit. The design data collection unit collects design data from web services and applications. For example, the design data collection unit collects user behavior data such as click rates and heat maps. The design data collection unit can also collect design data from web pages using scraping technology. The design data collection unit can also acquire design data through an API. For example, the design data collection unit measures user click rates and generates heat maps. Using scraping technology, design elements from web pages are extracted and stored in a database. Design data is acquired in real time through the API and used for analysis. The design analysis unit analyzes the collected design data. For example, the design analysis unit analyzes design elements using image analysis technology. The design analysis unit can also analyze text data included in designs using text analysis technology. The design analysis unit can also analyze design patterns using pattern recognition technology. For example, the design analysis unit analyzes the placement and color of design elements using image analysis technology. The system uses text analysis technology to analyze the meaning of text data contained in the design. It uses pattern recognition technology to extract design patterns and evaluate their effectiveness. The high-conversion design proposal unit automatically proposes high-conversion designs based on the analyzed design data. For example, the high-conversion design proposal unit proposes layout changes to emphasize areas where users frequently click. The high-conversion design proposal unit can also propose visually appealing color combinations. The high-conversion design proposal unit can also propose personalized designs optimized for individual users based on user behavior data. For example, the high-conversion design proposal unit changes the layout of buttons to emphasize areas where users frequently click. It proposes visually appealing color combinations to maximize the effectiveness of the design.Based on user behavior data, the system proposes designs optimized for individual users and improves conversion rates. As a result, the design improvement system according to the embodiment significantly reduces the time required to create, implement, and evaluate designs, and enables operation without A / B testing.

[0030] The design data collection unit can collect at least one of user behavior data, such as click rates or heat maps. For example, the design data collection unit measures click rates to determine which parts users click. The design data collection unit also generates heat maps to visualize the degree of user gaze concentration. For example, to measure click rates, the design data collection unit collects user click data and calculates a value by dividing the number of clicks by the number of impressions. To generate a heat map, the design data collection unit collects user gaze tracking data and visually displays the degree of gaze concentration. This allows the effectiveness of a design to be evaluated based on user behavior data.

[0031] The design analysis unit can propose layout changes to emphasize areas where users frequently click. For example, the design analysis unit identifies areas where users frequently click and proposes layout changes to emphasize those areas. The design analysis unit can also analyze user gaze data and optimize the layout based on the degree of gaze concentration. The design analysis unit can also analyze user operation history and improve the layout based on operation patterns. For example, the design analysis unit can identify areas where users frequently click and change the button placement to emphasize those areas. The design analysis unit can analyze gaze data and optimize the layout to emphasize areas where gaze concentration is high. The design analysis unit can analyze operation history and improve the layout to emphasize areas where users frequently operate. This maximizes the effectiveness of the design based on user behavior.

[0032] The high-conversion design proposal unit can propose visually attractive color combinations. The high-conversion design proposal unit, for example, proposes visually attractive color combinations to maximize the effect of the design. The high-conversion design proposal unit can also propose color combinations based on color theory. The high-conversion design proposal unit can also propose color combinations based on user test results. For example, the high-conversion design proposal unit proposes designs that combine complementary or similar colors based on color theory. The high-conversion design proposal unit proposes color combinations that users prefer based on user test results. This makes it possible to propose visually attractive designs.

[0033] The design data collection unit collects user gaze tracking data, and the design analysis unit can analyze the effectiveness of the design based on the gaze tracking data. For example, the design data collection unit collects user gaze data using an gaze tracking device and analyzes gaze movements. The design data collection unit can also measure the user's gaze concentration level using the gaze tracking device. The design data collection unit can also analyze the user's gaze movement pattern using the gaze tracking device. For example, the design data collection unit collects user gaze data using an gaze tracking device and analyzes gaze movements. The gaze tracking device measures the user's gaze concentration level. The gaze tracking device analyzes the user's gaze movement pattern. The design analysis unit analyzes the effectiveness of the design based on the collected gaze tracking data. For example, the design analysis unit identifies areas with high gaze concentration and evaluates the design effectiveness of those areas. The design analysis unit can also evaluate the effectiveness of the design based on the gaze movement patterns. The design analysis unit can also identify areas for improvement in the design based on the gaze data. For example, the design analysis unit identifies areas with high gaze concentration and evaluates the design effectiveness of those areas. It evaluates the design effectiveness based on gaze movement patterns. It identifies areas for improvement in the design based on gaze data. This allows the design effectiveness to be evaluated based on user gaze data.

[0034] The design data collection unit analyzes the user's operation history in detail, and the design analysis unit can identify areas for design improvement based on the operation history. The design data collection unit, for example, collects user click data and scroll data and analyzes the operation history in detail. The design data collection unit can also collect user tap data and analyze operation patterns. The design data collection unit can also collect user gesture data and analyze the operation history. For example, the design data collection unit collects user click data and scroll data and analyzes the operation history in detail. The design data collection unit collects user tap data and analyzes operation patterns. The design analysis unit collects user gesture data and analyzes the operation history. The design analysis unit identifies areas for design improvement based on the collected operation history. For example, the design analysis unit identifies areas where the user frequently clicks and improves the design of those areas. The design analysis unit can also identify areas where scrolling is frequently performed based on the user scroll data and improve the design of those areas. The design analysis unit can also identify areas for design improvement based on the user's operation patterns. For example, the design analysis unit identifies areas where the user frequently clicks and improves the design of those areas. Based on user scrolling data, areas with high scrolling frequency are identified and the design of those areas is improved. Based on user operation patterns, design improvements are identified. This allows design improvements to be identified based on the user's operation history.

[0035] The design data collection unit collects multimodal data including voice commands and gesture inputs, and the design analysis unit can analyze operation patterns based on the multimodal data. For example, the design data collection unit collects user voice commands and analyzes them using voice recognition technology. The design data collection unit can also collect user gesture inputs and analyze them using gesture recognition technology. The design data collection unit can also collect user text inputs and analyze them using text analysis technology. For example, the design data collection unit collects user voice commands and analyzes them using voice recognition technology. The design data collection unit collects user gesture inputs and analyzes them using gesture recognition technology. The design analysis unit collects user text inputs and analyzes them using text analysis technology. The design analysis unit analyzes operation patterns based on the collected multimodal data. For example, the design analysis unit analyzes user operation patterns based on voice commands. The design analysis unit can also analyze operation patterns based on gesture inputs. The design analysis unit can also analyze operation patterns based on text inputs. For example, the design analysis unit analyzes user operation patterns based on voice commands. The design analysis unit can also analyze operation patterns based on gesture inputs. Analyzes operation patterns based on text input, allowing for analysis of operation patterns based on multimodal data including voice commands and gesture input.

[0036] The design data collection unit integrates design data from different devices, and the design analysis unit can evaluate consistency between devices based on the design data. The design data collection unit collects and integrates design data from different devices, such as smartphones, tablets, and PCs. The design data collection unit can also collect design data according to the characteristics of each device. The design data collection unit can also compare design data between devices and evaluate consistency. For example, the design data collection unit collects and integrates design data from different devices, such as smartphones, tablets, and PCs. The design data collection unit collects design data according to the characteristics of each device. The design data collection unit compares design data between devices and evaluates consistency. The design analysis unit evaluates consistency between devices based on the collected design data. For example, the design analysis unit compares the appearance and operability of the design on each device and evaluates consistency. The design analysis unit can also evaluate the uniformity of the design between devices. The design analysis unit can also identify design differences between devices and propose improvements. For example, the design analysis unit compares the appearance and operability of the design on each device and evaluates consistency. The design analysis unit can evaluate the uniformity of the design between devices. The design analysis unit can also identify design differences between devices and propose improvements. This allows you to evaluate the consistency of your design across different devices.

[0037] The high-conversion design proposal unit can propose personalized designs optimized for individual users based on user behavior data. The high-conversion design proposal unit can propose designs optimized for individual users based on, for example, the user's click data and scroll data. The high-conversion design proposal unit can also propose personalized designs based on the user's operation history. The high-conversion design proposal unit can also make proposals that maximize the effectiveness of the design based on the user's behavior data. For example, the high-conversion design proposal unit can propose designs optimized for individual users based on the user's click data and scroll data. It proposes personalized designs based on the user's operation history. It makes proposals that maximize the effectiveness of the design based on the user's behavior data. This makes it possible to propose designs optimized for individual users.

[0038] The high-conversion design proposal unit can learn from past success cases and propose new designs based on successful patterns. The high-conversion design proposal unit can, for example, learn from past success cases and propose new designs based on similar successful patterns. The high-conversion design proposal unit can also propose new designs based on past high-conversion designs. The high-conversion design proposal unit can also make proposals to maximize the effectiveness of the design based on successful patterns. For example, the high-conversion design proposal unit can learn from past success cases and propose new designs based on similar successful patterns. It proposes new designs based on past high-conversion designs. It makes proposals to maximize the effectiveness of the design based on successful patterns. This makes it possible to propose new designs based on past success cases.

[0039] The high-conversion design proposal unit can make design proposals that take into account the cultural background of users from different cultural spheres or regions. The high-conversion design proposal unit, for example, makes design proposals that take into account the cultural background of users from different cultural spheres. The high-conversion design proposal unit can also propose designs that take into account the cultural background of each region. The high-conversion design proposal unit can also make proposals that maximize the effect of the design based on the cultural background. For example, the high-conversion design proposal unit makes design proposals that take into account the cultural background of users from different cultural spheres. A design is proposed that takes into account the cultural background of each region. A proposal is made that maximizes the effect of the design based on the cultural background. This makes it possible to make design proposals that take into account the cultural background of users from different cultural spheres or regions.

[0040] The high-conversion design proposal unit can include dynamic elements such as audio and animation in the design proposal to generate a design that attracts the user's attention. The high-conversion design proposal unit can, for example, include audio elements in the design proposal to generate a design that attracts the user's attention. The high-conversion design proposal unit can also include animation elements in the design proposal to generate a design that attracts the user's attention. The high-conversion design proposal unit can also include interactive elements in the design proposal to generate a design that attracts the user's attention. For example, the high-conversion design proposal unit can include audio elements in the design proposal to generate a design that attracts the user's attention. The high-conversion design proposal unit can include animation elements in the design proposal to generate a design that attracts the user's attention. The high-conversion design proposal unit can include interactive elements in the design proposal to generate a design that attracts the user's attention. In this way, by making design proposals that include dynamic elements such as audio and animation, it is possible to generate a design that attracts the user's attention.

[0041] The automatic design implementation unit can simultaneously generate code that is compatible with different platforms (iOS, Android, Web). The automatic design implementation unit, for example, simultaneously generates code that is compatible with different platforms (iOS, Android, Web). The automatic design implementation unit can also implement design elements in a format appropriate for each platform. The automatic design implementation unit can also implement common design elements in a format appropriate for each platform. For example, the automatic design implementation unit simultaneously generates code that is compatible with different platforms (iOS, Android, Web). Implements design elements in a format appropriate for each platform. Implements common design elements in a format appropriate for each platform. This makes it possible to simultaneously generate code that is compatible with different platforms.

[0042] The automatic design implementation unit automatically considers accessibility requirements when implementing a design, making it possible to realize a design that is easy to use for multiple users. The automatic design implementation unit, for example, automatically considers accessibility requirements when implementing a design. The automatic design implementation unit can also automatically generate a color design that is easy to see for users with color vision deficiency. The automatic design implementation unit can also implement a design that takes into account accessibility requirements such as font size and voice readability. For example, the automatic design implementation unit automatically considers accessibility requirements when implementing a design. It automatically generates a color design that is easy to see for users with color vision deficiency. It implements a design that takes into account accessibility requirements such as font size and voice readability. In this way, accessibility requirements can be automatically considered and a design that is easy to use for multiple users can be realized.

[0043] The automatic design implementation unit can reflect a user's voice commands and automatically generate an interface that supports voice operation. The automatic design implementation unit, for example, analyzes a user's voice commands and automatically generates an interface that supports voice operation. The automatic design implementation unit can also analyze a voice command using voice recognition technology and generate an interface. The automatic design implementation unit can also adjust the layout of the interface based on the voice command. For example, the automatic design implementation unit analyzes a user's voice commands and automatically generates an interface that supports voice operation. The voice command is analyzed using voice recognition technology and an interface is generated. The interface layout is adjusted based on the voice command. In this way, it is possible to reflect a user's voice commands and automatically generate an interface that supports voice operation.

[0044] The automatic design implementation unit can reflect user feedback in real time during design implementation and make instantaneous design modifications. The automatic design implementation unit, for example, collects user feedback in real time during design implementation and makes instantaneous modifications to the design. The automatic design implementation unit can also modify the design based on user comments and ratings. The automatic design implementation unit can also modify the design based on feedback collected in real time. For example, the automatic design implementation unit collects user feedback in real time during design implementation and makes instantaneous modifications to the design. The design is modified based on user comments and ratings. The design is modified based on feedback collected in real time. This makes it possible to reflect user feedback in real time and make instantaneous modifications to the design.

[0045] The design evaluation unit can analyze user behavior data in time series and evaluate the effects before and after a design change in detail. The design evaluation unit can, for example, analyze user click data and scroll data in time series and evaluate the effects before and after a design change. The design evaluation unit can also analyze user operation history in time series and evaluate the effects before and after a design change. The design evaluation unit can also comprehensively evaluate the effects of the design based on the time series data. For example, the design evaluation unit can analyze user click data and scroll data in time series and evaluate the effects before and after a design change. The design evaluation unit can analyze user operation history in time series and evaluate the effects before and after a design change. The design effectiveness is comprehensively evaluated based on the time series data. This allows user behavior data to be analyzed in time series and the effects before and after a design change to be evaluated in detail.

[0046] When evaluating a design, the design evaluation unit can compare it with competitors' designs and perform a relative evaluation. For example, the design evaluation unit collects design data from competitors and compares it with the company's own design. The design evaluation unit can also perform a relative evaluation based on the click rate and gaze data of competitors. The design evaluation unit can also evaluate the effectiveness of a design by comparing it with competitors' designs. For example, the design evaluation unit collects design data from competitors and compares it with the company's own design. A relative evaluation is performed based on the click rate and gaze data of competitors. The effectiveness of a design is evaluated by comparing it with competitors' designs. This allows the company to perform a relative evaluation by comparing it with competitors' designs when evaluating a design.

[0047] The design evaluation unit can collect user voice feedback and analyze the voice data to perform design evaluation. The design evaluation unit, for example, collects user voice feedback and analyzes the voice data to perform design evaluation. The design evaluation unit can also analyze the voice data using voice recognition technology to evaluate the effectiveness of the design. The design evaluation unit can also identify areas for improvement in the design based on the voice data and perform evaluation. For example, the design evaluation unit collects user voice feedback and analyzes the voice data to perform design evaluation. The design evaluation unit can analyze the voice data using voice recognition technology to evaluate the effectiveness of the design. The design evaluation unit can identify areas for improvement in the design based on the voice data and perform evaluation. In this way, it is possible to collect user voice feedback and analyze the voice data to perform design evaluation.

[0048] The design evaluation unit can display the results of the design evaluation in a visual dashboard to enable intuitive understanding. The design evaluation unit, for example, displays the results of the design evaluation in a visual dashboard to enable intuitive understanding by the user. The design evaluation unit can also display the evaluation scores in graphs or charts. The design evaluation unit can also provide a visual dashboard that includes interactive elements. For example, the design evaluation unit can display the results of the design evaluation in a visual dashboard to enable intuitive understanding by the user. Displays the evaluation scores in graphs or charts. Provides a visual dashboard that includes interactive elements. This allows the results of the design evaluation to be displayed in a visual dashboard to enable intuitive understanding by the user.

[0049] The design evaluation unit can analyze user behavior data in time series and evaluate the effects before and after a design change in detail. The design evaluation unit can, for example, analyze user click data and scroll data in time series and evaluate the effects before and after a design change. The design evaluation unit can also analyze user operation history in time series and evaluate the effects before and after a design change. The design evaluation unit can also comprehensively evaluate the effects of the design based on the time series data. For example, the design evaluation unit can analyze user click data and scroll data in time series and evaluate the effects before and after a design change. The design evaluation unit can analyze user operation history in time series and evaluate the effects before and after a design change. The design effectiveness is comprehensively evaluated based on the time series data. This allows user behavior data to be analyzed in time series and the effects before and after a design change to be evaluated in detail.

[0050] When evaluating a design, the design evaluation unit can compare it with competitors' designs and perform a relative evaluation. For example, the design evaluation unit collects design data from competitors and compares it with the company's own design. The design evaluation unit can also perform a relative evaluation based on the click rate and gaze data of competitors. The design evaluation unit can also evaluate the effectiveness of a design by comparing it with competitors' designs. For example, the design evaluation unit collects design data from competitors and compares it with the company's own design. A relative evaluation is performed based on the click rate and gaze data of competitors. The effectiveness of a design is evaluated by comparing it with competitors' designs. This allows the company to perform a relative evaluation by comparing it with competitors' designs when evaluating a design.

[0051] The design evaluation unit can collect user voice feedback and analyze the voice data to perform design evaluation. The design evaluation unit, for example, collects user voice feedback and analyzes the voice data to perform design evaluation. The design evaluation unit can also analyze the voice data using voice recognition technology to evaluate the effectiveness of the design. The design evaluation unit can also identify areas for improvement in the design based on the voice data and perform evaluation. For example, the design evaluation unit collects user voice feedback and analyzes the voice data to perform design evaluation. The design evaluation unit can analyze the voice data using voice recognition technology to evaluate the effectiveness of the design. The design evaluation unit can identify areas for improvement in the design based on the voice data and perform evaluation. In this way, it is possible to collect user voice feedback and analyze the voice data to perform design evaluation.

[0052] The design evaluation unit can display the results of the design evaluation in a visual dashboard to enable intuitive understanding. The design evaluation unit, for example, displays the results of the design evaluation in a visual dashboard to enable intuitive understanding by the user. The design evaluation unit can also display the evaluation scores in graphs or charts. The design evaluation unit can also provide a visual dashboard that includes interactive elements. For example, the design evaluation unit can display the results of the design evaluation in a visual dashboard to enable intuitive understanding by the user. Displays the evaluation scores in graphs or charts. Provides a visual dashboard that includes interactive elements. This allows the results of the design evaluation to be displayed in a visual dashboard to enable intuitive understanding by the user.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The design improvement system may further include a voice analysis unit that collects user voice feedback and analyzes the voice data to perform design evaluation. For example, the voice analysis unit collects user voice feedback and analyzes the voice data using voice recognition technology. The voice analysis unit may also identify and evaluate design improvements based on the voice data. This makes it possible to collect user voice feedback and analyze the voice data to perform design evaluation.

[0055] The design improvement system further integrates design data from different devices, and the design analysis unit can evaluate consistency between devices based on the design data. For example, the design data collection unit collects and integrates design data from different devices, such as smartphones, tablets, and PCs. The design data collection unit can also collect design data according to the characteristics of each device. This allows the design consistency between different devices to be evaluated.

[0056] The design improvement system further analyzes the user's operation history in detail, and the design analysis unit can identify areas for improvement in the design based on the operation history. For example, the design data collection unit collects user click data and scroll data and analyzes the operation history in detail. The design data collection unit can also collect user tap data and analyze operation patterns. This makes it possible to identify areas for improvement in the design based on the user's operation history.

[0057] The design improvement system can also learn from past success stories and propose new designs based on successful patterns. For example, the high-conversion design proposal unit can learn from past success stories and propose new designs based on similar success patterns. The high-conversion design proposal unit can also propose new designs based on past high-conversion designs. This makes it possible to propose new designs based on past success stories.

[0058] The design improvement system can further propose designs that take into account the cultural backgrounds of users from different cultural spheres or regions. For example, the high-conversion design proposal unit proposes designs that take into account the cultural backgrounds of users from different cultural spheres. The high-conversion design proposal unit can also propose designs that take into account the cultural backgrounds of each region. This makes it possible to propose designs that take into account the cultural backgrounds of users from different cultural spheres or regions.

[0059] The design improvement system can also compare designs with competitors' designs and perform relative evaluations when evaluating designs. For example, the design evaluation department collects design data from competitors and compares it with the company's own designs. The design evaluation department can also perform relative evaluations based on competitors' click rates and gaze data. This allows the system to compare designs with competitors' designs and perform relative evaluations when evaluating designs.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The design data collection unit collects design data from web services and applications. For example, it collects user behavior data such as click rates and heat maps. It can also collect design data from web pages using scraping technology. It can also obtain design data in real time through APIs. Step 2: The design analysis department analyzes the collected design data. For example, image analysis technology is used to analyze the placement and color of design elements. Text analysis technology is used to analyze the meaning of the text data contained in the design. Furthermore, pattern recognition technology is used to extract design patterns and evaluate their effectiveness. Step 3: The high-conversion design suggestion unit automatically suggests high-conversion designs based on the analyzed design data. For example, it suggests layout changes to emphasize areas that users click frequently. It also suggests visually appealing color combinations to maximize the effectiveness of the design. It also suggests personalized designs optimized for individual users based on user behavior data.

[0062] (Example 2) The design improvement system according to an embodiment of the present invention collects design data for web services and applications, analyzes it using a generative AI, and automatically proposes high-conversion designs. This significantly reduces the time required for design creation, implementation, and evaluation, and enables operation without A / B testing.

[0063] A design improvement system according to an embodiment includes a design data collection unit, a design analysis unit, and a high-conversion design proposal unit. The design data collection unit collects design data from web services and applications. For example, the design data collection unit collects user behavior data such as click rates and heat maps. The design data collection unit can also collect design data from web pages using scraping technology. The design data collection unit can also acquire design data through an API. For example, the design data collection unit measures user click rates and generates heat maps. Using scraping technology, design elements from web pages are extracted and stored in a database. Design data is acquired in real time through the API and used for analysis. The design analysis unit analyzes the collected design data. For example, the design analysis unit analyzes design elements using image analysis technology. The design analysis unit can also analyze text data included in designs using text analysis technology. The design analysis unit can also analyze design patterns using pattern recognition technology. For example, the design analysis unit analyzes the placement and color of design elements using image analysis technology. The system uses text analysis technology to analyze the meaning of text data contained in the design. It uses pattern recognition technology to extract design patterns and evaluate their effectiveness. The high-conversion design proposal unit automatically proposes high-conversion designs based on the analyzed design data. For example, the high-conversion design proposal unit proposes layout changes to emphasize areas where users frequently click. The high-conversion design proposal unit can also propose visually appealing color combinations. The high-conversion design proposal unit can also propose personalized designs optimized for individual users based on user behavior data. For example, the high-conversion design proposal unit changes the layout of buttons to emphasize areas where users frequently click. It proposes visually appealing color combinations to maximize the effectiveness of the design.Based on user behavior data, the system proposes designs optimized for individual users and improves conversion rates. As a result, the design improvement system according to the embodiment significantly reduces the time required to create, implement, and evaluate designs, and enables operation without A / B testing.

[0064] The design data collection unit can collect at least one of user behavior data, such as click rates or heat maps. For example, the design data collection unit measures click rates to determine which parts users click. The design data collection unit also generates heat maps to visualize the degree of user gaze concentration. For example, to measure click rates, the design data collection unit collects user click data and calculates a value by dividing the number of clicks by the number of impressions. To generate a heat map, the design data collection unit collects user gaze tracking data and visually displays the degree of gaze concentration. This allows the effectiveness of a design to be evaluated based on user behavior data.

[0065] The design analysis unit can propose layout changes to emphasize areas where users frequently click. For example, the design analysis unit identifies areas where users frequently click and proposes layout changes to emphasize those areas. The design analysis unit can also analyze user gaze data and optimize the layout based on the degree of gaze concentration. The design analysis unit can also analyze user operation history and improve the layout based on operation patterns. For example, the design analysis unit can identify areas where users frequently click and change the button placement to emphasize those areas. The design analysis unit can analyze gaze data and optimize the layout to emphasize areas where gaze concentration is high. The design analysis unit can analyze operation history and improve the layout to emphasize areas where users frequently operate. This maximizes the effectiveness of the design based on user behavior.

[0066] The high-conversion design proposal unit can propose visually attractive color combinations. The high-conversion design proposal unit, for example, proposes visually attractive color combinations to maximize the effect of the design. The high-conversion design proposal unit can also propose color combinations based on color theory. The high-conversion design proposal unit can also propose color combinations based on user test results. For example, the high-conversion design proposal unit proposes designs that combine complementary or similar colors based on color theory. The high-conversion design proposal unit proposes color combinations that users prefer based on user test results. This makes it possible to propose visually attractive designs.

[0067] The design data collection unit collects user emotion data, and the design analysis unit can perform design evaluation based on the emotion data. The design data collection unit, for example, collects user facial expression data using a camera and calculates an emotion score using an emotion estimation algorithm. The design data collection unit can also collect user voice data and estimate emotions using voice analysis technology. The design data collection unit can also collect user biometric data (heart rate and electrodermal activity) using a sensor and analyze emotions using an emotion estimation algorithm. For example, the design data collection unit collects user facial expression data using a camera and calculates an emotion score using an emotion estimation algorithm. The design data collection unit collects user voice data and estimates emotions using voice analysis technology. The design analysis unit collects user biometric data using a sensor and analyzes emotions using an emotion estimation algorithm. The design analysis unit performs design evaluation based on the collected emotion data. For example, the design analysis unit gives high marks to design elements that the user expressed positive emotions about. The design analysis unit can also identify design elements that the user expressed negative emotions about as areas for improvement. The design analysis unit can also comprehensively evaluate the effectiveness of the design based on the emotion data. For example, the design analysis unit will give high marks to design elements that users feel positive about, and identify design elements that users feel negative about as areas for improvement. The design effectiveness is then comprehensively evaluated based on the emotion data. This allows the effectiveness of the design to be evaluated based on the user's emotions.

[0068] The design data collection unit collects user gaze tracking data, and the design analysis unit can analyze the effectiveness of the design based on the gaze tracking data. For example, the design data collection unit collects user gaze data using an gaze tracking device and analyzes gaze movements. The design data collection unit can also measure the user's gaze concentration level using the gaze tracking device. The design data collection unit can also analyze the user's gaze movement pattern using the gaze tracking device. For example, the design data collection unit collects user gaze data using an gaze tracking device and analyzes gaze movements. The gaze tracking device measures the user's gaze concentration level. The gaze tracking device analyzes the user's gaze movement pattern. The design analysis unit analyzes the effectiveness of the design based on the collected gaze tracking data. For example, the design analysis unit identifies areas with high gaze concentration and evaluates the design effectiveness of those areas. The design analysis unit can also evaluate the effectiveness of the design based on the gaze movement patterns. The design analysis unit can also identify areas for improvement in the design based on the gaze data. For example, the design analysis unit identifies areas with high gaze concentration and evaluates the design effectiveness of those areas. It evaluates the design effectiveness based on gaze movement patterns. It identifies areas for improvement in the design based on gaze data. This allows the design effectiveness to be evaluated based on user gaze data.

[0069] The design data collection unit analyzes the user's operation history in detail, and the design analysis unit can identify areas for design improvement based on the operation history. The design data collection unit, for example, collects user click data and scroll data and analyzes the operation history in detail. The design data collection unit can also collect user tap data and analyze operation patterns. The design data collection unit can also collect user gesture data and analyze the operation history. For example, the design data collection unit collects user click data and scroll data and analyzes the operation history in detail. The design data collection unit collects user tap data and analyzes operation patterns. The design analysis unit collects user gesture data and analyzes the operation history. The design analysis unit identifies areas for design improvement based on the collected operation history. For example, the design analysis unit identifies areas where the user frequently clicks and improves the design of those areas. The design analysis unit can also identify areas where scrolling is frequently performed based on the user scroll data and improve the design of those areas. The design analysis unit can also identify areas for design improvement based on the user's operation patterns. For example, the design analysis unit identifies areas where the user frequently clicks and improves the design of those areas. Based on user scrolling data, areas with high scrolling frequency are identified and the design of those areas is improved. Based on user operation patterns, design improvements are identified. This allows design improvements to be identified based on the user's operation history.

[0070] The design data collection unit collects multimodal data including voice commands and gesture inputs, and the design analysis unit can analyze operation patterns based on the multimodal data. For example, the design data collection unit collects user voice commands and analyzes them using voice recognition technology. The design data collection unit can also collect user gesture inputs and analyze them using gesture recognition technology. The design data collection unit can also collect user text inputs and analyze them using text analysis technology. For example, the design data collection unit collects user voice commands and analyzes them using voice recognition technology. The design data collection unit collects user gesture inputs and analyzes them using gesture recognition technology. The design analysis unit collects user text inputs and analyzes them using text analysis technology. The design analysis unit analyzes operation patterns based on the collected multimodal data. For example, the design analysis unit analyzes user operation patterns based on voice commands. The design analysis unit can also analyze operation patterns based on gesture inputs. The design analysis unit can also analyze operation patterns based on text inputs. For example, the design analysis unit analyzes user operation patterns based on voice commands. The design analysis unit can also analyze operation patterns based on gesture inputs. Analyzes operation patterns based on text input, allowing for analysis of operation patterns based on multimodal data including voice commands and gesture input.

[0071] The design data collection unit integrates design data from different devices, and the design analysis unit can evaluate consistency between devices based on the design data. The design data collection unit collects and integrates design data from different devices, such as smartphones, tablets, and PCs. The design data collection unit can also collect design data according to the characteristics of each device. The design data collection unit can also compare design data between devices and evaluate consistency. For example, the design data collection unit collects and integrates design data from different devices, such as smartphones, tablets, and PCs. The design data collection unit collects design data according to the characteristics of each device. The design data collection unit compares design data between devices and evaluates consistency. The design analysis unit evaluates consistency between devices based on the collected design data. For example, the design analysis unit compares the appearance and operability of the design on each device and evaluates consistency. The design analysis unit can also evaluate the uniformity of the design between devices. The design analysis unit can also identify design differences between devices and propose improvements. For example, the design analysis unit compares the appearance and operability of the design on each device and evaluates consistency. The design analysis unit can evaluate the uniformity of the design between devices. The design analysis unit can also identify design differences between devices and propose improvements. This allows you to evaluate the consistency of your design across different devices.

[0072] The design data collection unit collects user emotion data in real time, and the design analysis unit can propose design improvements based on the emotion data. For example, the design data collection unit collects user facial expression data in real time using a camera and calculates an emotion score using an emotion estimation algorithm. The design data collection unit can also collect user voice data in real time and estimate emotions using voice analysis technology. The design data collection unit can also collect user biometric data (heart rate and electrodermal activity) in real time and analyze emotions using an emotion estimation algorithm. For example, the design data collection unit collects user facial expression data in real time using a camera and calculates an emotion score using an emotion estimation algorithm. The design data collection unit collects user voice data in real time and estimates emotions using voice analysis technology. The design analysis unit collects user biometric data in real time and analyzes emotions using an emotion estimation algorithm. The design analysis unit proposes design improvements based on the collected emotion data. For example, the design analysis unit proposes design improvements that emphasize design elements for which the user expressed positive emotions. The design analysis unit can also propose improvements to design elements for which the user expressed negative emotions. The design analysis unit can also comprehensively evaluate the effectiveness of the design based on the emotional data and suggest areas for improvement. For example, the design analysis unit can suggest design improvements that emphasize design elements for which the user expressed positive emotions, or suggest improvements to design elements for which the user expressed negative emotions. The design analysis unit comprehensively evaluates the effectiveness of the design based on the emotional data and suggests areas for improvement. This makes it possible to suggest design improvements in real time based on the user's emotional data.

[0073] The high-conversion design proposal unit can use the emotion estimation function to make design proposals based on the user's emotions. The high-conversion design proposal unit, for example, uses the emotion estimation function to automatically generate a design that elicits positive emotions based on the user's emotion data. The high-conversion design proposal unit can also select colors and adjust layouts based on the user's emotion data. The high-conversion design proposal unit can also make proposals to maximize the effect of the design based on the user's emotion data. For example, the high-conversion design proposal unit uses the emotion estimation function to automatically generate a design that elicits positive emotions based on the user's emotion data. The high-conversion design proposal unit selects colors and adjusts layouts based on the user's emotion data. The high-conversion design proposal unit makes proposals to maximize the effect of the design based on the user's emotion data. In this way, by making design proposals based on the user's emotions, it is possible to automatically generate a design that elicits positive emotions.

[0074] The high-conversion design proposal unit can propose personalized designs optimized for individual users based on user behavior data. The high-conversion design proposal unit can propose designs optimized for individual users based on, for example, the user's click data and scroll data. The high-conversion design proposal unit can also propose personalized designs based on the user's operation history. The high-conversion design proposal unit can also make proposals that maximize the effectiveness of the design based on the user's behavior data. For example, the high-conversion design proposal unit can propose designs optimized for individual users based on the user's click data and scroll data. It proposes personalized designs based on the user's operation history. It makes proposals that maximize the effectiveness of the design based on the user's behavior data. This makes it possible to propose designs optimized for individual users.

[0075] The high-conversion design proposal unit can learn from past success cases and propose new designs based on successful patterns. The high-conversion design proposal unit can, for example, learn from past success cases and propose new designs based on similar successful patterns. The high-conversion design proposal unit can also propose new designs based on past high-conversion designs. The high-conversion design proposal unit can also make proposals to maximize the effectiveness of the design based on successful patterns. For example, the high-conversion design proposal unit can learn from past success cases and propose new designs based on similar successful patterns. It proposes new designs based on past high-conversion designs. It makes proposals to maximize the effectiveness of the design based on successful patterns. This makes it possible to propose new designs based on past success cases.

[0076] The high-conversion design proposal unit can make design proposals that take into account the cultural background of users from different cultural spheres or regions. The high-conversion design proposal unit, for example, makes design proposals that take into account the cultural background of users from different cultural spheres. The high-conversion design proposal unit can also propose designs that take into account the cultural background of each region. The high-conversion design proposal unit can also make proposals that maximize the effect of the design based on the cultural background. For example, the high-conversion design proposal unit makes design proposals that take into account the cultural background of users from different cultural spheres. A design is proposed that takes into account the cultural background of each region. A proposal is made that maximizes the effect of the design based on the cultural background. This makes it possible to make design proposals that take into account the cultural background of users from different cultural spheres or regions.

[0077] The high-conversion design proposal unit can include dynamic elements such as audio and animation in the design proposal to generate a design that attracts the user's attention. The high-conversion design proposal unit can, for example, include audio elements in the design proposal to generate a design that attracts the user's attention. The high-conversion design proposal unit can also include animation elements in the design proposal to generate a design that attracts the user's attention. The high-conversion design proposal unit can also include interactive elements in the design proposal to generate a design that attracts the user's attention. For example, the high-conversion design proposal unit can include audio elements in the design proposal to generate a design that attracts the user's attention. The high-conversion design proposal unit can include animation elements in the design proposal to generate a design that attracts the user's attention. The high-conversion design proposal unit can include interactive elements in the design proposal to generate a design that attracts the user's attention. In this way, by making design proposals that include dynamic elements such as audio and animation, it is possible to generate a design that attracts the user's attention.

[0078] The high-conversion design proposal unit can use the emotion estimation function to monitor changes in a user's emotions in real time and propose design changes according to the emotions. The high-conversion design proposal unit can, for example, use the emotion estimation function to monitor changes in a user's emotions in real time and propose design changes according to the emotions. The high-conversion design proposal unit can also propose color adjustments or layout changes based on changes in a user's emotions. The high-conversion design proposal unit can also make proposals to maximize the effectiveness of the design based on user emotion data. For example, the high-conversion design proposal unit can use the emotion estimation function to monitor changes in a user's emotions in real time and propose design changes according to the emotions. The high-conversion design proposal unit can propose color adjustments or layout changes based on changes in a user's emotions. The high-conversion design proposal unit makes proposals to maximize the effectiveness of the design based on user emotion data. This makes it possible to monitor changes in a user's emotions in real time and propose design changes according to the emotions.

[0079] The automatic design implementation unit uses the emotion estimation function to implement a design based on the user's emotions and can generate code that elicits positive emotions. The automatic design implementation unit, for example, uses the emotion estimation function to implement a design that elicits positive emotions based on the user's emotion data. The automatic design implementation unit can also generate HTML and CSS code based on the user's emotion data. The automatic design implementation unit can also generate JavaScript code based on the user's emotion data. For example, the automatic design implementation unit uses the emotion estimation function to implement a design that elicits positive emotions based on the user's emotion data. HTML and CSS code are generated based on the user's emotion data. JavaScript code is generated based on the user's emotion data. In this way, it is possible to implement a design based on the user's emotions and generate code that elicits positive emotions.

[0080] The automatic design implementation unit can simultaneously generate code that is compatible with different platforms (iOS, Android, Web). The automatic design implementation unit, for example, simultaneously generates code that is compatible with different platforms (iOS, Android, Web). The automatic design implementation unit can also implement design elements in a format appropriate for each platform. The automatic design implementation unit can also implement common design elements in a format appropriate for each platform. For example, the automatic design implementation unit simultaneously generates code that is compatible with different platforms (iOS, Android, Web). Implements design elements in a format appropriate for each platform. Implements common design elements in a format appropriate for each platform. This makes it possible to simultaneously generate code that is compatible with different platforms.

[0081] The automatic design implementation unit automatically considers accessibility requirements when implementing a design, making it possible to realize a design that is easy to use for multiple users. The automatic design implementation unit, for example, automatically considers accessibility requirements when implementing a design. The automatic design implementation unit can also automatically generate a color design that is easy to see for users with color vision deficiency. The automatic design implementation unit can also implement a design that takes into account accessibility requirements such as font size and voice readability. For example, the automatic design implementation unit automatically considers accessibility requirements when implementing a design. It automatically generates a color design that is easy to see for users with color vision deficiency. It implements a design that takes into account accessibility requirements such as font size and voice readability. In this way, accessibility requirements can be automatically considered and a design that is easy to use for multiple users can be realized.

[0082] The automatic design implementation unit can reflect a user's voice commands and automatically generate an interface that supports voice operation. The automatic design implementation unit, for example, analyzes a user's voice commands and automatically generates an interface that supports voice operation. The automatic design implementation unit can also analyze a voice command using voice recognition technology and generate an interface. The automatic design implementation unit can also adjust the layout of the interface based on the voice command. For example, the automatic design implementation unit analyzes a user's voice commands and automatically generates an interface that supports voice operation. The voice command is analyzed using voice recognition technology and an interface is generated. The interface layout is adjusted based on the voice command. In this way, it is possible to reflect a user's voice commands and automatically generate an interface that supports voice operation.

[0083] The automatic design implementation unit can reflect user feedback in real time during design implementation and make instantaneous design modifications. The automatic design implementation unit, for example, collects user feedback in real time during design implementation and makes instantaneous modifications to the design. The automatic design implementation unit can also modify the design based on user comments and ratings. The automatic design implementation unit can also modify the design based on feedback collected in real time. For example, the automatic design implementation unit collects user feedback in real time during design implementation and makes instantaneous modifications to the design. The design is modified based on user comments and ratings. The design is modified based on feedback collected in real time. This makes it possible to reflect user feedback in real time and make instantaneous modifications to the design.

[0084] The automatic design implementation unit can implement design changes in real time according to the user's emotions using the emotion estimation function. The automatic design implementation unit, for example, uses the emotion estimation function to analyze the user's emotion data in real time and implement design changes in accordance with the emotions. The automatic design implementation unit can also implement color adjustments and layout changes in real time based on the user's emotion data. The automatic design implementation unit can also implement changes in real time to maximize the effect of the design based on the emotion data. For example, the automatic design implementation unit uses the emotion estimation function to analyze the user's emotion data in real time and implement design changes in accordance with the emotions. Color adjustments and layout changes are implemented in real time based on the user's emotion data. Changes are implemented in real time to maximize the effect of the design based on the emotion data. In this way, the emotion estimation function can be used to implement design changes in real time according to the user's emotions.

[0085] The design evaluation unit can use the emotion estimation function to evaluate the design based on the user's emotion data and provide feedback based on the emotion. The design evaluation unit, for example, uses the emotion estimation function to evaluate the design based on the user's emotion data. The design evaluation unit can also evaluate the effectiveness of the design based on the user's emotion data. The design evaluation unit can also identify areas for improvement in the design based on the emotion data and provide feedback. For example, the design evaluation unit uses the emotion estimation function to evaluate the design based on the user's emotion data. The design effectiveness is evaluated based on the user's emotion data. The areas for improvement in the design are identified based on the emotion data and provide feedback. In this way, the emotion estimation function can be used to evaluate the design based on the user's emotion data and provide feedback based on the emotion.

[0086] The design evaluation unit can analyze user behavior data in time series and evaluate the effects before and after a design change in detail. The design evaluation unit can, for example, analyze user click data and scroll data in time series and evaluate the effects before and after a design change. The design evaluation unit can also analyze user operation history in time series and evaluate the effects before and after a design change. The design evaluation unit can also comprehensively evaluate the effects of the design based on the time series data. For example, the design evaluation unit can analyze user click data and scroll data in time series and evaluate the effects before and after a design change. The design evaluation unit can analyze user operation history in time series and evaluate the effects before and after a design change. The design effectiveness is comprehensively evaluated based on the time series data. This allows user behavior data to be analyzed in time series and the effects before and after a design change to be evaluated in detail.

[0087] When evaluating a design, the design evaluation unit can compare it with competitors' designs and perform a relative evaluation. For example, the design evaluation unit collects design data from competitors and compares it with the company's own design. The design evaluation unit can also perform a relative evaluation based on the click rate and gaze data of competitors. The design evaluation unit can also evaluate the effectiveness of a design by comparing it with competitors' designs. For example, the design evaluation unit collects design data from competitors and compares it with the company's own design. A relative evaluation is performed based on the click rate and gaze data of competitors. The effectiveness of a design is evaluated by comparing it with competitors' designs. This allows the company to perform a relative evaluation by comparing it with competitors' designs when evaluating a design.

[0088] The design evaluation unit can collect user voice feedback and analyze the voice data to perform design evaluation. The design evaluation unit, for example, collects user voice feedback and analyzes the voice data to perform design evaluation. The design evaluation unit can also analyze the voice data using voice recognition technology to evaluate the effectiveness of the design. The design evaluation unit can also identify areas for improvement in the design based on the voice data and perform evaluation. For example, the design evaluation unit collects user voice feedback and analyzes the voice data to perform design evaluation. The design evaluation unit can analyze the voice data using voice recognition technology to evaluate the effectiveness of the design. The design evaluation unit can identify areas for improvement in the design based on the voice data and perform evaluation. In this way, it is possible to collect user voice feedback and analyze the voice data to perform design evaluation.

[0089] The design evaluation unit can display the results of the design evaluation in a visual dashboard to enable intuitive understanding. The design evaluation unit, for example, displays the results of the design evaluation in a visual dashboard to enable intuitive understanding by the user. The design evaluation unit can also display the evaluation scores in graphs or charts. The design evaluation unit can also provide a visual dashboard that includes interactive elements. For example, the design evaluation unit can display the results of the design evaluation in a visual dashboard to enable intuitive understanding by the user. Displays the evaluation scores in graphs or charts. Provides a visual dashboard that includes interactive elements. This allows the results of the design evaluation to be displayed in a visual dashboard to enable intuitive understanding by the user.

[0090] The design evaluation unit can use the emotion estimation function to monitor changes in the user's emotions in real time and provide feedback based on the emotions. The design evaluation unit, for example, can use the emotion estimation function to monitor changes in the user's emotions in real time and provide feedback based on the emotions. The design evaluation unit can also provide feedback that has a relaxing effect based on the user's changes in emotions. The design evaluation unit can also evaluate the effectiveness of the design based on the emotion data and provide feedback. For example, the design evaluation unit can use the emotion estimation function to monitor changes in the user's emotions in real time and provide feedback based on the emotions. Feedback that has a relaxing effect based on the user's changes in emotions is provided. The design evaluation unit can evaluate the effectiveness of the design based on the emotion data and provide feedback. In this way, the emotion estimation function can be used to monitor changes in the user's emotions in real time and provide feedback based on the emotions.

[0091] The design evaluation unit can use the emotion estimation function to evaluate the design based on the user's emotion data and provide feedback based on the emotion. The design evaluation unit, for example, uses the emotion estimation function to evaluate the design based on the user's emotion data. The design evaluation unit can also evaluate the effectiveness of the design based on the user's emotion data. The design evaluation unit can also identify areas for improvement in the design based on the emotion data and provide feedback. For example, the design evaluation unit uses the emotion estimation function to evaluate the design based on the user's emotion data. The design effectiveness is evaluated based on the user's emotion data. The areas for improvement in the design are identified based on the emotion data and provide feedback. In this way, the emotion estimation function can be used to evaluate the design based on the user's emotion data and provide feedback based on the emotion.

[0092] The design evaluation unit can analyze user behavior data in time series and evaluate the effects before and after a design change in detail. The design evaluation unit can, for example, analyze user click data and scroll data in time series and evaluate the effects before and after a design change. The design evaluation unit can also analyze user operation history in time series and evaluate the effects before and after a design change. The design evaluation unit can also comprehensively evaluate the effects of the design based on the time series data. For example, the design evaluation unit can analyze user click data and scroll data in time series and evaluate the effects before and after a design change. The design evaluation unit can analyze user operation history in time series and evaluate the effects before and after a design change. The design effectiveness is comprehensively evaluated based on the time series data. This allows user behavior data to be analyzed in time series and the effects before and after a design change to be evaluated in detail.

[0093] When evaluating a design, the design evaluation unit can compare it with competitors' designs and perform a relative evaluation. For example, the design evaluation unit collects design data from competitors and compares it with the company's own design. The design evaluation unit can also perform a relative evaluation based on the click rate and gaze data of competitors. The design evaluation unit can also evaluate the effectiveness of a design by comparing it with competitors' designs. For example, the design evaluation unit collects design data from competitors and compares it with the company's own design. A relative evaluation is performed based on the click rate and gaze data of competitors. The effectiveness of a design is evaluated by comparing it with competitors' designs. This allows the company to perform a relative evaluation by comparing it with competitors' designs when evaluating a design.

[0094] The design evaluation unit can collect user voice feedback and analyze the voice data to perform design evaluation. The design evaluation unit, for example, collects user voice feedback and analyzes the voice data to perform design evaluation. The design evaluation unit can also analyze the voice data using voice recognition technology to evaluate the effectiveness of the design. The design evaluation unit can also identify areas for improvement in the design based on the voice data and perform evaluation. For example, the design evaluation unit collects user voice feedback and analyzes the voice data to perform design evaluation. The design evaluation unit can analyze the voice data using voice recognition technology to evaluate the effectiveness of the design. The design evaluation unit can identify areas for improvement in the design based on the voice data and perform evaluation. In this way, it is possible to collect user voice feedback and analyze the voice data to perform design evaluation.

[0095] The design evaluation unit can display the results of the design evaluation in a visual dashboard to enable intuitive understanding. The design evaluation unit, for example, displays the results of the design evaluation in a visual dashboard to enable intuitive understanding by the user. The design evaluation unit can also display the evaluation scores in graphs or charts. The design evaluation unit can also provide a visual dashboard that includes interactive elements. For example, the design evaluation unit can display the results of the design evaluation in a visual dashboard to enable intuitive understanding by the user. Displays the evaluation scores in graphs or charts. Provides a visual dashboard that includes interactive elements. This allows the results of the design evaluation to be displayed in a visual dashboard to enable intuitive understanding by the user.

[0096] The design evaluation unit can use the emotion estimation function to monitor changes in the user's emotions in real time and provide feedback based on the emotions. The design evaluation unit, for example, can use the emotion estimation function to monitor changes in the user's emotions in real time and provide feedback based on the emotions. The design evaluation unit can also provide feedback that has a relaxing effect based on the user's changes in emotions. The design evaluation unit can also evaluate the effectiveness of the design based on the emotion data and provide feedback. For example, the design evaluation unit can use the emotion estimation function to monitor changes in the user's emotions in real time and provide feedback based on the emotions. Feedback that has a relaxing effect based on the user's changes in emotions is provided. The design evaluation unit can evaluate the effectiveness of the design based on the emotion data and provide feedback. In this way, the emotion estimation function can be used to monitor changes in the user's emotions in real time and provide feedback based on the emotions.

[0097] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0098] The design improvement system may further include a voice analysis unit that collects user voice feedback and analyzes the voice data to perform design evaluation. For example, the voice analysis unit collects user voice feedback and analyzes the voice data using voice recognition technology. The voice analysis unit may also identify and evaluate design improvements based on the voice data. This makes it possible to collect user voice feedback and analyze the voice data to perform design evaluation.

[0099] The design improvement system further integrates design data from different devices, and the design analysis unit can evaluate consistency between devices based on the design data. For example, the design data collection unit collects and integrates design data from different devices, such as smartphones, tablets, and PCs. The design data collection unit can also collect design data according to the characteristics of each device. This allows the design consistency between different devices to be evaluated.

[0100] The design improvement system further collects user emotion data, and the design analysis unit can evaluate the design based on the emotion data. For example, the design data collection unit collects user facial expression data using a camera and calculates an emotion score using an emotion estimation algorithm. The design data collection unit can also collect user voice data and estimate emotions using voice analysis technology. This allows the effectiveness of the design to be evaluated based on the user's emotions.

[0101] The design improvement system further analyzes the user's operation history in detail, and the design analysis unit can identify areas for improvement in the design based on the operation history. For example, the design data collection unit collects user click data and scroll data and analyzes the operation history in detail. The design data collection unit can also collect user tap data and analyze operation patterns. This makes it possible to identify areas for improvement in the design based on the user's operation history.

[0102] The design improvement system can further use an emotion estimation function to monitor changes in a user's emotions in real time and propose design changes according to the emotions. For example, the design analysis unit can use the emotion estimation function to monitor changes in a user's emotions in real time and propose design changes according to the emotions. The design analysis unit can also propose color adjustments and layout changes based on changes in the user's emotions. This makes it possible to monitor changes in a user's emotions in real time and propose design changes according to the emotions.

[0103] The design improvement system can also learn from past success stories and propose new designs based on successful patterns. For example, the high-conversion design proposal unit can learn from past success stories and propose new designs based on similar success patterns. The high-conversion design proposal unit can also propose new designs based on past high-conversion designs. This makes it possible to propose new designs based on past success stories.

[0104] The design improvement system can further propose designs that take into account the cultural backgrounds of users from different cultural spheres or regions. For example, the high-conversion design proposal unit proposes designs that take into account the cultural backgrounds of users from different cultural spheres. The high-conversion design proposal unit can also propose designs that take into account the cultural backgrounds of each region. This makes it possible to propose designs that take into account the cultural backgrounds of users from different cultural spheres or regions.

[0105] The design improvement system can further use an emotion estimation function to evaluate a design based on the user's emotion data and provide emotion-based feedback. For example, the design evaluation unit can use the emotion estimation function to evaluate a design based on the user's emotion data. The design evaluation unit can also evaluate the effectiveness of a design based on the user's emotion data. This makes it possible to use the emotion estimation function to evaluate a design based on the user's emotion data and provide emotion-based feedback.

[0106] The design improvement system can also compare designs with competitors' designs and perform relative evaluations when evaluating designs. For example, the design evaluation department collects design data from competitors and compares it with the company's own designs. The design evaluation department can also perform relative evaluations based on competitors' click rates and gaze data. This allows the system to compare designs with competitors' designs and perform relative evaluations when evaluating designs.

[0107] The design improvement system further uses an emotion estimation function to collect user emotion data in real time, and the design analysis unit can propose design improvements based on the emotion data. For example, the design data collection unit collects user facial expression data in real time using a camera and calculates an emotion score using an emotion estimation algorithm. The design data collection unit can also collect user voice data in real time and estimate emotions using voice analysis technology. This allows design improvements to be proposed in real time based on the user emotion data.

[0108] The processing flow of the second embodiment will be briefly explained below.

[0109] Step 1: The design data collection unit collects design data from web services and applications. For example, it collects user behavior data such as click rates and heat maps. It can also collect design data from web pages using scraping technology. It can also obtain design data in real time through APIs. Step 2: The design analysis department analyzes the collected design data. For example, image analysis technology is used to analyze the placement and color of design elements. Text analysis technology is used to analyze the meaning of the text data contained in the design. Furthermore, pattern recognition technology is used to extract design patterns and evaluate their effectiveness. Step 3: The high-conversion design suggestion unit automatically suggests high-conversion designs based on the analyzed design data. For example, it suggests layout changes to emphasize areas that users click frequently. It also suggests visually appealing color combinations to maximize the effectiveness of the design. It also suggests personalized designs optimized for individual users based on user behavior data.

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0112] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0120] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0121] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0123] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0124] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0128] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0129] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0130] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0131] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0132] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0134] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0135] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0136] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0138] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0139] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0140] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0143] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0144] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0145] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0146] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0147] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0149] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0150] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0151] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0152] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0153] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0154] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0155] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0156] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0157] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0158] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0159] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0160] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0161] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0162] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0163] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0164] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0165] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0166] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0167] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0168] 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.

[0169] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0170] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0171] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0172] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0173] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0174] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0175] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0176] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A design data collection department that collects design data for web services and applications; a design analysis unit that analyzes the design data collected by the design data collection unit; a high-conversion design suggestion unit that automatically suggests a high-conversion design based on the design data analyzed by the design analysis unit. A system characterized by:

2. The design data collection unit Collect user behavior data, such as click-through rates or heat maps.

2. The system of claim 1.

3. The design analysis unit Suggest layout changes to highlight areas where users click frequently 2. The system of claim 1.

4. The high conversion design proposal unit Suggest visually appealing color combinations 2. The system of claim 1.

5. The design data collection unit Collect multimodal data, including voice commands and gesture input, The design analysis unit Analyzing operation patterns based on the multimodal data 2. The system of claim 1.

6. The design data collection unit Collect user emotion data in real time, The design analysis unit Propose design improvements based on the emotion data 2. The system of claim 1.

7. The high conversion design proposal unit Propose designs based on user emotions 2. The system of claim 1.

8. The automated implementation part of the design is Implementing design based on user emotions and generating code that elicits positive emotions 2. The system of claim 1.

Images