system

Abstract

The system according to this embodiment aims to enable users to effectively utilize unused functions of their smartphones. [Solution] The system according to the embodiment comprises an analysis unit, a generation unit, a setting unit, and a guide unit. The analysis unit analyzes the user's smartphone usage patterns. The generation unit automatically generates customized explanatory videos about unused functions identified by the analysis unit. The setting unit adds the functions introduced in the explanatory videos generated by the generation unit. The guide unit guides the user on how to further customize the functions set by the setting unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult for users to effectively utilize the unused functions of smartphones.

[0005] The system according to the embodiment aims to enable users to effectively utilize the unused functions of smartphones.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a generation unit, a setting unit, and a guide unit. The analysis unit analyzes the user's smartphone usage patterns. The generation unit automatically generates customized explanatory videos about unused functions identified by the analysis unit. The setting unit adds the functions introduced in the explanatory videos generated by the generation unit. The guide unit guides the user on how to further customize the functions set by the setting unit. [Effects of the Invention]

[0007] The system according to this embodiment can enable users to effectively utilize unused functions of their smartphones. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​smartphone navigation system according to an embodiment of the present invention is an innovative application that utilizes generative AI to analyze the user's smartphone usage patterns in detail and proposes previously unused functions optimized for the user. This AI smartphone navigation system clearly explains specific setting methods and customization methods to the user through customized explanatory videos automatically generated by the AI. Furthermore, if the user wishes to add a function introduced in the explanatory video, the basic settings are automatically completed with a single click. In addition, the AI ​​also guides the user on how to customize the function to a more advanced level, supporting the user in effectively utilizing their smartphone in a short amount of time. For example, the AI ​​smartphone navigation system analyzes the user's smartphone usage patterns in detail. The generative AI collects data such as the user's operation history and the frequency of app usage to identify previously unused functions. For example, if a user frequently uses the camera app but never uses the editing function, the generative AI will propose this editing function. Next, the generative AI automatically generates a customized explanatory video regarding the identified unused function. This video clearly explains specific setting methods and customization methods. For example, if the editing function of the camera app is proposed, the video will show how to use the editing function and the specific operation procedures. Furthermore, if a user wants to add a feature introduced in the explanatory video, the basic settings are automatically completed with a single click. The generating AI minimizes user interaction and completes the setup quickly. For example, if the settings for adding editing functions are introduced in the video, the user can complete the setup simply by pressing the "Add" button. The generating AI also guides the user on how to customize the function in more detail. After the user completes the basic settings, the generating AI provides a video explanation of how to further customize the function. For example, it shows how to further customize the editing functions of the camera app or how to apply specific filters. In this way, the AI ​​smartphone navigation system supports users in making effective use of their smartphones. From elderly people who are not tech-savvy to busy business people and young people who seek out new technologies, everyone can fully unlock the potential of their smartphone.Users can discover their own unique ways of using their smartphones, maximizing the efficiency and convenience of their daily lives. This allows the AI ​​smartphone navigation system to analyze users' smartphone usage patterns in detail and optimize and suggest unused features.

[0029] The AI ​​smartphone navigation system according to this embodiment comprises an analysis unit, a generation unit, a setting unit, and a guide unit. The analysis unit analyzes the user's smartphone usage patterns. The analysis unit collects data such as the user's operation history and app usage frequency, and identifies unused functions. For example, if the analysis unit frequently uses the camera app but never uses the editing function, it suggests the editing function. The generation unit automatically generates a customized explanatory video about the identified unused function. For example, if the generation unit suggests the editing function of the camera app, it generates a video showing how to use the editing function and specific operating procedures. The generation unit uses a generation AI to automatically generate content based on the user's usage patterns. For example, the generation AI takes the user's operation history as input and outputs an explanatory video about the unused function. The setting unit adds the function introduced in the explanatory video with a single click. For example, the setting unit allows the user to simply press the "Add" button, and the basic settings are automatically completed. The setting unit uses AI to minimize user operation and complete the settings quickly. For example, the setting unit automatically performs settings based on the content of the video generated by the generation AI. The guide unit provides video explanations of advanced customization methods for the configured functions. For example, the guide unit generates videos demonstrating how to further customize the editing functions of a camera app or how to apply specific filters. Using AI, the guide unit guides the user through more detailed customization methods after the user has completed the basic settings. For example, the guide unit explains customization methods based on the content of the videos generated by the AI. As a result, the AI ​​smartphone navigation system according to this embodiment can analyze the user's smartphone usage patterns in detail and optimize and suggest unused functions.

[0030] The analytics department conducts a detailed analysis of users' smartphone usage patterns. Specifically, it collects a wide range of data, including user operation history, app usage frequency, usage time, and usage location, and statistically analyzes this data. For example, if a user tends to frequently use a particular app at a specific time, the department will suggest features and apps related to that time period. Similarly, if a user uses a particular app in a specific location, the department will suggest features and apps related to that location. Furthermore, the analytics department learns user operation patterns and identifies unused features and apps. For example, if a user frequently uses the camera app but never uses the editing function, the department will suggest the editing function. In this way, the analytics department gains a detailed understanding of user usage patterns and provides the foundational data necessary to make optimal suggestions.

[0031] The generation unit automatically generates customized explanatory videos about identified unused features. Specifically, it uses a generation AI to automatically generate content based on the user's usage patterns. The generation AI receives data such as the user's operation history and app usage frequency as input and outputs explanatory videos about unused features. For example, if it suggests the editing function of the camera app, it will generate a video showing how to use the editing function and specific operating procedures. This video includes visual explanations and specific operating procedures to make it easy for the user to understand. Furthermore, the generation unit customizes the content of the video according to the user's usage patterns. For example, it provides content that matches the user's skill level, from simple explanations for beginners to detailed operating procedures for advanced users. In this way, the generation unit helps users effectively learn and utilize unused features.

[0032] The settings section allows users to add features introduced in the explanatory video with a single click. Specifically, basic settings are automatically completed simply by the user pressing the "Add" button. The settings section uses AI to minimize user interaction and complete the setup quickly. For example, it automatically performs settings based on the content of videos generated by the generation AI. When a user watches the video and presses the "Add" button, the settings section automatically performs the necessary settings, allowing the user to immediately use the new features. Furthermore, the settings section suggests optimal settings based on the user's usage patterns. For example, if a user frequently uses a particular app, settings related to that app will be prioritized. The settings section also collects user feedback and continuously improves the accuracy and effectiveness of the settings. In this way, the settings section helps users quickly and easily utilize new features.

[0033] The guide section provides video tutorials explaining advanced customization methods for the configured functions. Specifically, it generates videos demonstrating how to further customize the editing functions of the camera app or how to apply specific filters. Using AI, the guide section guides users through more detailed customization methods after they have completed the basic settings. For example, it explains customization methods based on the content of videos generated by the generation AI. After the user completes the basic settings, the guide section suggests more advanced functions and customization methods to help users make more effective use of their smartphones. Furthermore, the guide section customizes the video content according to the user's skill level and usage patterns. For example, it provides content tailored to the user's needs, from simple customization methods for beginners to detailed customization methods for advanced users. In this way, the guide section helps users make the most of their smartphone's functions and improves the user experience.

[0034] The analysis unit can collect data such as user operation history and app usage frequency to identify unused features. For example, the analysis unit collects data such as the number of taps, scroll direction, and usage time. The analysis unit can also collect data on app usage frequency to identify unused features. For example, the analysis unit collects data such as the number of uses per day and usage time per week. This allows the analysis unit to identify unused features based on user operation history and app usage frequency. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user operation history and app usage frequency data into AI and have the AI ​​perform the identification of unused features.

[0035] The generation unit can automatically generate customized explanatory videos about identified unused features. For example, if the generation unit proposes the editing function of a camera app, it will generate a video showing how to use the editing function and specific operating procedures. The generation unit uses a generation AI to automatically generate content based on the user's usage patterns. For example, the generation AI takes the user's operation history as input and outputs an explanatory video about unused features. The generation unit can use the generation AI to generate customized explanatory videos. For example, the generation AI generates an explanatory video about unused features based on the user's operation history and app usage frequency data. This enables the automatic generation of customized explanatory videos about identified unused features. Some or all of the above-described processes in the generation unit may be performed using the generation AI or not. For example, the generation unit can input the user's operation history and app usage frequency data into the generation AI and have the generation AI generate the explanatory video.

[0036] The settings section allows users to add features introduced in the explanatory video with a single click. For example, the settings section automatically completes basic settings simply by the user pressing the "Add" button. The settings section uses AI to minimize user interaction and complete settings quickly. For example, the settings section automatically configures settings based on the content of a video generated by a generation AI. This allows users to add features introduced in the explanatory video with a single click. Some or all of the above-described processes in the settings section may be performed using AI or not. For example, the settings section can input the content of a video generated by a generation AI into the AI, allowing the AI ​​to automate the settings process.

[0037] The guide unit can provide video tutorials explaining advanced customization methods for configured functions. For example, the guide unit can generate videos demonstrating how to further customize the editing functions of a camera app or how to apply specific filters. Using AI, the guide unit guides users through more detailed customization methods after they have completed the basic settings. For example, the guide unit explains customization methods based on the content of videos generated by the generation AI. This allows for video tutorials explaining advanced customization methods for configured functions. Some or all of the above-described processes in the guide unit may be performed using AI or not. For example, the guide unit can input the content of videos generated by the generation AI into the AI ​​and have the AI ​​perform the explanation of customization methods.

[0038] The analysis unit can analyze a user's past operation history and identify the most unused functions. For example, the analysis unit can analyze the functions of apps that a user frequently uses and identify unused sub-functions. It can also analyze patterns in the apps a user uses during specific time periods and suggest unused functions suitable for those times. Furthermore, the analysis unit can analyze the history of apps a user uses in specific locations and identify unused functions related to those locations. This allows the analysis unit to identify the most unused functions based on the user's past operation history. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input data on the user's past operation history into an AI and have the AI ​​perform the task of identifying the most unused functions.

[0039] The analysis unit can identify unused features by considering the user's lifestyle and daily behavior patterns. For example, the analysis unit can analyze the user's daily commute route and suggest unused features that are useful during the commute. It can also analyze the user's weekend activities and suggest unused features that are useful on weekends. Furthermore, the analysis unit can analyze the user's routines during specific time periods and identify unused features related to those routines. This allows for the identification of unused features based on the user's lifestyle and daily behavior patterns. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input data on the user's lifestyle and daily behavior patterns into an AI and have the AI ​​perform the identification of unused features.

[0040] The analysis unit can identify highly relevant unused features by considering the user's geographical location. For example, if the user is in a specific city, the analysis unit can suggest unused features related to that city. Furthermore, if the user is traveling, the analysis unit can suggest unused features that would be useful at their travel destination. Additionally, if the user is at home, the analysis unit can identify unused features available at home. This allows for the identification of highly relevant unused features based on the user's geographical location. Some or all of the above processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the user's geographical location data into an AI and have the AI ​​identify highly relevant unused features.

[0041] The analytics unit can analyze a user's social media activity and identify relevant unused features. For example, it can analyze the content a user frequently posts and suggest unused features related to that content. It can also analyze the activity of accounts a user follows and identify relevant unused features. Furthermore, it can analyze the activity of groups a user participates in and suggest unused features related to those groups. This allows for the identification of relevant unused features based on the user's social media activity. Some or all of the above processing in the analytics unit may be performed using AI or not. For example, the analytics unit can input data on the user's social media activity into an AI and have the AI ​​identify relevant unused features.

[0042] The generation unit can adjust the level of detail in the video based on the importance of unused features. For example, the generation unit can generate a video with a detailed explanation for unused features of high importance. It can also generate a video with a concise explanation for unused features of low importance. Furthermore, the generation unit can adjust the length of the video according to its importance to provide the user with the most relevant information. This allows the level of detail in the video to be adjusted based on the importance of unused features. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input data on the importance of unused features into a generation AI and have the generation AI perform the adjustment of the level of detail in the video.

[0043] The generation unit can apply different video generation algorithms depending on the category of unused function. For example, for entertainment functions, the generation unit can apply an algorithm that generates visually appealing videos. For productivity improvement functions, the generation unit can also apply an algorithm that generates videos emphasizing efficient operating methods. Furthermore, for health management functions, the generation unit can apply an algorithm that generates videos tailored to the user's health status. This allows for the application of different video generation algorithms depending on the category of unused function. Some or all of the above processing in the generation unit may be performed using a generation AI, or without a generation AI. For example, the generation unit can input data on unused function categories into the generation AI and have the generation AI execute the application of video generation algorithms.

[0044] The generation unit can determine the priority of videos based on the timing of proposed unused features. For example, for features that users use during a specific time period, the generation unit will prioritize generating videos tailored to that time period. The generation unit can also prioritize generating videos tailored to features related to specific events. Furthermore, the generation unit can prioritize generating videos tailored to features that users use in a specific location. This allows the generation unit to determine the priority of videos based on the timing of proposed unused features. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input data on the timing of proposed unused features into the generation AI and have the generation AI perform the video priority determination.

[0045] The generation unit can adjust the order of videos based on the relevance of unused features. For example, the generation unit may introduce highly relevant unused features at the beginning of the video. It may also introduce less relevant unused features in the latter half of the video. Furthermore, the generation unit can adjust the order of videos according to their relevance to provide the user with the most relevant information. This allows the order of videos to be adjusted based on the relevance of unused features. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input data on the relevance of unused features into a generation AI and have the generation AI perform the adjustment of the video order.

[0046] The settings unit can analyze the user's past settings history and select the optimal settings method. For example, the settings unit can automatically apply settings that the user has frequently used in the past. The settings unit can also prioritize suggesting settings methods (voice, text, etc.) that the user has used in the past. Furthermore, the settings unit can predict and suggest settings to be used during specific time periods based on the user's past settings history. This allows the optimal settings method to be selected based on the user's past settings history. Some or all of the above processes in the settings unit may be performed using AI or not. For example, the settings unit can input data on the user's past settings history into an AI and have the AI ​​select the optimal settings method.

[0047] The settings unit can customize the settings based on the user's current living situation. For example, if the user is at work, the settings unit will prioritize suggesting work-related settings. If the user is on vacation, the settings unit can also prioritize suggesting relaxing settings. Furthermore, if the user is participating in a specific event, the settings unit can suggest settings related to that event. This allows the settings to be customized based on the user's current living situation. Some or all of the above processing in the settings unit may be performed using AI or not. For example, the settings unit can input data on the user's current living situation into the AI ​​and have the AI ​​perform the customization of the settings.

[0048] The settings unit can select the optimal settings method by considering the user's geographical location information. For example, if the user is in a specific city, the settings unit can suggest settings related to that city. Furthermore, if the user is traveling, the settings unit can suggest settings that will be useful at their travel destination. In addition, if the user is at home, the settings unit can identify settings available at home. This allows the optimal settings method to be selected based on the user's geographical location information. Some or all of the above processing in the settings unit may be performed using AI, or not. For example, the settings unit can input the user's geographical location data into an AI and have the AI ​​select the optimal settings method.

[0049] The settings unit can analyze the user's social media activity and suggest settings. For example, it can analyze the content the user frequently posts and suggest settings related to that content. It can also analyze the activity of accounts the user follows and identify relevant settings. Furthermore, it can analyze the activity of groups the user participates in and suggest settings related to those groups. This allows the settings unit to suggest settings based on the user's social media activity. Some or all of the above processing in the settings unit may be performed using AI or not. For example, the settings unit can input data on the user's social media activity into an AI and have the AI ​​perform the suggestion of settings.

[0050] The guide unit can provide the optimal guiding method by referring to the user's past customization history. For example, the guide unit can suggest similar customization methods based on the user's past customizations. The guide unit can also prioritize providing guiding methods (text, video, etc.) that the user has used in the past. Furthermore, the guide unit can predict and suggest customization methods to be performed at a specific time of day based on the user's past customization history. This allows the guide unit to provide the optimal guiding method based on the user's past customization history. Some or all of the above processing in the guide unit may be performed using AI or not. For example, the guide unit can input data on the user's past customization history into AI and have the AI ​​perform the task of providing the optimal guiding method.

[0051] The guide unit can customize the customization method based on the user's current living situation. For example, if the user is at work, the guide unit will prioritize suggesting work-related customization methods. Similarly, if the user is on vacation, the guide unit can prioritize suggesting relaxation-oriented customization methods. Furthermore, if the user is participating in a specific event, the guide unit can suggest customization methods related to that event. This allows the customization method to be tailored to the user's current living situation. Some or all of the above processing in the guide unit may be performed using AI or not. For example, the guide unit can input data on the user's current living situation into the AI ​​and have the AI ​​perform the customization of the customization method.

[0052] The guide unit can provide the optimal customization method by taking into account the user's geographical location information. For example, if the user is in a specific city, the guide unit can suggest a customization method relevant to that city. Furthermore, if the user is traveling, the guide unit can suggest a customization method that will be useful at their travel destination. In addition, if the user is at home, the guide unit can identify a customization method that can be used at home. This allows the guide unit to provide the optimal customization method based on the user's geographical location information. Some or all of the above processing in the guide unit may be performed using AI or not. For example, the guide unit can input the user's geographical location data into an AI and have the AI ​​perform the task of providing the optimal customization method.

[0053] The guide unit can analyze a user's social media activity and suggest customization methods. For example, the guide unit can analyze the content a user frequently posts and suggest customization methods related to that content. It can also analyze the activity of accounts a user follows and identify relevant customization methods. Furthermore, the guide unit can analyze the activity of groups a user participates in and suggest customization methods related to those groups. This allows the guide unit to suggest customization methods based on the user's social media activity. Some or all of the above processing in the guide unit may be performed using AI or not. For example, the guide unit can input data on the user's social media activity into an AI and have the AI ​​suggest customization methods.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The analysis unit can consider a user's past search history when analyzing their smartphone usage patterns. For example, it can analyze keywords and topics that users frequently search for and suggest unused features related to them. The analysis unit can also identify unused features related to seasons or events based on what users searched for during a specific period. Furthermore, the analysis unit can analyze what users searched for in specific locations and suggest unused features related to those locations. This allows for the identification of optimal unused features based on the user's search history. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user search history data into AI and have the AI ​​identify unused features.

[0056] The settings unit can analyze the user's past settings history and select the optimal settings method. For example, it can automatically apply settings that the user has frequently used in the past. The settings unit can also prioritize suggesting settings methods (voice, text, etc.) that the user has used in the past. Furthermore, the settings unit can predict and suggest settings to be used during specific time periods based on the user's past settings history. This allows the optimal settings method to be selected based on the user's past settings history. Some or all of the above processes in the settings unit may be performed using AI or not. For example, the settings unit can input data on the user's past settings history into an AI and have the AI ​​select the optimal settings method.

[0057] The analysis unit can identify highly relevant unused features by considering the user's geographical location. For example, if the user is in a specific city, the analysis unit can suggest unused features related to that city. Furthermore, if the user is traveling, the analysis unit can suggest unused features that would be useful at their travel destination. Additionally, if the user is at home, the analysis unit can identify unused features available at home. This allows for the identification of highly relevant unused features based on the user's geographical location. Some or all of the above processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the user's geographical location data into an AI and have the AI ​​identify highly relevant unused features.

[0058] The settings unit can customize the settings based on the user's current living situation. For example, if the user is at work, the settings unit will prioritize suggesting work-related settings. If the user is on vacation, the settings unit can also prioritize suggesting relaxing settings. Furthermore, if the user is participating in a specific event, the settings unit can suggest settings related to that event. This allows the settings to be customized based on the user's current living situation. Some or all of the above processing in the settings unit may be performed using AI or not. For example, the settings unit can input data on the user's current living situation into the AI ​​and have the AI ​​perform the customization of the settings.

[0059] The analytics unit can analyze a user's social media activity and identify relevant unused features. For example, it can analyze the content a user frequently posts and suggest unused features related to that content. It can also analyze the activity of accounts a user follows and identify relevant unused features. Furthermore, it can analyze the activity of groups a user participates in and suggest unused features related to those groups. This allows for the identification of relevant unused features based on the user's social media activity. Some or all of the above processing in the analytics unit may be performed using AI or not. For example, the analytics unit can input data on the user's social media activity into an AI and have the AI ​​identify relevant unused features.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The analysis department analyzes the user's smartphone usage patterns. Specifically, it collects data such as the user's operation history and app usage frequency to identify unused functions. For example, if a user frequently uses the camera app but never uses the editing function, the department will suggest the editing function. Step 2: The generation unit automatically generates customized explanatory videos about the identified unused features. The generation unit uses a generation AI to automatically generate content based on the user's usage patterns. For example, if it suggests the editing function of the camera app, it will generate a video showing how to use the editing function and specific operating procedures. Step 3: The settings section allows you to add the features introduced in the explanatory video with a single click. For example, the user simply presses the "Add" button, and the basic settings are automatically completed. The settings section uses AI to minimize user interaction and complete the setup quickly. Step 4: The guide section provides video tutorials explaining advanced customization methods for the configured features. For example, it generates videos demonstrating how to further customize the editing functions of the camera app or how to apply specific filters. Using AI, the guide section provides further detailed customization instructions after the user has completed the basic settings.

[0062] (Example of form 2) The AI ​​smartphone navigation system according to an embodiment of the present invention is an innovative application that utilizes generative AI to analyze the user's smartphone usage patterns in detail and proposes previously unused functions optimized for the user. This AI smartphone navigation system clearly explains specific setting methods and customization methods to the user through customized explanatory videos automatically generated by the AI. Furthermore, if the user wishes to add a function introduced in the explanatory video, the basic settings are automatically completed with a single click. In addition, the AI ​​also guides the user on how to customize the function to a more advanced level, supporting the user in effectively utilizing their smartphone in a short amount of time. For example, the AI ​​smartphone navigation system analyzes the user's smartphone usage patterns in detail. The generative AI collects data such as the user's operation history and the frequency of app usage to identify previously unused functions. For example, if a user frequently uses the camera app but never uses the editing function, the generative AI will propose this editing function. Next, the generative AI automatically generates a customized explanatory video regarding the identified unused function. This video clearly explains specific setting methods and customization methods. For example, if the editing function of the camera app is proposed, the video will show how to use the editing function and the specific operation procedures. Furthermore, if a user wants to add a feature introduced in the explanatory video, the basic settings are automatically completed with a single click. The generating AI minimizes user interaction and completes the setup quickly. For example, if the settings for adding editing functions are introduced in the video, the user can complete the setup simply by pressing the "Add" button. The generating AI also guides the user on how to customize the function in more detail. After the user completes the basic settings, the generating AI provides a video explanation of how to further customize the function. For example, it shows how to further customize the editing functions of the camera app or how to apply specific filters. In this way, the AI ​​smartphone navigation system supports users in making effective use of their smartphones. From elderly people who are not tech-savvy to busy business people and young people who seek out new technologies, everyone can fully unlock the potential of their smartphone.Users can discover their own unique ways of using their smartphones, maximizing the efficiency and convenience of their daily lives. This allows the AI ​​smartphone navigation system to analyze users' smartphone usage patterns in detail and optimize and suggest unused features.

[0063] The AI ​​smartphone navigation system according to this embodiment comprises an analysis unit, a generation unit, a setting unit, and a guide unit. The analysis unit analyzes the user's smartphone usage patterns. The analysis unit collects data such as the user's operation history and app usage frequency, and identifies unused functions. For example, if the analysis unit frequently uses the camera app but never uses the editing function, it suggests the editing function. The generation unit automatically generates a customized explanatory video about the identified unused function. For example, if the generation unit suggests the editing function of the camera app, it generates a video showing how to use the editing function and specific operating procedures. The generation unit uses a generation AI to automatically generate content based on the user's usage patterns. For example, the generation AI takes the user's operation history as input and outputs an explanatory video about the unused function. The setting unit adds the function introduced in the explanatory video with a single click. For example, the setting unit allows the user to simply press the "Add" button, and the basic settings are automatically completed. The setting unit uses AI to minimize user operation and complete the settings quickly. For example, the setting unit automatically performs settings based on the content of the video generated by the generation AI. The guide unit provides video explanations of advanced customization methods for the configured functions. For example, the guide unit generates videos demonstrating how to further customize the editing functions of a camera app or how to apply specific filters. Using AI, the guide unit guides the user through more detailed customization methods after the user has completed the basic settings. For example, the guide unit explains customization methods based on the content of the videos generated by the AI. As a result, the AI ​​smartphone navigation system according to this embodiment can analyze the user's smartphone usage patterns in detail and optimize and suggest unused functions.

[0064] The analytics department conducts a detailed analysis of users' smartphone usage patterns. Specifically, it collects a wide range of data, including user operation history, app usage frequency, usage time, and usage location, and statistically analyzes this data. For example, if a user tends to frequently use a particular app at a specific time, the department will suggest features and apps related to that time period. Similarly, if a user uses a particular app in a specific location, the department will suggest features and apps related to that location. Furthermore, the analytics department learns user operation patterns and identifies unused features and apps. For example, if a user frequently uses the camera app but never uses the editing function, the department will suggest the editing function. In this way, the analytics department gains a detailed understanding of user usage patterns and provides the foundational data necessary to make optimal suggestions.

[0065] The generation unit automatically generates customized explanatory videos about identified unused features. Specifically, it uses a generation AI to automatically generate content based on the user's usage patterns. The generation AI receives data such as the user's operation history and app usage frequency as input and outputs explanatory videos about unused features. For example, if it suggests the editing function of the camera app, it will generate a video showing how to use the editing function and specific operating procedures. This video includes visual explanations and specific operating procedures to make it easy for the user to understand. Furthermore, the generation unit customizes the content of the video according to the user's usage patterns. For example, it provides content that matches the user's skill level, from simple explanations for beginners to detailed operating procedures for advanced users. In this way, the generation unit helps users effectively learn and utilize unused features.

[0066] The settings section allows users to add features introduced in the explanatory video with a single click. Specifically, basic settings are automatically completed simply by the user pressing the "Add" button. The settings section uses AI to minimize user interaction and complete the setup quickly. For example, it automatically performs settings based on the content of videos generated by the generation AI. When a user watches the video and presses the "Add" button, the settings section automatically performs the necessary settings, allowing the user to immediately use the new features. Furthermore, the settings section suggests optimal settings based on the user's usage patterns. For example, if a user frequently uses a particular app, settings related to that app will be prioritized. The settings section also collects user feedback and continuously improves the accuracy and effectiveness of the settings. In this way, the settings section helps users quickly and easily utilize new features.

[0067] The guide section provides video tutorials explaining advanced customization methods for the configured functions. Specifically, it generates videos demonstrating how to further customize the editing functions of the camera app or how to apply specific filters. Using AI, the guide section guides users through more detailed customization methods after they have completed the basic settings. For example, it explains customization methods based on the content of videos generated by the generation AI. After the user completes the basic settings, the guide section suggests more advanced functions and customization methods to help users make more effective use of their smartphones. Furthermore, the guide section customizes the video content according to the user's skill level and usage patterns. For example, it provides content tailored to the user's needs, from simple customization methods for beginners to detailed customization methods for advanced users. In this way, the guide section helps users make the most of their smartphone's functions and improves the user experience.

[0068] The analysis unit can collect data such as user operation history and app usage frequency to identify unused features. For example, the analysis unit collects data such as the number of taps, scroll direction, and usage time. The analysis unit can also collect data on app usage frequency to identify unused features. For example, the analysis unit collects data such as the number of uses per day and usage time per week. This allows the analysis unit to identify unused features based on user operation history and app usage frequency. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user operation history and app usage frequency data into AI and have the AI ​​perform the identification of unused features.

[0069] The generation unit can automatically generate customized explanatory videos about identified unused features. For example, if the generation unit proposes the editing function of a camera app, it will generate a video showing how to use the editing function and specific operating procedures. The generation unit uses a generation AI to automatically generate content based on the user's usage patterns. For example, the generation AI takes the user's operation history as input and outputs an explanatory video about unused features. The generation unit can use the generation AI to generate customized explanatory videos. For example, the generation AI generates an explanatory video about unused features based on the user's operation history and app usage frequency data. This enables the automatic generation of customized explanatory videos about identified unused features. Some or all of the above-described processes in the generation unit may be performed using the generation AI or not. For example, the generation unit can input the user's operation history and app usage frequency data into the generation AI and have the generation AI generate the explanatory video.

[0070] The settings section allows users to add features introduced in the explanatory video with a single click. For example, the settings section automatically completes basic settings simply by the user pressing the "Add" button. The settings section uses AI to minimize user interaction and complete settings quickly. For example, the settings section automatically configures settings based on the content of a video generated by a generation AI. This allows users to add features introduced in the explanatory video with a single click. Some or all of the above-described processes in the settings section may be performed using AI or not. For example, the settings section can input the content of a video generated by a generation AI into the AI, allowing the AI ​​to automate the settings process.

[0071] The guide unit can provide video tutorials explaining advanced customization methods for configured functions. For example, the guide unit can generate videos demonstrating how to further customize the editing functions of a camera app or how to apply specific filters. Using AI, the guide unit guides users through more detailed customization methods after they have completed the basic settings. For example, the guide unit explains customization methods based on the content of videos generated by the generation AI. This allows for video tutorials explaining advanced customization methods for configured functions. Some or all of the above-described processes in the guide unit may be performed using AI or not. For example, the guide unit can input the content of videos generated by the generation AI into the AI ​​and have the AI ​​perform the explanation of customization methods.

[0072] The analysis unit can estimate the user's emotions and adjust the usage pattern analysis method based on the estimated user emotions. For example, if the user is stressed, the analysis unit can provide a simplified interface to reduce the burden of analysis. If the user is relaxed, the analysis unit can perform a detailed analysis and suggest more unused features. Furthermore, if the user is in a hurry, the analysis unit can perform a rapid analysis and suggest immediately available unused features. This allows the usage pattern analysis method to be adjusted 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into AI and have the AI ​​adjust the usage pattern analysis method.

[0073] The analysis unit can analyze a user's past operation history and identify the most unused functions. For example, the analysis unit can analyze the functions of apps that a user frequently uses and identify unused sub-functions. It can also analyze patterns in the apps a user uses during specific time periods and suggest unused functions suitable for those times. Furthermore, the analysis unit can analyze the history of apps a user uses in specific locations and identify unused functions related to those locations. This allows the analysis unit to identify the most unused functions based on the user's past operation history. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input data on the user's past operation history into an AI and have the AI ​​perform the task of identifying the most unused functions.

[0074] The analysis unit can identify unused features by considering the user's lifestyle and daily behavior patterns. For example, the analysis unit can analyze the user's daily commute route and suggest unused features that are useful during the commute. It can also analyze the user's weekend activities and suggest unused features that are useful on weekends. Furthermore, the analysis unit can analyze the user's routines during specific time periods and identify unused features related to those routines. This allows for the identification of unused features based on the user's lifestyle and daily behavior patterns. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input data on the user's lifestyle and daily behavior patterns into an AI and have the AI ​​perform the identification of unused features.

[0075] The analysis unit can estimate the user's emotions and determine the priority of unused features based on the estimated emotions. For example, if the user is stressed, the analysis unit will prioritize suggesting features that promote relaxation. It can also prioritize suggesting entertainment features if the user is excited. Furthermore, if the user is tired, it can prioritize suggesting relaxation features. This allows the system to determine the priority of unused features based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into an AI and have the AI ​​determine the priority of unused features.

[0076] The analysis unit can identify highly relevant unused features by considering the user's geographical location. For example, if the user is in a specific city, the analysis unit can suggest unused features related to that city. Furthermore, if the user is traveling, the analysis unit can suggest unused features that would be useful at their travel destination. Additionally, if the user is at home, the analysis unit can identify unused features available at home. This allows for the identification of highly relevant unused features based on the user's geographical location. Some or all of the above processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the user's geographical location data into an AI and have the AI ​​identify highly relevant unused features.

[0077] The analytics unit can analyze a user's social media activity and identify relevant unused features. For example, it can analyze the content a user frequently posts and suggest unused features related to that content. It can also analyze the activity of accounts a user follows and identify relevant unused features. Furthermore, it can analyze the activity of groups a user participates in and suggest unused features related to those groups. This allows for the identification of relevant unused features based on the user's social media activity. Some or all of the above processing in the analytics unit may be performed using AI or not. For example, the analytics unit can input data on the user's social media activity into an AI and have the AI ​​identify relevant unused features.

[0078] The generation unit can estimate the user's emotions and adjust the presentation of the explanatory video based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate an explanatory video that proceeds at a leisurely pace. If the user is in a hurry, the generation unit can also generate an explanatory video that emphasizes the shortest route. Furthermore, if the user is excited, the generation unit can generate an explanatory video with visually stimulating effects. This allows the presentation of the explanatory video to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using or without a generative AI. For example, the generation unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of the explanatory video.

[0079] The generation unit can adjust the level of detail in the video based on the importance of unused features. For example, the generation unit can generate a video with a detailed explanation for unused features of high importance. It can also generate a video with a concise explanation for unused features of low importance. Furthermore, the generation unit can adjust the length of the video according to its importance to provide the user with the most relevant information. This allows the level of detail in the video to be adjusted based on the importance of unused features. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input data on the importance of unused features into a generation AI and have the generation AI perform the adjustment of the level of detail in the video.

[0080] The generation unit can apply different video generation algorithms depending on the category of unused function. For example, for entertainment functions, the generation unit can apply an algorithm that generates visually appealing videos. For productivity improvement functions, the generation unit can also apply an algorithm that generates videos emphasizing efficient operating methods. Furthermore, for health management functions, the generation unit can apply an algorithm that generates videos tailored to the user's health status. This allows for the application of different video generation algorithms depending on the category of unused function. Some or all of the above processing in the generation unit may be performed using a generation AI, or without a generation AI. For example, the generation unit can input data on unused function categories into the generation AI and have the generation AI execute the application of video generation algorithms.

[0081] The generation unit can estimate the user's emotions and adjust the length of the explanatory video based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise explanatory video. If the user is relaxed, the generation unit can also generate a longer explanatory video with more detailed explanations. Furthermore, if the user is excited, the generation unit can generate an explanatory video with visually stimulating effects. This allows the length of the explanatory video to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using or without a generative AI. For example, the generation unit can input user emotion data into a generative AI and have the generative AI adjust the length of the explanatory video.

[0082] The generation unit can determine the priority of videos based on the timing of proposed unused features. For example, for features that users use during a specific time period, the generation unit will prioritize generating videos tailored to that time period. The generation unit can also prioritize generating videos tailored to features related to specific events. Furthermore, the generation unit can prioritize generating videos tailored to features that users use in a specific location. This allows the generation unit to determine the priority of videos based on the timing of proposed unused features. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input data on the timing of proposed unused features into the generation AI and have the generation AI perform the video priority determination.

[0083] The generation unit can adjust the order of videos based on the relevance of unused features. For example, the generation unit may introduce highly relevant unused features at the beginning of the video. It may also introduce less relevant unused features in the latter half of the video. Furthermore, the generation unit can adjust the order of videos according to their relevance to provide the user with the most relevant information. This allows the order of videos to be adjusted based on the relevance of unused features. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input data on the relevance of unused features into a generation AI and have the generation AI perform the adjustment of the video order.

[0084] The settings unit can estimate the user's emotions and adjust the settings method based on the estimated emotions. For example, if the user is stressed, the settings unit can provide a simple settings method to reduce the burden of settings. If the user is relaxed, the settings unit can also provide detailed settings options and suggest a customizable settings method. Furthermore, if the user is in a hurry, the settings unit can provide a way to complete the settings quickly. This allows the settings method to be adjusted 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the settings unit may be performed using AI or not. For example, the settings unit can input user emotion data into AI and have the AI ​​perform the adjustment of the settings method.

[0085] The settings unit can analyze the user's past settings history and select the optimal settings method. For example, the settings unit can automatically apply settings that the user has frequently used in the past. The settings unit can also prioritize suggesting settings methods (voice, text, etc.) that the user has used in the past. Furthermore, the settings unit can predict and suggest settings to be used during specific time periods based on the user's past settings history. This allows the optimal settings method to be selected based on the user's past settings history. Some or all of the above processes in the settings unit may be performed using AI or not. For example, the settings unit can input data on the user's past settings history into an AI and have the AI ​​select the optimal settings method.

[0086] The settings unit can customize the settings based on the user's current living situation. For example, if the user is at work, the settings unit will prioritize suggesting work-related settings. If the user is on vacation, the settings unit can also prioritize suggesting relaxing settings. Furthermore, if the user is participating in a specific event, the settings unit can suggest settings related to that event. This allows the settings to be customized based on the user's current living situation. Some or all of the above processing in the settings unit may be performed using AI or not. For example, the settings unit can input data on the user's current living situation into the AI ​​and have the AI ​​perform the customization of the settings.

[0087] The settings unit can estimate the user's emotions and determine the priority of settings based on the estimated emotions. For example, if the user is stressed, the settings unit will prioritize suggesting relaxing settings. It can also prioritize suggesting entertainment settings if the user is excited. Furthermore, it can prioritize suggesting relaxation settings if the user is tired. This allows the settings unit to determine the priority of settings based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the settings unit may be performed using AI or not. For example, the settings unit can input user emotion data into an AI and have the AI ​​determine the priority of settings.

[0088] The settings unit can select the optimal settings method by considering the user's geographical location information. For example, if the user is in a specific city, the settings unit can suggest settings related to that city. Furthermore, if the user is traveling, the settings unit can suggest settings that will be useful at their travel destination. In addition, if the user is at home, the settings unit can identify settings available at home. This allows the optimal settings method to be selected based on the user's geographical location information. Some or all of the above processing in the settings unit may be performed using AI, or not. For example, the settings unit can input the user's geographical location data into an AI and have the AI ​​select the optimal settings method.

[0089] The settings unit can analyze the user's social media activity and suggest settings. For example, it can analyze the content the user frequently posts and suggest settings related to that content. It can also analyze the activity of accounts the user follows and identify relevant settings. Furthermore, it can analyze the activity of groups the user participates in and suggest settings related to those groups. This allows the settings unit to suggest settings based on the user's social media activity. Some or all of the above processing in the settings unit may be performed using AI or not. For example, the settings unit can input data on the user's social media activity into an AI and have the AI ​​perform the suggestion of settings.

[0090] The guide unit can estimate the user's emotions and adjust the customization guide based on those emotions. For example, if the user is nervous, the guide unit can provide a simple and easy-to-understand guide. If the user is relaxed, the guide unit can also provide a guide with more detailed information. Furthermore, if the user is in a hurry, the guide unit can provide a guide that quickly explains the customization method. This allows the customization guide to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the guide unit may be performed using AI or not. For example, the guide unit can input user emotion data into an AI and have the AI ​​adjust the customization guide.

[0091] The guide unit can provide the optimal guiding method by referring to the user's past customization history. For example, the guide unit can suggest similar customization methods based on the user's past customizations. The guide unit can also prioritize providing guiding methods (text, video, etc.) that the user has used in the past. Furthermore, the guide unit can predict and suggest customization methods to be performed at a specific time of day based on the user's past customization history. This allows the guide unit to provide the optimal guiding method based on the user's past customization history. Some or all of the above processing in the guide unit may be performed using AI or not. For example, the guide unit can input data on the user's past customization history into AI and have the AI ​​perform the task of providing the optimal guiding method.

[0092] The guide unit can customize the customization method based on the user's current living situation. For example, if the user is at work, the guide unit will prioritize suggesting work-related customization methods. Similarly, if the user is on vacation, the guide unit can prioritize suggesting relaxation-oriented customization methods. Furthermore, if the user is participating in a specific event, the guide unit can suggest customization methods related to that event. This allows the customization method to be tailored to the user's current living situation. Some or all of the above processing in the guide unit may be performed using AI or not. For example, the guide unit can input data on the user's current living situation into the AI ​​and have the AI ​​perform the customization of the customization method.

[0093] The guide unit can estimate the user's emotions and determine the priority of customization methods based on the estimated emotions. For example, if the user is stressed, the guide unit will prioritize suggesting relaxing customization methods. It can also prioritize suggesting entertainment customization methods if the user is excited. Furthermore, if the user is tired, it can prioritize suggesting relaxation customization methods. This allows the guide unit to determine the priority of customization methods based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the guide unit may be performed using AI or not. For example, the guide unit can input user emotion data into an AI and have the AI ​​determine the priority of customization methods.

[0094] The guide unit can provide the optimal customization method by taking into account the user's geographical location information. For example, if the user is in a specific city, the guide unit can suggest a customization method relevant to that city. Furthermore, if the user is traveling, the guide unit can suggest a customization method that will be useful at their travel destination. In addition, if the user is at home, the guide unit can identify a customization method that can be used at home. This allows the guide unit to provide the optimal customization method based on the user's geographical location information. Some or all of the above processing in the guide unit may be performed using AI or not. For example, the guide unit can input the user's geographical location data into an AI and have the AI ​​perform the task of providing the optimal customization method.

[0095] The guide unit can analyze a user's social media activity and suggest customization methods. For example, the guide unit can analyze the content a user frequently posts and suggest customization methods related to that content. It can also analyze the activity of accounts a user follows and identify relevant customization methods. Furthermore, the guide unit can analyze the activity of groups a user participates in and suggest customization methods related to those groups. This allows the guide unit to suggest customization methods based on the user's social media activity. Some or all of the above processing in the guide unit may be performed using AI or not. For example, the guide unit can input data on the user's social media activity into an AI and have the AI ​​suggest customization methods.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The analysis unit can consider a user's past search history when analyzing their smartphone usage patterns. For example, it can analyze keywords and topics that users frequently search for and suggest unused features related to them. The analysis unit can also identify unused features related to seasons or events based on what users searched for during a specific period. Furthermore, the analysis unit can analyze what users searched for in specific locations and suggest unused features related to those locations. This allows for the identification of optimal unused features based on the user's search history. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user search history data into AI and have the AI ​​identify unused features.

[0098] The generation unit can estimate the user's emotions and adjust the audio tone of the explanatory video based on the estimated emotions. For example, if the user is relaxed, the explanatory video can be generated with a calm audio tone. If the user is in a hurry, the explanatory video can be generated with a fast and clear audio tone. Furthermore, if the user is excited, the explanatory video can be generated with an energetic audio tone. This allows the audio tone of the explanatory video to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using the generative AI or not. For example, the generation unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the audio tone of the explanatory video.

[0099] The settings unit can analyze the user's past settings history and select the optimal settings method. For example, it can automatically apply settings that the user has frequently used in the past. The settings unit can also prioritize suggesting settings methods (voice, text, etc.) that the user has used in the past. Furthermore, the settings unit can predict and suggest settings to be used during specific time periods based on the user's past settings history. This allows the optimal settings method to be selected based on the user's past settings history. Some or all of the above processes in the settings unit may be performed using AI or not. For example, the settings unit can input data on the user's past settings history into an AI and have the AI ​​select the optimal settings method.

[0100] The guide unit can estimate the user's emotions and adjust the customization guide based on those emotions. For example, if the user is nervous, the guide unit can provide a simple and easy-to-understand guide. If the user is relaxed, the guide unit can also provide a guide with more detailed information. Furthermore, if the user is in a hurry, the guide unit can provide a guide that quickly explains the customization method. This allows the customization guide to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the guide unit may be performed using AI or not. For example, the guide unit can input user emotion data into an AI and have the AI ​​adjust the customization guide.

[0101] The analysis unit can identify highly relevant unused features by considering the user's geographical location. For example, if the user is in a specific city, the analysis unit can suggest unused features related to that city. Furthermore, if the user is traveling, the analysis unit can suggest unused features that would be useful at their travel destination. Additionally, if the user is at home, the analysis unit can identify unused features available at home. This allows for the identification of highly relevant unused features based on the user's geographical location. Some or all of the above processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the user's geographical location data into an AI and have the AI ​​identify highly relevant unused features.

[0102] The generation unit can estimate the user's emotions and adjust the length of the explanatory video based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise explanatory video. If the user is relaxed, the generation unit can also generate a longer explanatory video with more detailed explanations. Furthermore, if the user is excited, the generation unit can generate an explanatory video with visually stimulating effects. This allows the length of the explanatory video to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using or without a generative AI. For example, the generation unit can input user emotion data into a generative AI and have the generative AI adjust the length of the explanatory video.

[0103] The settings unit can customize the settings based on the user's current living situation. For example, if the user is at work, the settings unit will prioritize suggesting work-related settings. If the user is on vacation, the settings unit can also prioritize suggesting relaxing settings. Furthermore, if the user is participating in a specific event, the settings unit can suggest settings related to that event. This allows the settings to be customized based on the user's current living situation. Some or all of the above processing in the settings unit may be performed using AI or not. For example, the settings unit can input data on the user's current living situation into the AI ​​and have the AI ​​perform the customization of the settings.

[0104] The guide unit can estimate the user's emotions and determine the priority of customization methods based on the estimated emotions. For example, if the user is stressed, the guide unit will prioritize suggesting relaxing customization methods. It can also prioritize suggesting entertainment customization methods if the user is excited. Furthermore, if the user is tired, it can prioritize suggesting relaxation customization methods. This allows the guide unit to determine the priority of customization methods based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the guide unit may be performed using AI or not. For example, the guide unit can input user emotion data into an AI and have the AI ​​determine the priority of customization methods.

[0105] The analytics unit can analyze a user's social media activity and identify relevant unused features. For example, it can analyze the content a user frequently posts and suggest unused features related to that content. It can also analyze the activity of accounts a user follows and identify relevant unused features. Furthermore, it can analyze the activity of groups a user participates in and suggest unused features related to those groups. This allows for the identification of relevant unused features based on the user's social media activity. Some or all of the above processing in the analytics unit may be performed using AI or not. For example, the analytics unit can input data on the user's social media activity into an AI and have the AI ​​identify relevant unused features.

[0106] The settings unit can estimate the user's emotions and determine the priority of settings based on the estimated emotions. For example, if the user is stressed, the settings unit will prioritize suggesting relaxing settings. It can also prioritize suggesting entertainment settings if the user is excited. Furthermore, it can prioritize suggesting relaxation settings if the user is tired. This allows the settings unit to determine the priority of settings based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the settings unit may be performed using AI or not. For example, the settings unit can input user emotion data into an AI and have the AI ​​determine the priority of settings.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The analysis department analyzes the user's smartphone usage patterns. Specifically, it collects data such as the user's operation history and app usage frequency to identify unused functions. For example, if a user frequently uses the camera app but never uses the editing function, the department will suggest the editing function. Step 2: The generation unit automatically generates customized explanatory videos about the identified unused features. The generation unit uses a generation AI to automatically generate content based on the user's usage patterns. For example, if it suggests the editing function of the camera app, it will generate a video showing how to use the editing function and specific operating procedures. Step 3: The settings section allows you to add the features introduced in the explanatory video with a single click. For example, the user simply presses the "Add" button, and the basic settings are automatically completed. The settings section uses AI to minimize user interaction and complete the setup quickly. Step 4: The guide section provides video tutorials explaining advanced customization methods for the configured features. For example, it generates videos demonstrating how to further customize the editing functions of the camera app or how to apply specific filters. Using AI, the guide section provides further detailed customization instructions after the user has completed the basic settings.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the analysis unit, generation unit, setting unit, and guide 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 collects data such as the user's operation history and the frequency of app usage to identify unused functions. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and automatically generates a customized explanatory video about the identified unused functions. The setting unit is implemented by the control unit 46A of the smart device 14 and adds the functions introduced in the explanatory video with a single click. The guide unit is implemented by the identification processing unit 290 of the data processing unit 12 and explains in a video how to further customize the set functions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating 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.

[0126] 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.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0128] Each of the multiple elements described above, including the analysis unit, generation unit, setting unit, and guide 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 collects data such as the user's operation history and the frequency of app usage to identify unused functions. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and automatically generates a customized explanatory video about the identified unused functions. The setting unit is implemented by the control unit 46A of the smart glasses 214 and adds the functions introduced in the explanatory video with a single click. The guide unit is implemented by the identification processing unit 290 of the data processing unit 12 and explains in a video how to further customize the set functions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the analysis unit, generation unit, setting unit, and guide 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 collects data such as the user's operation history and application usage frequency to identify unused functions. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and automatically generates a customized explanatory video about the identified unused functions. The setting unit is implemented by the control unit 46A of the headset terminal 314 and adds the functions introduced in the explanatory video with a single click. The guide unit is implemented by the identification processing unit 290 of the data processing unit 12 and explains advanced customization methods for the set functions in a video. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the analysis unit, generation unit, setting unit, and guide 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 collects data such as the user's operation history and the frequency of application use to identify unused functions. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and automatically generates customized explanatory videos about the identified unused functions. The setting unit is implemented by the control unit 46A of the robot 414 and adds the functions introduced in the explanatory video with a single click. The guide unit is implemented by the identification processing unit 290 of the data processing unit 12 and explains advanced customization methods for the set functions in a video. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) The analysis department analyzes users' smartphone usage patterns, A generation unit that automatically generates customized explanatory videos regarding unused functions identified by the analysis unit, A setting unit to add functions introduced in the explanatory video generated by the generation unit, The system includes a guide unit that provides guidance on how to perform advanced customization of the functions set by the setting unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit is We collect data such as user operation history and app usage frequency to identify unused features. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Automatically generate customized explanatory videos for identified unused features. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned setting unit is, Add the features shown in the explanatory video with a single click. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned guide portion is This video explains how to perform advanced customization of the configured functions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit is We estimate user emotions and adjust the analysis method of usage patterns based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit is Analyze the user's past activity history to identify the most unused features. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit is Identifying unused features by considering the user's lifestyle and daily behavior patterns. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit is It estimates user sentiment and prioritizes unused features based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit is Identify relevant unused features by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is Analyze users' social media activity and identify relevant unused features. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is It estimates the user's emotions and adjusts the presentation of the explanatory video based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is Adjust video detail based on the importance of unused features. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is Apply different video generation algorithms depending on the category of unused features. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is It estimates the user's emotions and adjusts the length of the explanatory video based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is Prioritize videos based on when unused features were suggested. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is Adjust the order of videos based on the relevance of unused features. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned setting unit is, It estimates the user's emotions and adjusts the settings based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned setting unit is, The system analyzes the user's past settings history to select the optimal configuration method. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned setting unit is, Customize the settings based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned setting unit is, It estimates the user's emotions and determines the priority of settings based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned setting unit is, The optimal configuration method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned setting unit is, Analyze users' social media activity and suggest settings. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned guide portion is It estimates the user's emotions and adjusts the customization guide based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned guide portion is Referencing the user's past customization history provides the optimal guidance method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned guide portion is Customize the customization method based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned guide portion is It estimates the user's emotions and prioritizes customization methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned guide portion is We provide the optimal customization method considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned guide portion is Analyze users' social media activity and suggest customization methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The analysis department analyzes users' smartphone usage patterns, A generation unit that automatically generates customized explanatory videos regarding unused functions identified by the analysis unit, A setting unit to add functions introduced in the explanatory video generated by the generation unit, The system includes a guide unit that provides guidance on how to perform advanced customization of the functions set by the setting unit. A system characterized by the following features.

2. The aforementioned analysis unit is We collect data such as user operation history and app usage frequency to identify unused features. The system according to feature 1.

3. The generating unit is Automatically generate customized explanatory videos for identified unused features. The system according to feature 1.

4. The aforementioned setting unit is, Add the features shown in the explanatory video with a single click. The system according to feature 1.

5. The aforementioned guide section is This video explains how to perform advanced customization of the configured functions. The system according to feature 1.

6. The aforementioned analysis unit is We estimate user emotions and adjust the analysis method of usage patterns based on the estimated user emotions. The system according to feature 1.

7. The aforementioned analysis unit is Analyze the user's past activity history to identify the most unused features. The system according to feature 1.

8. The aforementioned analysis unit is Identifying unused features by considering the user's lifestyle and daily behavior patterns. The system according to feature 1.

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