system

Abstract

The system according to this embodiment aims to improve usability by automatically generating a user-specific UI. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects assets of existing services. The analysis unit analyzes the assets collected by the collection unit. The analysis unit analyzes user feedback based on the assets analyzed by the analysis unit. The generation unit generates a user-specific UI based on the analysis results obtained by the analysis unit. The provision unit provides the UI generated by the generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the UI of complex web services is difficult for users to operate, especially for the elderly and disabled, with low accessibility.

[0005] The system according to the embodiment aims to automatically generate a UI tailored to the user and improve the operability.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects assets of existing services. The analysis unit analyzes the assets collected by the collection unit. The analysis unit analyzes user feedback based on the assets analyzed by the analysis unit. The generation unit generates a user-specific UI based on the analysis results obtained by the analysis unit. The provision unit provides the UI generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can automatically generate a user-specific UI and improve usability. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The platform according to an embodiment of the present invention is a system that utilizes a generative AI agent to reconstruct the UI of existing web services and provide a user-oriented experience. This system reduces the cost of renewal for service providers and maximizes the value provided to users by having the generative AI agent handle the analysis of existing services, analysis of past feedback, and the generation and assistance of user-specific UIs. Specifically, without altering the service or internal structure to be renewed, the generative AI agent handles the analysis of existing UIs, logic, and assets, and the construction of new UIs, automatically building a user-oriented UI. For example, the generative AI agent analyzes the assets of existing services and past user feedback. For example, it collects and analyzes data such as which parts users find difficult to operate and which functions are difficult to use. Next, the generative AI agent provides a flexible UI that is tailored to the user and context. For example, it enables users to intuitively operate the service through natural language chat or voice input. In this way, the user's work efficiency can be significantly improved. Furthermore, this platform can be applied to a wide range of fields, from B2C and B2B to internal systems, and contributes to bridging the digital divide by providing an environment that takes accessibility into consideration. For example, by providing a simple and easily accessible UI for the elderly and people with disabilities, participation in the digital society can be promoted. This mechanism is expected to reduce costs, as the platform provides a one-stop solution for the enormous costs and manpower previously required to renew existing services. Furthermore, since it does not modify existing systems, it can enhance user value without affecting existing integration with other systems. As a result, the platform can reconstruct the UI of existing web services and provide a user-oriented experience.

[0029] The platform according to this embodiment comprises a collection unit, an analysis unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects assets of existing services. Assets of existing services include, but are not limited to, databases, APIs, and user interfaces. The collection unit extracts necessary data from, for example, the database of an existing service. The collection unit can also obtain data from external services through APIs. Furthermore, the collection unit can also analyze elements of user interfaces and collect necessary information. For example, the collection unit extracts user operation history from a database and provides it to the analysis unit. Data obtained through APIs is analyzed by the analysis unit. Elements of user interfaces are analyzed by the collection unit, and necessary information is extracted. The analysis unit analyzes the assets collected by the collection unit. The analysis is performed by, for example, data mining, statistical analysis, and machine learning algorithms, but is not limited to these methods. For example, the analysis unit uses data mining techniques to extract patterns and trends from the collected data. The analysis unit can also use statistical analysis to analyze the distribution and correlation of data. Furthermore, the analysis unit can also classify and predict data using machine learning algorithms. For example, the analysis unit can use data mining techniques to extract common patterns from user operation history. It can use statistical analysis to analyze the correlation between user operation history and feedback. It can use machine learning algorithms to predict future operations from user operation history. The analysis unit analyzes user feedback based on the assets analyzed by the analysis unit. User feedback includes, but is not limited to, survey results, reviews, and comments. For example, the analysis unit can analyze survey results using text mining techniques. The analysis unit can also analyze reviews and comments using sentiment analysis techniques. Furthermore, the analysis unit can use trend analysis to understand feedback trends. For example, the analysis unit can extract user opinions from survey results using text mining techniques. It can understand user sentiment from reviews and comments using sentiment analysis techniques.Trend analysis is used to understand feedback trends. The generation unit generates a user-specific UI based on the analysis results obtained by the analysis unit. The UI includes, but is not limited to, web interfaces, mobile applications, and desktop applications. For example, the generation unit uses generational AI to generate a customized UI based on the user's operation history and feedback. The generation unit can also use natural language processing technology to generate a UI that users can intuitively operate through natural language chat or voice input. Furthermore, the generation unit can also generate a customized UI based on the user's past operation history and preferences. For example, the generation unit uses generational AI to generate the optimal UI from the user's operation history. It uses natural language processing technology to generate a UI that users can operate in natural language. It generates a customized UI based on the user's past operation history and preferences. The delivery unit provides the UI generated by the generation unit. Delivery is done, for example, through a web browser or through a mobile application, but is not limited to these methods. For example, the delivery unit provides the generated UI to the user through a web browser. The delivery unit can also provide the generated UI to the user through a mobile application. Furthermore, the service provider can also provide the generated UI to users through a desktop application. For example, the service provider can display the generated UI through a web browser, a mobile application, or a desktop application. This allows the platform according to the embodiment to maximize user value by collecting, analyzing, and interpreting assets from existing services, and generating and providing a user-specific UI.

[0030] The data collection unit collects assets from existing services. These assets include, but are not limited to, databases, APIs, and user interfaces. For example, the data collection unit extracts necessary data from existing service databases. It can also obtain data from external services via APIs. Furthermore, the data collection unit can analyze user interface elements and collect necessary information. For instance, the data collection unit extracts user operation history from the database and provides it to the analysis unit. Data obtained via APIs is analyzed by the analysis unit. User interface elements are analyzed by the data collection unit, and the necessary information is extracted. In extracting data from databases, the data collection unit uses SQL queries to efficiently obtain data that meets specific criteria. For example, when extracting user operation history, it filters operation logs within a specific period to extract only the necessary information. When obtaining data from external services via APIs, it uses protocols such as RESTful APIs and GraphQL, and securely obtains data using authentication tokens. Furthermore, when analyzing user interface elements, the system uses DOM analysis technology to analyze the structure of web pages and collect information on specific elements (e.g., buttons and input fields). This allows the data collection unit to efficiently collect necessary assets from diverse data sources and provide them to the analysis unit. The data collection unit can flexibly configure the frequency and timing of data collection, accommodating various needs such as real-time data collection and periodic batch processing. For example, if real-time data collection is required, it can use WebSocket or streaming APIs to achieve immediate data acquisition. On the other hand, if periodic batch processing is more appropriate, it can use a scheduler to periodically collect data and provide it to the analysis unit. This allows the data collection unit to improve the overall flexibility and efficiency of the system.

[0031] The analysis unit analyzes the assets collected by the collection unit. Analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms, but is not limited to these examples. For instance, the analysis unit may use data mining techniques to extract patterns and trends from the collected data. It can also use statistical analysis to analyze data distribution and correlations. Furthermore, it can use machine learning algorithms to classify and predict data. For example, the analysis unit may use data mining techniques to extract common patterns from user operation history. Statistical analysis may be used to analyze the correlation between user operation history and feedback. Machine learning algorithms may be used to predict future operations from user operation history. The analysis unit may also use data mining techniques to perform clustering and association rule mining to identify user behavior patterns and highly related operations. For example, clustering algorithms may be used to identify user groups with similar operation histories and extract characteristics from each group. Association rule mining may also be used to clarify how specific operations relate to other operations. In statistical analysis, regression analysis and correlation analysis are used to quantitatively evaluate the relationship between user operation history and feedback. For example, regression analysis is used to model the impact of a specific operation on user feedback and identify areas for improvement in operation. In machine learning algorithms, supervised and unsupervised learning are used to classify and predict data. For example, supervised learning is used to build a model that predicts future operations based on past operation history and feedback. Unsupervised learning is used to discover the latent structure of the data and gain new insights. As a result, the analysis unit can analyze the collected data from multiple angles and gain insights based on user behavior and feedback.

[0032] The Analysis Department analyzes user feedback based on assets analyzed by the Analysis Department. User feedback includes, but is not limited to, survey results, reviews, and comments. For example, the Analysis Department analyzes survey results using text mining techniques. The Analysis Department can also analyze reviews and comments using sentiment analysis techniques. Furthermore, the Analysis Department can use trend analysis to understand the trends in feedback. For example, the Analysis Department extracts user opinions from survey results using text mining techniques. It understands user emotions from reviews and comments using sentiment analysis techniques. It understands the trends in feedback using trend analysis. The Analysis Department applies natural language processing algorithms using text mining techniques to extract important keywords and phrases from survey results, reviews, and comments. For example, it uses topic modeling to classify user opinions by theme and evaluate the frequency and importance of each theme. In sentiment analysis techniques, it uses sentiment dictionaries and machine learning models to classify positive, negative, and neutral emotions from user feedback and evaluate the intensity of those emotions. For example, it analyzes the text of reviews and comments to quantitatively evaluate what emotions users are feeling. In trend analysis, time-series data is used to understand the trends and fluctuations in feedback. For example, by analyzing how the frequency and content of feedback change over time, users' interests and frustrations during a specific period can be identified. This allows the analysis department to analyze user feedback from multiple perspectives and clearly understand user needs and problems.

[0033] The generation unit generates a user-specific UI based on the analysis results obtained by the analysis unit. The UI includes, but is not limited to, web interfaces, mobile applications, and desktop applications. For example, the generation unit uses generative AI to generate a customized UI based on the user's operation history and feedback. The generation unit can also use natural language processing technology to generate a UI that users can intuitively operate through natural language chat or voice input. Furthermore, the generation unit can generate a customized UI based on the user's past operation history and preferences. For example, the generation unit uses generative AI to generate the optimal UI from the user's operation history. It uses natural language processing technology to generate a UI that users can operate in natural language. It generates a customized UI based on the user's past operation history and preferences. The generation unit uses generative AI to analyze the user's operation history and feedback and automatically generate the optimal UI design. For example, it uses a deep learning model to propose UI layouts and color schemes based on the user's operation patterns and preferences. It also uses natural language processing technology to generate interfaces that users can operate through natural language chat or voice input. For example, it integrates chatbots and voice assistants to provide an intuitive user experience. Furthermore, the generation unit generates a personalized UI based on the user's past operation history and preferences. For example, it prioritizes displaying frequently used functions and settings to improve user convenience. This allows the generation unit to provide a customized UI tailored to the user's needs, thereby improving the user experience.

[0034] The provider provides the UI generated by the generator. This provision can be, but is not limited to, methods such as providing the UI through a web browser or a mobile application. For example, the provider provides the generated UI to the user through a web browser. The provider can also provide the generated UI to the user through a mobile application. Furthermore, the provider can provide the generated UI to the user through a desktop application. For example, the provider displays the generated UI through a web browser, a mobile application, or a desktop application. When providing the generated UI to the user, the provider employs responsive design to provide a UI that adapts to various devices and screen sizes. For example, the UI provided through a web browser is designed to display optimally on different devices such as desktops, tablets, and smartphones. The UI provided through a mobile application supports different platforms such as iOS and Android, providing the convenience of a native application. The UI provided through a desktop application supports different operating systems such as Windows and macOS, achieving high performance and usability. When providing the generated UI, the provider also considers security and privacy, ensuring the safe protection of user data. For example, SSL / TLS encryption is used to protect data transmission and reception, and user authentication and access control are implemented to prevent unauthorized access. This allows the service provider to offer users a secure and comfortable UI, thereby improving the user experience.

[0035] The data collection unit can collect past user feedback. For example, it can collect reviews from the past year. The data collection unit can also collect feedback during a specific event period. For example, it can extract reviews from the past year from a database. It can obtain feedback during a specific event period via an API. By collecting past user feedback, it is possible to generate a UI that reflects user needs. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input past user feedback into an AI and have the AI ​​perform the feedback collection.

[0036] The analysis unit can analyze the collected assets and identify which parts users find difficult to use. For example, the analysis unit can identify buttons with low click-through rates. The analysis unit can also analyze the frequency of error messages. For example, the analysis unit can identify buttons with low click-through rates using data mining techniques. The frequency of error messages can be analyzed using statistical analysis. This allows for the identification of areas where users find the operation difficult, thereby clearly identifying areas for UI improvement. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected assets into an AI and have the AI ​​identify the areas where users find the operation difficult.

[0037] The generation unit can generate a user interface (UI) that users can intuitively operate through natural language chat or voice input. For example, the generation unit can generate a chatbot. The generation unit can also generate a voice assistant. For example, the generation unit can use natural language processing technology to generate a UI that users can operate through a chatbot. For example, it can use speech recognition technology to generate a UI that users can operate through a voice assistant. This improves usability by generating a UI that users can operate intuitively. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input the user's operation history into a generation AI and have the generation AI execute the generation of a UI that can be operated through natural language chat or voice input.

[0038] The service provider can provide the generated UI to the user. The service provider can provide it, for example, through a web browser. Alternatively, the service provider can also provide it through a mobile application. For example, the service provider can provide the generated UI to the user through a web browser. Or it can provide it to the user through a mobile application. By providing the generated UI to the user, user convenience can be improved. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the generated UI into an AI and have the AI ​​execute a method for providing it to the user.

[0039] The generation unit can generate a customized UI based on the user's past operation history and preferences. For example, the generation unit can generate a UI based on the user's click history. The generation unit can also generate a UI based on the user's browsing history. For example, the generation unit can analyze the user's click history and generate an optimal UI. It can analyze the user's browsing history and generate a customized UI. By generating a customized UI based on the user's past operation history and preferences, user satisfaction can be improved. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's operation history into a generation AI and have the generation AI execute the generation of a customized UI.

[0040] The data collection unit can select the most suitable assets by referring to the user's past operation history during collection. For example, the data collection unit can prioritize collecting assets that the user has frequently used in the past. The data collection unit can also predict and collect assets that will be used during specific time periods based on the user's past operation history. For example, the data collection unit can analyze the user's past operation history and select the most efficient assets. This allows for the efficient collection of assets that meet the user's needs by selecting the most suitable assets by referring to the user's past operation history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user operation history data into a generating AI and have the generating AI perform the selection of the most suitable assets.

[0041] The data collection unit can filter data based on the user's current usage and areas of interest during collection. For example, the data collection unit can prioritize collecting assets related to the features the user is currently using. The data collection unit can also filter and collect relevant assets based on the user's areas of interest. For example, the data collection unit can analyze the user's current usage in real time and collect the most suitable assets. This allows for efficient collection of assets that meet the user's needs by filtering based on the user's current usage and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's current usage data into a generating AI and have the generating AI perform the filtering.

[0042] The collection unit can prioritize the collection of highly relevant assets by considering the user's geographical location information during the collection process. For example, if the user is in a specific region, the collection unit will prioritize the collection of assets related to that region. The collection unit can also suggest the most suitable assets based on the user's geographical location information. For example, if the user is on the move, the collection unit will collect highly relevant assets based on the user's current location. This allows for the efficient collection of assets that meet the user's needs by prioritizing the collection of highly relevant assets by considering the user's geographical location information. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant assets.

[0043] The data collection unit can analyze the user's social media activity and collect relevant assets during the collection process. For example, the data collection unit can collect relevant assets based on information shared by the user on social media. The data collection unit can also predict and collect assets of interest based on the user's social media activity. For example, the data collection unit can collect assets related to accounts that the user follows on social media. This allows for the efficient collection of assets that meet the user's needs by analyzing the user's social media activity and collecting relevant assets. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant assets.

[0044] The analysis unit can improve the accuracy of its analysis by considering the interrelationships between assets during the analysis process. For example, the analysis unit analyzes the relationships between assets and performs the analysis while considering these interrelationships. Furthermore, the analysis unit can improve the accuracy of the analysis results based on the interrelationships between assets. For example, the analysis unit selects the optimal analysis method while considering the interrelationships between assets. By improving the accuracy of the analysis while considering the interrelationships between assets, more accurate analysis results can be obtained. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input data on the interrelationships between assets into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0045] The analysis unit can perform analysis while considering the attribute information of the asset submitter. For example, the analysis unit can adjust the analysis results based on the attribute information of the asset submitter. The analysis unit can also select the optimal analysis method while considering the submitter's attribute information. For example, the analysis unit can improve the accuracy of the analysis results based on the submitter's attribute information. As a result, more accurate analysis results can be obtained by performing analysis while considering the attribute information of the asset submitter. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the submitter's attribute information into a generating AI and have the generating AI perform the analysis.

[0046] The analysis unit can perform analysis while considering the geographical distribution of assets. For example, the analysis unit can adjust the analysis results based on the geographical distribution of assets. The analysis unit can also select the optimal analysis method while considering the geographical distribution. For example, the analysis unit can improve the accuracy of the analysis results based on the geographical distribution. As a result, more accurate analysis results can be obtained by performing analysis while considering the geographical distribution of assets. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input geographical distribution data into a generating AI and have the generating AI perform the analysis.

[0047] The analysis unit can improve the accuracy of its analysis by referring to relevant literature for the asset during the analysis process. For example, the analysis unit can adjust the analysis results by referring to relevant literature for the asset. The analysis unit can also select the optimal analysis method based on the relevant literature. For example, the analysis unit can improve the accuracy of the analysis results by referring to relevant literature. By improving the accuracy of the analysis by referring to relevant literature for the asset, more accurate analysis results can be obtained. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input relevant literature data into a generating AI and have the generating AI perform the analysis.

[0048] The analysis unit can predict current feedback by referring to past feedback data during analysis. For example, the analysis unit predicts current feedback based on past feedback data. The analysis unit can also select the optimal analysis method by referring to past feedback data. For example, the analysis unit analyzes feedback trends based on past feedback data. This allows for more accurate analysis by predicting current feedback by referring to past feedback data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input past feedback data into a generating AI and have the generating AI perform a prediction of current feedback.

[0049] The analysis unit can apply different analytical methods to each feedback category during the analysis. For example, the analysis unit can select the optimal analytical method for each feedback category. The analysis unit can also use different analytical criteria for each feedback category. For example, the analysis unit can adjust the analysis results for each feedback category. This allows for more accurate analysis by applying different analytical methods to each feedback category. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input feedback category data into a generating AI and have the generating AI apply different analytical methods.

[0050] The analysis department can determine the priority of analyses based on the timing of feedback submissions. For example, the analysis department can determine the priority of analyses based on the timing of feedback submissions. The analysis department can also select the optimal analysis method based on the submission timing. For example, the analysis department can adjust the analysis results taking the submission timing into consideration. This makes it possible to perform more efficient analyses by determining the priority of analyses based on the timing of feedback submissions. Some or all of the above processes in the analysis department may be performed using AI or not. For example, the analysis department can input feedback submission timing data into a generating AI and have the generating AI perform the determination of analysis priorities.

[0051] The analysis unit can perform analysis by referring to relevant market data on feedback. For example, the analysis unit can adjust the analysis results based on the relevant market data on feedback. The analysis unit can also select the optimal analysis method by referring to the relevant market data. For example, the analysis unit can analyze the trends of feedback based on the relevant market data. This makes it possible to perform analysis more accurately by referring to the relevant market data on feedback. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input relevant market data into a generating AI and have the generating AI perform the analysis.

[0052] The generation unit can generate the optimal UI by referring to the user's past operation history during generation. For example, the generation unit can generate the optimal UI based on the UI the user has frequently used in the past. The generation unit can also predict and generate the UI to be used at a specific time period based on the user's past operation history. For example, the generation unit can analyze the user's past operation history and generate the most efficient UI. In this way, by generating the optimal UI by referring to the user's past operation history, a UI that meets the user's needs can be provided. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's operation history data into a generation AI and have the generation AI execute the generation of the optimal UI.

[0053] The generation unit can customize the UI based on the user's current usage during generation. For example, the generation unit generates a UI related to the function the user is currently using. The generation unit can also analyze the user's current usage in real time and generate the optimal UI. For example, the generation unit provides a customized UI based on the user's current usage. This allows the UI to be customized based on the user's current usage, thereby providing a UI that meets the user's needs. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's current usage data into a generation AI and have the generation AI perform the UI customization.

[0054] The generation unit can generate the optimal UI by considering the user's geographical location information during the generation process. For example, if the user is in a specific region, the generation unit will generate a UI relevant to that region. The generation unit can also suggest the optimal UI based on the user's geographical location information. For example, if the user is on the move, the generation unit will generate a highly relevant UI based on their current location. In this way, by generating the optimal UI while considering the user's geographical location information, a UI that meets the user's needs can be provided. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's geographical location information into the generation AI and have the generation AI execute the generation of the optimal UI.

[0055] The generation unit can analyze the user's social media activity during generation and customize the UI accordingly. For example, the generation unit can generate relevant UI based on information shared by the user on social media. It can also predict and generate UI of interest based on the user's social media activity. For example, the generation unit can generate UI related to accounts the user follows on social media. This allows for the provision of UI tailored to user needs by analyzing the user's social media activity and customizing the UI accordingly. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input user social media activity data into a generation AI and have the generation AI perform the UI customization.

[0056] The service provider can select the optimal display method by referring to the user's past operation history at the time of service provision. For example, the service provider can provide the optimal display method based on the display methods the user has frequently used in the past. The service provider can also predict and provide the display method to be used during a specific time period based on the user's past operation history. For example, the service provider can analyze the user's past operation history and select the most efficient display method. This makes it possible to provide a display that meets the user's needs by selecting the optimal display method by referring to the user's past operation history. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's operation history data into a generating AI and have the generating AI perform the selection of the optimal display method.

[0057] The service provider can select the optimal display method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the service provider can provide a display method optimized for a larger screen. For example, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. This allows for display tailored to the user's needs by selecting the optimal display method based on the user's device information. Some or all of the above-described processes in the service provider may be performed using AI, or they may not. For example, the service provider can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The data collection unit can select the most suitable assets by referring to the user's past operation history during data collection. For example, it can prioritize collecting assets that the user has frequently used in the past. It can also predict and collect assets that the user will use during specific time periods based on the user's past operation history. This allows for efficient collection of assets that meet the user's needs by selecting the most suitable assets by referring to the user's past operation history. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input user operation history data into a generating AI and have the generating AI select the most suitable assets.

[0060] The analysis unit can improve the accuracy of the analysis by considering the interrelationships between assets during the analysis process. For example, it can analyze the relationships between assets and perform the analysis while considering these interrelationships. It can also improve the accuracy of the analysis results based on the interrelationships between assets. By improving the accuracy of the analysis by considering the interrelationships between assets, more accurate analysis results can be obtained. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input data on the interrelationships between assets into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0061] The generation unit can customize the UI based on the user's current usage during generation. For example, it can generate a UI related to the function the user is currently using. It can also analyze the user's current usage in real time and generate the optimal UI. This allows for the provision of a UI that meets the user's needs by customizing the UI based on the user's current usage. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's current usage data into the generation AI and have the generation AI perform the UI customization.

[0062] The service provider can select the optimal display method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, it can provide a display method that matches the screen size. If the user is using a tablet, it can also provide a display method optimized for the larger screen. By selecting the optimal display method considering the user's device information, it becomes possible to display content that meets the user's needs. Some or all of the above processing in the service provider may be performed using AI, or it may be performed without AI. For example, the service provider can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[0063] The analysis unit can apply different analytical methods to each feedback category during the analysis. For example, it can select the optimal analytical method for each feedback category. It can also use different analytical criteria for each feedback category. By applying different analytical methods to each feedback category, more accurate analysis becomes possible. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input feedback category data into a generating AI and have the generating AI execute the application of different analytical methods.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The collection unit collects assets from existing services. These assets include databases, APIs, and user interfaces. The collection unit extracts necessary data from existing service databases and obtains data from external services via APIs. It also analyzes user interface elements and collects necessary information. Step 2: The analysis unit analyzes the assets collected by the collection unit. The analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms. For example, data mining techniques are used to extract patterns and trends, statistical analysis is used to analyze the distribution and correlation of data, and machine learning algorithms are used to classify and predict data. Step 3: The analysis department analyzes user feedback based on the assets analyzed by the analysis department. User feedback includes survey results, reviews, and comments. For example, text mining techniques are used to analyze survey results, sentiment analysis techniques are used to analyze reviews and comments, and trend analysis is used to understand the trends in feedback. Step 4: The generation unit generates a user-specific UI based on the analysis results obtained by the analysis unit. The UI includes web interfaces, mobile applications, desktop applications, etc. For example, it uses generation AI to generate a customized UI based on the user's operation history and feedback, and uses natural language processing technology to generate a UI that the user can operate in natural language. Step 5: The providing unit provides the UI generated by the generating unit. The provision is done through a web browser, mobile application, or desktop application. For example, the generated UI is provided to the user through a web browser, mobile application, or desktop application.

[0066] (Example of form 2) The platform according to an embodiment of the present invention is a system that utilizes a generative AI agent to reconstruct the UI of existing web services and provide a user-oriented experience. This system reduces the cost of renewal for service providers and maximizes the value provided to users by having the generative AI agent handle the analysis of existing services, analysis of past feedback, and the generation and assistance of user-specific UIs. Specifically, without altering the service or internal structure to be renewed, the generative AI agent handles the analysis of existing UIs, logic, and assets, and the construction of new UIs, automatically building a user-oriented UI. For example, the generative AI agent analyzes the assets of existing services and past user feedback. For example, it collects and analyzes data such as which parts users find difficult to operate and which functions are difficult to use. Next, the generative AI agent provides a flexible UI that is tailored to the user and context. For example, it enables users to intuitively operate the service through natural language chat or voice input. In this way, the user's work efficiency can be significantly improved. Furthermore, this platform can be applied to a wide range of fields, from B2C and B2B to internal systems, and contributes to bridging the digital divide by providing an environment that takes accessibility into consideration. For example, by providing a simple and easily accessible UI for the elderly and people with disabilities, participation in the digital society can be promoted. This mechanism is expected to reduce costs, as the platform provides a one-stop solution for the enormous costs and manpower previously required to renew existing services. Furthermore, since it does not modify existing systems, it can enhance user value without affecting existing integration with other systems. As a result, the platform can reconstruct the UI of existing web services and provide a user-oriented experience.

[0067] The platform according to this embodiment comprises a collection unit, an analysis unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects assets of existing services. Assets of existing services include, but are not limited to, databases, APIs, and user interfaces. The collection unit extracts necessary data from, for example, the database of an existing service. The collection unit can also obtain data from external services through APIs. Furthermore, the collection unit can also analyze elements of user interfaces and collect necessary information. For example, the collection unit extracts user operation history from a database and provides it to the analysis unit. Data obtained through APIs is analyzed by the analysis unit. Elements of user interfaces are analyzed by the collection unit, and necessary information is extracted. The analysis unit analyzes the assets collected by the collection unit. The analysis is performed by, for example, data mining, statistical analysis, and machine learning algorithms, but is not limited to these methods. For example, the analysis unit uses data mining techniques to extract patterns and trends from the collected data. The analysis unit can also use statistical analysis to analyze the distribution and correlation of data. Furthermore, the analysis unit can also classify and predict data using machine learning algorithms. For example, the analysis unit can use data mining techniques to extract common patterns from user operation history. It can use statistical analysis to analyze the correlation between user operation history and feedback. It can use machine learning algorithms to predict future operations from user operation history. The analysis unit analyzes user feedback based on the assets analyzed by the analysis unit. User feedback includes, but is not limited to, survey results, reviews, and comments. For example, the analysis unit can analyze survey results using text mining techniques. The analysis unit can also analyze reviews and comments using sentiment analysis techniques. Furthermore, the analysis unit can use trend analysis to understand feedback trends. For example, the analysis unit can extract user opinions from survey results using text mining techniques. It can understand user sentiment from reviews and comments using sentiment analysis techniques.Trend analysis is used to understand feedback trends. The generation unit generates a user-specific UI based on the analysis results obtained by the analysis unit. The UI includes, but is not limited to, web interfaces, mobile applications, and desktop applications. For example, the generation unit uses generational AI to generate a customized UI based on the user's operation history and feedback. The generation unit can also use natural language processing technology to generate a UI that users can intuitively operate through natural language chat or voice input. Furthermore, the generation unit can also generate a customized UI based on the user's past operation history and preferences. For example, the generation unit uses generational AI to generate the optimal UI from the user's operation history. It uses natural language processing technology to generate a UI that users can operate in natural language. It generates a customized UI based on the user's past operation history and preferences. The delivery unit provides the UI generated by the generation unit. Delivery is done, for example, through a web browser or through a mobile application, but is not limited to these methods. For example, the delivery unit provides the generated UI to the user through a web browser. The delivery unit can also provide the generated UI to the user through a mobile application. Furthermore, the service provider can also provide the generated UI to users through a desktop application. For example, the service provider can display the generated UI through a web browser, a mobile application, or a desktop application. This allows the platform according to the embodiment to maximize user value by collecting, analyzing, and interpreting assets from existing services, and generating and providing a user-specific UI.

[0068] The data collection unit collects assets from existing services. These assets include, but are not limited to, databases, APIs, and user interfaces. For example, the data collection unit extracts necessary data from existing service databases. It can also obtain data from external services via APIs. Furthermore, the data collection unit can analyze user interface elements and collect necessary information. For instance, the data collection unit extracts user operation history from the database and provides it to the analysis unit. Data obtained via APIs is analyzed by the analysis unit. User interface elements are analyzed by the data collection unit, and the necessary information is extracted. In extracting data from databases, the data collection unit uses SQL queries to efficiently obtain data that meets specific criteria. For example, when extracting user operation history, it filters operation logs within a specific period to extract only the necessary information. When obtaining data from external services via APIs, it uses protocols such as RESTful APIs and GraphQL, and securely obtains data using authentication tokens. Furthermore, when analyzing user interface elements, the system uses DOM analysis technology to analyze the structure of web pages and collect information on specific elements (e.g., buttons and input fields). This allows the data collection unit to efficiently collect necessary assets from diverse data sources and provide them to the analysis unit. The data collection unit can flexibly configure the frequency and timing of data collection, accommodating various needs such as real-time data collection and periodic batch processing. For example, if real-time data collection is required, it can use WebSocket or streaming APIs to achieve immediate data acquisition. On the other hand, if periodic batch processing is more appropriate, it can use a scheduler to periodically collect data and provide it to the analysis unit. This allows the data collection unit to improve the overall flexibility and efficiency of the system.

[0069] The analysis unit analyzes the assets collected by the collection unit. Analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms, but is not limited to these examples. For instance, the analysis unit may use data mining techniques to extract patterns and trends from the collected data. It can also use statistical analysis to analyze data distribution and correlations. Furthermore, it can use machine learning algorithms to classify and predict data. For example, the analysis unit may use data mining techniques to extract common patterns from user operation history. Statistical analysis may be used to analyze the correlation between user operation history and feedback. Machine learning algorithms may be used to predict future operations from user operation history. The analysis unit may also use data mining techniques to perform clustering and association rule mining to identify user behavior patterns and highly related operations. For example, clustering algorithms may be used to identify user groups with similar operation histories and extract characteristics from each group. Association rule mining may also be used to clarify how specific operations relate to other operations. In statistical analysis, regression analysis and correlation analysis are used to quantitatively evaluate the relationship between user operation history and feedback. For example, regression analysis is used to model the impact of a specific operation on user feedback and identify areas for improvement in operation. In machine learning algorithms, supervised and unsupervised learning are used to classify and predict data. For example, supervised learning is used to build a model that predicts future operations based on past operation history and feedback. Unsupervised learning is used to discover the latent structure of the data and gain new insights. As a result, the analysis unit can analyze the collected data from multiple angles and gain insights based on user behavior and feedback.

[0070] The Analysis Department analyzes user feedback based on assets analyzed by the Analysis Department. User feedback includes, but is not limited to, survey results, reviews, and comments. For example, the Analysis Department analyzes survey results using text mining techniques. The Analysis Department can also analyze reviews and comments using sentiment analysis techniques. Furthermore, the Analysis Department can use trend analysis to understand the trends in feedback. For example, the Analysis Department extracts user opinions from survey results using text mining techniques. It understands user emotions from reviews and comments using sentiment analysis techniques. It understands the trends in feedback using trend analysis. The Analysis Department applies natural language processing algorithms using text mining techniques to extract important keywords and phrases from survey results, reviews, and comments. For example, it uses topic modeling to classify user opinions by theme and evaluate the frequency and importance of each theme. In sentiment analysis techniques, it uses sentiment dictionaries and machine learning models to classify positive, negative, and neutral emotions from user feedback and evaluate the intensity of those emotions. For example, it analyzes the text of reviews and comments to quantitatively evaluate what emotions users are feeling. In trend analysis, time-series data is used to understand the trends and fluctuations in feedback. For example, by analyzing how the frequency and content of feedback change over time, users' interests and frustrations during a specific period can be identified. This allows the analysis department to analyze user feedback from multiple perspectives and clearly understand user needs and problems.

[0071] The generation unit generates a user-specific UI based on the analysis results obtained by the analysis unit. The UI includes, but is not limited to, web interfaces, mobile applications, and desktop applications. For example, the generation unit uses generative AI to generate a customized UI based on the user's operation history and feedback. The generation unit can also use natural language processing technology to generate a UI that users can intuitively operate through natural language chat or voice input. Furthermore, the generation unit can generate a customized UI based on the user's past operation history and preferences. For example, the generation unit uses generative AI to generate the optimal UI from the user's operation history. It uses natural language processing technology to generate a UI that users can operate in natural language. It generates a customized UI based on the user's past operation history and preferences. The generation unit uses generative AI to analyze the user's operation history and feedback and automatically generate the optimal UI design. For example, it uses a deep learning model to propose UI layouts and color schemes based on the user's operation patterns and preferences. It also uses natural language processing technology to generate interfaces that users can operate through natural language chat or voice input. For example, it integrates chatbots and voice assistants to provide an intuitive user experience. Furthermore, the generation unit generates a personalized UI based on the user's past operation history and preferences. For example, it prioritizes displaying frequently used functions and settings to improve user convenience. This allows the generation unit to provide a customized UI tailored to the user's needs, thereby improving the user experience.

[0072] The provider provides the UI generated by the generator. This provision can be, but is not limited to, methods such as providing the UI through a web browser or a mobile application. For example, the provider provides the generated UI to the user through a web browser. The provider can also provide the generated UI to the user through a mobile application. Furthermore, the provider can provide the generated UI to the user through a desktop application. For example, the provider displays the generated UI through a web browser, a mobile application, or a desktop application. When providing the generated UI to the user, the provider employs responsive design to provide a UI that adapts to various devices and screen sizes. For example, the UI provided through a web browser is designed to display optimally on different devices such as desktops, tablets, and smartphones. The UI provided through a mobile application supports different platforms such as iOS and Android, providing the convenience of a native application. The UI provided through a desktop application supports different operating systems such as Windows and macOS, achieving high performance and usability. When providing the generated UI, the provider also considers security and privacy, ensuring the safe protection of user data. For example, SSL / TLS encryption is used to protect data transmission and reception, and user authentication and access control are implemented to prevent unauthorized access. This allows the service provider to offer users a secure and comfortable UI, thereby improving the user experience.

[0073] The data collection unit can collect past user feedback. For example, it can collect reviews from the past year. The data collection unit can also collect feedback during a specific event period. For example, it can extract reviews from the past year from a database. It can obtain feedback during a specific event period via an API. By collecting past user feedback, it is possible to generate a UI that reflects user needs. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input past user feedback into an AI and have the AI ​​perform the feedback collection.

[0074] The analysis unit can analyze the collected assets and identify which parts users find difficult to use. For example, the analysis unit can identify buttons with low click-through rates. The analysis unit can also analyze the frequency of error messages. For example, the analysis unit can identify buttons with low click-through rates using data mining techniques. The frequency of error messages can be analyzed using statistical analysis. This allows for the identification of areas where users find the operation difficult, thereby clearly identifying areas for UI improvement. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected assets into an AI and have the AI ​​identify the areas where users find the operation difficult.

[0075] The generation unit can generate a user interface (UI) that users can intuitively operate through natural language chat or voice input. For example, the generation unit can generate a chatbot. The generation unit can also generate a voice assistant. For example, the generation unit can use natural language processing technology to generate a UI that users can operate through a chatbot. For example, it can use speech recognition technology to generate a UI that users can operate through a voice assistant. This improves usability by generating a UI that users can operate intuitively. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input the user's operation history into a generation AI and have the generation AI execute the generation of a UI that can be operated through natural language chat or voice input.

[0076] The service provider can provide the generated UI to the user. The service provider can provide it, for example, through a web browser. Alternatively, the service provider can also provide it through a mobile application. For example, the service provider can provide the generated UI to the user through a web browser. Or it can provide it to the user through a mobile application. By providing the generated UI to the user, user convenience can be improved. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the generated UI into an AI and have the AI ​​execute a method for providing it to the user.

[0077] The generation unit can generate a customized UI based on the user's past operation history and preferences. For example, the generation unit can generate a UI based on the user's click history. The generation unit can also generate a UI based on the user's browsing history. For example, the generation unit can analyze the user's click history and generate an optimal UI. It can analyze the user's browsing history and generate a customized UI. By generating a customized UI based on the user's past operation history and preferences, user satisfaction can be improved. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's operation history into a generation AI and have the generation AI execute the generation of a customized UI.

[0078] The data collection unit can estimate the user's emotions and determine the priority of assets to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting simple and intuitive assets to alleviate stress. Conversely, if the user is relaxed, the data collection unit may prioritize collecting assets containing detailed information. For example, if the user is in a hurry, the data collection unit will prioritize collecting assets that can be quickly operated. This allows for the efficient collection of assets that meet the user's needs by prioritizing assets based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of assets.

[0079] The data collection unit can select the most suitable assets by referring to the user's past operation history during collection. For example, the data collection unit can prioritize collecting assets that the user has frequently used in the past. The data collection unit can also predict and collect assets that will be used during specific time periods based on the user's past operation history. For example, the data collection unit can analyze the user's past operation history and select the most efficient assets. This allows for the efficient collection of assets that meet the user's needs by selecting the most suitable assets by referring to the user's past operation history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user operation history data into a generating AI and have the generating AI perform the selection of the most suitable assets.

[0080] The data collection unit can filter data based on the user's current usage and areas of interest during collection. For example, the data collection unit can prioritize collecting assets related to the features the user is currently using. The data collection unit can also filter and collect relevant assets based on the user's areas of interest. For example, the data collection unit can analyze the user's current usage in real time and collect the most suitable assets. This allows for efficient collection of assets that meet the user's needs by filtering based on the user's current usage and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's current usage data into a generating AI and have the generating AI perform the filtering.

[0081] The data collection unit can estimate the user's emotions and adjust the timing of asset acquisition based on the estimated emotions. For example, if the user is stressed, the data collection unit will quickly collect assets to alleviate the stress. Conversely, if the user is relaxed, the data collection unit can slowly collect assets containing detailed information. For example, if the user is in a hurry, the data collection unit will quickly collect assets that can be operated quickly. This allows for efficient collection of assets that meet the user's needs by adjusting the timing of asset acquisition based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the timing of asset acquisition.

[0082] The collection unit can prioritize the collection of highly relevant assets by considering the user's geographical location information during the collection process. For example, if the user is in a specific region, the collection unit will prioritize the collection of assets related to that region. The collection unit can also suggest the most suitable assets based on the user's geographical location information. For example, if the user is on the move, the collection unit will collect highly relevant assets based on the user's current location. This allows for the efficient collection of assets that meet the user's needs by prioritizing the collection of highly relevant assets by considering the user's geographical location information. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant assets.

[0083] The data collection unit can analyze the user's social media activity and collect relevant assets during the collection process. For example, the data collection unit can collect relevant assets based on information shared by the user on social media. The data collection unit can also predict and collect assets of interest based on the user's social media activity. For example, the data collection unit can collect assets related to accounts that the user follows on social media. This allows for the efficient collection of assets that meet the user's needs by analyzing the user's social media activity and collecting relevant assets. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant assets.

[0084] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated emotions. For example, if the user is stressed, the analysis unit can use simple analysis criteria to reduce stress. Alternatively, if the user is relaxed, the analysis unit can use detailed analysis criteria. For example, if the user is in a hurry, the analysis unit can use criteria that allow for quick analysis. This allows for analysis tailored to the user's needs by adjusting the analysis criteria based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the analysis criteria.

[0085] The analysis unit can improve the accuracy of its analysis by considering the interrelationships between assets during the analysis process. For example, the analysis unit analyzes the relationships between assets and performs the analysis while considering these interrelationships. Furthermore, the analysis unit can improve the accuracy of the analysis results based on the interrelationships between assets. For example, the analysis unit selects the optimal analysis method while considering the interrelationships between assets. By improving the accuracy of the analysis while considering the interrelationships between assets, more accurate analysis results can be obtained. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input data on the interrelationships between assets into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0086] The analysis unit can perform analysis while considering the attribute information of the asset submitter. For example, the analysis unit can adjust the analysis results based on the attribute information of the asset submitter. The analysis unit can also select the optimal analysis method while considering the submitter's attribute information. For example, the analysis unit can improve the accuracy of the analysis results based on the submitter's attribute information. As a result, more accurate analysis results can be obtained by performing analysis while considering the attribute information of the asset submitter. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the submitter's attribute information into a generating AI and have the generating AI perform the analysis.

[0087] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a simple and highly visible display method. It can also provide a display method that includes detailed information if the user is relaxed. For example, if the user is in a hurry, the analysis unit can provide a concise display method. By adjusting the display method of the analysis results based on the user's emotions, a user-friendly display is possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the display method.

[0088] The analysis unit can perform analysis while considering the geographical distribution of assets. For example, the analysis unit can adjust the analysis results based on the geographical distribution of assets. The analysis unit can also select the optimal analysis method while considering the geographical distribution. For example, the analysis unit can improve the accuracy of the analysis results based on the geographical distribution. As a result, more accurate analysis results can be obtained by performing analysis while considering the geographical distribution of assets. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input geographical distribution data into a generating AI and have the generating AI perform the analysis.

[0089] The analysis unit can improve the accuracy of its analysis by referring to relevant literature for the asset during the analysis process. For example, the analysis unit can adjust the analysis results by referring to relevant literature for the asset. The analysis unit can also select the optimal analysis method based on the relevant literature. For example, the analysis unit can improve the accuracy of the analysis results by referring to relevant literature. By improving the accuracy of the analysis by referring to relevant literature for the asset, more accurate analysis results can be obtained. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input relevant literature data into a generating AI and have the generating AI perform the analysis.

[0090] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated emotions. For example, if the user is stressed, the analysis unit can use simple analysis criteria to reduce stress. Alternatively, if the user is relaxed, the analysis unit can use detailed analysis criteria. For example, if the user is in a hurry, the analysis unit can use criteria that allow for quick analysis. This allows for analysis tailored to the user's needs by adjusting the analysis criteria based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the analysis criteria.

[0091] The analysis unit can predict current feedback by referring to past feedback data during analysis. For example, the analysis unit predicts current feedback based on past feedback data. The analysis unit can also select the optimal analysis method by referring to past feedback data. For example, the analysis unit analyzes feedback trends based on past feedback data. This allows for more accurate analysis by predicting current feedback by referring to past feedback data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input past feedback data into a generating AI and have the generating AI perform a prediction of current feedback.

[0092] The analysis unit can apply different analytical methods to each feedback category during the analysis. For example, the analysis unit can select the optimal analytical method for each feedback category. The analysis unit can also use different analytical criteria for each feedback category. For example, the analysis unit can adjust the analysis results for each feedback category. This allows for more accurate analysis by applying different analytical methods to each feedback category. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input feedback category data into a generating AI and have the generating AI apply different analytical methods.

[0093] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a simple and highly visible display method. It can also provide a display method that includes detailed information if the user is relaxed. For example, if the user is in a hurry, the analysis unit can provide a concise display method. This allows for a user-friendly display by adjusting the display method of the analysis results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the display method.

[0094] The analysis department can determine the priority of analyses based on the timing of feedback submissions. For example, the analysis department can determine the priority of analyses based on the timing of feedback submissions. The analysis department can also select the optimal analysis method based on the submission timing. For example, the analysis department can adjust the analysis results taking the submission timing into consideration. This makes it possible to perform more efficient analyses by determining the priority of analyses based on the timing of feedback submissions. Some or all of the above processes in the analysis department may be performed using AI or not. For example, the analysis department can input feedback submission timing data into a generating AI and have the generating AI perform the determination of analysis priorities.

[0095] The analysis unit can perform analysis by referring to relevant market data on feedback. For example, the analysis unit can adjust the analysis results based on the relevant market data on feedback. The analysis unit can also select the optimal analysis method by referring to the relevant market data. For example, the analysis unit can analyze the trends of feedback based on the relevant market data. This makes it possible to perform analysis more accurately by referring to the relevant market data on feedback. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input relevant market data into a generating AI and have the generating AI perform the analysis.

[0096] The generation unit can estimate the user's emotions and adjust the way the generated UI is presented based on the estimated emotions. For example, if the user is stressed, the generation unit can generate a simple and highly visible UI. If the user is relaxed, the generation unit can also generate a UI that includes detailed information. For example, if the user is in a hurry, the generation unit can generate a UI that can be operated quickly. This allows for a user-friendly UI by adjusting the presentation of the generated UI based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using or without a generation AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI adjust the presentation of the UI.

[0097] The generation unit can generate the optimal UI by referring to the user's past operation history during generation. For example, the generation unit can generate the optimal UI based on the UI the user has frequently used in the past. The generation unit can also predict and generate the UI to be used at a specific time period based on the user's past operation history. For example, the generation unit can analyze the user's past operation history and generate the most efficient UI. In this way, by generating the optimal UI by referring to the user's past operation history, a UI that meets the user's needs can be provided. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's operation history data into a generation AI and have the generation AI execute the generation of the optimal UI.

[0098] The generation unit can customize the UI based on the user's current usage during generation. For example, the generation unit generates a UI related to the function the user is currently using. The generation unit can also analyze the user's current usage in real time and generate the optimal UI. For example, the generation unit provides a customized UI based on the user's current usage. This allows the UI to be customized based on the user's current usage, thereby providing a UI that meets the user's needs. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's current usage data into a generation AI and have the generation AI perform the UI customization.

[0099] The generation unit can estimate the user's emotions and determine the priority of the UI to be generated based on the estimated user emotions. For example, if the user is stressed, the generation unit will prioritize generating a UI that can be operated quickly to alleviate stress. Conversely, if the user is relaxed, the generation unit can prioritize generating a UI that contains detailed information. For example, if the user is in a hurry, the generation unit will immediately generate a UI that can be operated quickly. This allows for the provision of a UI that meets the user's needs by determining the priority of the UI to be generated based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using or without a generation AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI determine the UI priorities.

[0100] The generation unit can generate the optimal UI by considering the user's geographical location information during the generation process. For example, if the user is in a specific region, the generation unit will generate a UI relevant to that region. The generation unit can also suggest the optimal UI based on the user's geographical location information. For example, if the user is on the move, the generation unit will generate a highly relevant UI based on their current location. In this way, by generating the optimal UI while considering the user's geographical location information, a UI that meets the user's needs can be provided. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's geographical location information into the generation AI and have the generation AI execute the generation of the optimal UI.

[0101] The generation unit can analyze the user's social media activity during generation and customize the UI accordingly. For example, the generation unit can generate relevant UI based on information shared by the user on social media. It can also predict and generate UI of interest based on the user's social media activity. For example, the generation unit can generate UI related to accounts the user follows on social media. This allows for the provision of UI tailored to user needs by analyzing the user's social media activity and customizing the UI accordingly. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input user social media activity data into a generation AI and have the generation AI perform the UI customization.

[0102] The service provider can estimate the user's emotions and adjust the display method of the UI based on the estimated emotions. For example, if the user is stressed, the service provider can provide a simple and highly visible display method. Alternatively, if the user is relaxed, the service provider can provide a display method that includes detailed information. For example, if the user is in a hurry, the service provider can provide a concise display method. This allows for a user-friendly display by adjusting the UI display method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the display method.

[0103] The service provider can select the optimal display method by referring to the user's past operation history at the time of service provision. For example, the service provider can provide the optimal display method based on the display methods the user has frequently used in the past. The service provider can also predict and provide the display method to be used during a specific time period based on the user's past operation history. For example, the service provider can analyze the user's past operation history and select the most efficient display method. This makes it possible to provide a display that meets the user's needs by selecting the optimal display method by referring to the user's past operation history. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's operation history data into a generating AI and have the generating AI perform the selection of the optimal display method.

[0104] The service provider can estimate the user's emotions and adjust the UI operation procedures based on the estimated emotions. For example, if the user is stressed, the service provider can simplify the operation procedures. Conversely, if the user is relaxed, the service provider can provide detailed operation procedures. For example, if the user is in a hurry, the service provider can provide procedures that allow for quick operation. In this way, by adjusting the UI operation procedures based on the user's emotions, user-friendly operation procedures can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform the adjustment of operation procedures.

[0105] The service provider can select the optimal display method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the service provider can provide a display method optimized for a larger screen. For example, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. This allows for display tailored to the user's needs by selecting the optimal display method based on the user's device information. Some or all of the above-described processes in the service provider may be performed using AI, or they may not. For example, the service provider can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[0106] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0107] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is feeling stressed, it can quickly provide analysis results to alleviate the stress. Conversely, if the user is relaxed, it can perform a more detailed analysis. By determining the priority of analysis based on the user's emotions, it becomes possible to perform analysis that meets the user's needs. Emotion estimation is achieved using an emotion engine or generative AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI determine the priority of analysis.

[0108] The data collection unit can estimate the user's emotions and determine the type of assets to collect based on those emotions. For example, if the user is stressed, it will prioritize collecting simple and intuitive assets to alleviate stress. Conversely, if the user is relaxed, it may collect assets containing detailed information. This allows for efficient collection of assets that meet the user's needs by determining the type of assets based on their emotions. Emotion estimation is achieved using an emotion engine or generative AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the type of assets to collect.

[0109] The generation unit can estimate the user's emotions and adjust the colors of the generated UI based on those emotions. For example, if the user is stressed, it can generate a UI with calming colors. Conversely, if the user is relaxed, it can generate a UI with bright colors. By adjusting the colors of the generated UI based on the user's emotions, a comfortable UI can be provided to the user. Emotion estimation is achieved using an emotion engine or a generation AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the UI colors.

[0110] The service provider can estimate the user's emotions and adjust the UI interactions based on those emotions. For example, if the user is stressed, it can provide simple and intuitive interactions. Conversely, if the user is relaxed, it can provide more detailed interactions. By adjusting the UI interactions based on the user's emotions, it can provide a user-friendly UI. Emotion estimation is achieved using an emotion engine or generative AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the interactions.

[0111] The generation unit can estimate the user's emotions and adjust the UI layout based on those emotions. For example, if the user is stressed, it can generate a simple and highly visible layout. Conversely, if the user is relaxed, it can generate a layout that includes detailed information. By adjusting the UI layout based on the user's emotions, it is possible to provide a user-friendly UI. Emotion estimation is achieved using an emotion engine or a generation AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform the layout adjustments.

[0112] The data collection unit can select the most suitable assets by referring to the user's past operation history during data collection. For example, it can prioritize collecting assets that the user has frequently used in the past. It can also predict and collect assets that the user will use during specific time periods based on the user's past operation history. This allows for efficient collection of assets that meet the user's needs by selecting the most suitable assets by referring to the user's past operation history. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input user operation history data into a generating AI and have the generating AI select the most suitable assets.

[0113] The analysis unit can improve the accuracy of the analysis by considering the interrelationships between assets during the analysis process. For example, it can analyze the relationships between assets and perform the analysis while considering these interrelationships. It can also improve the accuracy of the analysis results based on the interrelationships between assets. By improving the accuracy of the analysis by considering the interrelationships between assets, more accurate analysis results can be obtained. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input data on the interrelationships between assets into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0114] The generation unit can customize the UI based on the user's current usage during generation. For example, it can generate a UI related to the function the user is currently using. It can also analyze the user's current usage in real time and generate the optimal UI. This allows for the provision of a UI that meets the user's needs by customizing the UI based on the user's current usage. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's current usage data into the generation AI and have the generation AI perform the UI customization.

[0115] The service provider can select the optimal display method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, it can provide a display method that matches the screen size. If the user is using a tablet, it can also provide a display method optimized for the larger screen. By selecting the optimal display method considering the user's device information, it becomes possible to display content that meets the user's needs. Some or all of the above processing in the service provider may be performed using AI, or it may be performed without AI. For example, the service provider can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[0116] The analysis unit can apply different analytical methods to each feedback category during the analysis. For example, it can select the optimal analytical method for each feedback category. It can also use different analytical criteria for each feedback category. By applying different analytical methods to each feedback category, more accurate analysis becomes possible. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input feedback category data into a generating AI and have the generating AI execute the application of different analytical methods.

[0117] The following briefly describes the processing flow for example form 2.

[0118] Step 1: The collection unit collects assets from existing services. These assets include databases, APIs, and user interfaces. The collection unit extracts necessary data from existing service databases and obtains data from external services via APIs. It also analyzes user interface elements and collects necessary information. Step 2: The analysis unit analyzes the assets collected by the collection unit. The analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms. For example, data mining techniques are used to extract patterns and trends, statistical analysis is used to analyze the distribution and correlation of data, and machine learning algorithms are used to classify and predict data. Step 3: The analysis department analyzes user feedback based on the assets analyzed by the analysis department. User feedback includes survey results, reviews, and comments. For example, text mining techniques are used to analyze survey results, sentiment analysis techniques are used to analyze reviews and comments, and trend analysis is used to understand the trends in feedback. Step 4: The generation unit generates a user-specific UI based on the analysis results obtained by the analysis unit. The UI includes web interfaces, mobile applications, desktop applications, etc. For example, it uses generation AI to generate a customized UI based on the user's operation history and feedback, and uses natural language processing technology to generate a UI that the user can operate in natural language. Step 5: The providing unit provides the UI generated by the generating unit. The provision is done through a web browser, mobile application, or desktop application. For example, the generated UI is provided to the user through a web browser, mobile application, or desktop application.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0121] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] Each of the multiple elements described above, including the collection unit, analysis unit, data analysis unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects assets of existing services. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the collected assets. The data analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes user feedback. The generation unit is implemented by the control unit 46A of the smart device 14 and generates a user-specific UI. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the generated UI. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0123] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0124] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0126] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0127] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0129] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0130] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0131] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0132] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0133] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0135] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0137] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0138] Each of the multiple elements described above, including the collection unit, analysis unit, data analysis unit, generation unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects assets of existing services. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the collected assets. The data analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes user feedback. The generation unit is implemented by the control unit 46A of the smart glasses 214 and generates a user-specific UI. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the generated UI. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0139] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0140] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0141] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0142] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0143] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0145] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0146] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0147] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0148] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0149] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0150] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0151] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0153] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0154] Each of the multiple elements described above, including the collection unit, analysis unit, data analysis unit, generation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects assets of existing services. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected assets. The data analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes user feedback. The generation unit is implemented by the control unit 46A of the headset terminal 314 and generates a user-specific UI. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the generated UI. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0155] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0156] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0157] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0158] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0159] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0161] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0162] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0163] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0164] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0165] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0166] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0167] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0168] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0169] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0170] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0171] Each of the multiple elements described above, including the collection unit, analysis unit, data analysis unit, generation unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects assets of existing services. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected assets. The data analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes user feedback. The generation unit is implemented by the control unit 46A of the robot 414 and generates a user-specific UI. The provision unit is implemented by the control unit 46A of the robot 414 and provides the generated UI. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0172] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0173] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0174] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0175] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0176] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0177] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0178] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0179] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0180] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0181] 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.

[0182] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0183] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0184] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0185] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0186] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0187] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0188] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0189] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0190] (Note 1) The collection unit collects assets from existing services, An analysis unit analyzes the assets collected by the aforementioned collection unit, An analysis unit analyzes user feedback based on the assets analyzed by the aforementioned analysis unit, A generation unit generates a user-specific UI based on the analysis results obtained by the aforementioned analysis unit, The system comprises a providing unit that provides the UI generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect past user feedback. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, We analyze the collected assets to identify where users are experiencing difficulties in their operations. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Generate a user interface that users can intuitively interact with through natural language chat and voice input. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Provide the generated UI to the user. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is Generate a customized UI based on the user's past activity history and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and determines the priority of assets to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is During data collection, the system selects the most suitable assets by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is During data collection, filtering is performed based on the user's current usage patterns and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of asset acquisition based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant assets, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system analyzes the user's social media activity and collects relevant assets. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, We estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, consider the interrelationships between assets to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, the attribute information of the asset submitter is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the geographical distribution of assets will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, we refer to relevant literature for the assets to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is We estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit is During analysis, past feedback data is referenced to predict current feedback. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit is During the analysis, different analytical methods are applied to each feedback category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit is During the analysis, prioritize the analysis based on when feedback is submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit is During the analysis, we will refer to relevant market data for feedback. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is It estimates the user's emotions and adjusts the UI representation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The generating unit is During generation, the system references the user's past operation history to generate the optimal UI. The system described in Appendix 1, characterized by the features described herein. (Note 27) The generating unit is During creation, the UI is customized based on the user's current usage. The system described in Appendix 1, characterized by the features described herein. (Note 28) The generating unit is It estimates the user's emotions and determines the priority of the UI to generate based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The generating unit is During generation, the optimal UI is generated considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The generating unit is During generation, the UI is customized by analyzing the user's social media activity. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the UI is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, When providing the service, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, It estimates the user's emotions and adjusts the UI operation procedures based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned supply unit is, When providing the service, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection unit collects assets from existing services, An analysis unit analyzes the assets collected by the aforementioned collection unit, An analysis unit analyzes user feedback based on the assets analyzed by the aforementioned analysis unit, A generation unit generates a user-specific UI based on the analysis results obtained by the aforementioned analysis unit, The system comprises a providing unit that provides the UI generated by the generation unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect past user feedback. The system according to feature 1.

3. The aforementioned analysis unit, We analyze the collected assets to identify where users are experiencing difficulties in their operations. The system according to feature 1.

4. The generating unit is Generate a user interface that users can intuitively interact with through natural language chat and voice input. The system according to feature 1.

5. The aforementioned supply unit is, Provide the generated UI to the user. The system according to feature 1.

6. The generating unit is Generate a customized UI based on the user's past activity history and preferences. The system according to feature 1.

7. The aforementioned collection unit is It estimates the user's emotions and determines the priority of assets to collect based on the estimated user emotions. The system according to feature 1.

8. The aforementioned collection unit is During data collection, the system selects the most suitable assets by referring to the user's past operation history. The system according to feature 1.

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