system

Abstract

To provide a user-friendly design and a customizable UI.SOLUTION: A system according to an embodiment comprises a design unit, a UI provision unit, a proposal unit, a support unit, and a switching unit. The design unit provides a user-friendly design. The UI provision unit provides a customizable UI. The proposal unit predicts a user input and proposes a complementary candidate on the basis of the prediction results. The support unit supports the use of functions. The switching unit switches a theme mode tailored to illumination conditions.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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques leave room for improvement in providing an intuitive and easy-to-use user interface design.

[0005] The system according to the embodiment aims to provide a user-friendly design and a customizable UI. [Means for solving the problem]

[0006] The system according to the embodiment includes a design unit, a UI providing unit, a suggestion unit, a support unit, and a switching unit. The design unit provides a design that is easy for the user to use. The UI providing unit provides a customizable UI. The suggestion unit predicts user input and suggests completion candidates based on the predicted results. The support unit supports the use of functions. The switching unit switches theme modes according to lighting conditions. Effect of the Invention

[0007] The system according to the embodiment can provide a user-friendly design and a customizable UI. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process program 56 executed on the RAM 30.

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The system according to the embodiment of the present invention is a system that provides a user-friendly design. The system provides an intuitive interface and an easy-to-use layout. Users can adjust the appearance and arrangement to create a UI that suits their personal preferences. In addition, the system predicts user input and suggests completion candidates to enable efficient operation. In addition, the system provides UI options for people with disabilities to improve accessibility. It also has an interactive guide that teaches users how to use new functions and applications, supporting smooth operation. It also provides a dark mode / light mode that switches themes according to lighting conditions to reduce visual fatigue. It also has a function to accept feedback from users and provide information useful for improving the UI. It achieves secure data management while protecting privacy and increases reliability. It is also characterized by multi-platform support that provides a consistent UI experience on different devices and operating systems. In addition, an AI assistant interacts with the user and quickly responds to UI operation guidance and questions. As a result, the system that provides a user-friendly design can improve the user's operation efficiency and increase accessibility.

[0029] A system for providing a user-friendly design according to an embodiment includes a design unit, a UI providing unit, a suggestion unit, a support unit, and a switching unit. The design unit provides a user-friendly design. The design unit provides, for example, an intuitive interface and an easy-to-use layout. The design unit enables a user to adjust the appearance and arrangement to create a UI that matches individual preferences. For example, the design unit can change colors, change layouts, add functions, and the like. The UI providing unit provides a customizable UI. The UI providing unit, for example, predicts a user's input and suggests completion candidates based on the predicted results. The UI providing unit can select completion candidates based on the user's past input history and frequent patterns. The suggestion unit predicts a user's input and suggests completion candidates based on the predicted results. The suggestion unit predicts a user's input using, for example, a machine learning algorithm or a statistical model. The suggestion unit can select completion candidates based on the user's past input history and frequent patterns. The support unit supports the use of a function. The support unit provides, for example, a help document, chat support, telephone support, and the like. The support unit provides information required when a user uses a function and supports smooth operation. The switching unit switches the theme mode according to lighting conditions. The switching unit provides theme modes such as a dark mode, a light mode, and a custom mode. The switching unit can automatically select the theme mode based on a user's preference and environmental conditions. As a result, the system providing a user-friendly design according to the embodiment can improve the user's operation efficiency and enhance accessibility.

[0030] The design department provides a user-friendly design. For example, the design department provides an intuitive interface and an easy-to-use layout. Specifically, the design department devise the layout of buttons and menus so that users can operate intuitively, and adopts visually easy-to-understand icons and colors. In order to enable users to adjust the appearance and layout and create a UI that suits their personal preferences, the design department provides a drag-and-drop function so that users can easily move and resize elements. In addition, a wealth of customization options are provided so that users can change colors, change layouts, add functions, and so on. For example, users can use a color picker to change the background color or text color, or select a layout template and adjust it to their preferences. In addition, the design department also provides a function that allows users to save their designs and reuse them later. This allows users to easily call up and reuse designs that they have created once, saving time and effort. The design department also has a function to collect user feedback and use it to improve the design. For example, it provides a feedback form that allows users to give their opinions on the usability of the design, and improves the design based on the results. This allows the design department to continue to provide designs that always meet the latest user needs.

[0031] The UI providing unit provides a customizable UI. For example, the UI providing unit predicts a user's input and suggests completion candidates based on the predicted result. Specifically, when the user starts inputting, the UI providing unit displays appropriate completion candidates in real time based on the past input history and frequent patterns. This allows the user to perform input work efficiently. The UI providing unit uses a machine learning algorithm to learn the user's input pattern and provide more accurate completion candidates. For example, if a user frequently uses a specific word or phrase, the word or phrase is preferentially displayed as a completion candidate. In addition, the UI providing unit can adjust the display timing and order of the completion candidates according to the user's input speed and typing style. This allows the user to perform input work smoothly without stress. Furthermore, the UI providing unit supports keyboard shortcuts and gesture operations to simplify the operation when the user selects a completion candidate. For example, the UI providing unit allows the user to quickly select a completion candidate by pressing a specific key. In addition, the UI providing unit allows the user to customize the display method and contents of the completion candidates to suit his or her preferences. This allows the UI providing unit to make the user's input work more efficient and provide a comfortable operating environment.

[0032] The suggestion unit predicts the user's input and suggests completion candidates based on the predicted result. Specifically, the suggestion unit predicts the user's input using a machine learning algorithm or a statistical model. The suggestion unit can select completion candidates based on the user's past input history and frequent patterns. For example, the suggestion unit learns words and phrases that the user often uses in a specific context and suggests completion candidates based on the learned words and phrases. The suggestion unit utilizes natural language processing technology to understand the user's input content and provide completion candidates suitable for the context. For example, if the user inputs "schedule a meeting," the suggestion unit suggests appropriate completion candidates such as "adjust," "confirm," and "set." In addition, the suggestion unit can adjust the display timing and order of the completion candidates according to the user's input speed and typing style. This allows the user to perform input work smoothly without stress. In addition, the suggestion unit supports keyboard shortcuts and gesture operations to simplify the operation when the user selects a completion candidate. For example, the user can quickly select a completion candidate by pressing a specific key. In addition, the suggestion unit allows the user to customize the display method and content of the completion candidates to suit his or her preferences. As a result, the suggestion unit can make the user's input work more efficient and provide a comfortable operating environment.

[0033] The support department supports the use of the functions. Specifically, the support department provides help documents, chat support, telephone support, etc. The help documents include detailed explanations and operation procedures to make it easy for users to understand each function of the system. In addition, it has a search function, so that users can quickly find the information they need. Chat support responds to users' questions in real time and supports problem solving. For example, if a user asks about how to use a specific function, chat support provides an immediate answer and can share the screen or perform remote operation if necessary. Telephone support allows users to solve problems while talking directly with an operator. This makes it possible to provide support quickly and accurately even in cases where complex problems or urgent responses are required. In addition, the support department also has a function to collect user feedback and use it to improve the system. For example, it conducts a survey on the quality of support and satisfaction of users who have received support, and improves the support system based on the results. In this way, the support department can enable users to use the system smoothly and increase satisfaction.

[0034] The switching unit switches the theme mode according to the lighting conditions. Specifically, the switching unit provides theme modes such as a dark mode, a light mode, and a custom mode. The dark mode darkens the background and displays text and icons brightly to improve visibility in low-light environments and reduce eye fatigue. The light mode brightens the background and displays text and icons darkly to ensure visibility in bright environments. The custom mode allows the user to adjust the color and contrast of the theme according to his or her preferences. The switching unit can automatically select the theme mode based on the user's preferences and environmental conditions. For example, the system detects the brightness of the environment from the device's illuminance sensor and automatically switches to an appropriate theme mode accordingly. In addition, the user can manually switch the theme mode, and can easily change it from the setting menu. Furthermore, the switching unit also provides a function of automatically switching the theme mode according to the time of day. For example, the system automatically switches to the light mode during the day and the dark mode at night to improve the user's visibility and comfort. As a result, the switching unit can always enjoy optimal visibility and comfort and improve operation efficiency.

[0035] The system for providing a user-friendly design includes a feedback unit that collects feedback from users, and an improvement unit that improves a UI that the user can select based on the feedback collected by the feedback unit. The feedback unit collects feedback from users. The feedback unit collects feedback using, for example, questionnaires, user reviews, usage logs, etc. The feedback unit collects opinions and requests from users, and uses them to improve the UI. The improvement unit improves the UI that the user can select based on the feedback collected by the feedback unit. The improvement unit, for example, changes the layout of the UI, adds or deletes functions, etc. The improvement unit provides a UI that is easier to use by reflecting the user's feedback. In this way, the system for providing a user-friendly design can improve the UI based on the user's feedback and improve the user experience. A part or all of the above-mentioned processing in the feedback unit and the improvement unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback unit can input the user's feedback data to the generation AI and cause the generation AI to analyze the feedback. The improvement unit can improve the UI based on the feedback data analyzed by the generation AI.

[0036] The system for providing a user-friendly design includes a dialogue unit that dialogues with a user, and a control unit that controls at least one of a design unit, a UI providing unit, a suggestion unit, a support unit, and a switching unit based on the user's dialogue with the dialogue unit. The dialogue unit dialogues with the user using, for example, technologies such as voice recognition, text chat, and gesture recognition. The dialogue unit responds to the user's input and questions and provides necessary information. The control unit controls at least one of the design unit, the UI providing unit, the suggestion unit, the support unit, and the switching unit based on the user's dialogue with the dialogue unit. The control unit controls the operation of each unit based on, for example, the user's input, and performs automatic adjustment of the system. As a result, the system for providing a user-friendly design can control each unit based on the dialogue with the user and provide a more personalized experience. A part or all of the above-mentioned processing in the dialogue unit and the control unit may be performed, for example, using AI, or may be performed without using AI. For example, the dialogue unit can input the user's dialogue data to the generation AI and cause the generation AI to execute analysis of the dialogue. The control unit can control each part based on the dialogue data analyzed by the generation AI.

[0037] The UI providing unit can provide UI options for persons with disabilities. The UI providing unit provides UI options such as voice reading, enlarged display, and color vision correction. The UI providing unit provides necessary support for persons with disabilities when using the system, improving accessibility. As a result, the UI providing unit can provide UI options for persons with disabilities and improve accessibility. Some or all of the above-mentioned processing in the UI providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the UI providing unit can input UI options based on the needs of persons with disabilities to the generation AI and have the generation AI suggest the optimal UI option.

[0038] The UI providing unit can recognize the user's facial expressions and gestures and customize the UI in real time. The UI providing unit recognizes the user's facial expressions and gestures using technologies such as face recognition by a camera and a motion sensor. The UI providing unit dynamically changes the UI based on the user's facial expressions and gestures to enable more intuitive operation. For example, the UI providing unit provides a UI with a bright color tone when the user smiles. In addition, the UI providing unit can also activate a specific function when the user waves his or her hand. This allows the UI providing unit to recognize the user's facial expressions and gestures and dynamically customize the UI to enable more intuitive operation. Part or all of the above-mentioned processing in the UI providing unit may be performed, for example, using AI or may be performed without using AI. For example, the UI providing unit can input the user's facial expression data to the generation AI and cause the generation AI to perform an analysis of the facial expression.

[0039] The switching unit can track the user's eye movement and automatically select an appropriate theme mode. The switching unit tracks the user's eye movement using technologies such as an eye tracking device and a camera sensor. The switching unit selects an optimal theme mode based on the user's eye movement to reduce visual fatigue. For example, the switching unit switches to a dark mode when the user continues to look at the screen for a long time. The switching unit can also switch to a light mode when the user is working in a bright environment. Thereby, the switching unit can reduce visual fatigue by tracking the user's eye movement and automatically selecting an optimal theme mode. A part or all of the above-mentioned processing in the switching unit may be performed using, for example, AI, or may be performed without using AI. For example, the switching unit can input the user's gaze data to the generation AI and cause the generation AI to perform gaze analysis.

[0040] The UI providing unit can change the customizable UI based on the health data of the user. The UI providing unit collects health data such as, for example, heart rate, number of steps, and sleep pattern, and adjusts the UI according to the health condition of the user. The UI providing unit provides a healthier operating environment based on the health data of the user. For example, when the heart rate of the user is high, the UI providing unit provides a UI with a relaxing color tone. In addition, when the number of steps of the user is low, the UI providing unit can also display a message encouraging exercise. In this way, the UI providing unit can provide a healthier operating environment by changing the UI based on the health data of the user. A part or all of the above-mentioned processing in the UI providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the UI providing unit can input the health data of the user to the generation AI and cause the generation AI to execute an analysis of the health condition.

[0041] The UI providing unit can customize the UI based on the user's learning style. The UI providing unit identifies a learning style, such as visual, auditory, or experiential, and provides a UI according to the learning style. The UI providing unit provides a more effective learning environment based on the user's learning style. For example, the UI providing unit provides a UI that makes extensive use of diagrams and graphs to a visual user. Also, audio guides can be provided to an auditory user. This allows the UI providing unit to customize the UI based on the user's learning style, thereby improving learning efficiency. A part or all of the above-mentioned processing in the UI providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the UI providing unit can input the user's learning data into the generating AI and cause the generating AI to perform an analysis of the learning style.

[0042] The switching unit can predict and switch the theme mode according to the user's life rhythm. The switching unit identifies life rhythms such as wake-up time, bedtime, and activity patterns, and provides a theme mode according to the life rhythm. The switching unit provides a more comfortable operating environment based on the user's life rhythm. For example, the switching unit switches to a dark mode when the user is working at night. The switching unit can also switch to a light mode when the user is working in the morning. This allows the switching unit to provide a more comfortable operating environment by switching the theme mode according to the user's life rhythm. A part or all of the above-mentioned processing in the switching unit may be performed, for example, using AI, or may be performed without using AI. For example, the switching unit can input the user's life rhythm data to the generation AI and cause the generation AI to execute an analysis of the life rhythm.

[0043] The dialogue unit can customize the content of the dialogue with the user based on the user's preferences or interests. The dialogue unit identifies the user's preferences and interests using, for example, past selection history, survey results, etc., and provides dialogue content according to the user's preferences and interests. The dialogue unit provides a more personalized dialogue based on the user's preferences and interests. For example, the dialogue unit conducts a dialogue about a topic that the user liked in the past. The dialogue unit can also provide new information based on the user's interests. This allows the dialogue unit to provide a more personalized dialogue by customizing the content of the dialogue based on the user's preferences and interests. Some or all of the above-mentioned processing in the dialogue unit may be performed, for example, using AI, or may be performed without using AI. For example, the dialogue unit can input the user's preference data to the generation AI and cause the generation AI to perform an analysis of the preferences.

[0044] The switching unit can switch the theme mode according to the sound environment around the user. The switching unit detects, for example, the surrounding noise level, the type of sound, and the like, and provides a theme mode according to the detected noise level. The switching unit provides a more comfortable operating environment based on the sound environment of the user. For example, the switching unit switches to the light mode when the surroundings are quiet. The switching unit can also switch to the dark mode when the surroundings are noisy. This allows the switching unit to provide a more comfortable operating environment by switching the theme mode according to the sound environment around the user. A part or all of the above-mentioned processing in the switching unit may be performed, for example, using AI, or may be performed without using AI. For example, the switching unit can input the sound environment data of the user to the generation AI and cause the generation AI to execute an analysis of the sound environment.

[0045] The UI providing unit can adjust the UI based on the user's lifestyle or daily behavioral patterns. The UI providing unit, for example, identifies daily behavioral patterns, hobbies, occupations, etc., and provides a UI according to the identified patterns. The UI providing unit provides a more personalized operating environment based on the user's lifestyle. For example, when the user is at work, the UI providing unit provides a UI that makes it easy for the user to concentrate. In addition, when the user is relaxed, the UI providing unit can also provide a UI that allows the user to relax. This allows the UI providing unit to provide a more personalized operating environment by adjusting the UI based on the user's lifestyle and daily behavioral patterns. Some or all of the above-mentioned processing in the UI providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the UI providing unit can input the user's behavioral data into the generation AI and cause the generation AI to perform an analysis of the behavioral pattern.

[0046] The switching unit can switch theme modes in cooperation with the user's smart home devices. The switching unit can cooperate with smart home devices such as smart lights and smart thermostats to provide theme modes according to the smart home devices. The switching unit provides a more integrated operating environment based on the user's smart home devices. For example, the switching unit can switch theme modes according to the brightness of the smart lights. The switching unit can also switch theme modes according to the set temperature of the smart thermostat. This allows the switching unit to provide a more integrated operating environment by switching theme modes in cooperation with the user's smart home devices. Some or all of the above-mentioned processing in the switching unit may be performed, for example, using AI or may be performed without using AI. For example, the switching unit can input data of the smart home device to the generation AI and cause the generation AI to perform analysis of the device.

[0047] The design unit can analyze the user's past design preferences and propose an optimal design template. The design unit proposes an optimal design template based on, for example, the color and layout selected by the user in the past. The design unit provides a more personalized design template based on the user's past design preferences. For example, the design unit customizes a design template based on the font and icon style that the user has liked in the past. The design unit can also analyze the user's past design preferences and propose design templates that match the season and events. This allows the design unit to provide a more personalized design template by analyzing the user's past design preferences. Some or all of the above-mentioned processing in the design unit may be performed, for example, using AI or may be performed without using AI. For example, the design unit can input the user's preference data to the generation AI and cause the generation AI to perform an analysis of the preferences.

[0048] The design unit can dynamically change the design based on the user's current activity and environment when generating the design. For example, when the user is at work, the design unit provides a simple design that allows the user to concentrate easily. The design unit provides a more appropriate design based on the user's current activity and environment. For example, when the user is relaxing, the design unit provides a relaxing color tone and layout. In addition, when the user is out, the design unit can also provide a design with high visibility. This allows the design unit to dynamically change the design based on the user's current activity and environment to provide a more appropriate design. Part or all of the above-mentioned processing in the design unit may be performed, for example, using AI or may be performed without using AI. For example, the design unit can input the user's activity data to the generation AI and cause the generation AI to perform an analysis of the activity.

[0049] When generating a design, the design department can incorporate design elements specific to the region in consideration of the geographical location information of the user. For example, if the user is in Japan, the design department incorporates traditional Japanese design elements. The design department provides a more personalized design based on the geographical location information of the user. For example, if the user is in America, the design department incorporates design elements based on American culture. Also, if the user is in Europe, the design department can incorporate historical design elements of Europe. This allows the design department to provide a more personalized design by incorporating design elements specific to the region in consideration of the geographical location information of the user. A part or all of the above-mentioned processing in the design department may be performed, for example, using AI, or may be performed without using AI. For example, the design department can input the geographical location data of the user to the generation AI and cause the generation AI to perform an analysis of the location information.

[0050] When generating a design, the design unit can analyze the user's social media activity and propose a design based on a trend. The design unit proposes a design based on, for example, colors and styles that the user often uses on social media. The design unit provides a more personalized design based on the user's social media activity. For example, the design unit incorporates the design style of an influencer that the user follows. The design unit can also propose a design based on trends and hashtags in which the user participates. This allows the design unit to provide a more personalized design by analyzing the user's social media activity and proposing a design based on a trend. Some or all of the above-mentioned processing in the design unit may be performed, for example, using AI or may be performed without using AI. For example, the design unit can input the user's social media data into the generation AI and cause the generation AI to perform trend analysis.

[0051] The UI providing unit can analyze the user's past operation history and propose an optimal UI layout. For example, the UI providing unit preferentially arranges functions that the user has frequently used in the past. The UI providing unit provides a more efficient UI layout based on the user's past operation history. For example, the UI providing unit proposes a layout based on a UI style that the user has preferred in the past. The UI providing unit can also analyze the user's past operation history and propose an efficient UI layout. As a result, the UI providing unit can provide a more efficient UI layout by analyzing the user's past operation history. A part or all of the above-mentioned processing in the UI providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the UI providing unit can input the user's operation history data to the generation AI and cause the generation AI to perform analysis of the history.

[0052] The UI providing unit can dynamically change the UI based on the user's current activity and environment when providing the UI. For example, when the user is at work, the UI providing unit provides a UI that makes it easy for the user to concentrate. The UI providing unit provides a more appropriate UI based on the user's current activity and environment. For example, when the user is relaxing, the UI providing unit provides a UI that allows the user to relax. In addition, when the user is out, the UI providing unit can also provide a UI with high visibility. Thereby, the UI providing unit can provide a more appropriate UI by dynamically changing the UI based on the user's current activity and environment. A part or all of the above-mentioned processing in the UI providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the UI providing unit can input the user's activity data to the generation AI and cause the generation AI to perform an analysis of the activity.

[0053] When providing the UI, the UI providing unit can incorporate region-specific UI elements in consideration of the geographical location information of the user. For example, when the user is in Japan, the UI providing unit incorporates traditional Japanese UI elements. The UI providing unit provides a more personalized UI based on the geographical location information of the user. For example, when the user is in America, the UI providing unit incorporates UI elements based on American culture. Also, when the user is in Europe, the UI providing unit can incorporate historical UI elements of Europe. As a result, the UI providing unit can provide a more personalized UI by incorporating region-specific UI elements in consideration of the geographical location information of the user. A part or all of the above-mentioned processing in the UI providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the UI providing unit can input the geographical location data of the user to the generation AI and cause the generation AI to execute an analysis of the location information.

[0054] When providing the UI, the UI providing unit can analyze the social media activity of the user and propose a UI based on a trend. The UI providing unit proposes a UI based on, for example, colors and styles that the user often uses on social media. The UI providing unit provides a more personalized UI based on the social media activity of the user. For example, the UI providing unit incorporates the UI style of an influencer that the user follows. The UI providing unit can also propose a UI based on trends and hashtags in which the user participates. This allows the UI providing unit to provide a more personalized UI by analyzing the social media activity of the user and proposing a UI based on a trend. A part or all of the above-mentioned processing in the UI providing unit may be performed, for example, using AI or may be performed without using AI. For example, the UI providing unit can input the social media data of the user to the generation AI and cause the generation AI to perform trend analysis.

[0055] At the time of making a proposal, the suggestion unit can analyze the user's past preferences and make an optimal proposal. The suggestion unit makes a proposal based on, for example, products or services that the user liked in the past. The suggestion unit provides a more personalized proposal based on the user's past preferences. For example, the suggestion unit analyzes the user's past preferences and makes a proposal that matches the season or event. The suggestion unit can also make a highly relevant proposal based on the user's past preferences. As a result, the suggestion unit can provide a more personalized proposal by analyzing the user's past preferences. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's preference data to the generation AI and cause the generation AI to perform an analysis of the preferences.

[0056] The suggestion unit can dynamically change the content of the suggestion based on the user's current activity and environment at the time of suggestion. For example, when the user is at work, the suggestion unit makes a suggestion related to work. The suggestion unit provides a more appropriate suggestion based on the user's current activity and environment. For example, when the user is relaxing, the suggestion unit makes a suggestion that allows the user to relax. In addition, when the user is out, the suggestion unit can also make a suggestion related to going out. Thereby, the suggestion unit can provide a more appropriate suggestion by dynamically changing the content of the suggestion based on the user's current activity and environment. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the user's activity data to the generation AI and cause the generation AI to execute an analysis of the activity.

[0057] The suggestion unit can make a region-specific suggestion taking into account the geographical location information of the user at the time of suggestion. For example, when the user is in Japan, the suggestion unit makes a suggestion based on Japanese culture. The suggestion unit provides a more personalized suggestion based on the geographical location information of the user. For example, when the user is in America, the suggestion unit makes a suggestion based on American culture. Also, when the user is in Europe, the suggestion unit can make a suggestion based on European culture. Thereby, the suggestion unit can provide a more personalized suggestion by making a region-specific suggestion taking into account the geographical location information of the user. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the geographical location data of the user to the generation AI and cause the generation AI to execute an analysis of the location information.

[0058] At the time of making a proposal, the suggestion unit can analyze the user's social media activity and make a proposal based on a trend. The suggestion unit makes a proposal based on, for example, products and services that the user frequently uses on social media. The suggestion unit provides a more personalized proposal based on the user's social media activity. For example, the suggestion unit incorporates suggestions from influencers that the user follows. The suggestion unit can also make a proposal based on trends and hashtags in which the user participates. This allows the suggestion unit to provide a more personalized proposal by analyzing the user's social media activity and making a proposal based on a trend. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the user's social media data to the generation AI and cause the generation AI to perform trend analysis.

[0059] The support unit can analyze the user's past support history and propose an optimal support method at the time of support. For example, the support unit makes a proposal based on the support method that the user liked in the past. The support unit provides a more efficient support method based on the user's past support history. For example, the support unit analyzes the user's past support history and proposes an efficient support method. The support unit can also propose highly relevant support based on the support content that the user received in the past. In this way, the support unit can provide a more efficient support method by analyzing the user's past support history. A part or all of the above-mentioned processing in the support unit may be performed, for example, using AI, or may be performed without using AI. For example, the support unit can input the user's support history data to the generation AI and cause the generation AI to execute the analysis of the history.

[0060] The support unit can dynamically change the support content based on the user's current activity and environment during support. For example, when the user is at work, the support unit provides work-related support. The support unit provides more appropriate support based on the user's current activity and environment. For example, when the user is relaxed, the support unit provides support that allows the user to relax. In addition, when the user is out, the support unit can also provide support related to going out. This allows the support unit to dynamically change the support content based on the user's current activity and environment, thereby providing more appropriate support. Part or all of the above-mentioned processing in the support unit may be performed, for example, using AI, or may be performed without using AI. For example, the support unit can input the user's activity data into the generation AI and cause the generation AI to perform an analysis of the activity.

[0061] The support unit can provide region-specific support by taking into account the geographical location information of the user when providing support. For example, when the user is in Japan, the support unit provides support based on Japanese culture. The support unit provides more personalized support based on the geographical location information of the user. For example, when the user is in America, the support unit provides support based on American culture. Also, when the user is in Europe, the support unit can provide support based on European culture. This allows the support unit to provide more personalized support by providing region-specific support by taking into account the geographical location information of the user. Part or all of the above-mentioned processing in the support unit may be performed, for example, using AI or may be performed without using AI. For example, the support unit can input the geographical location data of the user to the generation AI and cause the generation AI to perform analysis of the location information.

[0062] During support, the support unit can analyze the user's social media activities and provide support based on trends. The support unit provides support based on, for example, products and services that the user frequently uses on social media. The support unit provides more personalized support based on the user's social media activities. For example, the support unit adopts the support style of an influencer that the user follows. The support unit can also provide support based on trends and hashtags in which the user participates. This allows the support unit to analyze the user's social media activities and provide support based on trends, thereby providing more personalized support. Part or all of the above-mentioned processing in the support unit may be performed, for example, using AI, or may be performed without using AI. For example, the support unit can input the user's social media data into the generation AI and cause the generation AI to perform trend analysis.

[0063] At the time of switching, the switching unit can analyze the user's past theme preferences and propose an optimal theme. For example, the switching unit makes a proposal based on themes that the user liked in the past. The switching unit provides a more personalized theme based on the user's past theme preferences. For example, the switching unit analyzes the user's past theme preferences and proposes a theme that matches a season or an event. The switching unit can also propose a highly relevant theme based on the user's past theme preferences. Thereby, the switching unit can provide a more personalized theme by analyzing the user's past theme preferences. A part or all of the above-mentioned processing in the switching unit may be performed, for example, using AI, or may be performed without using AI. For example, the switching unit can input the user's theme preference data to the generation AI and cause the generation AI to execute an analysis of the preferences.

[0064] The switching unit can dynamically change the theme based on the user's current activity and environment at the time of switching. For example, when the user is at work, the switching unit provides a theme that is easy to concentrate on. The switching unit provides a more appropriate theme based on the user's current activity and environment. For example, when the user is relaxing, the switching unit provides a relaxing theme. In addition, when the user is out, the switching unit can also provide a highly visible theme. Thereby, the switching unit can dynamically change the theme based on the user's current activity and environment to provide a more appropriate theme. Part or all of the above-mentioned processing in the switching unit may be performed, for example, using AI, or may be performed without using AI. For example, the switching unit can input the user's activity data to the generation AI and cause the generation AI to execute an analysis of the activity.

[0065] The switching unit can incorporate a region-specific theme in consideration of the geographical location information of the user at the time of switching. For example, when the user is in Japan, the switching unit incorporates a traditional Japanese theme. The switching unit provides a more personalized theme based on the geographical location information of the user. For example, when the user is in America, the switching unit incorporates a theme based on American culture. Also, when the user is in Europe, the switching unit can incorporate a European historical theme. This allows the switching unit to provide a more personalized theme by incorporating a region-specific theme in consideration of the geographical location information of the user. A part or all of the above-mentioned processing in the switching unit may be performed, for example, using AI, or may be performed without using AI. For example, the switching unit can input the geographical location data of the user to the generation AI and cause the generation AI to execute an analysis of the location information.

[0066] At the time of switching, the switching unit can analyze the social media activity of the user and suggest a theme based on a trend. The switching unit, for example, suggests a theme based on colors and styles that the user often uses on social media. The switching unit provides a more personalized theme based on the social media activity of the user. For example, the switching unit incorporates a theme style of an influencer that the user follows. The switching unit can also suggest a theme based on trends and hashtags in which the user participates. In this way, the switching unit can provide a more personalized theme by analyzing the social media activity of the user and suggesting a theme based on a trend. A part or all of the above-mentioned processing in the switching unit may be performed, for example, using AI or may be performed without using AI. For example, the switching unit can input the social media data of the user to the generation AI and cause the generation AI to perform trend analysis.

[0067] The feedback unit can analyze the user's past feedback history and propose an optimal collection method when collecting feedback. The feedback unit makes a proposal based on, for example, a feedback collection method that the user has preferred in the past. The feedback unit provides a more efficient feedback collection method based on the user's past feedback history. For example, the feedback unit analyzes the user's past feedback history and proposes an efficient feedback collection method. The feedback unit can also propose a highly relevant feedback collection method based on feedback content provided by the user in the past. In this way, the feedback unit can provide a more efficient feedback collection method by analyzing the user's past feedback history. A part or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback unit can input the user's feedback history data to the generation AI and cause the generation AI to perform analysis of the history.

[0068] The feedback unit can collect region-specific feedback in consideration of the geographical location information of the user when collecting feedback. For example, if the user is in Japan, the feedback unit collects feedback based on Japanese culture. The feedback unit provides more personalized feedback based on the geographical location information of the user. For example, if the user is in America, the feedback unit collects feedback based on American culture. Also, if the user is in Europe, the feedback unit can collect feedback based on European culture. This allows the feedback unit to provide more personalized feedback by collecting region-specific feedback in consideration of the geographical location information of the user. Part or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI or may be performed without using AI. For example, the feedback unit can input the geographical location data of the user to the generation AI and cause the generation AI to perform analysis of the location information.

[0069] At the time of improvement, the improvement unit can propose an optimal improvement method by analyzing the user's past feedback. The improvement unit proposes an improvement method based on, for example, feedback provided by the user in the past. The improvement unit provides a more efficient UI improvement method based on the user's past feedback. For example, the improvement unit analyzes the user's past feedback history and proposes an efficient improvement method. The improvement unit can also propose a highly relevant improvement method based on the feedback content provided by the user in the past. In this way, the improvement unit can provide a more efficient UI improvement method by analyzing the user's past feedback. A part or all of the above-mentioned processing in the improvement unit may be performed, for example, using AI, or may be performed without using AI. For example, the improvement unit can input the user's feedback data to the generation AI and cause the generation AI to execute an analysis of the feedback.

[0070] The improvement unit can make region-specific improvements by taking into account the geographical location information of the user at the time of improvement. For example, when the user is in Japan, the improvement unit makes UI improvements based on Japanese culture. The improvement unit provides more personalized UI improvements based on the geographical location information of the user. For example, when the user is in America, the improvement unit makes UI improvements based on American culture. Also, when the user is in Europe, the improvement unit can make UI improvements based on European culture. Thereby, the improvement unit can provide more personalized UI improvements by making region-specific improvements by taking into account the geographical location information of the user. Part or all of the above-mentioned processing in the improvement unit may be performed, for example, using AI or may be performed without using AI. For example, the improvement unit can input the geographical location data of the user to the generation AI and cause the generation AI to execute an analysis of the location information.

[0071] The dialogue unit can analyze the user's past dialogue history during dialogue to suggest an optimal dialogue method. The dialogue unit makes a suggestion based on, for example, a dialogue style that the user has preferred in the past. The dialogue unit provides a more efficient dialogue method based on the user's past dialogue history. For example, the dialogue unit analyzes the user's past dialogue history and suggests an efficient dialogue method. The dialogue unit can also suggest a highly relevant dialogue based on the content of the dialogue the user has had in the past. This allows the dialogue unit to provide a more efficient dialogue method by analyzing the user's past dialogue history. A part or all of the above-mentioned processing in the dialogue unit may be performed, for example, using AI, or may be performed without using AI. For example, the dialogue unit can input the user's dialogue history data to the generation AI and cause the generation AI to perform analysis of the history.

[0072] The dialogue unit can conduct a dialogue specific to a region in consideration of the geographical location information of the user during the dialogue. For example, when the user is in Japan, the dialogue unit conducts a dialogue based on Japanese culture. The dialogue unit provides a more personalized dialogue based on the geographical location information of the user. For example, when the user is in America, the dialogue unit conducts a dialogue based on American culture. Also, when the user is in Europe, the dialogue unit can conduct a dialogue based on European culture. This allows the dialogue unit to provide a more personalized dialogue by conducting a dialogue specific to a region in consideration of the geographical location information of the user. A part or all of the above-mentioned processing in the dialogue unit may be performed, for example, using AI or may be performed without using AI. For example, the dialogue unit can input the geographical location data of the user to the generation AI and cause the generation AI to execute an analysis of the location information.

[0073] During control, the control unit can analyze the user's past operation history and propose an optimal control method. The control unit makes a proposal based on, for example, a control method that the user liked in the past. The control unit provides a more efficient control method based on the user's past operation history. For example, the control unit analyzes the user's past operation history and proposes an efficient control method. The control unit can also propose a highly relevant control based on the operation content performed by the user in the past. In this way, the control unit can provide a more efficient control method by analyzing the user's past operation history. A part or all of the above-mentioned processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input the user's operation history data to the generation AI and cause the generation AI to perform an analysis of the history.

[0074] The control unit can perform region-specific control in consideration of the geographical location information of the user during control. For example, when the user is in Japan, the control unit performs control based on Japanese culture. The control unit provides more personalized control based on the geographical location information of the user. For example, when the user is in America, the control unit performs control based on American culture. Also, when the user is in Europe, the control unit can perform control based on European culture. This allows the control unit to provide more personalized control by performing region-specific control in consideration of the geographical location information of the user. Part or all of the above-mentioned processing in the control unit may be performed, for example, using AI or may be performed without using AI. For example, the control unit can input the geographical location data of the user to the generation AI and cause the generation AI to perform analysis of the location information.

[0075] During control, the control unit can analyze the user's social media activities and perform control based on trends. The control unit performs control based on, for example, products and services that the user frequently uses on social media. The control unit provides more personalized control based on the user's social media activities. For example, the control unit incorporates the control style of an influencer that the user follows. The control unit can also perform control based on trends and hashtags in which the user participates. This allows the control unit to provide more personalized control by analyzing the user's social media activities and performing control based on trends. Some or all of the above-mentioned processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input the user's social media data to the generation AI and cause the generation AI to perform trend analysis.

[0076] The control unit can select an optimal control method in consideration of the device information of the user during control. For example, when the user is using a smartphone, the control unit provides a control method that matches the screen size. The control unit provides more appropriate control based on the device information of the user. For example, when the user is using a tablet, the control unit provides a control method optimized for a large screen. In addition, when the user is using a smartwatch, the control unit can also provide a simple and highly visible control method. As a result, the control unit can provide more appropriate control by selecting an optimal control method in consideration of the device information of the user. Part or all of the above-mentioned processing in the control unit may be performed, for example, using AI or may be performed without using AI. For example, the control unit can input the device information of the user to the generation AI and cause the generation AI to perform an analysis of the device.

[0077] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0078] The design department can incorporate design elements specific to a region in consideration of the geographic location information of the user. For example, if the user is in Japan, traditional Japanese design elements can be incorporated. Also, if the user is in America, design elements based on American culture can be incorporated. This allows the design department to provide a more personalized design by incorporating design elements specific to a region in consideration of the geographic location information of the user.

[0079] The suggestion unit can analyze the user's past preferences and make optimal suggestions. For example, the suggestion unit makes suggestions based on products and services that the user liked in the past. The suggestion unit can also analyze the user's past preferences and make suggestions that match the season or events. This allows the suggestion unit to provide more personalized suggestions by analyzing the user's past preferences.

[0080] The support unit can analyze the user's past support history and propose an optimal support method. For example, the proposal is made based on the support method that the user preferred in the past. In addition, the support unit can analyze the user's past support history and propose an efficient support method. In this way, the support unit can provide a more efficient support method by analyzing the user's past support history.

[0081] The switching unit can predict and switch the theme mode according to the user's life rhythm. For example, if the user works at night, the mode can be switched to dark mode. Also, if the user works in the morning, the mode can be switched to light mode. In this way, the switching unit can provide a more comfortable operating environment by switching the theme mode according to the user's life rhythm.

[0082] The feedback unit may collect region-specific feedback in consideration of the geographic location information of the user. For example, if the user is in Japan, feedback based on Japanese culture may be collected. Also, if the user is in America, feedback based on American culture may be collected. This allows the feedback unit to provide more personalized feedback by collecting region-specific feedback in consideration of the geographic location information of the user.

[0083] The process flow of the first embodiment will be briefly described below.

[0084] Step 1: The design team provides a user-friendly design. The design team provides an intuitive interface and easy-to-use layout, allowing users to adjust the appearance and arrangement to create a UI that suits their personal preferences. For example, they can change colors, modify the layout, add features, etc. Step 2: The UI provider provides a customizable UI. The UI provider predicts the user's input and suggests completion candidates based on the predicted results. The UI provider can select completion candidates based on the user's past input history and frequent patterns. Step 3: The suggestion unit predicts the user's input and proposes completion candidates based on the predicted results. The suggestion unit can predict the user's input using machine learning algorithms and statistical models, and select completion candidates based on the user's past input history and frequent patterns. Step 4: The support department supports the use of the function. The support department provides help documents, chat support, telephone support, etc., to provide the information necessary for users to use the function and support smooth operation. Step 5: The switching unit switches the theme mode according to the lighting conditions. The switching unit can provide theme modes such as a dark mode, a light mode, and a custom mode, and automatically select a theme mode based on the user's preferences and environmental conditions.

[0085] (Example 2) The system according to the embodiment of the present invention is a system that provides a user-friendly design. The system provides an intuitive interface and an easy-to-use layout. Users can adjust the appearance and arrangement to create a UI that suits their personal preferences. In addition, the system predicts user input and suggests completion candidates to enable efficient operation. In addition, the system provides UI options for people with disabilities to improve accessibility. It also has an interactive guide that teaches users how to use new functions and applications, supporting smooth operation. It also provides a dark mode / light mode that switches themes according to lighting conditions to reduce visual fatigue. It also has a function to accept feedback from users and provide information useful for improving the UI. It achieves secure data management while protecting privacy and increases reliability. It is also characterized by multi-platform support that provides a consistent UI experience on different devices and operating systems. In addition, an AI assistant interacts with the user and quickly responds to UI operation guidance and questions. As a result, the system that provides a user-friendly design can improve the user's operation efficiency and increase accessibility.

[0086] A system for providing a user-friendly design according to an embodiment includes a design unit, a UI providing unit, a suggestion unit, a support unit, and a switching unit. The design unit provides a user-friendly design. The design unit provides, for example, an intuitive interface and an easy-to-use layout. The design unit enables a user to adjust the appearance and arrangement to create a UI that matches individual preferences. For example, the design unit can change colors, change layouts, add functions, and the like. The UI providing unit provides a customizable UI. The UI providing unit, for example, predicts a user's input and suggests completion candidates based on the predicted results. The UI providing unit can select completion candidates based on the user's past input history and frequent patterns. The suggestion unit predicts a user's input and suggests completion candidates based on the predicted results. The suggestion unit predicts a user's input using, for example, a machine learning algorithm or a statistical model. The suggestion unit can select completion candidates based on the user's past input history and frequent patterns. The support unit supports the use of a function. The support unit provides, for example, a help document, chat support, telephone support, and the like. The support unit provides information required when a user uses a function and supports smooth operation. The switching unit switches the theme mode according to lighting conditions. The switching unit provides theme modes such as a dark mode, a light mode, and a custom mode. The switching unit can automatically select the theme mode based on a user's preference and environmental conditions. As a result, the system providing a user-friendly design according to the embodiment can improve the user's operation efficiency and enhance accessibility.

[0087] The design department provides a user-friendly design. For example, the design department provides an intuitive interface and an easy-to-use layout. Specifically, the design department devise the layout of buttons and menus so that users can operate intuitively, and adopts visually easy-to-understand icons and colors. In order to enable users to adjust the appearance and layout and create a UI that suits their personal preferences, the design department provides a drag-and-drop function so that users can easily move and resize elements. In addition, a wealth of customization options are provided so that users can change colors, change layouts, add functions, and so on. For example, users can use a color picker to change the background color or text color, or select a layout template and adjust it to their preferences. In addition, the design department also provides a function that allows users to save their designs and reuse them later. This allows users to easily call up and reuse designs that they have created once, saving time and effort. The design department also has a function to collect user feedback and use it to improve the design. For example, it provides a feedback form that allows users to give their opinions on the usability of the design, and improves the design based on the results. This allows the design department to continue to provide designs that always meet the latest user needs.

[0088] The UI providing unit provides a customizable UI. For example, the UI providing unit predicts a user's input and suggests completion candidates based on the predicted result. Specifically, when the user starts inputting, the UI providing unit displays appropriate completion candidates in real time based on the past input history and frequent patterns. This allows the user to perform input work efficiently. The UI providing unit uses a machine learning algorithm to learn the user's input pattern and provide more accurate completion candidates. For example, if a user frequently uses a specific word or phrase, the word or phrase is preferentially displayed as a completion candidate. In addition, the UI providing unit can adjust the display timing and order of the completion candidates according to the user's input speed and typing style. This allows the user to perform input work smoothly without stress. Furthermore, the UI providing unit supports keyboard shortcuts and gesture operations to simplify the operation when the user selects a completion candidate. For example, the user can quickly select a completion candidate by pressing a specific key. In addition, the UI providing unit allows the user to customize the display method and contents of the completion candidates to suit his or her preferences. This allows the UI providing unit to make the user's input work more efficient and provide a comfortable operating environment.

[0089] The suggestion unit predicts the user's input and suggests completion candidates based on the predicted result. Specifically, the suggestion unit predicts the user's input using a machine learning algorithm or a statistical model. The suggestion unit can select completion candidates based on the user's past input history and frequent patterns. For example, the suggestion unit learns words and phrases that the user often uses in a specific context and suggests completion candidates based on the learned words and phrases. The suggestion unit utilizes natural language processing technology to understand the user's input content and provide completion candidates suitable for the context. For example, if the user inputs "schedule a meeting," the suggestion unit suggests appropriate completion candidates such as "adjust," "confirm," and "set." In addition, the suggestion unit can adjust the display timing and order of the completion candidates according to the user's input speed and typing style. This allows the user to perform input work smoothly without stress. In addition, the suggestion unit supports keyboard shortcuts and gesture operations to simplify the operation when the user selects a completion candidate. For example, the user can quickly select a completion candidate by pressing a specific key. In addition, the suggestion unit allows the user to customize the display method and content of the completion candidates to suit his or her preferences. As a result, the suggestion unit can make the user's input work more efficient and provide a comfortable operating environment.

[0090] The support department supports the use of the functions. Specifically, the support department provides help documents, chat support, telephone support, etc. The help documents include detailed explanations and operation procedures to make it easy for users to understand each function of the system. In addition, it has a search function, so that users can quickly find the information they need. Chat support responds to users' questions in real time and supports problem solving. For example, if a user asks about how to use a specific function, chat support provides an immediate answer and can share the screen or perform remote operation if necessary. Telephone support allows users to solve problems while talking directly with an operator. This makes it possible to provide support quickly and accurately even in cases where complex problems or urgent responses are required. In addition, the support department also has a function to collect user feedback and use it to improve the system. For example, it conducts a survey on the quality of support and satisfaction of users who have received support, and improves the support system based on the results. In this way, the support department can enable users to use the system smoothly and increase satisfaction.

[0091] The switching unit switches the theme mode according to the lighting conditions. Specifically, the switching unit provides theme modes such as a dark mode, a light mode, and a custom mode. The dark mode darkens the background and displays text and icons brightly to improve visibility in low-light environments and reduce eye fatigue. The light mode brightens the background and displays text and icons darkly to ensure visibility in bright environments. The custom mode allows the user to adjust the color and contrast of the theme according to his or her preferences. The switching unit can automatically select the theme mode based on the user's preferences and environmental conditions. For example, the system detects the brightness of the environment from the device's illuminance sensor and automatically switches to an appropriate theme mode accordingly. In addition, the user can manually switch the theme mode, and can easily change it from the setting menu. Furthermore, the switching unit also provides a function of automatically switching the theme mode according to the time of day. For example, the system automatically switches to the light mode during the day and the dark mode at night to improve the user's visibility and comfort. As a result, the switching unit can always enjoy optimal visibility and comfort and improve operation efficiency.

[0092] The system for providing a user-friendly design includes a feedback unit that collects feedback from users, and an improvement unit that improves a UI that the user can select based on the feedback collected by the feedback unit. The feedback unit collects feedback from users. The feedback unit collects feedback using, for example, questionnaires, user reviews, usage logs, etc. The feedback unit collects opinions and requests from users, and uses them to improve the UI. The improvement unit improves the UI that the user can select based on the feedback collected by the feedback unit. The improvement unit, for example, changes the layout of the UI, adds or deletes functions, etc. The improvement unit provides a UI that is easier to use by reflecting the user's feedback. In this way, the system for providing a user-friendly design can improve the UI based on the user's feedback and improve the user experience. A part or all of the above-mentioned processing in the feedback unit and the improvement unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback unit can input the user's feedback data to the generation AI and cause the generation AI to analyze the feedback. The improvement unit can improve the UI based on the feedback data analyzed by the generation AI.

[0093] The system for providing a user-friendly design includes a dialogue unit that dialogues with a user, and a control unit that controls at least one of a design unit, a UI providing unit, a suggestion unit, a support unit, and a switching unit based on the user's dialogue with the dialogue unit. The dialogue unit dialogues with the user using, for example, technologies such as voice recognition, text chat, and gesture recognition. The dialogue unit responds to the user's input and questions and provides necessary information. The control unit controls at least one of the design unit, the UI providing unit, the suggestion unit, the support unit, and the switching unit based on the user's dialogue with the dialogue unit. The control unit controls the operation of each unit based on, for example, the user's input, and performs automatic adjustment of the system. As a result, the system for providing a user-friendly design can control each unit based on the dialogue with the user and provide a more personalized experience. A part or all of the above-mentioned processing in the dialogue unit and the control unit may be performed, for example, using AI, or may be performed without using AI. For example, the dialogue unit can input the user's dialogue data to the generation AI and cause the generation AI to execute analysis of the dialogue. The control unit can control each part based on the dialogue data analyzed by the generation AI.

[0094] The UI providing unit can provide UI options for persons with disabilities. The UI providing unit provides UI options such as voice reading, enlarged display, and color vision correction. The UI providing unit provides necessary support for persons with disabilities when using the system, improving accessibility. As a result, the UI providing unit can provide UI options for persons with disabilities and improve accessibility. Some or all of the above-mentioned processing in the UI providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the UI providing unit can input UI options based on the needs of persons with disabilities to the generation AI and have the generation AI suggest the optimal UI option.

[0095] The UI providing unit can recognize the user's facial expressions and gestures and customize the UI in real time. The UI providing unit recognizes the user's facial expressions and gestures using technologies such as face recognition by a camera and a motion sensor. The UI providing unit dynamically changes the UI based on the user's facial expressions and gestures to enable more intuitive operation. For example, the UI providing unit provides a UI with a bright color tone when the user smiles. In addition, the UI providing unit can also activate a specific function when the user waves his or her hand. This allows the UI providing unit to recognize the user's facial expressions and gestures and dynamically customize the UI to enable more intuitive operation. Part or all of the above-mentioned processing in the UI providing unit may be performed, for example, using AI or may be performed without using AI. For example, the UI providing unit can input the user's facial expression data to the generation AI and cause the generation AI to perform an analysis of the facial expression.

[0096] The switching unit can track the user's eye movement and automatically select an appropriate theme mode. The switching unit tracks the user's eye movement using technologies such as an eye tracking device and a camera sensor. The switching unit selects an optimal theme mode based on the user's eye movement to reduce visual fatigue. For example, the switching unit switches to a dark mode when the user continues to look at the screen for a long time. The switching unit can also switch to a light mode when the user is working in a bright environment. Thereby, the switching unit can reduce visual fatigue by tracking the user's eye movement and automatically selecting an optimal theme mode. A part or all of the above-mentioned processing in the switching unit may be performed using, for example, AI, or may be performed without using AI. For example, the switching unit can input the user's gaze data to the generation AI and cause the generation AI to perform gaze analysis.

[0097] The UI providing unit can change the customizable UI based on the health data of the user. The UI providing unit collects health data such as, for example, heart rate, number of steps, and sleep pattern, and adjusts the UI according to the health condition of the user. The UI providing unit provides a healthier operating environment based on the health data of the user. For example, when the heart rate of the user is high, the UI providing unit provides a UI with a relaxing color tone. In addition, when the number of steps of the user is low, the UI providing unit can also display a message encouraging exercise. In this way, the UI providing unit can provide a healthier operating environment by changing the UI based on the health data of the user. A part or all of the above-mentioned processing in the UI providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the UI providing unit can input the health data of the user to the generation AI and cause the generation AI to execute an analysis of the health condition.

[0098] The UI providing unit can customize the UI based on the user's learning style. The UI providing unit identifies a learning style, such as visual, auditory, or experiential, and provides a UI according to the learning style. The UI providing unit provides a more effective learning environment based on the user's learning style. For example, the UI providing unit provides a UI that makes extensive use of diagrams and graphs to a visual user. Also, audio guides can be provided to an auditory user. This allows the UI providing unit to customize the UI based on the user's learning style, thereby improving learning efficiency. A part or all of the above-mentioned processing in the UI providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the UI providing unit can input the user's learning data into the generating AI and cause the generating AI to perform an analysis of the learning style.

[0099] The switching unit can predict and switch the theme mode according to the user's life rhythm. The switching unit identifies life rhythms such as wake-up time, bedtime, and activity patterns, and provides a theme mode according to the life rhythm. The switching unit provides a more comfortable operating environment based on the user's life rhythm. For example, the switching unit switches to a dark mode when the user is working at night. The switching unit can also switch to a light mode when the user is working in the morning. This allows the switching unit to provide a more comfortable operating environment by switching the theme mode according to the user's life rhythm. A part or all of the above-mentioned processing in the switching unit may be performed, for example, using AI, or may be performed without using AI. For example, the switching unit can input the user's life rhythm data to the generation AI and cause the generation AI to execute an analysis of the life rhythm.

[0100] The dialogue unit can customize the content of the dialogue with the user based on the user's preferences or interests. The dialogue unit identifies the user's preferences and interests using, for example, past selection history, survey results, etc., and provides dialogue content according to the user's preferences and interests. The dialogue unit provides a more personalized dialogue based on the user's preferences and interests. For example, the dialogue unit conducts a dialogue about a topic that the user liked in the past. The dialogue unit can also provide new information based on the user's interests. This allows the dialogue unit to provide a more personalized dialogue by customizing the content of the dialogue based on the user's preferences and interests. Some or all of the above-mentioned processing in the dialogue unit may be performed, for example, using AI, or may be performed without using AI. For example, the dialogue unit can input the user's preference data to the generation AI and cause the generation AI to perform an analysis of the preferences.

[0101] The switching unit can switch the theme mode according to the sound environment around the user. The switching unit detects, for example, the surrounding noise level, the type of sound, and the like, and provides a theme mode according to the detected noise level. The switching unit provides a more comfortable operating environment based on the sound environment of the user. For example, the switching unit switches to the light mode when the surroundings are quiet. The switching unit can also switch to the dark mode when the surroundings are noisy. This allows the switching unit to provide a more comfortable operating environment by switching the theme mode according to the sound environment around the user. A part or all of the above-mentioned processing in the switching unit may be performed, for example, using AI, or may be performed without using AI. For example, the switching unit can input the sound environment data of the user to the generation AI and cause the generation AI to execute an analysis of the sound environment.

[0102] The UI providing unit can adjust the UI based on the user's lifestyle or daily behavioral patterns. The UI providing unit, for example, identifies daily behavioral patterns, hobbies, occupations, etc., and provides a UI according to the identified patterns. The UI providing unit provides a more personalized operating environment based on the user's lifestyle. For example, when the user is at work, the UI providing unit provides a UI that makes it easy for the user to concentrate. In addition, when the user is relaxed, the UI providing unit can also provide a UI that allows the user to relax. This allows the UI providing unit to provide a more personalized operating environment by adjusting the UI based on the user's lifestyle and daily behavioral patterns. Some or all of the above-mentioned processing in the UI providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the UI providing unit can input the user's behavioral data into the generation AI and cause the generation AI to perform an analysis of the behavioral pattern.

[0103] The switching unit can switch theme modes in cooperation with the user's smart home devices. The switching unit can cooperate with smart home devices such as smart lights and smart thermostats to provide theme modes according to the smart home devices. The switching unit provides a more integrated operating environment based on the user's smart home devices. For example, the switching unit can switch theme modes according to the brightness of the smart lights. The switching unit can also switch theme modes according to the set temperature of the smart thermostat. This allows the switching unit to provide a more integrated operating environment by switching theme modes in cooperation with the user's smart home devices. Some or all of the above-mentioned processing in the switching unit may be performed, for example, using AI or may be performed without using AI. For example, the switching unit can input data of the smart home device to the generation AI and cause the generation AI to perform analysis of the device.

[0104] The design unit can estimate the user's emotion and adjust the color tone and layout of the design based on the estimated user's emotion. The design unit estimates the user's emotion using technologies such as facial expression recognition, voice analysis, and biometrics. The design unit provides a more comfortable operating environment based on the user's emotion. For example, the design unit provides calm colors and a simple layout when the user is stressed. The design unit can also provide bright colors and a playful layout when the user is relaxed. This allows the design unit to provide a more comfortable operating environment by adjusting the color tone and layout of the design based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the design unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the design unit can input the user's emotion data to the generation AI and cause the generation AI to perform emotion analysis.

[0105] The design unit can analyze the user's past design preferences and propose an optimal design template. The design unit proposes an optimal design template based on, for example, the color and layout selected by the user in the past. The design unit provides a more personalized design template based on the user's past design preferences. For example, the design unit customizes a design template based on the font and icon style that the user has liked in the past. The design unit can also analyze the user's past design preferences and propose design templates that match the season and events. This allows the design unit to provide a more personalized design template by analyzing the user's past design preferences. Some or all of the above-mentioned processing in the design unit may be performed, for example, using AI or may be performed without using AI. For example, the design unit can input the user's preference data to the generation AI and cause the generation AI to perform an analysis of the preferences.

[0106] The design unit can dynamically change the design based on the user's current activity and environment when generating the design. For example, when the user is at work, the design unit provides a simple design that allows the user to concentrate easily. The design unit provides a more appropriate design based on the user's current activity and environment. For example, when the user is relaxing, the design unit provides a relaxing color tone and layout. In addition, when the user is out, the design unit can also provide a design with high visibility. This allows the design unit to dynamically change the design based on the user's current activity and environment to provide a more appropriate design. Part or all of the above-mentioned processing in the design unit may be performed, for example, using AI or may be performed without using AI. For example, the design unit can input the user's activity data to the generation AI and cause the generation AI to perform an analysis of the activity.

[0107] The design unit can estimate the user's emotion and dynamically change the design elements based on the estimated user's emotion. The design unit estimates the user's emotion using technologies such as facial expression recognition, voice analysis, and biometrics. The design unit provides a more comfortable operating environment based on the user's emotion. For example, the design unit provides design elements that are less visually stimulating when the user is tired. Also, the design unit can provide design elements that are more visually stimulating when the user is excited. This allows the design unit to provide a more comfortable operating environment by dynamically changing the design elements based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the design unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the design unit can input the user's emotion data to the generation AI and cause the generation AI to perform emotion analysis.

[0108] When generating a design, the design department can incorporate design elements specific to the region in consideration of the geographical location information of the user. For example, if the user is in Japan, the design department incorporates traditional Japanese design elements. The design department provides a more personalized design based on the geographical location information of the user. For example, if the user is in America, the design department incorporates design elements based on American culture. Also, if the user is in Europe, the design department can incorporate historical design elements of Europe. This allows the design department to provide a more personalized design by incorporating design elements specific to the region in consideration of the geographical location information of the user. A part or all of the above-mentioned processing in the design department may be performed, for example, using AI, or may be performed without using AI. For example, the design department can input the geographical location data of the user to the generation AI and cause the generation AI to perform an analysis of the location information.

[0109] When generating a design, the design unit can analyze the user's social media activity and propose a design based on a trend. The design unit proposes a design based on, for example, colors and styles that the user often uses on social media. The design unit provides a more personalized design based on the user's social media activity. For example, the design unit incorporates the design style of an influencer that the user follows. The design unit can also propose a design based on trends and hashtags in which the user participates. This allows the design unit to provide a more personalized design by analyzing the user's social media activity and proposing a design based on a trend. Some or all of the above-mentioned processing in the design unit may be performed, for example, using AI or may be performed without using AI. For example, the design unit can input the user's social media data into the generation AI and cause the generation AI to perform trend analysis.

[0110] The UI providing unit can estimate the user's emotion and adjust the layout and functions of the UI based on the estimated user's emotion. The UI providing unit estimates the user's emotion using technologies such as facial expression recognition, voice analysis, and biometrics. The UI providing unit provides a more comfortable operating environment based on the user's emotion. For example, the UI providing unit provides a simple and intuitive UI layout when the user is stressed. In addition, the UI providing unit can also provide a UI layout including detailed information when the user is relaxed. This allows the UI providing unit to provide a more comfortable operating environment by adjusting the layout and functions of the UI based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the UI providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the UI providing unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion analysis.

[0111] The UI providing unit can analyze the user's past operation history and propose an optimal UI layout. For example, the UI providing unit preferentially arranges functions that the user has frequently used in the past. The UI providing unit provides a more efficient UI layout based on the user's past operation history. For example, the UI providing unit proposes a layout based on a UI style that the user has preferred in the past. The UI providing unit can also analyze the user's past operation history and propose an efficient UI layout. As a result, the UI providing unit can provide a more efficient UI layout by analyzing the user's past operation history. A part or all of the above-mentioned processing in the UI providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the UI providing unit can input the user's operation history data to the generation AI and cause the generation AI to perform analysis of the history.

[0112] The UI providing unit can dynamically change the UI based on the user's current activity and environment when providing the UI. For example, when the user is at work, the UI providing unit provides a UI that makes it easy for the user to concentrate. The UI providing unit provides a more appropriate UI based on the user's current activity and environment. For example, when the user is relaxing, the UI providing unit provides a UI that allows the user to relax. In addition, when the user is out, the UI providing unit can also provide a UI with high visibility. Thereby, the UI providing unit can provide a more appropriate UI by dynamically changing the UI based on the user's current activity and environment. A part or all of the above-mentioned processing in the UI providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the UI providing unit can input the user's activity data to the generation AI and cause the generation AI to perform an analysis of the activity.

[0113] The UI providing unit can estimate the user's emotion and provide UI customization options based on the estimated user's emotion. The UI providing unit estimates the user's emotion using technologies such as facial expression recognition, voice analysis, and biometrics. The UI providing unit provides a more comfortable operating environment based on the user's emotion. For example, the UI providing unit provides a less visually stimulating customization option when the user is tired. The UI providing unit can also provide a more visually stimulating customization option when the user is excited. This allows the UI providing unit to provide a more comfortable operating environment by providing UI customization options based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the UI providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the UI providing unit can input the user's emotion data to the generation AI and cause the generation AI to perform emotion analysis.

[0114] When providing the UI, the UI providing unit can incorporate region-specific UI elements in consideration of the geographical location information of the user. For example, when the user is in Japan, the UI providing unit incorporates traditional Japanese UI elements. The UI providing unit provides a more personalized UI based on the geographical location information of the user. For example, when the user is in America, the UI providing unit incorporates UI elements based on American culture. Also, when the user is in Europe, the UI providing unit can incorporate historical UI elements of Europe. As a result, the UI providing unit can provide a more personalized UI by incorporating region-specific UI elements in consideration of the geographical location information of the user. A part or all of the above-mentioned processing in the UI providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the UI providing unit can input the geographical location data of the user to the generation AI and cause the generation AI to execute an analysis of the location information.

[0115] When providing the UI, the UI providing unit can analyze the social media activity of the user and propose a UI based on a trend. The UI providing unit proposes a UI based on, for example, colors and styles that the user often uses on social media. The UI providing unit provides a more personalized UI based on the social media activity of the user. For example, the UI providing unit incorporates the UI style of an influencer that the user follows. The UI providing unit can also propose a UI based on trends and hashtags in which the user participates. This allows the UI providing unit to provide a more personalized UI by analyzing the social media activity of the user and proposing a UI based on a trend. A part or all of the above-mentioned processing in the UI providing unit may be performed, for example, using AI or may be performed without using AI. For example, the UI providing unit can input the social media data of the user to the generation AI and cause the generation AI to perform trend analysis.

[0116] The suggestion unit can estimate the user's emotion and adjust the expression method of the proposal based on the estimated user's emotion. The suggestion unit estimates the user's emotion using technologies such as facial expression recognition, voice analysis, and biometrics. The suggestion unit provides a more appropriate proposal based on the user's emotion. For example, the suggestion unit makes a simple and highly visible proposal when the user is nervous. Also, the suggestion unit can make a proposal including detailed information when the user is relaxed. This allows the suggestion unit to provide a more appropriate proposal by adjusting the expression method of the proposal based on the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion analysis.

[0117] At the time of making a proposal, the suggestion unit can analyze the user's past preferences and make an optimal proposal. The suggestion unit makes a proposal based on, for example, products or services that the user liked in the past. The suggestion unit provides a more personalized proposal based on the user's past preferences. For example, the suggestion unit analyzes the user's past preferences and makes a proposal that matches the season or event. The suggestion unit can also make a highly relevant proposal based on the user's past preferences. As a result, the suggestion unit can provide a more personalized proposal by analyzing the user's past preferences. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's preference data to the generation AI and cause the generation AI to perform an analysis of the preferences.

[0118] The suggestion unit can dynamically change the content of the suggestion based on the user's current activity and environment at the time of suggestion. For example, when the user is at work, the suggestion unit makes a suggestion related to work. The suggestion unit provides a more appropriate suggestion based on the user's current activity and environment. For example, when the user is relaxing, the suggestion unit makes a suggestion that allows the user to relax. In addition, when the user is out, the suggestion unit can also make a suggestion related to going out. Thereby, the suggestion unit can provide a more appropriate suggestion by dynamically changing the content of the suggestion based on the user's current activity and environment. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the user's activity data to the generation AI and cause the generation AI to execute an analysis of the activity.

[0119] The suggestion unit can estimate the user's emotion and determine the priority of suggestions based on the estimated user's emotion. The suggestion unit estimates the user's emotion using technologies such as facial expression recognition, voice analysis, and biometrics. The suggestion unit provides more appropriate suggestions based on the user's emotion. For example, the suggestion unit prioritizes relaxing suggestions when the user is tired. Also, the suggestion unit can prioritize visually stimulating suggestions when the user is excited. In this way, the suggestion unit can provide more appropriate suggestions by determining the priority of suggestions based on the user's emotion. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotion data to the generation AI and cause the generation AI to perform emotion analysis.

[0120] The suggestion unit can make a region-specific suggestion taking into account the geographical location information of the user at the time of suggestion. For example, when the user is in Japan, the suggestion unit makes a suggestion based on Japanese culture. The suggestion unit provides a more personalized suggestion based on the geographical location information of the user. For example, when the user is in America, the suggestion unit makes a suggestion based on American culture. Also, when the user is in Europe, the suggestion unit can make a suggestion based on European culture. Thereby, the suggestion unit can provide a more personalized suggestion by making a region-specific suggestion taking into account the geographical location information of the user. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the geographical location data of the user to the generation AI and cause the generation AI to execute an analysis of the location information.

[0121] At the time of making a proposal, the suggestion unit can analyze the user's social media activity and make a proposal based on a trend. The suggestion unit makes a proposal based on, for example, products and services that the user frequently uses on social media. The suggestion unit provides a more personalized proposal based on the user's social media activity. For example, the suggestion unit incorporates suggestions from influencers that the user follows. The suggestion unit can also make a proposal based on trends and hashtags in which the user participates. This allows the suggestion unit to provide a more personalized proposal by analyzing the user's social media activity and making a proposal based on a trend. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the user's social media data to the generation AI and cause the generation AI to perform trend analysis.

[0122] The support unit can estimate the user's emotion and adjust the content and method of support based on the estimated user's emotion. The support unit estimates the user's emotion using technologies such as facial expression recognition, voice analysis, and biometrics. The support unit provides more appropriate support based on the user's emotion. For example, the support unit provides simple and intuitive support when the user is stressed. In addition, the support unit can also provide support including detailed information when the user is relaxed. This allows the support unit to provide more appropriate support by adjusting the content and method of support based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the support unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the support unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion analysis.

[0123] The support unit can analyze the user's past support history and propose an optimal support method at the time of support. For example, the support unit makes a proposal based on the support method that the user liked in the past. The support unit provides a more efficient support method based on the user's past support history. For example, the support unit analyzes the user's past support history and proposes an efficient support method. The support unit can also propose highly relevant support based on the support content that the user received in the past. In this way, the support unit can provide a more efficient support method by analyzing the user's past support history. A part or all of the above-mentioned processing in the support unit may be performed, for example, using AI, or may be performed without using AI. For example, the support unit can input the user's support history data to the generation AI and cause the generation AI to execute the analysis of the history.

[0124] The support unit can dynamically change the support content based on the user's current activity and environment during support. For example, when the user is at work, the support unit provides work-related support. The support unit provides more appropriate support based on the user's current activity and environment. For example, when the user is relaxed, the support unit provides support that allows the user to relax. In addition, when the user is out, the support unit can also provide support related to going out. This allows the support unit to dynamically change the support content based on the user's current activity and environment, thereby providing more appropriate support. Part or all of the above-mentioned processing in the support unit may be performed, for example, using AI, or may be performed without using AI. For example, the support unit can input the user's activity data into the generation AI and cause the generation AI to perform an analysis of the activity.

[0125] The support unit can estimate the user's emotion and determine the priority of support based on the estimated user's emotion. The support unit estimates the user's emotion using technologies such as facial expression recognition, voice analysis, and biometrics. The support unit provides more appropriate support based on the user's emotion. For example, the support unit prioritizes relaxing support when the user is tired. Also, the support unit can prioritize visually stimulating support when the user is excited. In this way, the support unit can provide more appropriate support by determining the priority of support based on the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the support unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the support unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion analysis.

[0126] The support unit can provide region-specific support by taking into account the geographical location information of the user when providing support. For example, when the user is in Japan, the support unit provides support based on Japanese culture. The support unit provides more personalized support based on the geographical location information of the user. For example, when the user is in America, the support unit provides support based on American culture. Also, when the user is in Europe, the support unit can provide support based on European culture. This allows the support unit to provide more personalized support by providing region-specific support by taking into account the geographical location information of the user. Part or all of the above-mentioned processing in the support unit may be performed, for example, using AI or may be performed without using AI. For example, the support unit can input the geographical location data of the user to the generation AI and cause the generation AI to perform analysis of the location information.

[0127] During support, the support unit can analyze the user's social media activities and provide support based on trends. The support unit provides support based on, for example, products and services that the user frequently uses on social media. The support unit provides more personalized support based on the user's social media activities. For example, the support unit adopts the support style of an influencer that the user follows. The support unit can also provide support based on trends and hashtags in which the user participates. This allows the support unit to analyze the user's social media activities and provide support based on trends, thereby providing more personalized support. Part or all of the above-mentioned processing in the support unit may be performed, for example, using AI, or may be performed without using AI. For example, the support unit can input the user's social media data into the generation AI and cause the generation AI to perform trend analysis.

[0128] The switching unit can estimate the user's emotion and switch the theme mode based on the estimated user's emotion. The switching unit estimates the user's emotion using technologies such as facial expression recognition, voice analysis, and biometrics. The switching unit provides a more comfortable operating environment based on the user's emotion. For example, the switching unit switches to a calm theme mode when the user is stressed. The switching unit can also switch to a bright theme mode when the user is relaxed. This allows the switching unit to provide a more comfortable operating environment by switching the theme mode based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the switching unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the switching unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion analysis.

[0129] At the time of switching, the switching unit can analyze the user's past theme preferences and propose an optimal theme. For example, the switching unit makes a proposal based on themes that the user liked in the past. The switching unit provides a more personalized theme based on the user's past theme preferences. For example, the switching unit analyzes the user's past theme preferences and proposes a theme that matches a season or an event. The switching unit can also propose a highly relevant theme based on the user's past theme preferences. Thereby, the switching unit can provide a more personalized theme by analyzing the user's past theme preferences. A part or all of the above-mentioned processing in the switching unit may be performed, for example, using AI, or may be performed without using AI. For example, the switching unit can input the user's theme preference data to the generation AI and cause the generation AI to execute an analysis of the preferences.

[0130] The switching unit can dynamically change the theme based on the user's current activity and environment at the time of switching. For example, when the user is at work, the switching unit provides a theme that is easy to concentrate on. The switching unit provides a more appropriate theme based on the user's current activity and environment. For example, when the user is relaxing, the switching unit provides a relaxing theme. In addition, when the user is out, the switching unit can also provide a highly visible theme. Thereby, the switching unit can dynamically change the theme based on the user's current activity and environment to provide a more appropriate theme. Part or all of the above-mentioned processing in the switching unit may be performed, for example, using AI, or may be performed without using AI. For example, the switching unit can input the user's activity data to the generation AI and cause the generation AI to execute an analysis of the activity.

[0131] The switching unit can estimate the user's emotion and determine the priority order of theme modes based on the estimated user's emotion. The switching unit estimates the user's emotion using technologies such as facial expression recognition, voice analysis, and biometrics. The switching unit provides a more appropriate theme based on the user's emotion. For example, the switching unit prioritizes a relaxing theme when the user is tired. Also, the switching unit can prioritize a visually stimulating theme when the user is excited. In this way, the switching unit can provide a more appropriate theme by determining the priority order of theme modes based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the switching unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the switching unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion analysis.

[0132] The switching unit can incorporate a region-specific theme in consideration of the geographical location information of the user at the time of switching. For example, when the user is in Japan, the switching unit incorporates a traditional Japanese theme. The switching unit provides a more personalized theme based on the geographical location information of the user. For example, when the user is in America, the switching unit incorporates a theme based on American culture. Also, when the user is in Europe, the switching unit can incorporate a European historical theme. This allows the switching unit to provide a more personalized theme by incorporating a region-specific theme in consideration of the geographical location information of the user. A part or all of the above-mentioned processing in the switching unit may be performed, for example, using AI, or may be performed without using AI. For example, the switching unit can input the geographical location data of the user to the generation AI and cause the generation AI to execute an analysis of the location information.

[0133] At the time of switching, the switching unit can analyze the social media activity of the user and suggest a theme based on a trend. The switching unit, for example, suggests a theme based on colors and styles that the user often uses on social media. The switching unit provides a more personalized theme based on the social media activity of the user. For example, the switching unit incorporates a theme style of an influencer that the user follows. The switching unit can also suggest a theme based on trends and hashtags in which the user participates. In this way, the switching unit can provide a more personalized theme by analyzing the social media activity of the user and suggesting a theme based on a trend. A part or all of the above-mentioned processing in the switching unit may be performed, for example, using AI or may be performed without using AI. For example, the switching unit can input the social media data of the user to the generation AI and cause the generation AI to perform trend analysis.

[0134] The feedback unit can estimate the user's emotion and adjust the feedback collection method based on the estimated user's emotion. The feedback unit estimates the user's emotion using technologies such as facial expression recognition, voice analysis, and biometrics. The feedback unit collects more appropriate feedback based on the user's emotion. For example, the feedback unit provides a simple and intuitive feedback collection method when the user is stressed. Also, the feedback unit can provide a feedback collection method including detailed information when the user is relaxed. This allows the feedback unit to collect more appropriate feedback by adjusting the feedback collection method based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the feedback unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the feedback unit can input the user's emotion data to the generation AI and cause the generation AI to perform emotion analysis.

[0135] The feedback unit can analyze the user's past feedback history and propose an optimal collection method when collecting feedback. The feedback unit makes a proposal based on, for example, a feedback collection method that the user has preferred in the past. The feedback unit provides a more efficient feedback collection method based on the user's past feedback history. For example, the feedback unit analyzes the user's past feedback history and proposes an efficient feedback collection method. The feedback unit can also propose a highly relevant feedback collection method based on feedback content provided by the user in the past. In this way, the feedback unit can provide a more efficient feedback collection method by analyzing the user's past feedback history. A part or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback unit can input the user's feedback history data to the generation AI and cause the generation AI to perform analysis of the history.

[0136] The feedback unit can estimate the user's emotion and determine the priority of feedback based on the estimated user's emotion. The feedback unit estimates the user's emotion using technologies such as facial expression recognition, voice analysis, and biometrics. The feedback unit collects more appropriate feedback based on the user's emotion. For example, when the user is tired, the feedback unit prioritizes feedback that allows the user to relax. Also, when the user is excited, the feedback unit can prioritize visually stimulating feedback. This allows the feedback unit to collect more appropriate feedback by determining the priority of feedback based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the feedback unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the feedback unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion analysis.

[0137] The feedback unit can collect region-specific feedback in consideration of the geographical location information of the user when collecting feedback. For example, if the user is in Japan, the feedback unit collects feedback based on Japanese culture. The feedback unit provides more personalized feedback based on the geographical location information of the user. For example, if the user is in America, the feedback unit collects feedback based on American culture. Also, if the user is in Europe, the feedback unit can collect feedback based on European culture. This allows the feedback unit to provide more personalized feedback by collecting region-specific feedback in consideration of the geographical location information of the user. Part or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI or may be performed without using AI. For example, the feedback unit can input the geographical location data of the user to the generation AI and cause the generation AI to perform analysis of the location information.

[0138] The improvement unit can estimate the user's emotion and adjust the UI improvement method based on the estimated user's emotion. The improvement unit estimates the user's emotion using technologies such as facial expression recognition, voice analysis, and biometrics. The improvement unit provides a more appropriate UI improvement based on the user's emotion. For example, the improvement unit provides a simple and intuitive UI improvement method when the user is stressed. Also, the improvement unit can provide a UI improvement method including detailed information when the user is relaxed. This allows the improvement unit to provide a more appropriate UI improvement by adjusting the UI improvement method based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the improvement unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the improvement unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion analysis.

[0139] At the time of improvement, the improvement unit can propose an optimal improvement method by analyzing the user's past feedback. The improvement unit proposes an improvement method based on, for example, feedback provided by the user in the past. The improvement unit provides a more efficient UI improvement method based on the user's past feedback. For example, the improvement unit analyzes the user's past feedback history and proposes an efficient improvement method. The improvement unit can also propose a highly relevant improvement method based on the feedback content provided by the user in the past. In this way, the improvement unit can provide a more efficient UI improvement method by analyzing the user's past feedback. A part or all of the above-mentioned processing in the improvement unit may be performed, for example, using AI, or may be performed without using AI. For example, the improvement unit can input the user's feedback data to the generation AI and cause the generation AI to execute an analysis of the feedback.

[0140] The improvement unit can estimate the user's emotion and determine the UI improvement priority based on the estimated user's emotion. The improvement unit estimates the user's emotion using technologies such as facial expression recognition, voice analysis, and biometrics. The improvement unit provides a more appropriate UI improvement based on the user's emotion. For example, when the user is tired, the improvement unit prioritizes a relaxing UI improvement. Also, when the user is excited, the improvement unit can prioritize a visually stimulating UI improvement. In this way, the improvement unit can provide a more appropriate UI improvement by determining the UI improvement priority based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the improvement unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the improvement unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion analysis.

[0141] The improvement unit can make region-specific improvements by taking into account the geographical location information of the user at the time of improvement. For example, when the user is in Japan, the improvement unit makes UI improvements based on Japanese culture. The improvement unit provides more personalized UI improvements based on the geographical location information of the user. For example, when the user is in America, the improvement unit makes UI improvements based on American culture. Also, when the user is in Europe, the improvement unit can make UI improvements based on European culture. Thereby, the improvement unit can provide more personalized UI improvements by making region-specific improvements by taking into account the geographical location information of the user. Part or all of the above-mentioned processing in the improvement unit may be performed, for example, using AI or may be performed without using AI. For example, the improvement unit can input the geographical location data of the user to the generation AI and cause the generation AI to execute an analysis of the location information.

[0142] The dialogue unit can estimate the user's emotion and adjust the content and method of the dialogue based on the estimated user's emotion. The dialogue unit estimates the user's emotion using technologies such as facial expression recognition, voice analysis, and biometrics. The dialogue unit provides a more appropriate dialogue based on the user's emotion. For example, the dialogue unit can dialogue in a calm voice when the user is nervous. Also, the dialogue unit can dialogue in a bright voice when the user is relaxed. This allows the dialogue unit to provide a more appropriate dialogue by adjusting the content and method of the dialogue based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the dialogue unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the dialogue unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion analysis.

[0143] The dialogue unit can analyze the user's past dialogue history during dialogue to suggest an optimal dialogue method. The dialogue unit makes a suggestion based on, for example, a dialogue style that the user has preferred in the past. The dialogue unit provides a more efficient dialogue method based on the user's past dialogue history. For example, the dialogue unit analyzes the user's past dialogue history and suggests an efficient dialogue method. The dialogue unit can also suggest a highly relevant dialogue based on the content of the dialogue the user has had in the past. This allows the dialogue unit to provide a more efficient dialogue method by analyzing the user's past dialogue history. A part or all of the above-mentioned processing in the dialogue unit may be performed, for example, using AI, or may be performed without using AI. For example, the dialogue unit can input the user's dialogue history data to the generation AI and cause the generation AI to perform analysis of the history.

[0144] The dialogue unit can estimate the user's emotion and determine the priority of dialogues based on the estimated user's emotion. The dialogue unit estimates the user's emotion using technologies such as facial expression recognition, voice analysis, and biometrics. The dialogue unit provides a more appropriate dialogue based on the user's emotion. For example, when the user is tired, the dialogue unit prioritizes a dialogue that allows the user to relax. Also, when the user is excited, the dialogue unit can prioritize a visually stimulating dialogue. This allows the dialogue unit to provide a more appropriate dialogue by determining the priority of dialogues based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the dialogue unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the dialogue unit can input the user's emotion data to the generation AI and cause the generation AI to perform emotion analysis.

[0145] The dialogue unit can conduct a dialogue specific to a region in consideration of the geographical location information of the user during the dialogue. For example, when the user is in Japan, the dialogue unit conducts a dialogue based on Japanese culture. The dialogue unit provides a more personalized dialogue based on the geographical location information of the user. For example, when the user is in America, the dialogue unit conducts a dialogue based on American culture. Also, when the user is in Europe, the dialogue unit can conduct a dialogue based on European culture. This allows the dialogue unit to provide a more personalized dialogue by conducting a dialogue specific to a region in consideration of the geographical location information of the user. A part or all of the above-mentioned processing in the dialogue unit may be performed, for example, using AI or may be performed without using AI. For example, the dialogue unit can input the geographical location data of the user to the generation AI and cause the generation AI to execute an analysis of the location information.

[0146] The control unit can estimate the user's emotion and adjust the content and method of control based on the estimated user's emotion. The control unit estimates the user's emotion using technologies such as facial expression recognition, voice analysis, and biometrics. The control unit provides more appropriate control based on the user's emotion. For example, the control unit provides a simple and intuitive control method when the user is stressed. The control unit can also provide a control method including detailed information when the user is relaxed. This allows the control unit to provide more appropriate control by adjusting the content and method of control based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the control unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the control unit can input the user's emotion data to the generation AI and cause the generation AI to perform emotion analysis.

[0147] During control, the control unit can analyze the user's past operation history and propose an optimal control method. The control unit makes a proposal based on, for example, a control method that the user liked in the past. The control unit provides a more efficient control method based on the user's past operation history. For example, the control unit analyzes the user's past operation history and proposes an efficient control method. The control unit can also propose a highly relevant control based on the operation content performed by the user in the past. In this way, the control unit can provide a more efficient control method by analyzing the user's past operation history. A part or all of the above-mentioned processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input the user's operation history data to the generation AI and cause the generation AI to perform an analysis of the history.

[0148] The control unit can estimate the user's emotion and determine the priority of control based on the estimated user's emotion. The control unit estimates the user's emotion using technologies such as facial expression recognition, voice analysis, and biometrics. The control unit provides more appropriate control based on the user's emotion. For example, the control unit prioritizes relaxing control when the user is tired. Also, the control unit can prioritize visually stimulating control when the user is excited. In this way, the control unit can provide more appropriate control by determining the priority of control based on the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the control unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the control unit can input the user's emotion data to the generation AI and cause the generation AI to perform emotion analysis.

[0149] The control unit can perform region-specific control in consideration of the geographical location information of the user during control. For example, when the user is in Japan, the control unit performs control based on Japanese culture. The control unit provides more personalized control based on the geographical location information of the user. For example, when the user is in America, the control unit performs control based on American culture. Also, when the user is in Europe, the control unit can perform control based on European culture. This allows the control unit to provide more personalized control by performing region-specific control in consideration of the geographical location information of the user. Part or all of the above-mentioned processing in the control unit may be performed, for example, using AI or may be performed without using AI. For example, the control unit can input the geographical location data of the user to the generation AI and cause the generation AI to perform analysis of the location information.

[0150] During control, the control unit can analyze the user's social media activities and perform control based on trends. The control unit performs control based on, for example, products and services that the user frequently uses on social media. The control unit provides more personalized control based on the user's social media activities. For example, the control unit incorporates the control style of an influencer that the user follows. The control unit can also perform control based on trends and hashtags in which the user participates. This allows the control unit to provide more personalized control by analyzing the user's social media activities and performing control based on trends. Some or all of the above-mentioned processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input the user's social media data to the generation AI and cause the generation AI to perform trend analysis.

[0151] The control unit can select an optimal control method in consideration of the device information of the user during control. For example, when the user is using a smartphone, the control unit provides a control method that matches the screen size. The control unit provides more appropriate control based on the device information of the user. For example, when the user is using a tablet, the control unit provides a control method optimized for a large screen. In addition, when the user is using a smartwatch, the control unit can also provide a simple and highly visible control method. As a result, the control unit can provide more appropriate control by selecting an optimal control method in consideration of the device information of the user. Part or all of the above-mentioned processing in the control unit may be performed, for example, using AI or may be performed without using AI. For example, the control unit can input the device information of the user to the generation AI and cause the generation AI to perform an analysis of the device.

[0152] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0153] The design unit can estimate the user's emotions and dynamically change the color tones and layout of the design based on the estimated emotions. For example, if the user is feeling stressed, calm colors and a simple layout can be provided. Also, if the user is feeling relaxed, bright colors and a playful layout can be provided. In this way, the design unit can provide a more comfortable operating environment by adjusting the design based on the user's emotions.

[0154] The feedback unit can estimate the user's emotion and adjust the feedback collection method based on the estimated emotion. For example, when the user is stressed, a simple and intuitive feedback collection method can be provided. Also, when the user is relaxed, a feedback collection method including detailed information can be provided. In this way, the feedback unit can collect more appropriate feedback by adjusting the feedback collection method based on the user's emotion.

[0155] The dialogue unit can estimate the user's emotions and adjust the content and method of the dialogue based on the estimated emotions. For example, if the user is nervous, the dialogue can be conducted in a calm voice. Also, if the user is relaxed, the dialogue can be conducted in a bright voice. In this way, the dialogue unit can provide a more appropriate dialogue by adjusting the content and method of the dialogue based on the user's emotions.

[0156] The support unit can estimate the user's emotions and adjust the content and method of support based on the estimated emotions. For example, when the user is feeling stressed, simple and intuitive support can be provided. Also, when the user is relaxed, support including detailed information can be provided. This allows the support unit to provide more appropriate support by adjusting the content and method of support based on the user's emotions.

[0157] The switching unit can estimate the user's emotion and switch the theme mode based on the estimated emotion. For example, when the user is stressed, the theme mode can be switched to a calm theme mode. When the user is relaxed, the theme mode can be switched to a bright theme mode. In this way, the switching unit can provide a more comfortable operating environment by switching the theme mode based on the user's emotion.

[0158] The design department can incorporate design elements specific to a region in consideration of the geographic location information of the user. For example, if the user is in Japan, traditional Japanese design elements can be incorporated. Also, if the user is in America, design elements based on American culture can be incorporated. This allows the design department to provide a more personalized design by incorporating design elements specific to a region in consideration of the geographic location information of the user.

[0159] The suggestion unit can analyze the user's past preferences and make optimal suggestions. For example, the suggestion unit makes suggestions based on products and services that the user liked in the past. The suggestion unit can also analyze the user's past preferences and make suggestions that match the season or events. This allows the suggestion unit to provide more personalized suggestions by analyzing the user's past preferences.

[0160] The support unit can analyze the user's past support history and propose an optimal support method. For example, the proposal is made based on the support method that the user preferred in the past. In addition, the support unit can analyze the user's past support history and propose an efficient support method. In this way, the support unit can provide a more efficient support method by analyzing the user's past support history.

[0161] The switching unit can predict and switch the theme mode according to the user's life rhythm. For example, if the user works at night, the mode can be switched to dark mode. Also, if the user works in the morning, the mode can be switched to light mode. In this way, the switching unit can provide a more comfortable operating environment by switching the theme mode according to the user's life rhythm.

[0162] The feedback unit may collect region-specific feedback in consideration of the geographic location information of the user. For example, if the user is in Japan, feedback based on Japanese culture may be collected. Also, if the user is in America, feedback based on American culture may be collected. This allows the feedback unit to provide more personalized feedback by collecting region-specific feedback in consideration of the geographic location information of the user.

[0163] The process flow of the second embodiment will be briefly described below.

[0164] Step 1: The design team provides a user-friendly design. The design team provides an intuitive interface and easy-to-use layout, allowing users to adjust the appearance and arrangement to create a UI that suits their personal preferences. For example, they can change colors, modify the layout, add features, etc. Step 2: The UI provider provides a customizable UI. The UI provider predicts the user's input and suggests completion candidates based on the predicted results. The UI provider can select completion candidates based on the user's past input history and frequent patterns. Step 3: The suggestion unit predicts the user's input and proposes completion candidates based on the predicted results. The suggestion unit can predict the user's input using machine learning algorithms and statistical models, and select completion candidates based on the user's past input history and frequent patterns. Step 4: The support department supports the use of the function. The support department provides help documents, chat support, telephone support, etc., to provide the information necessary for users to use the function and support smooth operation. Step 5: The switching unit switches the theme mode according to the lighting conditions. The switching unit can provide theme modes such as a dark mode, a light mode, and a custom mode, and automatically select a theme mode based on the user's preferences and environmental conditions.

[0165] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0166] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0167] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0168] For example, the design unit is realized by at least one of the smart device 14 and the data processing device 12. For example, the design unit is realized by the control unit 46A of the smart device 14, and enables the user to adjust the appearance and arrangement and create a UI that suits individual preferences. The UI providing unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and predicts the user's input and proposes completion candidates. The suggesting unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and predicts the user's input using a machine learning algorithm or a statistical model. The support unit is realized by, for example, the control unit 46A of the smart device 14, and provides help documents and chat support. The switching unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and switches the theme mode according to the lighting conditions. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0169] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0170] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0171] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0172] 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.

[0173] 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 the voice according to instructions from the processor 46.

[0174] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0175] 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 performed in a secure state.

[0176] Fig. 4 shows an example of 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.

[0177] 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.

[0178] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0179] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0180] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0181] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0182] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0183] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0184] For example, the design unit is realized by at least one of the smart glasses 214 and the data processing device 12. For example, the design unit is realized by the control unit 46A of the smart glasses 214, and enables the user to adjust the appearance and arrangement to create a UI that suits individual preferences. The UI providing unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and predicts the user's input and proposes completion candidates. The suggesting unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and predicts the user's input using a machine learning algorithm or a statistical model. The support unit is realized by, for example, the control unit 46A of the smart glasses 214, and provides help documents and chat support. The switching unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and switches the theme mode according to the lighting conditions. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0185] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0186] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0187] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0188] 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.

[0189] 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 the voice according to instructions from the processor 46.

[0190] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0191] 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 performed in a secure state.

[0192] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0193] 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.

[0194] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0195] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific 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.

[0196] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0197] 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 voice indicating a user input for 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.

[0198] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0199] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0200] For example, the design unit is realized by at least one of the headset type terminal 314 and the data processing device 12. For example, the design unit is realized by the control unit 46A of the headset type terminal 314, and enables the user to adjust the appearance and arrangement and create a UI that suits individual preferences. The UI providing unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and predicts the user's input and proposes completion candidates. The suggesting unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and predicts the user's input using a machine learning algorithm or a statistical model. The support unit is realized by, for example, the control unit 46A of the headset type terminal 314, and provides help documents and chat support. The switching unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and switches the theme mode according to the lighting conditions. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0201] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0202] 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.

[0203] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0204] 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.

[0205] 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 the voice according to instructions from the processor 46.

[0206] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0207] 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 performed in a secure state.

[0208] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0209] 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.

[0210] 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.

[0211] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0212] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0213] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0214] 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 a voice indicating a user input for 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.

[0215] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0216] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0217] For example, the design unit is realized by at least one of the robot 414 and the data processing device 12. For example, the design unit is realized by the control unit 46A of the robot 414, and enables the user to adjust the appearance and arrangement and create a UI that suits individual preferences. The UI providing unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and predicts the user's input and proposes completion candidates. The suggesting unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and predicts the user's input using a machine learning algorithm or a statistical model. The support unit is realized by, for example, the control unit 46A of the robot 414, and provides help documents and chat support. The switching unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and switches the theme mode according to the lighting conditions. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0218] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0219] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0220] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0221] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0222] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0223] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0224] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0225] 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.

[0226] 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 Universal Serial Bus (USB) 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.

[0227] In addition, 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 upon request from the data processing device 12.

[0228] 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.

[0229] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0230] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0231] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0232] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0233] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0234] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0235] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0236] (Appendix 1) The design department provides designs that are easy for users to use, A UI providing unit that provides a customizable UI; a suggestion unit that predicts a user's input and proposes completion candidates based on the prediction result; A support department that supports the use of the functions; A switching unit for switching the theme mode according to the lighting conditions. A system characterized by: (Appendix 2) a feedback unit for collecting feedback from users; and an improvement unit that improves a UI that can be selected by the user based on the feedback collected by the feedback unit. 2. The system of claim 1, (Appendix 3) A dialogue unit that dialogues with a user; a control unit that controls at least one of the design unit, the UI providing unit, the suggestion unit, the support unit, and the switching unit based on a dialogue between the user and the dialogue unit. 2. The system of claim 1, (Appendix 4) The UI providing unit is Providing UI options for people with disabilities 2. The system of claim 1, (Appendix 5) The UI providing unit is Recognizing the user's facial expressions and gestures and customizing the UI in real time 2. The system of claim 1, (Appendix 6) The switching unit is Tracking the user's eye movements and automatically selecting an appropriate theme mode 2. The system of claim 1, (Appendix 7) The UI providing unit is A customizable UI that changes based on the user's health data. 2. The system of claim 1, (Appendix 8) The UI providing unit is Customize the UI based on the user's learning style 2. The system of claim 1, (Appendix 9) The switching unit is Predict and switch theme modes according to the user's lifestyle 2. The system of claim 1, (Appendix 10) The dialogue unit includes: Customize the content of interactions with the user based on the user's preferences or interests 4. The system of claim 3. (Appendix 11) The switching unit is Switching theme modes according to the sound environment around the user 2. The system of claim 1, (Appendix 12) The UI providing unit is Adjusting the UI based on the user's lifestyle or daily behavior patterns 2. The system of claim 1, (Appendix 13) The switching unit is Switch theme modes in conjunction with the user's smart home devices 2. The system of claim 1, (Appendix 14) The design department: Estimate the user's emotions and adjust the color tone and layout of the design based on the estimated user emotions. 2. The system of claim 1, (Appendix 15) The design department: Analyze users' past design preferences and suggest optimal design templates 2. The system of claim 1, (Appendix 16) The design department: Dynamically change the design as it is generated based on the user's current activity and environment 2. The system of claim 1, (Appendix 17) The design department: Estimate user emotions and dynamically change design elements based on the estimated user emotions 2. The system of claim 1, (Appendix 18) The design department: Consider the user's geographic location when generating designs to incorporate local design elements 2. The system of claim 1, (Appendix 19) The design department: When generating designs, it analyzes users' social media activity and suggests designs based on trends. 2. The system of claim 1, (Appendix 20) The UI providing unit is Estimate user emotions and adjust UI layout and functions based on the estimated user emotions 2. The system of claim 1, (Appendix 21) The UI providing unit is Analyze the user's past operation history and propose the optimal UI layout 2. The system of claim 1, (Appendix 22) The UI providing unit is Dynamically change the UI as it is presented based on the user's current activity and environment 2. The system of claim 1, (Appendix 23) The UI providing unit is Infer user emotions and provide UI customization options based on the inferred user emotions 2. The system of claim 1, (Appendix 24) The UI providing unit is When providing a UI, take into account the user's geographic location and incorporate region-specific UI elements. 2. The system of claim 1, (Appendix 25) The UI providing unit is When providing UI, analyze users' social media activity and suggest UI based on trends. 2. The system of claim 1, (Appendix 26) The suggestion unit, Infer user emotions and adjust the presentation of suggestions based on the inferred user emotions 2. The system of claim 1, (Appendix 27) The suggestion unit, When making suggestions, the system analyzes the user's past preferences to provide optimal suggestions. 2. The system of claim 1, (Appendix 28) The suggestion unit, Dynamically change suggestions based on the user's current activity and environment 2. The system of claim 1, (Appendix 29) The suggestion unit, Estimate user sentiment and prioritize suggestions based on the estimated user sentiment 2. The system of claim 1, (Appendix 30) The suggestion unit, When making suggestions, the app takes into account the user's geographic location to provide location-specific suggestions. 2. The system of claim 1, (Appendix 31) The suggestion unit, When making suggestions, the app analyzes users' social media activity and makes suggestions based on trends. 2. The system of claim 1, (Appendix 32) The support portion is Estimate the user's emotions and adjust the content and method of support based on the estimated user's emotions 2. The system of claim 1, (Appendix 33) The support portion is When providing support, analyze the user's past support history and suggest the most appropriate support method 2. The system of claim 1, (Appendix 34) The support portion is Dynamically change support content based on the user's current activity and environment during support 2. The system of claim 1, (Appendix 35) The support portion is Estimate user's emotions and determine priority of support based on the estimated user's emotions 2. The system of claim 1, (Appendix 36) The support portion is When providing support, it takes into account the user's geographic location to provide location-specific support 2. The system of claim 1, (Appendix 37) The support portion is When providing support, analyze users' social media activity and provide support based on trends 2. The system of claim 1, (Appendix 38) The switching unit is Estimate the user's emotion and switch the theme mode based on the estimated user's emotion. 2. The system of claim 1, (Appendix 39) The switching unit is When switching, the system analyzes the user's past theme preferences to suggest the most suitable theme. 2. The system of claim 1, (Appendix 40) The switching unit is Dynamically change themes when switching based on the user's current activity and environment 2. The system of claim 1, (Appendix 41) The switching unit is A user's emotion is estimated, and a priority order of theme modes is determined based on the estimated user's emotion. 2. The system of claim 1, (Appendix 42) The switching unit is When switching, it takes into account the user's geographic location to incorporate a region-specific theme 2. The system of claim 1, (Appendix 43) The switching unit is When switching, it analyzes users' social media activity and suggests themes based on trends. 2. The system of claim 1, (Appendix 44) The feedback unit is Infer user sentiment and adjust feedback collection methods based on the inferred user sentiment 2. The system of claim 1, (Appendix 45) The feedback unit is When collecting feedback, analyze the user's past feedback history and suggest the best way to collect feedback 2. The system of claim 1, (Appendix 46) The feedback unit is Estimate user emotions and prioritize feedback based on the estimated user emotions 2. The system of claim 1, (Appendix 47) The feedback unit is When collecting feedback, take into account the geographic location of the user to collect region-specific feedback 2. The system of claim 1, (Appendix 48) The improvement unit includes: Infer user emotions and adjust UI improvements based on the inferred user emotions 2. The system of claim 1, (Appendix 49) The improvement unit includes: When making improvements, analyze past user feedback to suggest optimal improvement methods 2. The system of claim 1, (Appendix 50) The improvement unit includes: Estimate user emotions and prioritize UI improvements based on the estimated user emotions 2. The system of claim 1, (Appendix 51) The improvement unit includes: When making improvements, we take into account the user's geographic location to make region-specific improvements. 2. The system of claim 1, (Appendix 52) The dialogue unit includes: Estimate the user's emotions and adjust the content and method of the dialogue based on the estimated user's emotions. 2. The system of claim 1, (Appendix 53) The dialogue unit includes: During dialogue, the system analyzes the user's past dialogue history and suggests the most appropriate dialogue method. 2. The system of claim 1, (Appendix 54) The dialogue unit includes: Estimate user emotions and prioritize interactions based on the estimated user emotions 2. The system of claim 1, (Appendix 55) The dialogue unit includes: During dialogue, the user's geographic location is taken into account to provide region-specific dialogue. 2. The system of claim 1, (Appendix 56) The control unit is Estimate the user's emotions and adjust the content and method of control based on the estimated user's emotions. 2. The system of claim 1, (Appendix 57) The control unit is When controlling the system, the system analyzes the user's past operation history and suggests the optimal control method. 2. The system of claim 1, (Appendix 58) The control unit is Estimate the user's emotions and determine control priorities based on the estimated user's emotions. 2. The system of claim 1, (Appendix 59) The control unit is When controlling, the geographic location of the user is taken into account to provide region-specific control. 2. The system of claim 1, (Appendix 60) The control unit is During control, analyze users' social media activity and control based on trends 2. The system of claim 1, (Appendix 61) The control unit is When controlling, the optimal control method is selected taking into account the user's device information. 2. The system of claim 1, [Explanation of symbols]

[0237] 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. The design department provides designs that are easy for users to use, A UI providing unit that provides a customizable UI; a suggestion unit that predicts a user's input and proposes completion candidates based on the prediction result; A support department that supports the use of the functions; A switching unit for switching the theme mode according to the lighting conditions. A system characterized by:

2. a feedback unit for collecting feedback from users; and an improvement unit that improves a UI that the user can select based on the feedback collected by the feedback unit.

2. The system of claim 1 .

3. A dialogue unit that dialogues with a user; a control unit that controls at least one of the design unit, the UI providing unit, the suggestion unit, the support unit, and the switching unit based on a dialogue between the user and the dialogue unit.

2. The system of claim 1 .

4. The UI providing unit is Providing UI options for people with disabilities 2. The system of claim 1 .

5. The UI providing unit is Recognizing the user's facial expressions and gestures and customizing the UI in real time 2. The system of claim 1 .

6. The switching unit is Tracking the user's eye movements and automatically selecting an appropriate theme mode 2. The system of claim 1 .

7. The UI providing unit is A customizable UI that changes based on the user's health data.

2. The system of claim 1 .

8. The UI providing unit is Customize the UI based on the user's learning style 2. The system of claim 1 .

9. The switching unit is Predict and switch theme modes according to the user's lifestyle 2. The system of claim 1 .

Images