system

Abstract

An object of a system according to an embodiment is to personalize a terminal on the basis of personalized information of a user and improve usability and convenience.SOLUTION: A system includes an acquisition unit, an analysis unit, a generation unit, and a presentation unit. The acquisition unit acquires personalized information of a user. The analysis unit analyzes the use of the terminal and the source code based on the information acquired by the acquisition unit. The generation unit generates a UI and a source code based on a result of the analysis by the analysis unit. The presentation unit presents the UI generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to personalize systems that users were using for the first time or shared terminals in public spaces, posing challenges to ease of use and convenience.

[0005] The system according to the embodiment aims to personalize a terminal based on user personalization information, thereby improving ease of use and convenience. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, an analysis unit, a generation unit, and a presentation unit. The acquisition unit acquires personalized information of a user. The analysis unit analyzes the purpose of the terminal and source code based on the information acquired by the acquisition unit. The generation unit generates a UI and source code based on the results of the analysis by the analysis unit. The presentation unit presents the UI generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can personalize a terminal based on user personalization information, thereby improving ease of use and convenience. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention provides a user with a personalized, easy-to-use interface, even when using a system for the first time or a shared terminal in a public space. This system acquires personalization information from the large-scale language model (LLM) used by the user, analyzes the intended use and source code of the target terminal, and combines this information with the personalization information interpreted by the LLM to generate and present the optimal UI and source code for the user. For example, when using a public kiosk terminal, the user accesses their own LLM to acquire personalization information. Based on the intended use of the kiosk terminal, the LLM then generates an optimal UI and displays it on the terminal. This allows the user to use an optimized, easy-to-use interface even when using a terminal for the first time. This system personalizes all devices to the user, making the system easier for the user to use. This effect is particularly pronounced when using shared terminals in public spaces or systems for the first time.

[0029] A personalization system according to an embodiment includes an acquisition unit, an analysis unit, a generation unit, and a presentation unit. The acquisition unit acquires user personalization information. The personalization information includes, for example, the user's preferences, past usage history, and specific settings. The acquisition unit, for example, acquires information directly from the user's device. The acquisition unit can also collect personalization information through a cloud service. The acquisition unit can also collect information from the user's social media activities. The analysis unit analyzes the device's use and source code based on the information acquired by the acquisition unit. The analysis unit classifies the device's use into, for example, business use, entertainment use, etc. The analysis unit also analyzes the structure of the source code to obtain basic information for generating an optimal UI and source code. The analysis unit can also analyze the device's hardware configuration and network settings. The generation unit generates a new UI and source code based on the results of the analysis by the analysis unit. The generation unit, for example, generates a UI that takes usability into consideration. The generation unit can also generate optimal source code according to the device's performance. The generation unit can also generate a customized UI tailored to the user's preferences. The presentation unit displays the UI generated by the generation unit on the terminal. The presentation unit displays the generated UI in real time, for example. The presentation unit can also dynamically update the UI in response to user operations. Furthermore, the presentation unit can synchronously display the generated UI on multiple devices. As a result, the personalization system according to the embodiment can provide an optimized interface to the user by analyzing the purpose and source code of the terminal based on the user's personalization information and generating and presenting a new UI and source code.

[0030] The acquisition unit can acquire personalized information such as the user's preferences, past usage history, and specific settings. For example, the acquisition unit acquires the user's preferences from survey results or past selection history. The acquisition unit can also acquire the user's past usage history from applications used or websites accessed. Furthermore, the acquisition unit can also acquire device settings and application settings set by the user. This enables more accurate personalization by acquiring personalized information such as the user's preferences, past usage history, and specific settings.

[0031] The analysis unit can analyze the device's use and source code. For example, the analysis unit classifies the device's use into business use, entertainment use, etc. The analysis unit can also analyze the structure of the source code to obtain basic information for generating an optimal UI and source code. Furthermore, the analysis unit can analyze the device's hardware configuration and network settings. In this way, by analyzing the device's use and source code, basic information for generating an appropriate UI and source code can be obtained.

[0032] The generation unit can generate a new UI and source code based on the analysis results. For example, the generation unit generates a UI that takes usability into consideration. The generation unit can also generate optimal source code according to the performance of the terminal. Furthermore, the generation unit can generate a customized UI that matches the user's preferences. In this way, by generating a new UI and source code based on the analysis results, it is possible to provide the user with an optimal interface.

[0033] The presentation unit can display the generated UI on the terminal. For example, the presentation unit displays the generated UI in real time. The presentation unit can also dynamically update the UI in response to user operations. Furthermore, the presentation unit can synchronously display the generated UI on multiple devices. In this way, by displaying the generated UI on the terminal, it is possible to provide an interface that the user can intuitively operate.

[0034] The acquisition unit can analyze the user's past usage history and select the optimal acquisition method. For example, the acquisition unit prioritizes acquisition of personalized information from devices or applications that the user has frequently used in the past. The acquisition unit can also select the most efficient data acquisition method based on the user's past usage history. Furthermore, the acquisition unit can also select the optimal acquisition timing taking into account the time period and location of the user's past use. In this way, by analyzing the user's past usage history, the optimal acquisition method can be selected and personalized information can be acquired efficiently.

[0035] When acquiring personalized information, the acquisition unit can perform filtering based on the user's current project or area of ​​interest. For example, the acquisition unit acquires only information related to the project the user is currently working on. The acquisition unit can also preferentially acquire highly relevant information based on the user's area of ​​interest. Furthermore, the acquisition unit can also perform filtering and acquire related information based on topics in which the user has shown interest in the past. In this way, by filtering based on the user's current project or area of ​​interest, highly relevant information can be preferentially acquired.

[0036] When acquiring personalized information, the acquisition unit can select the optimal acquisition means depending on the user's input method. For example, if the user is using voice input, the acquisition unit acquires personalized information from voice data. Also, if the user is using text input, the acquisition unit can acquire personalized information from text data. Furthermore, if the user is using image input, the acquisition unit can acquire personalized information from image data. This allows personalized information to be acquired efficiently by selecting the optimal acquisition means depending on the user's input method.

[0037] When acquiring personalized information, the acquisition unit can prioritize acquiring highly relevant information by taking into account the user's geographical location information. For example, the acquisition unit prioritizes acquiring information related to the user's current location. The acquisition unit can also acquire highly relevant information based on places the user has visited in the past. Furthermore, the acquisition unit can also acquire information related to places the user plans to visit in the future. In this way, highly relevant information can be prioritized by taking into account the user's geographical location information.

[0038] When acquiring personalized information, the acquisition unit can analyze the user's social media activities and acquire related information. For example, the acquisition unit acquires information related to places where the user has checked in on social media. The acquisition unit can also analyze the content of the user's posts on social media to acquire related information. Furthermore, the acquisition unit can also acquire related information by referring to the activities of the user's friends on social media. In this way, highly relevant information can be acquired by analyzing the user's social media activities.

[0039] When acquiring personalized information, the acquisition unit can customize the acquisition method by reflecting the user's past feedback. The acquisition unit selects the optimal acquisition method, for example, based on feedback provided by the user in the past. The acquisition unit can also determine the priority of information to be acquired based on the user's past feedback. Furthermore, the acquisition unit can also reflect the user's feedback and continuously improve the acquisition method. In this way, the acquisition method can be customized by reflecting the user's past feedback, and more appropriate information can be acquired.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the terminal. For example, in the case of an important terminal, the analysis unit performs a detailed analysis to improve accuracy. In addition, in the case of a terminal with low importance, the analysis unit can perform a simplified analysis to shorten processing time. Furthermore, the analysis unit can also appropriately allocate analysis resources according to the importance of the terminal. In this way, by adjusting the level of detail of the analysis based on the importance of the terminal, analysis can be performed efficiently.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the terminal. For example, if the terminal is a mobile device, the analysis unit applies an analysis algorithm specialized for mobile devices. Furthermore, if the terminal is a desktop device, the analysis unit can also apply an analysis algorithm specialized for desktop devices. Furthermore, if the terminal is an IoT device, the analysis unit can also apply an analysis algorithm specialized for IoT devices. In this way, by applying different analysis algorithms depending on the category of the terminal, it is possible to provide appropriate analysis results.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can improve the accuracy of the current analysis, for example, based on the analysis results previously performed by the user. The analysis unit can also select the optimal analysis method from the user's past analysis results. Furthermore, the analysis unit can also adjust the analysis algorithm by referring to the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results.

[0043] The analysis unit can perform analysis taking into account the geographical distribution of terminals. For example, if terminals are concentrated in a specific area, the analysis unit performs analysis specialized for that area. In addition, if terminals are distributed over a wide area, the analysis unit can perform different analyses for each area. Furthermore, the analysis unit can also appropriately allocate analysis resources taking into account the geographical distribution of terminals. In this way, by taking into account the geographical distribution of terminals, appropriate analysis can be performed for each area.

[0044] During analysis, the analysis unit can improve the accuracy of the analysis by referring to literature related to the terminal. For example, the analysis unit can improve the accuracy of the analysis by referring to the latest research papers related to the terminal. The analysis unit can also improve the accuracy of the analysis by referring to patent documents related to the terminal. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to technical literature related to the terminal. In this way, by referring to literature related to the terminal, the accuracy of the analysis can be improved.

[0045] The analysis unit can perform the analysis while taking into account the market value of the device. For example, in the case of a device with a high market value, the analysis unit performs a detailed analysis to improve accuracy. In addition, in the case of a device with a low market value, the analysis unit can perform a simplified analysis to shorten processing time. Furthermore, the analysis unit can also appropriately allocate analysis resources according to the market value of the device. In this way, by taking into account the market value of the device, detailed analysis can be performed on important devices.

[0046] The generation unit can improve the accuracy of generation by taking into account the interrelationships between terminals during generation. For example, when a terminal is linked to other devices, the generation unit generates a UI and source code by taking into account the interrelationships. Furthermore, when a terminal is connected to a network, the generation unit can also generate a UI and source code by taking into account the network state. Furthermore, when a terminal uses a cloud service, the generation unit can also generate a UI and source code by taking into account cloud resources. This makes it possible to generate an appropriate UI and source code by taking into account the interrelationships between terminals.

[0047] The generation unit can generate the UI and source code taking into account the attribute information of the submitter of the terminal. For example, if the submitter is an engineer, the generation unit generates a UI and source code that include technical details. Furthermore, if the submitter is a general user, the generation unit can also generate a simple and easy-to-use UI and source code. Furthermore, if the submitter is a company, the generation unit can also generate a UI and source code that matches the company's brand image. In this way, by taking into account the attribute information of the submitter of the terminal, it is possible to generate a UI and source code that is optimal for the submitter.

[0048] The generation unit can weight generation based on the submission frequency of the terminal during generation. For example, the generation unit generates detailed UI and source code for a terminal with a high submission frequency. The generation unit can also generate simplified UI and source code for a terminal with a low submission frequency. Furthermore, the generation unit can also appropriately allocate generation resources according to the submission frequency. In this way, by weighting generation based on the submission frequency of the terminal, it is possible to generate appropriate UI and source code according to the submission frequency.

[0049] The generation unit can generate the UI and source code taking into account the geographical distribution of terminals. For example, if terminals are concentrated in a specific region, the generation unit generates a UI and source code that is specialized for that region. Also, if terminals are distributed over a wide area, the generation unit can generate different UIs and source code for each region. Furthermore, the generation unit can appropriately allocate generation resources taking into account the geographical distribution of terminals. In this way, by taking into account the geographical distribution of terminals, it is possible to generate UIs and source code that are appropriate for each region.

[0050] The generation unit can improve the accuracy of generation by referring to literature related to the terminal during generation. For example, the generation unit can improve the accuracy of generation by referring to the latest research papers related to the terminal. The generation unit can also improve the accuracy of generation by referring to patent documents related to the terminal. Furthermore, the generation unit can improve the accuracy of generation by referring to technical literature related to the terminal. In this way, the accuracy of generation can be improved by referring to literature related to the terminal.

[0051] The generation unit can generate the UI and source code taking into consideration the market value of the device. For example, the generation unit generates a detailed UI and source code for a device with a high market value. The generation unit can also generate a simplified UI and source code for a device with a low market value. Furthermore, the generation unit can also appropriately allocate generation resources according to the market value of the device. This makes it possible to generate a detailed UI and source code for important devices by taking into consideration the market value of the device.

[0052] When displaying the UI, the presentation unit can select the optimal display method by referring to the user's past operation history. For example, the presentation unit preferentially provides a display method that the user has used favorably in the past. The presentation unit can also suggest the optimal display method based on the user's past operation history. Furthermore, the presentation unit can select the optimal display method by taking into account the time period and location where the user used the UI in the past. In this way, the optimal display method can be provided by referring to the user's past operation history.

[0053] The presentation unit can customize the display content according to the user's current task when displaying the UI. For example, the presentation unit preferentially displays information related to the task the user is currently working on. The presentation unit can also provide optimal display content based on the user's current task. Furthermore, the presentation unit can also display related information based on tasks the user has performed in the past. In this way, by customizing the display content according to the user's current task, it is possible to provide optimal information for the user.

[0054] The presentation unit can select the optimal display method when displaying the UI by taking into consideration the user's device information. For example, if the user is using a smartphone, the presentation unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the presentation unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the presentation unit can also provide a simple and highly visible display method. In this way, the optimal display method can be provided by taking into consideration the user's device information.

[0055] The presentation unit can make the display content multilingual according to the user's language setting when displaying the UI. For example, the presentation unit automatically sets the UI language based on the language setting of the user's device. The presentation unit can also provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the presentation unit can also provide the UI in that language. This makes it possible to provide information in the language most suitable for the user by making the display content multilingual according to the user's language setting.

[0056] When displaying the UI, the presentation unit can analyze the user's social media activity and provide related information. For example, the presentation unit can provide information about places where the user has checked in on social media. The presentation unit can also analyze the content of the user's social media posts and provide information about related tourist spots and stores. Furthermore, the presentation unit can provide information about related places and events by referring to the activities of the user's friends on social media. In this way, highly relevant information can be provided by analyzing the user's social media activity.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] The acquisition unit can take the user's current health condition into consideration when acquiring personalized information for the user. For example, if the user is using a health management app, the acquisition unit acquires that data and generates an optimal UI and source code based on the user's health condition. The acquisition unit can also acquire heart rate and sleep data from the user's wearable device and provide an interface tailored to the user's physical condition. Furthermore, the acquisition unit can acquire diagnostic results received by the user at a medical institution and provide customized information based on the user's health condition. This enables personalization based on the user's health condition, making it possible to provide an interface that is more suitable for the user.

[0059] The analysis unit can take into account the device's purpose and the device's security level when analyzing source code. For example, if the device requires a high security level, the analysis unit can perform a detailed security check to minimize security risks. In addition, if the device operates at a low security level, the analysis unit can perform a quick analysis to improve usability. Furthermore, the analysis unit can select an appropriate analysis algorithm based on the device's security settings. This enables analysis according to the device's security level, providing a safe and easy-to-use interface for users.

[0060] The generator can take the user's learning style into consideration when generating new UIs and source code based on the analysis results. For example, if the user is a visual learner, the generator can generate a graphical UI and provide a lot of visual information. If the user is an auditory learner, the generator can also generate a UI that includes audio guidance. Furthermore, if the user is a tactile learner, the generator can generate an interactive UI and provide an interface that includes many elements that the user can directly operate. This allows the user to deepen their understanding and improve usability by providing an interface that suits their learning style.

[0061] The presentation unit can take into account the remaining battery level of the user's device when displaying the generated UI on the terminal. For example, when the device's remaining battery level is low, the presentation unit displays the UI in power-saving mode to reduce battery consumption. Also, when the battery level is sufficient, the presentation unit can display a high-resolution UI to provide the user with a high-quality interface. Furthermore, the presentation unit can adjust UI animations and effects according to the remaining battery level to optimize battery life. This can improve user convenience by providing an interface according to the device's remaining battery level.

[0062] The analysis unit can perform analysis taking into account the geographical distribution of terminals. For example, if terminals are concentrated in a specific area, the analysis unit can perform an analysis specific to that area. In addition, if terminals are distributed over a wide area, the analysis unit can perform different analyses for each area. Furthermore, the analysis unit can also appropriately allocate analysis resources by taking into account the geographical distribution of terminals. In this way, by taking into account the geographical distribution of terminals, it is possible to perform an appropriate analysis for each area.

[0063] The processing flow of the first embodiment will be briefly explained below.

[0064] Step 1: The acquisition unit acquires personalized information of the user. The personalized information includes, for example, the user's preferences, past usage history, and specific settings. For example, the acquisition unit acquires information directly from the user's device. The acquisition unit can also collect personalized information through a cloud service. Furthermore, the acquisition unit can also collect information from the user's social media activities. Step 2: The analysis unit analyzes the device's use and source code based on the information acquired by the acquisition unit. For example, the analysis unit classifies the device's use into business use, entertainment use, etc. The analysis unit also analyzes the structure of the source code to obtain basic information for generating an optimal UI and source code. The analysis unit can also analyze the device's hardware configuration and network settings. Step 3: The generation unit generates a new UI and source code based on the results of the analysis by the analysis unit. For example, the generation unit generates a UI that takes usability into consideration. The generation unit can also generate optimal source code according to the performance of the device. Furthermore, the generation unit can also generate a customized UI that matches the user's preferences. Step 4: The presentation unit displays the UI generated by the generation unit on the terminal. For example, the presentation unit displays the generated UI in real time. The presentation unit can also dynamically update the UI in response to user operations. Furthermore, the presentation unit can also synchronously display the generated UI on multiple devices.

[0065] (Example 2) A system according to an embodiment of the present invention provides a user with a personalized, easy-to-use interface, even when using a system for the first time or a shared terminal in a public space. This system acquires personalization information from the large-scale language model (LLM) used by the user, analyzes the intended use and source code of the target terminal, and combines this information with the personalization information interpreted by the LLM to generate and present the optimal UI and source code for the user. For example, when using a public kiosk terminal, the user accesses their own LLM to acquire personalization information. Based on the intended use of the kiosk terminal, the LLM then generates an optimal UI and displays it on the terminal. This allows the user to use an optimized, easy-to-use interface even when using a terminal for the first time. This system personalizes all devices to the user, making the system easier for the user to use. This effect is particularly pronounced when using shared terminals in public spaces or systems for the first time.

[0066] A personalization system according to an embodiment includes an acquisition unit, an analysis unit, a generation unit, and a presentation unit. The acquisition unit acquires user personalization information. The personalization information includes, for example, the user's preferences, past usage history, and specific settings. The acquisition unit, for example, acquires information directly from the user's device. The acquisition unit can also collect personalization information through a cloud service. The acquisition unit can also collect information from the user's social media activities. The analysis unit analyzes the device's use and source code based on the information acquired by the acquisition unit. The analysis unit classifies the device's use into, for example, business use, entertainment use, etc. The analysis unit also analyzes the structure of the source code to obtain basic information for generating an optimal UI and source code. The analysis unit can also analyze the device's hardware configuration and network settings. The generation unit generates a new UI and source code based on the results of the analysis by the analysis unit. The generation unit, for example, generates a UI that takes usability into consideration. The generation unit can also generate optimal source code according to the device's performance. The generation unit can also generate a customized UI tailored to the user's preferences. The presentation unit displays the UI generated by the generation unit on the terminal. The presentation unit displays the generated UI in real time, for example. The presentation unit can also dynamically update the UI in response to user operations. Furthermore, the presentation unit can synchronously display the generated UI on multiple devices. As a result, the personalization system according to the embodiment can provide an optimized interface to the user by analyzing the purpose and source code of the terminal based on the user's personalization information and generating and presenting a new UI and source code.

[0067] The acquisition unit can acquire personalized information such as the user's preferences, past usage history, and specific settings. For example, the acquisition unit acquires the user's preferences from survey results or past selection history. The acquisition unit can also acquire the user's past usage history from applications used or websites accessed. Furthermore, the acquisition unit can also acquire device settings and application settings set by the user. This enables more accurate personalization by acquiring personalized information such as the user's preferences, past usage history, and specific settings.

[0068] The analysis unit can analyze the device's use and source code. For example, the analysis unit classifies the device's use into business use, entertainment use, etc. The analysis unit can also analyze the structure of the source code to obtain basic information for generating an optimal UI and source code. Furthermore, the analysis unit can analyze the device's hardware configuration and network settings. In this way, by analyzing the device's use and source code, basic information for generating an appropriate UI and source code can be obtained.

[0069] The generation unit can generate a new UI and source code based on the analysis results. For example, the generation unit generates a UI that takes usability into consideration. The generation unit can also generate optimal source code according to the performance of the terminal. Furthermore, the generation unit can generate a customized UI that matches the user's preferences. In this way, by generating a new UI and source code based on the analysis results, it is possible to provide the user with an optimal interface.

[0070] The presentation unit can display the generated UI on the terminal. For example, the presentation unit displays the generated UI in real time. The presentation unit can also dynamically update the UI in response to user operations. Furthermore, the presentation unit can synchronously display the generated UI on multiple devices. In this way, by displaying the generated UI on the terminal, it is possible to provide an interface that the user can intuitively operate.

[0071] The acquisition unit can estimate the user's emotions and adjust the timing of acquiring personalized information based on the emotion data. For example, if the user is feeling stressed, the acquisition unit can delay acquiring personalized information and wait until the user is relaxed. Furthermore, if the user is relaxed, the acquisition unit can immediately acquire personalized information and quickly start processing it. Furthermore, if the user is in a hurry, the acquisition unit can prioritize acquiring only the most important personalized information and quickly process it. This allows the user's stress to be reduced and information to be acquired at the optimal time by adjusting the timing of acquiring personalized information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0072] The acquisition unit can analyze the user's past usage history and select the optimal acquisition method. For example, the acquisition unit prioritizes acquisition of personalized information from devices or applications that the user has frequently used in the past. The acquisition unit can also select the most efficient data acquisition method based on the user's past usage history. Furthermore, the acquisition unit can also select the optimal acquisition timing taking into account the time period and location of the user's past use. In this way, by analyzing the user's past usage history, the optimal acquisition method can be selected and personalized information can be acquired efficiently.

[0073] When acquiring personalized information, the acquisition unit can perform filtering based on the user's current project or area of ​​interest. For example, the acquisition unit acquires only information related to the project the user is currently working on. The acquisition unit can also preferentially acquire highly relevant information based on the user's area of ​​interest. Furthermore, the acquisition unit can also perform filtering and acquire related information based on topics in which the user has shown interest in the past. In this way, by filtering based on the user's current project or area of ​​interest, highly relevant information can be preferentially acquired.

[0074] When acquiring personalized information, the acquisition unit can select the optimal acquisition means depending on the user's input method. For example, if the user is using voice input, the acquisition unit acquires personalized information from voice data. Also, if the user is using text input, the acquisition unit can acquire personalized information from text data. Furthermore, if the user is using image input, the acquisition unit can acquire personalized information from image data. This allows personalized information to be acquired efficiently by selecting the optimal acquisition means depending on the user's input method.

[0075] The acquisition unit can estimate the user's emotions and determine the priority of personalized information to be acquired based on the emotion data. For example, when the user is feeling stressed, the acquisition unit prioritizes acquiring information that helps the user relax. Furthermore, when the user is relaxed, the acquisition unit can also prioritize acquiring information that improves work efficiency. Furthermore, when the user is in a hurry, the acquisition unit can also prioritize acquiring the most important information. In this way, by determining the priority of personalized information according to the user's emotions, it is possible to prioritize acquiring the information that is most important to the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0076] When acquiring personalized information, the acquisition unit can prioritize acquiring highly relevant information by taking into account the user's geographical location information. For example, the acquisition unit prioritizes acquiring information related to the user's current location. The acquisition unit can also acquire highly relevant information based on places the user has visited in the past. Furthermore, the acquisition unit can also acquire information related to places the user plans to visit in the future. In this way, highly relevant information can be prioritized by taking into account the user's geographical location information.

[0077] When acquiring personalized information, the acquisition unit can analyze the user's social media activities and acquire related information. For example, the acquisition unit acquires information related to places where the user has checked in on social media. The acquisition unit can also analyze the content of the user's posts on social media to acquire related information. Furthermore, the acquisition unit can also acquire related information by referring to the activities of the user's friends on social media. In this way, highly relevant information can be acquired by analyzing the user's social media activities.

[0078] When acquiring personalized information, the acquisition unit can customize the acquisition method by reflecting the user's past feedback. The acquisition unit selects the optimal acquisition method, for example, based on feedback provided by the user in the past. The acquisition unit can also determine the priority of information to be acquired based on the user's past feedback. Furthermore, the acquisition unit can also reflect the user's feedback and continuously improve the acquisition method. In this way, the acquisition method can be customized by reflecting the user's past feedback, and more appropriate information can be acquired.

[0079] The analysis unit can estimate the user's emotions and adjust the device's usage and source code analysis method based on the emotion data. For example, if the user is relaxed, the analysis unit can perform a detailed analysis to improve accuracy. Furthermore, if the user is in a hurry, the analysis unit can perform a quick analysis to provide results quickly. Furthermore, if the user is feeling stressed, the analysis unit can reduce the analysis load and ease the burden on the user. By adjusting the analysis method according to the user's emotions, it is possible to provide the user with optimal analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0080] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the terminal. For example, in the case of an important terminal, the analysis unit performs a detailed analysis to improve accuracy. In addition, in the case of a terminal with low importance, the analysis unit can perform a simplified analysis to shorten processing time. Furthermore, the analysis unit can also appropriately allocate analysis resources according to the importance of the terminal. In this way, by adjusting the level of detail of the analysis based on the importance of the terminal, analysis can be performed efficiently.

[0081] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the terminal. For example, if the terminal is a mobile device, the analysis unit applies an analysis algorithm specialized for mobile devices. Furthermore, if the terminal is a desktop device, the analysis unit can also apply an analysis algorithm specialized for desktop devices. Furthermore, if the terminal is an IoT device, the analysis unit can also apply an analysis algorithm specialized for IoT devices. In this way, by applying different analysis algorithms depending on the category of the terminal, it is possible to provide appropriate analysis results.

[0082] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can improve the accuracy of the current analysis, for example, based on the analysis results previously performed by the user. The analysis unit can also select the optimal analysis method from the user's past analysis results. Furthermore, the analysis unit can also adjust the analysis algorithm by referring to the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results.

[0083] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the emotion data. For example, if the user is in a hurry, the analysis unit can prioritize the most important analysis. Furthermore, if the user is relaxed, the analysis unit can also prioritize detailed analysis. Furthermore, if the user is feeling stressed, the analysis unit can prioritize analysis that requires less effort. In this way, by determining the priority of analysis according to the user's emotions, the analysis that is most important to the user can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0084] The analysis unit can perform analysis taking into account the geographical distribution of terminals. For example, if terminals are concentrated in a specific area, the analysis unit performs analysis specialized for that area. In addition, if terminals are distributed over a wide area, the analysis unit can perform different analyses for each area. Furthermore, the analysis unit can also appropriately allocate analysis resources taking into account the geographical distribution of terminals. In this way, by taking into account the geographical distribution of terminals, appropriate analysis can be performed for each area.

[0085] During analysis, the analysis unit can improve the accuracy of the analysis by referring to literature related to the terminal. For example, the analysis unit can improve the accuracy of the analysis by referring to the latest research papers related to the terminal. The analysis unit can also improve the accuracy of the analysis by referring to patent documents related to the terminal. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to technical literature related to the terminal. In this way, by referring to literature related to the terminal, the accuracy of the analysis can be improved.

[0086] The analysis unit can perform the analysis while taking into account the market value of the device. For example, in the case of a device with a high market value, the analysis unit performs a detailed analysis to improve accuracy. In addition, in the case of a device with a low market value, the analysis unit can perform a simplified analysis to shorten processing time. Furthermore, the analysis unit can also appropriately allocate analysis resources according to the market value of the device. In this way, by taking into account the market value of the device, detailed analysis can be performed on important devices.

[0087] The generation unit can estimate the user's emotions and determine the priority of UIs and source code to be generated based on the emotion data. For example, when the user is in a hurry, the generation unit can prioritize generating the most important UIs and source code. Furthermore, when the user is relaxed, the generation unit can also prioritize generating detailed UIs and source code. Furthermore, when the user is stressed, the generation unit can prioritize generating UIs and source code that are less burdensome. Thus, by determining the priority of UIs and source code to be generated according to the user's emotions, it is possible to prioritize generating the UIs and source code that are most important to the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0088] The generation unit can improve the accuracy of generation by taking into account the interrelationships between terminals during generation. For example, when a terminal is linked to other devices, the generation unit generates a UI and source code by taking into account the interrelationships. Furthermore, when a terminal is connected to a network, the generation unit can also generate a UI and source code by taking into account the network state. Furthermore, when a terminal uses a cloud service, the generation unit can also generate a UI and source code by taking into account cloud resources. This makes it possible to generate an appropriate UI and source code by taking into account the interrelationships between terminals.

[0089] The generation unit can generate the UI and source code taking into account the attribute information of the submitter of the terminal. For example, if the submitter is an engineer, the generation unit generates a UI and source code that include technical details. Furthermore, if the submitter is a general user, the generation unit can also generate a simple and easy-to-use UI and source code. Furthermore, if the submitter is a company, the generation unit can also generate a UI and source code that matches the company's brand image. In this way, by taking into account the attribute information of the submitter of the terminal, it is possible to generate a UI and source code that is optimal for the submitter.

[0090] The generation unit can weight generation based on the submission frequency of the terminal during generation. For example, the generation unit generates detailed UI and source code for a terminal with a high submission frequency. The generation unit can also generate simplified UI and source code for a terminal with a low submission frequency. Furthermore, the generation unit can also appropriately allocate generation resources according to the submission frequency. In this way, by weighting generation based on the submission frequency of the terminal, it is possible to generate appropriate UI and source code according to the submission frequency.

[0091] The generation unit can estimate the user's emotions and adjust the display method of the generated UI and source code based on the emotion data. For example, if the user is nervous, the generation unit provides a simple, highly visible display method. If the user is relaxed, the generation unit can also provide a display method that includes detailed information. If the user is in a hurry, the generation unit can also provide a display method that focuses on the main points. In this way, by adjusting the display method of the UI and source code according to the user's emotions, it is possible to provide an optimal display method for the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0092] The generation unit can generate the UI and source code taking into account the geographical distribution of terminals. For example, if terminals are concentrated in a specific region, the generation unit generates a UI and source code that is specialized for that region. Also, if terminals are distributed over a wide area, the generation unit can generate different UIs and source code for each region. Furthermore, the generation unit can appropriately allocate generation resources taking into account the geographical distribution of terminals. In this way, by taking into account the geographical distribution of terminals, it is possible to generate UIs and source code that are appropriate for each region.

[0093] The generation unit can improve the accuracy of generation by referring to literature related to the terminal during generation. For example, the generation unit can improve the accuracy of generation by referring to the latest research papers related to the terminal. The generation unit can also improve the accuracy of generation by referring to patent documents related to the terminal. Furthermore, the generation unit can improve the accuracy of generation by referring to technical literature related to the terminal. In this way, the accuracy of generation can be improved by referring to literature related to the terminal.

[0094] The generation unit can generate the UI and source code taking into consideration the market value of the device. For example, the generation unit generates a detailed UI and source code for a device with a high market value. The generation unit can also generate a simplified UI and source code for a device with a low market value. Furthermore, the generation unit can also appropriately allocate generation resources according to the market value of the device. This makes it possible to generate a detailed UI and source code for important devices by taking into consideration the market value of the device.

[0095] The presentation unit can estimate the user's emotions and adjust the UI display method based on the emotion data. For example, if the user is nervous, the presentation unit can provide an interface with subdued colors to reduce visual stress. Furthermore, if the user is having fun, the presentation unit can provide an interface with bright colors to make input work more enjoyable. Furthermore, if the user is tired, the presentation unit can provide a simple, highly visible interface to make input work easier. This allows the UI display method to be adjusted according to the user's emotions, thereby providing an optimal interface for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0096] When displaying the UI, the presentation unit can select the optimal display method by referring to the user's past operation history. For example, the presentation unit preferentially provides a display method that the user has used favorably in the past. The presentation unit can also suggest the optimal display method based on the user's past operation history. Furthermore, the presentation unit can select the optimal display method by taking into account the time period and location where the user used the UI in the past. In this way, the optimal display method can be provided by referring to the user's past operation history.

[0097] The presentation unit can customize the display content according to the user's current task when displaying the UI. For example, the presentation unit preferentially displays information related to the task the user is currently working on. The presentation unit can also provide optimal display content based on the user's current task. Furthermore, the presentation unit can also display related information based on tasks the user has performed in the past. In this way, by customizing the display content according to the user's current task, it is possible to provide optimal information for the user.

[0098] The presentation unit can estimate the user's emotions and adjust the UI operation procedure based on the emotion data. For example, if the user is nervous, the presentation unit can provide simple and intuitive operation procedures. Furthermore, if the user is relaxed, the presentation unit can provide detailed operation procedures. Furthermore, if the user is in a hurry, the presentation unit can provide procedures that can be performed quickly. In this way, by adjusting the UI operation procedure according to the user's emotions, it is possible to provide the optimal operation procedure for the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0099] The presentation unit can select the optimal display method when displaying the UI by taking into consideration the user's device information. For example, if the user is using a smartphone, the presentation unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the presentation unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the presentation unit can also provide a simple and highly visible display method. In this way, the optimal display method can be provided by taking into consideration the user's device information.

[0100] The presentation unit can make the display content multilingual according to the user's language setting when displaying the UI. For example, the presentation unit automatically sets the UI language based on the language setting of the user's device. The presentation unit can also provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the presentation unit can also provide the UI in that language. This makes it possible to provide information in the language most suitable for the user by making the display content multilingual according to the user's language setting.

[0101] When displaying the UI, the presentation unit can analyze the user's social media activity and provide related information. For example, the presentation unit can provide information about places where the user has checked in on social media. The presentation unit can also analyze the content of the user's social media posts and provide information about related tourist spots and stores. Furthermore, the presentation unit can provide information about related places and events by referring to the activities of the user's friends on social media. In this way, highly relevant information can be provided by analyzing the user's social media activity. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned acquisition unit, analysis unit, generation unit, and presentation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the presentation unit is realized by the output device 40 of the smart device 14 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned acquisition unit, analysis unit, generation unit, and presentation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the presentation unit is realized by the speaker 240 of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned acquisition unit, analysis unit, generation unit, and presentation unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the acquisition unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the presentation unit is realized by the display 343 of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned acquisition unit, analysis unit, generation unit, and presentation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the presentation unit is realized by the display device of the robot 414 or the specific processing unit 290 of the data processing device 12.

[0102] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0103] The acquisition unit can take the user's current health condition into consideration when acquiring personalized information for the user. For example, if the user is using a health management app, the acquisition unit acquires that data and generates an optimal UI and source code based on the user's health condition. The acquisition unit can also acquire heart rate and sleep data from the user's wearable device and provide an interface tailored to the user's physical condition. Furthermore, the acquisition unit can acquire diagnostic results received by the user at a medical institution and provide customized information based on the user's health condition. This enables personalization based on the user's health condition, making it possible to provide an interface that is more suitable for the user.

[0104] The analysis unit can take into account the device's purpose and the device's security level when analyzing source code. For example, if the device requires a high security level, the analysis unit can perform a detailed security check to minimize security risks. In addition, if the device operates at a low security level, the analysis unit can perform a quick analysis to improve usability. Furthermore, the analysis unit can select an appropriate analysis algorithm based on the device's security settings. This enables analysis according to the device's security level, providing a safe and easy-to-use interface for users.

[0105] The generator can take the user's learning style into consideration when generating new UIs and source code based on the analysis results. For example, if the user is a visual learner, the generator can generate a graphical UI and provide a lot of visual information. If the user is an auditory learner, the generator can also generate a UI that includes audio guidance. Furthermore, if the user is a tactile learner, the generator can generate an interactive UI and provide an interface that includes many elements that the user can directly operate. This allows the user to deepen their understanding and improve usability by providing an interface that suits their learning style.

[0106] The presentation unit can take into account the remaining battery level of the user's device when displaying the generated UI on the terminal. For example, when the device's remaining battery level is low, the presentation unit displays the UI in power-saving mode to reduce battery consumption. Also, when the battery level is sufficient, the presentation unit can display a high-resolution UI to provide the user with a high-quality interface. Furthermore, the presentation unit can adjust UI animations and effects according to the remaining battery level to optimize battery life. This can improve user convenience by providing an interface according to the device's remaining battery level.

[0107] The acquisition unit can estimate the user's emotions and adjust the method for acquiring personalized information based on the emotion data. For example, if the user is excited, the acquisition unit can quickly acquire information to maintain the user's excitement. If the user is calm, the acquisition unit can acquire detailed information to provide the user with a deeper understanding. Furthermore, if the user is feeling anxious, the acquisition unit can prioritize acquiring information that provides a sense of security to reduce the user's anxiety. This makes it possible to improve user satisfaction by adjusting the method for acquiring personalized information according to the user's emotions.

[0108] The analysis unit can estimate the user's emotions and adjust the level of analysis detail based on the emotion data. For example, if the user is relaxed, the analysis unit performs a detailed analysis to improve accuracy. If the user is in a hurry, the analysis unit can perform a quick analysis to provide results quickly. Furthermore, if the user is feeling stressed, the analysis unit can reduce the analysis load and ease the burden on the user. In this way, by adjusting the level of analysis detail according to the user's emotions, it is possible to provide the user with the optimal analysis results.

[0109] The generator can estimate the user's emotions and adjust the design of the generated UI and source code based on the emotion data. For example, if the user is in a happy mood, the generator can generate a bright and colorful UI to further enhance the user's mood. If the user is tired, the generator can generate a UI with a simple and calm design to reduce the user's fatigue. Furthermore, if the user is concentrating, the generator can generate a detailed UI with a lot of information to support the user's concentration. This makes it possible to improve the user experience by providing a UI and source code designed according to the user's emotions.

[0110] When displaying the generated UI on the terminal, the presentation unit can estimate the user's emotions and adjust the display method based on the emotion data. For example, if the user is nervous, the presentation unit can provide an interface with subdued colors to reduce visual stress. If the user is having fun, the presentation unit can provide an interface with bright colors to make input work more enjoyable. Furthermore, if the user is tired, the presentation unit can provide a simple, highly visible interface to make input work easier. In this way, by adjusting the UI display method according to the user's emotions, it is possible to provide an optimal interface for the user.

[0111] The acquisition unit can estimate the user's emotions and determine the priority of personalized information to be acquired based on the emotion data. For example, if the user is feeling stressed, information that helps the user relax can be acquired preferentially. Also, if the user is relaxed, information that improves work efficiency can be acquired preferentially. Furthermore, if the user is in a hurry, the most important information can be acquired preferentially. In this way, by determining the priority of personalized information according to the user's emotions, the information that is most important to the user can be acquired preferentially.

[0112] The analysis unit can perform analysis taking into account the geographical distribution of terminals. For example, if terminals are concentrated in a specific area, the analysis unit can perform an analysis specific to that area. In addition, if terminals are distributed over a wide area, the analysis unit can perform different analyses for each area. Furthermore, the analysis unit can also appropriately allocate analysis resources by taking into account the geographical distribution of terminals. In this way, by taking into account the geographical distribution of terminals, it is possible to perform an appropriate analysis for each area.

[0113] The processing flow of the second embodiment will be briefly explained below.

[0114] Step 1: The acquisition unit acquires personalized information of the user. The personalized information includes, for example, the user's preferences, past usage history, and specific settings. For example, the acquisition unit acquires information directly from the user's device. The acquisition unit can also collect personalized information through a cloud service. Furthermore, the acquisition unit can also collect information from the user's social media activities. Step 2: The analysis unit analyzes the device's use and source code based on the information acquired by the acquisition unit. For example, the analysis unit classifies the device's use into business use, entertainment use, etc. The analysis unit also analyzes the structure of the source code to obtain basic information for generating an optimal UI and source code. The analysis unit can also analyze the device's hardware configuration and network settings. Step 3: The generation unit generates a new UI and source code based on the results of the analysis by the analysis unit. For example, the generation unit generates a UI that takes usability into consideration. The generation unit can also generate optimal source code according to the performance of the device. Furthermore, the generation unit can also generate a customized UI that matches the user's preferences. Step 4: The presentation unit displays the UI generated by the generation unit on the terminal. For example, the presentation unit displays the generated UI in real time. The presentation unit can also dynamically update the UI in response to user operations. Furthermore, the presentation unit can also synchronously display the generated UI on multiple devices.

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0119] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0120] 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.

[0121] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0122] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0123] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0125] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0126] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0127] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0134] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0135] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0136] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0137] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0138] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0139] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0141] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0142] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0143] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0144] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0145] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0146] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0147] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0149] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0150] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0151] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0152] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0153] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0154] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0155] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0157] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0158] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0159] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0160] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0161] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0162] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0163] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0164] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0165] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0166] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0167] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0168] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0169] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0170] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0171] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0172] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0173] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0174] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0175] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0176] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0177] 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.

[0178] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0179] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0180] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0181] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0182] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0183] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0184] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0185] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0186] [Explanation of symbols]

[0187] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an acquisition unit that acquires personalized information of a user; an analysis unit that analyzes the purpose and source code of the terminal based on the information acquired by the acquisition unit; a generation unit that generates a UI and source code based on the results of the analysis by the analysis unit; a presentation unit that presents the UI generated by the generation unit. A system characterized by:

2. The acquisition unit Obtain personalization information about your preferences, past usage history, and specific settings 2. The system of claim 1.

3. The analysis unit Analyze the device's purpose and source code 2. The system of claim 1.

4. The generation unit Generate new UI and source code based on the analysis results 2. The system of claim 1.

5. The presentation unit Display the generated UI on the device 2. The system of claim 1.

6. The acquisition unit A method for estimating a user's emotion and determining the timing of acquiring personalized information based on the estimated user's emotion.

2. The system of claim 1.

7. The acquisition unit Analyze the user's past usage history and select the optimal acquisition method 2. The system of claim 1.

8. The acquisition unit Filtering personalization information based on the user's current projects and interests 2. The system of claim 1.

Images