system

Abstract

The system according to this embodiment aims to automatically customize smartphone settings based on user operation logs. [Solution] The system according to the embodiment comprises an analysis unit, a customization unit, a notification setting unit, and an application placement unit. The analysis unit analyzes the user's operation log. The customization unit customizes the smartphone settings based on the data analyzed by the analysis unit. The notification setting unit sets the notification sound based on the settings customized by the customization unit. The application placement unit places applications based on the settings customized by the customization unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, the settings of a smartphone have not been sufficiently automatically customized based on a user's operation log, and there is room for improvement.

[0005] The system according to the embodiment aims to automatically customize the settings of a smartphone based on a user's operation log.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a customization unit, a notification setting unit, and an application placement unit. The analysis unit analyzes the user's operation log. The customization unit customizes the smartphone settings based on the data analyzed by the analysis unit. The notification setting unit sets the notification sound based on the settings customized by the customization unit. The application placement unit places applications based on the settings customized by the customization unit. [Effects of the Invention]

[0007] The system according to this embodiment can automatically customize smartphone settings based on the user's operation log. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The smartphone customization system according to an embodiment of the present invention is a system that utilizes generative AI to automatically customize the design and settings of a smartphone based on the user's preferences and usage habits. This system analyzes the user's operation logs and predicts the user's characteristics to provide a smartphone optimized for individual needs. For example, for users who frequently use email and chat, it automatically sets notification sounds and automatically places apps on the home screen. For visually impaired users, it automatically sets the text-to-speech function and automatically enlarges the text size. In this way, the generative AI automatically customizes the design and settings of the smartphone based on each user's preferences and usage habits, thereby improving the efficiency of work using smartphones. Furthermore, it can also provide optimal support according to each employee's work style and role. For example, the generative AI analyzes operation logs and sets the home screen to place a specific application on the screen for users who frequently use that application. It also automatically adjusts the notification sound settings to match the user's preferences. As a result, users can use a smartphone tailored to their preferences, improving work efficiency. Furthermore, for visually impaired users, the generative AI automatically sets the text-to-speech function and enlarges the text size, making it easier to use the smartphone. This will create an environment where all employees can effectively utilize smartphones. This service is realized by acquiring smartphone operation logs and generating AI to analyze that data. By predicting user characteristics from the operation logs and providing each user with the optimal smartphone design, users will be able to use their smartphones as if they were an extension of their own hands and feet. As a result, the smartphone customization system can automatically customize the design and settings of smartphones based on the user's preferences and usage habits.

[0029] The smartphone customization system according to the embodiment comprises an analysis unit, a customization unit, a notification setting unit, and an application placement unit. The analysis unit analyzes the user's operation logs. For example, the analysis unit collects the user's operation logs and analyzes them using a generation AI. The analysis unit inputs the operation logs into the generation AI and predicts the user's usage habits and preferences. For example, the analysis unit identifies applications and operation patterns that the user frequently uses. The customization unit customizes the smartphone settings based on the data analyzed by the analysis unit. For example, the customization unit uses a generation AI to make settings based on the user's preferences. The customization unit inputs the analysis results into the generation AI and generates the optimal settings for the user. For example, the customization unit automatically sets notification sounds and application placement according to the user's preferences. The notification setting unit sets notification sounds based on the settings customized by the customization unit. For example, the notification setting unit uses a generation AI to set notification sounds based on the user's preferences. The notification setting unit inputs the customization results into the generation AI and generates the optimal notification sounds for the user. For example, the notification setting unit automatically sets the notification sounds that the user likes. The app placement unit places apps based on settings customized by the customization unit. The app placement unit uses, for example, a generation AI to place apps based on user preferences. The app placement unit inputs the customization results into the generation AI to generate the optimal app placement for the user. For example, the app placement unit places apps that the user frequently uses on the home screen. As a result, the smartphone customization system according to this embodiment can provide a smartphone optimized for individual needs by analyzing the user's operation log and automatically customizing the smartphone settings.

[0030] The analysis unit analyzes user operation logs. For example, the analysis unit collects user operation logs and analyzes them using generative AI. Specifically, the analysis unit collects detailed logs of operations performed by users on their smartphones and inputs this into the generative AI. The generative AI utilizes natural language processing and machine learning techniques to extract user behavior patterns and usage habits from the operation logs. For example, if a user tends to frequently use a particular app during a specific time period, the generative AI recognizes this pattern and records it as the user's usage habit. The generative AI can also analyze detailed information such as what operations the user prefers and which functions they use frequently. As a result, the analysis unit can efficiently analyze the vast amount of data obtained from user operation logs and predict user preferences and usage habits with high accuracy. Furthermore, the analysis unit can also detect changes in user preferences and usage habits by comparing past operation logs with current operation logs. This allows the analysis unit to respond flexibly to user needs and play an important role in smartphone customization.

[0031] The customization unit customizes smartphone settings based on data analyzed by the analysis unit. For example, the customization unit uses a generative AI to create settings based on user preferences. Specifically, the customization unit inputs user usage habits and preference data provided by the analysis unit into the generative AI to generate optimal settings for the user. The generative AI automatically adjusts settings such as notification sounds, app placement, screen brightness, and theme color, taking into account the user's preferences and usage patterns. For example, if a user frequently uses their smartphone at night, the generative AI automatically enables night mode and lowers the screen brightness. Also, if a user frequently uses a particular app, it sets that app to be placed in a prominent position on the home screen. Furthermore, the customization unit can continuously improve the accuracy of settings by collecting user feedback and incorporating it into the generative AI. This allows the customization unit to provide optimal smartphone settings tailored to user needs, improving user convenience.

[0032] The notification settings unit sets notification sounds based on settings customized by the customization unit. For example, the notification settings unit uses a generation AI to set notification sounds based on user preferences. Specifically, the notification settings unit inputs the analysis results provided by the customization unit into the generation AI and generates the optimal notification sound for the user. The generation AI analyzes notification sounds previously selected by the user and sounds preferred in specific situations, and proposes the optimal notification sound. For example, if a user prefers quiet notification sounds while working, the generation AI will set a quiet notification sound appropriate for that time of day. Also, if a user considers notifications from a specific contact important, it is possible to set the notification sound from that contact to be distinguished from other notification sounds. Furthermore, the notification settings unit can continuously improve notification sound settings by collecting user feedback and reflecting it in the generation AI. In this way, the notification settings unit can provide optimal notification sounds that meet the user's preferences and needs, improving user convenience.

[0033] The app placement unit arranges apps based on settings customized by the customization unit. For example, the app placement unit uses a generative AI to arrange apps based on user preferences. Specifically, the app placement unit inputs analysis results provided by the customization unit into the generative AI to generate the optimal app layout for the user. The generative AI analyzes apps that the user frequently uses and apps used at specific times of day, and proposes the optimal layout. For example, if a user frequently uses a news app during their morning commute, the generative AI will place that app in a prominent position on the home screen. It can also arrange apps in a way that considers ease of use, such as grouping business apps used by the user during work into a single folder. Furthermore, the app placement unit can continuously improve app placement by collecting user feedback and incorporating it into the generative AI. This allows the app placement unit to provide the optimal app layout tailored to user preferences and needs, thereby improving user convenience.

[0034] The customization section allows users to customize their smartphone settings based on their preferences using generative AI. For example, the customization section analyzes user operation logs using generative AI to predict user preferences. The customization section inputs the operation logs into the generative AI and generates optimal settings for the user. For instance, the customization section automatically sets the user's preferred notification sounds and app layout. This enables customization based on user preferences using generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0035] The notification settings unit can set notification sounds based on user preferences using a generation AI. For example, the notification settings unit analyzes user operation logs using the generation AI to predict user preferences. The notification settings unit inputs the operation logs into the generation AI and generates the optimal notification sound for the user. For example, the notification settings unit automatically sets the notification sound preferred by the user. This makes it possible to set notification sounds based on user preferences by using a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0036] The app placement unit can place applications on the home screen for users who frequently use specific applications, using a generation AI. For example, the app placement unit analyzes user operation logs using the generation AI to predict user preferences. The app placement unit inputs the operation logs into the generation AI to generate the optimal app placement for the user. For example, the app placement unit places frequently used apps on the home screen. This makes it possible to place frequently used applications on the home screen using the generation AI. The generation AI could be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0037] The customization unit can configure a text-to-speech function for visually impaired users using a generation AI. For example, the customization unit analyzes user operation logs using the generation AI to predict user characteristics. The customization unit inputs the operation logs into the generation AI and generates optimal settings for visually impaired users. For instance, the customization unit automatically configures the text-to-speech function for visually impaired users. This makes smartphone use easier for visually impaired users by automatically configuring the text-to-speech function. The generation AI could be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0038] The customization section can enlarge the text size for visually impaired users using a generation AI. For example, the customization section analyzes user operation logs using the generation AI to predict user characteristics. The customization section inputs the operation logs into the generation AI and generates optimal settings for visually impaired users. For example, the customization section automatically enlarges the text size for visually impaired users. This makes smartphone use easier for visually impaired users by automatically enlarging the text size. The generation AI could be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0039] The analysis unit can improve the accuracy of its analysis by referring to the user's past operation history when analyzing operation logs. For example, the analysis unit analyzes the user's past operation history using a generation AI. The analysis unit inputs operation logs into the generation AI and refers to past operation history. For example, the analysis unit prioritizes analyzing operation logs of applications that the user has frequently used in the past. The analysis unit can also improve accuracy by analyzing the current operation log based on the user's past operation patterns. Furthermore, the analysis unit can predict operations performed during specific time periods from the user's past operation history and reflect this in the analysis. This improves the accuracy of the analysis by referring to past operation history. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0040] The analysis unit can analyze operation logs while considering the user's device usage environment (e.g., indoors / outdoors, time of day). For example, the analysis unit uses a generation AI to analyze the user's device usage environment. The analysis unit inputs the operation logs into the generation AI and considers the device usage environment. For example, if the user is using the device outdoors, the analysis unit combines GPS data with the operation log analysis. Furthermore, if the user is using the device at night, the analysis unit can also consider lighting conditions in the operation log analysis. Additionally, if the user is using the device while moving, the analysis unit can utilize acceleration sensor data in the operation log analysis. This allows for more accurate analysis by considering the device usage environment. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0041] The analysis unit can perform analysis of operation logs while considering the user's geographical location. For example, the analysis unit can analyze the user's geographical location using a generation AI. The analysis unit inputs operation logs into the generation AI and takes geographical location information into account. For example, if the user is in a specific region, the analysis unit will prioritize analyzing operation logs related to that region. The analysis unit can also analyze operation logs while considering the geographical information of the travel destination if the user is traveling. Furthermore, if the user is at home, the analysis unit can perform analysis based on operation patterns at home. This allows for more accurate analysis by considering geographical location information. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0042] The analysis unit can analyze the user's social media activity when analyzing operation logs and complementarily analyze related data. For example, the analysis unit can analyze the user's social media activity using generative AI. The analysis unit inputs operation logs into the generative AI and analyzes social media activity. For example, the analysis unit analyzes the content that the user frequently posts on social media and reflects it in the operation logs. The analysis unit can also analyze operation logs based on the user's activity time on social media. Furthermore, the analysis unit can analyze operation logs considering the user's social media friendships. This enables a more comprehensive analysis by analyzing social media activity. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0043] The customization unit can perform optimal customization by referring to the user's past setting change history during customization. For example, the customization unit analyzes the user's past setting change history using a generation AI. The customization unit inputs operation logs into the generation AI and refers to the past setting change history. For example, the customization unit proposes the optimal customization based on the settings the user has changed in the past. The customization unit can also prioritize and propose frequently used settings from the user's past setting change history. Furthermore, the customization unit can analyze the user's past setting change history and propose the most efficient customization. This makes optimal customization possible by referring to the past setting change history. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0044] The customization section can adjust settings during customization, taking into account the user's device usage environment (e.g., indoors / outdoors, time of day). The customization section analyzes the user's device usage environment using, for example, a generative AI. The customization section inputs operation logs into the generative AI and considers the device usage environment. For example, if the user is using the device outdoors, the customization section can automatically adjust the screen brightness. It can also automatically enable night mode if the user is using the device at night. Furthermore, if the user is using the device while on the move, the customization section can suggest settings to reduce battery consumption. This allows for more appropriate customization by considering the device usage environment. The generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0045] The customization function can optimize settings by considering the user's geographical location during customization. For example, the customization function analyzes the user's geographical location using a generative AI. The customization function inputs operation logs into the generative AI and takes geographical location into account. For instance, if the user is in a specific region, the customization function prioritizes settings related to that region. Furthermore, if the user is traveling, the customization function can consider the geographical information of the travel destination when configuring settings. Also, if the user is at home, the customization function can optimize settings for home use. This allows for more appropriate settings by considering geographical location. The generative AI could be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0046] The customization unit can analyze the user's social media activity during customization and suggest relevant settings. For example, the customization unit can analyze the user's social media activity using generative AI. The customization unit inputs operation logs into the generative AI to analyze social media activity. For example, the customization unit suggests optimal settings based on the content the user frequently posts on social media. The customization unit can also adjust settings based on the user's social media activity time. Furthermore, the customization unit can suggest settings considering the user's social media friendships. This allows for more appropriate settings by analyzing social media activity. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0047] The notification settings unit can select the optimal notification sound by referring to the user's past notification sound setting history when setting a notification sound. For example, the notification settings unit analyzes the user's past notification sound setting history using a generation AI. The notification settings unit inputs operation logs into the generation AI and refers to the past notification sound setting history. For example, the notification settings unit suggests the optimal notification sound based on the notification sounds the user has set in the past. The notification settings unit can also prioritize suggesting frequently used notification sounds from the user's past notification sound setting history. Furthermore, the notification settings unit can analyze the user's past notification sound setting history and suggest the most efficient notification sound. In this way, the optimal notification sound is selected by referring to the past notification sound setting history. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0048] The notification settings unit can configure notification sounds while considering the user's device usage environment (e.g., indoors / outdoors, time of day). The notification settings unit analyzes the user's device usage environment using, for example, a generation AI. The notification settings unit inputs operation logs into the generation AI and considers the device usage environment. For example, if the user is using the device outdoors, the notification settings unit automatically adjusts the notification sound volume. It can also quiet the notification sound if the user is using the device at night. Furthermore, it can prioritize vibration notifications if the user is using the device while on the move. This allows for more appropriate notification sound settings by considering the device usage environment. The generation AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0049] The notification settings unit can select the optimal notification sound by considering the user's geographical location when setting notification sounds. For example, the notification settings unit analyzes the user's geographical location using a generation AI. The notification settings unit inputs operation logs into the generation AI and takes geographical location information into consideration. For example, if the user is in a specific region, the notification settings unit will prioritize setting notification sounds related to that region. The notification settings unit can also set notification sounds by considering the geographical information of the travel destination if the user is traveling. Furthermore, if the notification settings unit is at home, it can set notification sounds that are optimal for use at home. In this way, a more appropriate notification sound is selected by considering geographical location information. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0050] The notification settings unit can analyze the user's social media activity and suggest relevant notification sounds when setting notification sounds. For example, the notification settings unit analyzes the user's social media activity using a generative AI. The notification settings unit inputs operation logs into the generative AI and analyzes social media activity. For example, the notification settings unit suggests the optimal notification sound based on the content the user frequently posts on social media. The notification settings unit can also adjust the notification sound based on the user's social media activity time. Furthermore, the notification settings unit can suggest notification sounds considering the user's social media friendships. This allows for the suggestion of more appropriate notification sounds by analyzing social media activity. The generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0051] The app placement unit can optimize app placement by referring to the user's past app usage history. For example, the app placement unit analyzes the user's past app usage history using a generative AI. The app placement unit inputs operation logs into the generative AI and refers to the past app usage history. For example, the app placement unit places apps that the user has frequently used in the past on the home screen. The app placement unit can also prioritize the placement of frequently used apps based on the user's past app usage history. Furthermore, the app placement unit can analyze the user's past app usage history and propose the most efficient app placement. This enables optimal app placement by referring to past app usage history. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0052] The app placement unit can place apps while considering the user's device usage environment (e.g., indoors / outdoors, time of day). The app placement unit analyzes the user's device usage environment using, for example, a generative AI. The app placement unit inputs operation logs into the generative AI and considers the device usage environment. For example, if the user is using the device outdoors, the app placement unit will place key apps on the home screen. Furthermore, if the user is using the device at night, the app placement unit can prioritize placing apps related to night mode. Also, if the user is using the device while on the move, the app placement unit can place navigation apps on the home screen. This allows for more appropriate app placement by considering the device usage environment. The generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0053] The app placement unit can optimize app placement by considering the user's geographical location. For example, the app placement unit analyzes the user's geographical location using a generative AI. The app placement unit inputs operation logs into the generative AI and considers the geographical location. For example, if the user is in a specific region, the app placement unit prioritizes placing apps related to that region. Furthermore, if the user is traveling, the app placement unit can place apps considering the geographical information of the travel destination. Also, if the user is at home, the app placement unit can place apps best suited for home use. This allows for more appropriate app placement by considering geographical location. The generative AI could be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0054] The app placement unit can analyze a user's social media activity and suggest relevant apps when placing apps. For example, the app placement unit can analyze a user's social media activity using generative AI. The app placement unit inputs operation logs into the generative AI to analyze social media activity. For example, the app placement unit suggests the most suitable apps based on the content a user frequently posts on social media. The app placement unit can also adjust app placement based on the user's social media activity time. Furthermore, the app placement unit can suggest apps considering the user's social media friendships. This allows for the suggestion of more appropriate apps by analyzing social media activity. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The analysis unit can analyze user operation logs while considering the user's health data (e.g., heart rate, steps, sleep patterns). For example, the analysis unit can analyze the user's health data using a generative AI. The analysis unit inputs operation logs and health data into the generative AI, taking the user's health status into account. For instance, if the user is fatigued, the analysis unit simplifies the operation log analysis, extracting only the main operations. If the user is healthy, the analysis unit can analyze the operation log in detail and suggest specific customizations. Furthermore, if the user is exercising, the analysis unit can perform a rapid analysis and provide customization results immediately. This allows for more appropriate analysis by adjusting the operation log analysis method according to the user's health status.

[0057] The analysis unit can improve the accuracy of its analysis by referring to the user's past operation history when analyzing operation logs. For example, the analysis unit uses a generation AI to analyze the user's past operation history. The analysis unit inputs operation logs into the generation AI and refers to past operation history. For example, the analysis unit prioritizes analyzing operation logs of applications that the user has frequently used in the past. The analysis unit can also improve accuracy by analyzing the current operation log based on the user's past operation patterns. Furthermore, the analysis unit can predict operations performed during specific time periods based on the user's past operation history and incorporate this into the analysis. In this way, the accuracy of the analysis is improved by referring to past operation history.

[0058] The customization unit can perform optimal customization by referring to the user's past setting change history during the customization process. For example, the customization unit analyzes the user's past setting change history using a generation AI. The customization unit inputs operation logs into the generation AI and refers to the past setting change history. For example, the customization unit proposes the optimal customization based on the settings the user has changed in the past. The customization unit can also prioritize suggesting frequently used settings from the user's past setting change history. Furthermore, the customization unit can analyze the user's past setting change history and propose the most efficient customization. In this way, optimal customization becomes possible by referring to the past setting change history.

[0059] The analysis unit can perform analysis of operation logs while considering the user's device usage environment (e.g., indoors / outdoors, time of day). For example, the analysis unit uses a generation AI to analyze the user's device usage environment. The analysis unit inputs the operation logs into the generation AI and takes the device usage environment into account. For example, if the user is using the device outdoors, the analysis unit combines GPS data with the operation log analysis. The analysis unit can also consider lighting conditions when analyzing the operation logs if the user is using the device at night. Furthermore, if the user is using the device while moving, the analysis unit can utilize acceleration sensor data when analyzing the operation logs. This allows for more accurate analysis by considering the device usage environment.

[0060] The customization section can adjust settings during customization, taking into account the user's device usage environment (e.g., indoors / outdoors, time of day). For example, the customization section analyzes the user's device usage environment using a generative AI. The customization section inputs operation logs into the generative AI and considers the device usage environment. For instance, if the user is using the device outdoors, the customization section automatically adjusts the screen brightness. It can also automatically enable night mode if the user is using the device at night. Furthermore, if the user is using the device while on the move, the customization section can suggest settings to reduce battery consumption. This allows for more appropriate customization by considering the device usage environment.

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

[0062] Step 1: The analysis unit analyzes the user's operation logs. For example, the analysis unit collects user operation logs and analyzes them using a generation AI. The analysis unit inputs the operation logs into the generation AI and predicts the user's usage habits and preferences. For example, the analysis unit identifies applications and operation patterns that the user frequently uses. Step 2: The customization unit customizes the smartphone settings based on the data analyzed by the analysis unit. For example, the customization unit uses a generation AI to create settings based on the user's preferences. The customization unit inputs the analysis results into the generation AI and generates the optimal settings for the user. For example, the customization unit automatically sets notification sounds and app placement according to the user's preferences. Step 3: The notification settings unit sets the notification sound based on the settings customized by the customization unit. For example, the notification settings unit sets the notification sound based on the user's preferences using a generation AI. The notification settings unit inputs the customization results into the generation AI and generates the optimal notification sound for the user. For example, the notification settings unit automatically sets the notification sound that the user prefers. Step 4: The app placement unit places apps based on the settings customized by the customization unit. For example, the app placement unit uses a generation AI to place apps based on the user's preferences. The app placement unit inputs the customization results into the generation AI and generates the optimal app placement for the user. For example, the app placement unit places apps that the user frequently uses on the home screen.

[0063] (Example of form 2) The smartphone customization system according to an embodiment of the present invention is a system that utilizes generative AI to automatically customize the design and settings of a smartphone based on the user's preferences and usage habits. This system analyzes the user's operation logs and predicts the user's characteristics to provide a smartphone optimized for individual needs. For example, for users who frequently use email and chat, it automatically sets notification sounds and automatically places apps on the home screen. For visually impaired users, it automatically sets the text-to-speech function and automatically enlarges the text size. In this way, the generative AI automatically customizes the design and settings of the smartphone based on each user's preferences and usage habits, thereby improving the efficiency of work using smartphones. Furthermore, it can also provide optimal support according to each employee's work style and role. For example, the generative AI analyzes operation logs and sets the home screen to place a specific application on the screen for users who frequently use that application. It also automatically adjusts the notification sound settings to match the user's preferences. As a result, users can use a smartphone tailored to their preferences, improving work efficiency. Furthermore, for visually impaired users, the generative AI automatically sets the text-to-speech function and enlarges the text size, making it easier to use the smartphone. This will create an environment where all employees can effectively utilize smartphones. This service is realized by acquiring smartphone operation logs and generating AI to analyze that data. By predicting user characteristics from the operation logs and providing each user with the optimal smartphone design, users will be able to use their smartphones as if they were an extension of their own hands and feet. As a result, the smartphone customization system can automatically customize the design and settings of smartphones based on the user's preferences and usage habits.

[0064] The smartphone customization system according to the embodiment comprises an analysis unit, a customization unit, a notification setting unit, and an application placement unit. The analysis unit analyzes the user's operation logs. For example, the analysis unit collects the user's operation logs and analyzes them using a generation AI. The analysis unit inputs the operation logs into the generation AI and predicts the user's usage habits and preferences. For example, the analysis unit identifies applications and operation patterns that the user frequently uses. The customization unit customizes the smartphone settings based on the data analyzed by the analysis unit. For example, the customization unit uses a generation AI to make settings based on the user's preferences. The customization unit inputs the analysis results into the generation AI and generates the optimal settings for the user. For example, the customization unit automatically sets notification sounds and application placement according to the user's preferences. The notification setting unit sets notification sounds based on the settings customized by the customization unit. For example, the notification setting unit uses a generation AI to set notification sounds based on the user's preferences. The notification setting unit inputs the customization results into the generation AI and generates the optimal notification sounds for the user. For example, the notification setting unit automatically sets the notification sounds that the user likes. The app placement unit places apps based on settings customized by the customization unit. The app placement unit uses, for example, a generation AI to place apps based on user preferences. The app placement unit inputs the customization results into the generation AI to generate the optimal app placement for the user. For example, the app placement unit places apps that the user frequently uses on the home screen. As a result, the smartphone customization system according to this embodiment can provide a smartphone optimized for individual needs by analyzing the user's operation log and automatically customizing the smartphone settings.

[0065] The analysis unit analyzes user operation logs. For example, the analysis unit collects user operation logs and analyzes them using generative AI. Specifically, the analysis unit collects detailed logs of operations performed by users on their smartphones and inputs this into the generative AI. The generative AI utilizes natural language processing and machine learning techniques to extract user behavior patterns and usage habits from the operation logs. For example, if a user tends to frequently use a particular app during a specific time period, the generative AI recognizes this pattern and records it as the user's usage habit. The generative AI can also analyze detailed information such as what operations the user prefers and which functions they use frequently. As a result, the analysis unit can efficiently analyze the vast amount of data obtained from user operation logs and predict user preferences and usage habits with high accuracy. Furthermore, the analysis unit can also detect changes in user preferences and usage habits by comparing past operation logs with current operation logs. This allows the analysis unit to respond flexibly to user needs and play an important role in smartphone customization.

[0066] The customization unit customizes smartphone settings based on data analyzed by the analysis unit. For example, the customization unit uses a generative AI to create settings based on user preferences. Specifically, the customization unit inputs user usage habits and preference data provided by the analysis unit into the generative AI to generate optimal settings for the user. The generative AI automatically adjusts settings such as notification sounds, app placement, screen brightness, and theme color, taking into account the user's preferences and usage patterns. For example, if a user frequently uses their smartphone at night, the generative AI automatically enables night mode and lowers the screen brightness. Also, if a user frequently uses a particular app, it sets that app to be placed in a prominent position on the home screen. Furthermore, the customization unit can continuously improve the accuracy of settings by collecting user feedback and incorporating it into the generative AI. This allows the customization unit to provide optimal smartphone settings tailored to user needs, improving user convenience.

[0067] The notification settings unit sets notification sounds based on settings customized by the customization unit. For example, the notification settings unit uses a generation AI to set notification sounds based on user preferences. Specifically, the notification settings unit inputs the analysis results provided by the customization unit into the generation AI and generates the optimal notification sound for the user. The generation AI analyzes notification sounds previously selected by the user and sounds preferred in specific situations, and proposes the optimal notification sound. For example, if a user prefers quiet notification sounds while working, the generation AI will set a quiet notification sound appropriate for that time of day. Also, if a user considers notifications from a specific contact important, it is possible to set the notification sound from that contact to be distinguished from other notification sounds. Furthermore, the notification settings unit can continuously improve notification sound settings by collecting user feedback and reflecting it in the generation AI. In this way, the notification settings unit can provide optimal notification sounds that meet the user's preferences and needs, improving user convenience.

[0068] The app placement unit arranges apps based on settings customized by the customization unit. For example, the app placement unit uses a generative AI to arrange apps based on user preferences. Specifically, the app placement unit inputs analysis results provided by the customization unit into the generative AI to generate the optimal app layout for the user. The generative AI analyzes apps that the user frequently uses and apps used at specific times of day, and proposes the optimal layout. For example, if a user frequently uses a news app during their morning commute, the generative AI will place that app in a prominent position on the home screen. It can also arrange apps in a way that considers ease of use, such as grouping business apps used by the user during work into a single folder. Furthermore, the app placement unit can continuously improve app placement by collecting user feedback and incorporating it into the generative AI. This allows the app placement unit to provide the optimal app layout tailored to user preferences and needs, thereby improving user convenience.

[0069] The customization section allows users to customize their smartphone settings based on their preferences using generative AI. For example, the customization section analyzes user operation logs using generative AI to predict user preferences. The customization section inputs the operation logs into the generative AI and generates optimal settings for the user. For instance, the customization section automatically sets the user's preferred notification sounds and app layout. This enables customization based on user preferences using generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0070] The notification settings unit can set notification sounds based on user preferences using a generation AI. For example, the notification settings unit analyzes user operation logs using the generation AI to predict user preferences. The notification settings unit inputs the operation logs into the generation AI and generates the optimal notification sound for the user. For example, the notification settings unit automatically sets the notification sound preferred by the user. This makes it possible to set notification sounds based on user preferences by using a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0071] The app placement unit can place applications on the home screen for users who frequently use specific applications, using a generation AI. For example, the app placement unit analyzes user operation logs using the generation AI to predict user preferences. The app placement unit inputs the operation logs into the generation AI to generate the optimal app placement for the user. For example, the app placement unit places frequently used apps on the home screen. This makes it possible to place frequently used applications on the home screen using the generation AI. The generation AI could be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0072] The customization unit can configure a text-to-speech function for visually impaired users using a generation AI. For example, the customization unit analyzes user operation logs using the generation AI to predict user characteristics. The customization unit inputs the operation logs into the generation AI and generates optimal settings for visually impaired users. For instance, the customization unit automatically configures the text-to-speech function for visually impaired users. This makes smartphone use easier for visually impaired users by automatically configuring the text-to-speech function. The generation AI could be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0073] The customization section can enlarge the text size for visually impaired users using a generation AI. For example, the customization section analyzes user operation logs using the generation AI to predict user characteristics. The customization section inputs the operation logs into the generation AI and generates optimal settings for visually impaired users. For example, the customization section automatically enlarges the text size for visually impaired users. This makes smartphone use easier for visually impaired users by automatically enlarging the text size. The generation AI could be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0074] The analysis unit can estimate the user's emotions and adjust the analysis method of the operation log based on the estimated user emotions. For example, the analysis unit estimates the user's emotions using a generative AI. The analysis unit inputs the operation log into the generative AI and estimates the user's emotions. For example, if the user is stressed, the analysis unit simplifies the analysis of the operation log and extracts only the main operations. If the user is relaxed, the analysis unit can also analyze the operation log in detail and suggest fine-tuned customizations. Furthermore, if the user is in a hurry, the analysis unit can perform a rapid analysis and provide customized results immediately. This allows for more appropriate analysis by adjusting the analysis method of the operation log according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0075] The analysis unit can improve the accuracy of its analysis by referring to the user's past operation history when analyzing operation logs. For example, the analysis unit analyzes the user's past operation history using a generation AI. The analysis unit inputs operation logs into the generation AI and refers to past operation history. For example, the analysis unit prioritizes analyzing operation logs of applications that the user has frequently used in the past. The analysis unit can also improve accuracy by analyzing the current operation log based on the user's past operation patterns. Furthermore, the analysis unit can predict operations performed during specific time periods from the user's past operation history and reflect this in the analysis. This improves the accuracy of the analysis by referring to past operation history. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0076] The analysis unit can analyze operation logs while considering the user's device usage environment (e.g., indoors / outdoors, time of day). For example, the analysis unit uses a generation AI to analyze the user's device usage environment. The analysis unit inputs the operation logs into the generation AI and considers the device usage environment. For example, if the user is using the device outdoors, the analysis unit combines GPS data with the operation log analysis. Furthermore, if the user is using the device at night, the analysis unit can also consider lighting conditions in the operation log analysis. Additionally, if the user is using the device while moving, the analysis unit can utilize acceleration sensor data in the operation log analysis. This allows for more accurate analysis by considering the device usage environment. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0077] The analysis unit can estimate the user's emotions and determine the priority of analysis results based on the estimated emotions. For example, the analysis unit estimates the user's emotions using a generative AI. The analysis unit inputs operation logs into the generative AI to estimate the user's emotions. For example, if the user is stressed, the analysis unit prioritizes displaying important analysis results. If the user is relaxed, the analysis unit can also sequentially display detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can immediately display the necessary analysis results. This allows for the priority of important information by determining the priority of analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0078] The analysis unit can perform analysis of operation logs while considering the user's geographical location. For example, the analysis unit can analyze the user's geographical location using a generation AI. The analysis unit inputs operation logs into the generation AI and takes geographical location information into account. For example, if the user is in a specific region, the analysis unit will prioritize analyzing operation logs related to that region. The analysis unit can also analyze operation logs while considering the geographical information of the travel destination if the user is traveling. Furthermore, if the user is at home, the analysis unit can perform analysis based on operation patterns at home. This allows for more accurate analysis by considering geographical location information. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0079] The analysis unit can analyze the user's social media activity when analyzing operation logs and complementarily analyze related data. For example, the analysis unit can analyze the user's social media activity using generative AI. The analysis unit inputs operation logs into the generative AI and analyzes social media activity. For example, the analysis unit analyzes the content that the user frequently posts on social media and reflects it in the operation logs. The analysis unit can also analyze operation logs based on the user's activity time on social media. Furthermore, the analysis unit can analyze operation logs considering the user's social media friendships. This enables a more comprehensive analysis by analyzing social media activity. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0080] The customization unit can estimate the user's emotions and adjust the customization based on those emotions. For example, the customization unit might use generative AI to estimate the user's emotions. The customization unit inputs operation logs into the generative AI to estimate the user's emotions. For example, if the user is stressed, the customization unit might suggest simple settings. If the user is relaxed, the customization unit might offer more detailed customization options. If the user is in a hurry, the customization unit might offer an option to quickly change settings. This allows for more appropriate customization by adjusting the customization based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The customization unit can perform optimal customization by referring to the user's past setting change history during customization. For example, the customization unit analyzes the user's past setting change history using a generation AI. The customization unit inputs operation logs into the generation AI and refers to the past setting change history. For example, the customization unit proposes the optimal customization based on the settings the user has changed in the past. The customization unit can also prioritize and propose frequently used settings from the user's past setting change history. Furthermore, the customization unit can analyze the user's past setting change history and propose the most efficient customization. This makes optimal customization possible by referring to the past setting change history. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0082] The customization section can adjust settings during customization, taking into account the user's device usage environment (e.g., indoors / outdoors, time of day). The customization section analyzes the user's device usage environment using, for example, a generative AI. The customization section inputs operation logs into the generative AI and considers the device usage environment. For example, if the user is using the device outdoors, the customization section can automatically adjust the screen brightness. It can also automatically enable night mode if the user is using the device at night. Furthermore, if the user is using the device while on the move, the customization section can suggest settings to reduce battery consumption. This allows for more appropriate customization by considering the device usage environment. The generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0083] The customization unit can estimate the user's emotions and determine the priority of customizations based on the estimated emotions. For example, the customization unit estimates the user's emotions using generative AI. The customization unit inputs operation logs into the generative AI to estimate the user's emotions. For example, if the user is stressed, the customization unit prioritizes important customizations. If the user is relaxed, the customization unit can also sequentially perform detailed customizations. Furthermore, if the user is in a hurry, the customization unit can quickly perform necessary customizations. This allows for prioritizing important settings by determining customization priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The customization function can optimize settings by considering the user's geographical location during customization. For example, the customization function analyzes the user's geographical location using a generative AI. The customization function inputs operation logs into the generative AI and takes geographical location into account. For instance, if the user is in a specific region, the customization function prioritizes settings related to that region. Furthermore, if the user is traveling, the customization function can consider the geographical information of the travel destination when configuring settings. Also, if the user is at home, the customization function can optimize settings for home use. This allows for more appropriate settings by considering geographical location. The generative AI could be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0085] The customization unit can analyze the user's social media activity during customization and suggest relevant settings. For example, the customization unit can analyze the user's social media activity using generative AI. The customization unit inputs operation logs into the generative AI to analyze social media activity. For example, the customization unit suggests optimal settings based on the content the user frequently posts on social media. The customization unit can also adjust settings based on the user's social media activity time. Furthermore, the customization unit can suggest settings considering the user's social media friendships. This allows for more appropriate settings by analyzing social media activity. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0086] The notification settings unit can estimate the user's emotions and adjust the notification sound settings based on the estimated emotions. For example, the notification settings unit estimates the user's emotions using a generative AI. The notification settings unit inputs operation logs into the generative AI to estimate the user's emotions. For example, if the user is stressed, the notification settings unit sets a calm notification sound. It can also set a bright notification sound if the user is relaxed. Furthermore, if the user is in a hurry, it can set a notification sound that can be recognized quickly. By adjusting the notification sound settings according to the user's emotions, a more appropriate notification sound is set. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The notification settings unit can select the optimal notification sound by referring to the user's past notification sound setting history when setting a notification sound. For example, the notification settings unit analyzes the user's past notification sound setting history using a generation AI. The notification settings unit inputs operation logs into the generation AI and refers to the past notification sound setting history. For example, the notification settings unit suggests the optimal notification sound based on the notification sounds the user has set in the past. The notification settings unit can also prioritize suggesting frequently used notification sounds from the user's past notification sound setting history. Furthermore, the notification settings unit can analyze the user's past notification sound setting history and suggest the most efficient notification sound. In this way, the optimal notification sound is selected by referring to the past notification sound setting history. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0088] The notification settings unit can configure notification sounds while considering the user's device usage environment (e.g., indoors / outdoors, time of day). The notification settings unit analyzes the user's device usage environment using, for example, a generation AI. The notification settings unit inputs operation logs into the generation AI and considers the device usage environment. For example, if the user is using the device outdoors, the notification settings unit automatically adjusts the notification sound volume. It can also quiet the notification sound if the user is using the device at night. Furthermore, it can prioritize vibration notifications if the user is using the device while on the move. This allows for more appropriate notification sound settings by considering the device usage environment. The generation AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0089] The notification settings unit can estimate the user's emotions and determine the priority of notification sounds based on the estimated emotions. For example, the notification settings unit estimates the user's emotions using a generative AI. The notification settings unit inputs operation logs into the generative AI to estimate the user's emotions. For example, if the user is feeling stressed, the notification settings unit prioritizes important notification sounds. If the user is relaxed, the notification settings unit can also sequentially set detailed notification sounds. Furthermore, if the user is in a hurry, the notification settings unit can quickly set necessary notification sounds. This allows for prioritizing important notification sounds according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The notification settings unit can select the optimal notification sound by considering the user's geographical location when setting notification sounds. For example, the notification settings unit analyzes the user's geographical location using a generation AI. The notification settings unit inputs operation logs into the generation AI and takes geographical location information into consideration. For example, if the user is in a specific region, the notification settings unit will prioritize setting notification sounds related to that region. The notification settings unit can also set notification sounds by considering the geographical information of the travel destination if the user is traveling. Furthermore, if the notification settings unit is at home, it can set notification sounds that are optimal for use at home. In this way, a more appropriate notification sound is selected by considering geographical location information. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0091] The notification settings unit can analyze the user's social media activity and suggest relevant notification sounds when setting notification sounds. For example, the notification settings unit analyzes the user's social media activity using a generative AI. The notification settings unit inputs operation logs into the generative AI and analyzes social media activity. For example, the notification settings unit suggests the optimal notification sound based on the content the user frequently posts on social media. The notification settings unit can also adjust the notification sound based on the user's social media activity time. Furthermore, the notification settings unit can suggest notification sounds considering the user's social media friendships. This allows for the suggestion of more appropriate notification sounds by analyzing social media activity. The generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0092] The app placement unit can estimate the user's emotions and adjust the app placement based on those emotions. For example, the app placement unit uses generative AI to estimate the user's emotions. The app placement unit inputs operation logs into the generative AI to estimate the user's emotions. For example, if the user is stressed, the app placement unit places key apps on the home screen. If the user is relaxed, the app placement unit can also suggest a more detailed app placement. Furthermore, if the user is in a hurry, the app placement unit can prioritize apps that can be accessed quickly. This allows for more appropriate app placement by adjusting the app placement according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The app placement unit can optimize app placement by referring to the user's past app usage history. For example, the app placement unit analyzes the user's past app usage history using a generative AI. The app placement unit inputs operation logs into the generative AI and refers to the past app usage history. For example, the app placement unit places apps that the user has frequently used in the past on the home screen. The app placement unit can also prioritize the placement of frequently used apps based on the user's past app usage history. Furthermore, the app placement unit can analyze the user's past app usage history and propose the most efficient app placement. This enables optimal app placement by referring to past app usage history. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0094] The app placement unit can place apps while considering the user's device usage environment (e.g., indoors / outdoors, time of day). The app placement unit analyzes the user's device usage environment using, for example, a generative AI. The app placement unit inputs operation logs into the generative AI and considers the device usage environment. For example, if the user is using the device outdoors, the app placement unit will place key apps on the home screen. Furthermore, if the user is using the device at night, the app placement unit can prioritize placing apps related to night mode. Also, if the user is using the device while on the move, the app placement unit can place navigation apps on the home screen. This allows for more appropriate app placement by considering the device usage environment. The generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0095] The app placement unit can estimate the user's emotions and determine the priority of app placement based on the estimated emotions. For example, the app placement unit estimates the user's emotions using generative AI. The app placement unit inputs operation logs into the generative AI to estimate the user's emotions. For example, if the user is stressed, the app placement unit prioritizes important apps. If the user is relaxed, the app placement unit can also sequentially place detailed apps. Furthermore, if the user is in a hurry, the app placement unit can prioritize apps that can be accessed quickly. This allows for the prioritization of important apps by determining the app placement priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The app placement unit can optimize app placement by considering the user's geographical location. For example, the app placement unit analyzes the user's geographical location using a generative AI. The app placement unit inputs operation logs into the generative AI and considers the geographical location. For example, if the user is in a specific region, the app placement unit prioritizes placing apps related to that region. Furthermore, if the user is traveling, the app placement unit can place apps considering the geographical information of the travel destination. Also, if the user is at home, the app placement unit can place apps best suited for home use. This allows for more appropriate app placement by considering geographical location. The generative AI could be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0097] The app placement unit can analyze a user's social media activity and suggest relevant apps when placing apps. For example, the app placement unit can analyze a user's social media activity using generative AI. The app placement unit inputs operation logs into the generative AI to analyze social media activity. For example, the app placement unit suggests the most suitable apps based on the content a user frequently posts on social media. The app placement unit can also adjust app placement based on the user's social media activity time. Furthermore, the app placement unit can suggest apps considering the user's social media friendships. This allows for the suggestion of more appropriate apps by analyzing social media activity. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

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

[0099] The analysis unit can analyze user operation logs while considering the user's health data (e.g., heart rate, steps, sleep patterns). For example, the analysis unit can analyze the user's health data using a generative AI. The analysis unit inputs operation logs and health data into the generative AI, taking the user's health status into account. For instance, if the user is fatigued, the analysis unit simplifies the operation log analysis, extracting only the main operations. If the user is healthy, the analysis unit can analyze the operation log in detail and suggest specific customizations. Furthermore, if the user is exercising, the analysis unit can perform a rapid analysis and provide customization results immediately. This allows for more appropriate analysis by adjusting the operation log analysis method according to the user's health status.

[0100] The customization unit can estimate the user's emotions and adjust the customization based on those emotions. For example, the customization unit uses generative AI to estimate the user's emotions. The customization unit inputs operation logs into the generative AI to estimate the user's emotions. For example, if the user is feeling stressed, the customization unit will suggest simple settings. If the user is relaxed, the customization unit can also provide detailed customization options. Furthermore, if the user is in a hurry, the customization unit can provide an option to quickly change settings. This allows for more appropriate customization by adjusting the customization content according to the user's emotions.

[0101] The notification settings unit can estimate the user's emotions and adjust the notification sound settings based on those emotions. For example, the notification settings unit estimates the user's emotions using a generative AI. The notification settings unit inputs operation logs into the generative AI to estimate the user's emotions. For example, if the user is feeling stressed, the notification settings unit will set a calm notification sound. It can also set a bright notification sound if the user is relaxed. Furthermore, if the user is in a hurry, the notification settings unit will set a notification sound that can be recognized quickly. In this way, by adjusting the notification sound settings according to the user's emotions, a more appropriate notification sound is set.

[0102] The app placement unit can estimate the user's emotions and adjust the app placement based on those emotions. For example, the app placement unit uses generative AI to estimate the user's emotions. The app placement unit inputs operation logs into the generative AI to estimate the user's emotions. For example, if the user is feeling stressed, the app placement unit will place key apps on the home screen. If the user is relaxed, the app placement unit can also suggest a more detailed app placement. Furthermore, if the user is in a hurry, the app placement unit can prioritize apps that can be accessed quickly. This allows for more appropriate app placement by adjusting the app placement according to the user's emotions.

[0103] The analysis unit can improve the accuracy of its analysis by referring to the user's past operation history when analyzing operation logs. For example, the analysis unit uses a generation AI to analyze the user's past operation history. The analysis unit inputs operation logs into the generation AI and refers to past operation history. For example, the analysis unit prioritizes analyzing operation logs of applications that the user has frequently used in the past. The analysis unit can also improve accuracy by analyzing the current operation log based on the user's past operation patterns. Furthermore, the analysis unit can predict operations performed during specific time periods based on the user's past operation history and incorporate this into the analysis. In this way, the accuracy of the analysis is improved by referring to past operation history.

[0104] The customization unit can perform optimal customization by referring to the user's past setting change history during the customization process. For example, the customization unit analyzes the user's past setting change history using a generation AI. The customization unit inputs operation logs into the generation AI and refers to the past setting change history. For example, the customization unit proposes the optimal customization based on the settings the user has changed in the past. The customization unit can also prioritize suggesting frequently used settings from the user's past setting change history. Furthermore, the customization unit can analyze the user's past setting change history and propose the most efficient customization. In this way, optimal customization becomes possible by referring to the past setting change history.

[0105] The analysis unit can estimate the user's emotions and adjust the analysis method of the operation log based on the estimated user emotions. For example, the analysis unit estimates the user's emotions using generative AI. The analysis unit inputs the operation log into the generative AI and estimates the user's emotions. For example, if the user is stressed, the analysis unit simplifies the analysis of the operation log and extracts only the main operations. If the user is relaxed, the analysis unit can also analyze the operation log in detail and suggest fine-tuned customizations. Furthermore, if the user is in a hurry, the analysis unit can perform a rapid analysis and provide customized results immediately. This allows for more appropriate analysis by adjusting the analysis method of the operation log according to the user's emotions.

[0106] The analysis unit can perform analysis of operation logs while considering the user's device usage environment (e.g., indoors / outdoors, time of day). For example, the analysis unit uses a generation AI to analyze the user's device usage environment. The analysis unit inputs the operation logs into the generation AI and takes the device usage environment into account. For example, if the user is using the device outdoors, the analysis unit combines GPS data with the operation log analysis. The analysis unit can also consider lighting conditions when analyzing the operation logs if the user is using the device at night. Furthermore, if the user is using the device while moving, the analysis unit can utilize acceleration sensor data when analyzing the operation logs. This allows for more accurate analysis by considering the device usage environment.

[0107] The customization unit can estimate the user's emotions and determine the priority of customizations based on those emotions. For example, the customization unit uses generative AI to estimate the user's emotions. The customization unit inputs operation logs into the generative AI to estimate the user's emotions. For example, if the user is feeling stressed, the customization unit will prioritize important customizations. If the user is relaxed, the customization unit can also sequentially perform detailed customizations. Furthermore, if the user is in a hurry, the customization unit can quickly perform necessary customizations. In this way, by determining the priority of customizations according to the user's emotions, important settings can be prioritized.

[0108] The customization section can adjust settings during customization, taking into account the user's device usage environment (e.g., indoors / outdoors, time of day). For example, the customization section analyzes the user's device usage environment using a generative AI. The customization section inputs operation logs into the generative AI and considers the device usage environment. For instance, if the user is using the device outdoors, the customization section automatically adjusts the screen brightness. It can also automatically enable night mode if the user is using the device at night. Furthermore, if the user is using the device while on the move, the customization section can suggest settings to reduce battery consumption. This allows for more appropriate customization by considering the device usage environment.

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

[0110] Step 1: The analysis unit analyzes the user's operation logs. For example, the analysis unit collects user operation logs and analyzes them using a generation AI. The analysis unit inputs the operation logs into the generation AI and predicts the user's usage habits and preferences. For example, the analysis unit identifies applications and operation patterns that the user frequently uses. Step 2: The customization unit customizes the smartphone settings based on the data analyzed by the analysis unit. For example, the customization unit uses a generation AI to create settings based on the user's preferences. The customization unit inputs the analysis results into the generation AI and generates the optimal settings for the user. For example, the customization unit automatically sets notification sounds and app placement according to the user's preferences. Step 3: The notification settings unit sets the notification sound based on the settings customized by the customization unit. For example, the notification settings unit sets the notification sound based on the user's preferences using a generation AI. The notification settings unit inputs the customization results into the generation AI and generates the optimal notification sound for the user. For example, the notification settings unit automatically sets the notification sound that the user prefers. Step 4: The app placement unit places apps based on the settings customized by the customization unit. For example, the app placement unit uses a generation AI to place apps based on the user's preferences. The app placement unit inputs the customization results into the generation AI and generates the optimal app placement for the user. For example, the app placement unit places apps that the user frequently uses on the home screen.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements described above, including the analysis unit, customization unit, notification setting unit, and application placement unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart device 14 and the processor 28 of the data processing unit 12, and collects user operation logs and analyzes them using generated AI. The customization unit is implemented by the control unit 46A of the smart device 14 and the specific processing unit 290 of the data processing unit 12, and customizes the smartphone settings based on the analysis results. The notification setting unit is implemented by the control unit 46A of the smart device 14 and the specific processing unit 290 of the data processing unit 12, and sets notification sounds based on the user's preferences. The application placement unit is implemented by the control unit 46A of the smart device 14 and the specific processing unit 290 of the data processing unit 12, and places frequently used applications on the home screen. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

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

[0116] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the analysis unit, customization unit, notification setting unit, and application placement unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart glasses 214 and the processor 28 of the data processing unit 12, and collects user operation logs and analyzes them using generated AI. The customization unit is implemented by the control unit 46A of the smart glasses 214 and the specific processing unit 290 of the data processing unit 12, and customizes smartphone settings based on the analysis results. The notification setting unit is implemented by the control unit 46A of the smart glasses 214 and the specific processing unit 290 of the data processing unit 12, and sets notification sounds based on user preferences. The application placement unit is implemented by the control unit 46A of the smart glasses 214 and the specific processing unit 290 of the data processing unit 12, and places frequently used applications on the home screen. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

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

[0132] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the analysis unit, customization unit, notification setting unit, and application placement unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the headset terminal 314 and the processor 28 of the data processing unit 12, and collects user operation logs and analyzes them using generated AI. The customization unit is implemented by the control unit 46A of the headset terminal 314 and the specific processing unit 290 of the data processing unit 12, and customizes the smartphone settings based on the analysis results. The notification setting unit is implemented by the control unit 46A of the headset terminal 314 and the specific processing unit 290 of the data processing unit 12, and sets notification sounds based on the user's preferences. The application placement unit is implemented by the control unit 46A of the headset terminal 314 and the specific processing unit 290 of the data processing unit 12, and places frequently used applications on the home screen. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

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

[0148] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0155] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the analysis unit, customization unit, notification setting unit, and application placement unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the robot 414 and the processor 28 of the data processing unit 12, and collects user operation logs and analyzes them using generated AI. The customization unit is implemented by the control unit 46A of the robot 414 and the specific processing unit 290 of the data processing unit 12, and customizes smartphone settings based on the analysis results. The notification setting unit is implemented by the control unit 46A of the robot 414 and the specific processing unit 290 of the data processing unit 12, and sets notification sounds based on user preferences. The application placement unit is implemented by the control unit 46A of the robot 414 and the specific processing unit 290 of the data processing unit 12, and places frequently used applications on the home screen. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0164] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0166] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0167] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0168] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0172] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0173] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0174] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0175] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0176] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0177] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0179] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0180] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0181] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0182] (Note 1) An analysis unit that analyzes user operation logs, A customization unit that customizes the settings of the smartphone based on the data analyzed by the aforementioned analysis unit, A notification setting unit sets a notification sound based on the settings customized by the aforementioned customization unit, The system includes an application placement unit that places applications based on settings customized by the customization unit. A system characterized by the following features. (Note 2) The aforementioned customization unit is Generative AI customizes smartphone settings based on user preferences. The system described in Appendix 1, characterized by the features described herein. (Note 3) The notification setting unit is, AI generates notification sounds based on user preferences. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned application placement unit is The AI ​​generates a home screen for users who frequently use a particular application. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned customization unit is Use AI to set up a text-to-speech function for visually impaired users. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned customization unit is The AI ​​generates text to enlarge the text size for visually impaired users. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the analysis method of the operation logs based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, When analyzing operation logs, the system improves the accuracy of the analysis by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, When analyzing operation logs, the analysis takes into account the user's device usage environment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, When analyzing operation logs, the analysis takes into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, When analyzing operation logs, the system analyzes the user's social media activity and supplements it with related data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned customization unit is It estimates the user's emotions and adjusts the customization based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned customization unit is During customization, the system will refer to the user's past configuration change history to perform optimal customization. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned customization unit is When customizing, adjust settings considering the user's device usage environment. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned customization unit is It estimates the user's emotions and determines the priority of customization based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned customization unit is When customizing, the system takes the user's geographical location into consideration to optimize the settings. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned customization unit is During customization, the system analyzes the user's social media activity and suggests relevant settings. The system described in Appendix 1, characterized by the features described herein. (Note 19) The notification setting unit is, It estimates the user's emotions and adjusts the notification sound settings based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The notification setting unit is, When setting notification sounds, the system will refer to the user's past notification sound setting history to select the most suitable notification sound. The system described in Appendix 1, characterized by the features described herein. (Note 21) The notification setting unit is, When setting notification sounds, the settings should be configured considering the user's device usage environment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The notification setting unit is, It estimates the user's emotions and prioritizes notification sounds based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The notification setting unit is, When setting notification sounds, the system selects the most suitable notification sound by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The notification setting unit is, When setting notification sounds, the system analyzes the user's social media activity and suggests relevant notification sounds. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned application placement unit is It estimates the user's emotions and adjusts the placement of apps based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned application placement unit is When deploying apps, the system optimizes placement by referencing the user's past app usage history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned application placement unit is When deploying apps, consider the user's device environment when making placement decisions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned application placement unit is It estimates user sentiment and determines app placement priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned application placement unit is When deploying the app, the optimal placement is determined by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned application placement unit is When deploying apps, the system analyzes users' social media activity and suggests relevant apps. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

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

Claims

1. An analysis unit that analyzes user operation logs, A customization unit that customizes the settings of the smartphone based on the data analyzed by the aforementioned analysis unit, A notification setting unit sets a notification sound based on the settings customized by the aforementioned customization unit, The system includes an application placement unit that places applications based on settings customized by the customization unit. A system characterized by the following features.

2. The aforementioned customization unit is The generated AI customizes smartphone settings based on the user's preferences. The system according to feature 1.

3. The notification setting unit is, AI generates notification sounds based on user preferences. The system according to feature 1.

4. The aforementioned application placement unit is The AI ​​generates a home screen for users who frequently use a particular application, placing that application on their home screen. The system according to feature 1.

5. The aforementioned customization unit is The AI ​​generates a text-to-speech function for visually impaired users. The system according to feature 1.

6. The aforementioned customization unit is The AI ​​generates text to enlarge the text size for visually impaired users. The system according to feature 1.

7. The aforementioned analysis unit, The system estimates the user's emotions and adjusts the analysis method of the operation logs based on the estimated user emotions. The system according to feature 1.

8. The aforementioned analysis unit, When analyzing operation logs, the system improves the accuracy of the analysis by referring to the user's past operation history. The system according to feature 1.

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