system

Abstract

The system according to this embodiment aims to provide high-quality content in real time by utilizing the user's location information and environmental data. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, a display unit, and a communication unit. The collection unit collects the user's location information and environmental data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates content based on the information obtained by the analysis unit. The display unit displays the content generated by the generation unit. The communication unit provides a high-quality communication environment.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, content generation that utilizes the user's location information and environmental data in real time has not been sufficiently performed, and there is room for improvement.

[0005] The system according to the embodiment aims to provide high-quality content in real time by utilizing the user's location information and environmental data.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a generation unit, a display unit, and a communication unit. The data collection unit collects the user's location information and environmental data. The analysis unit analyzes the data collected by the data collection unit. The generation unit generates content based on the information obtained by the analysis unit. The display unit displays the content generated by the generation unit. The communication unit provides a high-quality communication environment. [Effects of the Invention]

[0007] The system according to this embodiment can provide high-quality content in real time by utilizing the user's location information and environmental data. [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, and the like. 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 system according to an embodiment of the present invention is a system that combines XR and generative AI to provide killer content for expanding private network sales opportunities. In this system, when a user wears XR glasses and participates in a virtual event, the generative AI analyzes on-site information in real time and generates mixed reality content in response. The generated content is displayed according to the user's vision. This allows the user to enjoy a new experience where the real and virtual worlds merge. Furthermore, since a high-quality communication environment is required for content delivery, a dedicated private network is provided to the store. This ensures a stable communication environment, allowing users to comfortably enjoy virtual events. For example, in a virtual event held within a store, as the user walks around wearing XR glasses, the generative AI analyzes surrounding information in real time and generates appropriate content. This allows the user to experience something as if they were participating in a real event. The generative AI can also customize content according to the user's preferences. For example, if a user likes a particular character, the system can be set to frequently feature that character. In this way, by providing killer content combining XR and generative AI, private network sales opportunities can be expanded. Users can enjoy a new experience, and stores can improve customer satisfaction by providing a high-quality communication environment. This allows the system to provide real-time virtual events by collecting and analyzing user location and environmental data, and displaying generated content.

[0029] The system according to the embodiment comprises a data collection unit, an analysis unit, a data generation unit, a display unit, and a communication unit. The data collection unit collects the user's location information and environmental data. The data collection unit can collect data such as GPS data, temperature, humidity, and noise level. The data collection unit can also collect this data in real time using AI. The analysis unit analyzes the data collected by the data collection unit. The analysis unit can, for example, understand the user's current situation based on the collected data and provide information for generating optimal content. The analysis unit can also analyze the collected data using AI. The data generation unit generates content based on the information obtained by the analysis unit. The data generation unit can also customize content according to the user's preferences using generation AI. For example, if the user likes a particular character, the data generation unit will generate content so that that character appears frequently. The display unit displays the content generated by the data generation unit. The display unit can display content according to the user's vision. The display unit can also display the generated content in an optimal form using AI. The communication unit provides a high-quality communication environment. The communications department provides an optimal communication environment based on criteria such as communication speed, latency, and connection stability. The communications department can also use AI to monitor the state of the communication environment and make adjustments as needed. This allows the system to collect and analyze user location and environmental data, and display generated content to provide real-time virtual events.

[0030] The data collection unit collects user location information and environmental data. Specifically, it uses GPS data to determine the user's precise location and temperature and humidity sensors to collect ambient environmental data. It is also equipped with a microphone to measure noise levels, allowing for real-time monitoring of the user's surrounding sound environment. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is sent to a cloud server for access by the analysis and generation units. Furthermore, the data collection unit uses AI to optimize the data collection process and collect data in real time. The AI ​​dynamically adjusts the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. For example, if the user is moving, the frequency of location information updates is increased, and if the user is stationary, the update frequency is decreased, resulting in efficient data collection. This allows the data collection unit to collect a wide range of data from various devices and understand the situation in real time.

[0031] The analysis unit analyzes the data collected by the data collection unit. Specifically, the analysis unit identifies the user's current location based on collected GPS data and analyzes environmental data such as temperature, humidity, and noise levels to understand the user's surroundings. The analysis unit uses AI to analyze this data in real time, enabling a detailed understanding of the user's current situation. For example, the AI ​​can use machine learning algorithms to extract patterns and trends from the collected data and predict user behavior and environmental changes. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term trend analysis and risk assessment. For example, based on historical environmental data, it can predict environmental fluctuations in specific areas and time periods and formulate future countermeasures. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing warnings early. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The generation unit generates content based on information obtained by the analysis unit. Specifically, the generation unit uses a generation AI to generate customized content tailored to the user's preferences and current situation. For example, if a user likes a particular character, the generation unit can generate content that frequently features that character. The generation AI utilizes natural language processing and image generation technologies to create stories and visuals that match the user's preferences. Furthermore, the generation unit can collect user feedback and continuously improve the quality of the generated content. For example, based on user feedback, the generation AI adjusts the content and presentation to provide more satisfying content. In addition, the generation unit can dynamically generate content based on real-time updated data and provide information relevant to the user's situation. This allows the generation unit to always provide users with fresh and engaging content, increasing user engagement.

[0033] The display unit displays content generated by the generation unit. Specifically, the display unit can display content in an optimal form to suit the user's vision. For example, if the user is using a smartphone, the display unit adjusts the content to match the smartphone's screen size and resolution for easy viewing. Furthermore, the display unit can use AI to customize the generated content according to the user's preferences. For example, if the user prefers a particular color or font, the display unit adjusts the content design accordingly. The display unit can also automatically adjust brightness and contrast to reduce the user's visual fatigue. This allows the display unit to provide the user with a comfortable and engaging visual experience. In addition, the display unit can dynamically display content based on real-time updated data, providing information relevant to the user's situation. This allows the display unit to always provide the user with the latest information and increase user engagement.

[0034] The communications department provides a high-quality communication environment. Specifically, it provides the optimal communication environment based on criteria such as communication speed, latency, and connection stability. The communications department uses AI to monitor the state of the communication environment in real time and make adjustments as needed. For example, if the communication speed decreases, the communications department automatically selects the optimal communication channel and restores the communication speed. Also, if latency increases, the communications department adjusts data prioritization and sends important data first to minimize latency. Furthermore, the communications department can use multiple communication methods in combination to ensure connection stability. For example, it can use Wi-Fi and mobile data communication simultaneously, and if one communication method becomes unstable, it will continue communication using the other method. In this way, the communications department can always provide users with a high-quality communication environment and smoothly realize real-time virtual events. In addition, the communications department can improve the overall system performance by continuously learning the state of the communication environment and automatically applying the optimal communication settings.

[0035] The generation unit includes a customization unit that performs customization according to user preferences. For example, if a user likes a particular character, the generation unit will generate content so that that character appears frequently. Using generation AI, the generation unit can identify user preferences based on the user's past selection history and survey results, and perform customization accordingly. For example, the generation unit can customize content based on characters and themes that the user has selected in the past. Furthermore, the generation unit can dynamically customize content based on the user's real-time reactions. For example, if a user shows a strong reaction to a particular character, the frequency of that character's appearance can be increased. This allows the generation unit to customize content according to user preferences.

[0036] The communications unit includes a monitoring unit that monitors the state of the communications environment and makes adjustments as needed. The communications unit evaluates the state of the communications environment based on criteria such as communications speed, latency, and connection stability. The communications unit can use AI to monitor the state of the communications environment in real time and make adjustments as needed. For example, if the communications speed decreases, the communications unit can adjust the bandwidth to improve the communications speed. Also, if the connection stability decreases, the communications unit can change the connection destination to provide a stable communications environment. In this way, the communications unit can provide a stable communications environment by monitoring the state of the communications environment and making adjustments as needed.

[0037] The data collection unit analyzes the user's past behavior history and selects the optimal data collection timing. For example, if the user was active during a specific time period in the past, the data collection unit will concentrate data collection during that time period. The data collection unit can use AI to analyze the user's past behavior history and select the optimal data collection timing. For example, if the user was frequently active in a specific location in the past, the data collection unit will prioritize data collection at that location. The data collection unit can also analyze the user's past behavior patterns and select the most efficient data collection timing. As a result, the data collection unit can efficiently collect data by selecting the optimal data collection timing based on the user's past behavior history.

[0038] The data collection unit filters data based on the user's current activity level during collection. For example, if the user is walking, the unit filters the data to be collected according to their movement speed. The data collection unit can use AI to filter data based on the user's current activity level. For example, if the user is sitting, the unit collects detailed environmental data. Furthermore, if the user is exercising, the unit can prioritize collecting data related to exercise. In this way, the data collection unit can efficiently collect the necessary data by filtering it based on the user's current activity level.

[0039] The data collection unit prioritizes collecting highly relevant data, taking into account the user's geographical location. For example, if the user is in a tourist area, the unit prioritizes collecting tourist information related to that location. The data collection unit can use AI to prioritize collecting highly relevant data, taking into account the user's geographical location. For example, if the user is in a commercial facility, the unit prioritizes collecting store information and sales information. Furthermore, if the user is using public transportation, the unit can prioritize collecting traffic information and service status. In this way, the data collection unit can obtain more useful data by prioritizing the collection of highly relevant data, taking into account the user's geographical location.

[0040] The data collection unit analyzes users' social media activity and collects relevant data during the collection process. For example, if a user posts about a specific event, the unit collects data related to that event. The data collection unit can also use AI to analyze users' social media activity and collect relevant data. For example, if a user checks in to a specific location, the unit collects data related to that location. Furthermore, if a user uses a specific hashtag, the unit can collect data related to that hashtag. This allows the data collection unit to efficiently collect relevant data by analyzing users' social media activity.

[0041] The analysis unit adjusts the level of detail of the analysis based on the importance of the collected data during the analysis. For example, the analysis unit performs a detailed analysis on high-importance data to provide highly accurate information. The analysis unit can use AI to adjust the level of detail of the analysis based on the importance of the collected data. For example, the analysis unit performs a simplified analysis on low-importance data to provide information efficiently. Furthermore, the analysis unit can allocate analysis resources according to the importance of the data to provide optimal analysis results. In this way, the analysis unit can perform efficient analysis by adjusting the level of detail of the analysis based on the importance of the collected data.

[0042] The analysis unit applies different analysis methods depending on the data category during analysis. For example, it applies image analysis methods to image data to extract detailed information. The analysis unit can also use AI to apply different analysis methods depending on the data category. For example, it applies natural language processing methods to text data to extract meaningful information. Furthermore, it can apply speech analysis methods to speech data to perform speech recognition and sentiment analysis. As a result, the analysis unit can provide more accurate analysis results by applying different analysis methods depending on the data category.

[0043] The analysis unit prioritizes analysis based on the data submission timing. For example, it prioritizes the analysis of the latest data and provides information in real time. The analysis unit can use AI to determine analysis priorities based on data submission timing. For example, it can postpone the analysis of older data, using resources efficiently. The analysis unit can also adjust the analysis schedule according to the data submission timing and provide information at the optimal time. As a result, the analysis unit can perform efficient analysis by prioritizing analysis based on data submission timing.

[0044] The analysis unit adjusts the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit prioritizes the analysis of highly relevant data, providing important information quickly. The analysis unit can use AI to adjust the order of analysis based on the relevance of the data. For example, the analysis unit postpones the analysis of less relevant data, using resources efficiently. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of the data to provide optimal analysis results. As a result, the analysis unit can quickly provide important information by adjusting the order of analysis based on the relevance of the data.

[0045] The generation unit adjusts the level of detail in the generation process based on the importance of the content. For example, the generation unit performs detailed generation for high-importance content to provide highly accurate information. The generation unit can use generation AI to adjust the level of detail in the generation process based on the importance of the content. For example, the generation unit performs simplified generation for low-importance content to provide information efficiently. Furthermore, the generation unit can allocate generation resources according to the importance of the content to provide optimal generation results. As a result, the generation unit can efficiently generate content by adjusting the level of detail in the generation process based on the importance of the content.

[0046] The generation unit applies different generation algorithms depending on the content category during generation. For example, for image content, the generation unit applies an image generation algorithm to generate detailed images. The generation unit can use generation AI to apply different generation algorithms depending on the content category. For example, for text content, the generation unit applies a text generation algorithm to generate meaningful text. Furthermore, the generation unit can apply a speech generation algorithm to audio content to generate natural-sounding audio. In this way, the generation unit can provide more accurate content by applying different generation algorithms depending on the content category.

[0047] The generation unit determines the generation priority based on the content submission date during the generation process. For example, the generation unit prioritizes the generation of the latest content, providing information in real time. The generation unit can use generation AI to determine the generation priority based on the content submission date. For example, the generation unit postpones the generation of older content, using resources efficiently. The generation unit can also adjust the generation schedule according to the content submission date, providing information at the optimal time. As a result, the generation unit can efficiently generate content by determining the generation priority based on the content submission date.

[0048] The generation unit adjusts the generation order based on the relevance of the content during generation. For example, the generation unit prioritizes the generation of highly relevant content, providing important information quickly. The generation unit can use generation AI to adjust the generation order based on the relevance of the content. For example, the generation unit postpones the generation of less relevant content, using resources efficiently. Furthermore, the generation unit can adjust the generation order according to the relevance of the content to provide optimal generation results. As a result, the generation unit can provide important information quickly by adjusting the generation order based on the relevance of the content.

[0049] The display unit selects the optimal display method by referring to the user's past visual history when displaying information. For example, the display unit prioritizes providing display methods that the user has previously preferred. The display unit can use AI to select the optimal display method by referring to the user's past visual history. For example, the display unit selects the display method with the highest visibility from the user's past visual history. The display unit can also analyze the user's past visual history and suggest the optimal display method. In this way, the display unit can provide the optimal display method by referring to the user's past visual history.

[0050] The display unit selects the optimal display method when displaying information, taking into account the user's device information. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. The display unit can use AI to select the optimal display method by considering the user's device information. For example, if the user is using a tablet, the display unit provides a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the display unit can provide a concise and highly visible display method. In this way, the display unit can provide the optimal display method by taking into account the user's device information.

[0051] The communications unit selects the optimal communication method by referring to past communication data during communication. For example, the communications unit prioritizes providing communication methods previously used by the user. The communications unit can use AI to select the optimal communication method by referring to past communication data. For example, the communications unit can select the most efficient communication method from the user's past communication data. Furthermore, the communications unit can analyze the user's past communication data and propose the optimal communication method. In this way, the communications unit can provide the optimal communication method by referring to past communication data.

[0052] The communications unit selects the optimal communication method by considering the user's geographical location during communication. For example, if the user is in an urban area, the communications unit provides a high-speed communication environment. The communications unit can use AI to select the optimal communication method by considering the user's geographical location. For example, if the user is in a suburban area, the communications unit provides a stable communication environment. Furthermore, if the user is on the move, the communications unit can minimize communication interruptions. In this way, the communications unit can provide the optimal communication method by considering the user's geographical location.

[0053] The customization unit selects the optimal customization method by referring to the user's past preference history during the customization process. For example, the customization unit prioritizes providing customization methods that the user has previously preferred. The customization unit can use generative AI to select the optimal customization method by referring to the user's past preference history. For example, the customization unit selects the most suitable customization method from the user's past preference history. Furthermore, the customization unit can analyze the user's past preference history and propose the optimal customization method. In this way, the customization unit can provide the optimal customization method by referring to the user's past preference history.

[0054] The customization unit selects the optimal customization method by considering the user's geographical location information during the customization process. For example, if the user is in an urban area, the customization unit will provide a customization suitable for urban areas. The customization unit can use generative AI to select the optimal customization method by considering the user's geographical location information. For example, if the user is in a suburban area, the customization unit will provide a customization suitable for suburban areas. Furthermore, if the user is on the move, the customization unit can provide a customization suitable for travel. In this way, the customization unit can provide the optimal customization method by considering the user's geographical location information.

[0055] The monitoring unit selects the optimal monitoring method by referring to past communication environment data during monitoring. For example, the monitoring unit selects the optimal monitoring method based on past communication environment data. The monitoring unit can also use AI to select the optimal monitoring method by referring to past communication environment data. For example, the monitoring unit selects the most efficient monitoring method from past communication environment data. Furthermore, the monitoring unit can analyze past communication environment data and propose the optimal monitoring method. Thus, the monitoring unit can provide the optimal monitoring method by referring to past communication environment data.

[0056] The monitoring unit selects the optimal monitoring method by considering the user's geographical location information during monitoring. For example, if the user is in an urban area, the monitoring unit provides monitoring suitable for urban areas. The monitoring unit can use AI to select the optimal monitoring method by considering the user's geographical location information. For example, if the user is in a suburban area, the monitoring unit provides monitoring suitable for suburban areas. Furthermore, if the user is on the move, the monitoring unit can provide monitoring suitable for movement. In this way, the monitoring unit can provide the optimal monitoring method by considering the user's geographical location information.

[0057] The monitoring unit monitors the user's current communication environment in real time during monitoring and makes adjustments as needed. For example, if the user senses a change in their communication environment, the monitoring unit will monitor it in real time and provide the optimal communication environment. The monitoring unit can use AI to monitor the user's current communication environment in real time and make adjustments as needed. For example, if the user reports a problem with their communication environment, the monitoring unit will monitor it in real time and resolve the problem. Furthermore, if the user requests an improvement in their communication environment, the monitoring unit can monitor it in real time and provide the optimal communication environment. In this way, the monitoring unit can provide the optimal communication environment by monitoring the user's current communication environment in real time.

[0058] The monitoring unit analyzes user communication data during monitoring and selects the optimal monitoring method. For example, the monitoring unit selects the optimal monitoring method based on user communication data. The monitoring unit can also use AI to analyze user communication data and select the optimal monitoring method. For example, the monitoring unit selects the most efficient monitoring method from user communication data. Furthermore, the monitoring unit can analyze user communication data and propose the optimal monitoring method. Thus, the monitoring unit can provide the optimal monitoring method by analyzing user communication data.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The data collection unit can analyze the user's past behavior history and select the optimal data collection timing. For example, if the user was active during a specific time period in the past, data collection can be concentrated during that time period. Also, if the user was frequently active in a specific location in the past, data collection at that location can be prioritized. Furthermore, by analyzing the user's past behavior patterns, the unit can select the most efficient data collection timing. As a result, the data collection unit can efficiently collect data by selecting the optimal data collection timing based on the user's past behavior history.

[0061] The communications unit can select the optimal communication method by considering the user's geographical location during communication. For example, if the user is in an urban area, it can provide a high-speed communication environment. If the user is in a suburban area, it can provide a stable communication environment. Furthermore, if the user is on the move, it can minimize communication interruptions. In this way, the communications unit can provide the optimal communication method by considering the user's geographical location.

[0062] The data collection unit can filter data based on the user's current activity level during collection. For example, if the user is walking, the data collected can be filtered according to their movement speed. If the user is sitting, detailed environmental data can be collected. Furthermore, if the user is exercising, data related to exercise can be prioritized for collection. In this way, the data collection unit can efficiently collect the necessary data by filtering it based on the user's current activity level.

[0063] The analysis unit can apply different analysis methods depending on the data category during analysis. For example, image analysis methods can be applied to image data to extract detailed information. Natural language processing methods can be applied to text data to extract meaningful information. Furthermore, speech analysis methods can be applied to speech data to perform speech recognition and sentiment analysis. As a result, the analysis unit can provide more accurate analysis results by applying different analysis methods depending on the data category.

[0064] The generation unit can apply different generation algorithms depending on the content category during generation. For example, it can apply an image generation algorithm to image content to generate detailed images, a text generation algorithm to text content to generate meaningful text, and a speech generation algorithm to audio content to generate natural-sounding audio. This allows the generation unit to provide more accurate content by applying different generation algorithms depending on the content category.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The data collection unit collects the user's location information and environmental data. The data collection unit can collect data such as GPS data, temperature, humidity, and noise levels. The data collection unit can also use AI to collect this data in real time. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses the collected data to understand the user's current situation and provides information to generate optimal content. The analysis unit can also use AI to analyze the collected data. Step 3: The generation unit generates content based on the information obtained by the analysis unit. The generation unit can also customize content according to user preferences using generation AI. For example, if a user likes a particular character, the generation unit will generate content that frequently features that character. Step 4: The display unit displays the content generated by the generation unit. The display unit can display the content according to the user's vision. The display unit can also use AI to display the generated content in the most optimal form. Step 5: The communications department provides a high-quality communication environment. The communications department provides the optimal communication environment based on criteria such as communication speed, latency, and connection stability. The communications department can also use AI to monitor the state of the communication environment and make adjustments as needed.

[0067] (Example of form 2) The system according to an embodiment of the present invention is a system that combines XR and generative AI to provide killer content for expanding private network sales opportunities. In this system, when a user wears XR glasses and participates in a virtual event, the generative AI analyzes on-site information in real time and generates mixed reality content in response. The generated content is displayed according to the user's vision. This allows the user to enjoy a new experience where the real and virtual worlds merge. Furthermore, since a high-quality communication environment is required for content delivery, a dedicated private network is provided to the store. This ensures a stable communication environment, allowing users to comfortably enjoy virtual events. For example, in a virtual event held within a store, as the user walks around wearing XR glasses, the generative AI analyzes surrounding information in real time and generates appropriate content. This allows the user to experience something as if they were participating in a real event. The generative AI can also customize content according to the user's preferences. For example, if a user likes a particular character, the system can be set to frequently feature that character. In this way, by providing killer content combining XR and generative AI, private network sales opportunities can be expanded. Users can enjoy a new experience, and stores can improve customer satisfaction by providing a high-quality communication environment. This allows the system to provide real-time virtual events by collecting and analyzing user location and environmental data, and displaying generated content.

[0068] The system according to the embodiment comprises a data collection unit, an analysis unit, a data generation unit, a display unit, and a communication unit. The data collection unit collects the user's location information and environmental data. The data collection unit can collect data such as GPS data, temperature, humidity, and noise level. The data collection unit can also collect this data in real time using AI. The analysis unit analyzes the data collected by the data collection unit. The analysis unit can, for example, understand the user's current situation based on the collected data and provide information for generating optimal content. The analysis unit can also analyze the collected data using AI. The data generation unit generates content based on the information obtained by the analysis unit. The data generation unit can also customize content according to the user's preferences using generation AI. For example, if the user likes a particular character, the data generation unit will generate content so that that character appears frequently. The display unit displays the content generated by the data generation unit. The display unit can display content according to the user's vision. The display unit can also display the generated content in an optimal form using AI. The communication unit provides a high-quality communication environment. The communications department provides an optimal communication environment based on criteria such as communication speed, latency, and connection stability. The communications department can also use AI to monitor the state of the communication environment and make adjustments as needed. This allows the system to collect and analyze user location and environmental data, and display generated content to provide real-time virtual events.

[0069] The data collection unit collects user location information and environmental data. Specifically, it uses GPS data to determine the user's precise location and temperature and humidity sensors to collect ambient environmental data. It is also equipped with a microphone to measure noise levels, allowing for real-time monitoring of the user's surrounding sound environment. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is sent to a cloud server for access by the analysis and generation units. Furthermore, the data collection unit uses AI to optimize the data collection process and collect data in real time. The AI ​​dynamically adjusts the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. For example, if the user is moving, the frequency of location information updates is increased, and if the user is stationary, the update frequency is decreased, resulting in efficient data collection. This allows the data collection unit to collect a wide range of data from various devices and understand the situation in real time.

[0070] The analysis unit analyzes the data collected by the data collection unit. Specifically, the analysis unit identifies the user's current location based on collected GPS data and analyzes environmental data such as temperature, humidity, and noise levels to understand the user's surroundings. The analysis unit uses AI to analyze this data in real time, enabling a detailed understanding of the user's current situation. For example, the AI ​​can use machine learning algorithms to extract patterns and trends from the collected data and predict user behavior and environmental changes. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term trend analysis and risk assessment. For example, based on historical environmental data, it can predict environmental fluctuations in specific areas and time periods and formulate future countermeasures. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing warnings early. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0071] The generation unit generates content based on information obtained by the analysis unit. Specifically, the generation unit uses a generation AI to generate customized content tailored to the user's preferences and current situation. For example, if a user likes a particular character, the generation unit can generate content that frequently features that character. The generation AI utilizes natural language processing and image generation technologies to create stories and visuals that match the user's preferences. Furthermore, the generation unit can collect user feedback and continuously improve the quality of the generated content. For example, based on user feedback, the generation AI adjusts the content and presentation to provide more satisfying content. In addition, the generation unit can dynamically generate content based on real-time updated data and provide information relevant to the user's situation. This allows the generation unit to always provide users with fresh and engaging content, increasing user engagement.

[0072] The display unit displays content generated by the generation unit. Specifically, the display unit can display content in an optimal form to suit the user's vision. For example, if the user is using a smartphone, the display unit adjusts the content to match the smartphone's screen size and resolution for easy viewing. Furthermore, the display unit can use AI to customize the generated content according to the user's preferences. For example, if the user prefers a particular color or font, the display unit adjusts the content design accordingly. The display unit can also automatically adjust brightness and contrast to reduce the user's visual fatigue. This allows the display unit to provide the user with a comfortable and engaging visual experience. In addition, the display unit can dynamically display content based on real-time updated data, providing information relevant to the user's situation. This allows the display unit to always provide the user with the latest information and increase user engagement.

[0073] The communications department provides a high-quality communication environment. Specifically, it provides the optimal communication environment based on criteria such as communication speed, latency, and connection stability. The communications department uses AI to monitor the state of the communication environment in real time and make adjustments as needed. For example, if the communication speed decreases, the communications department automatically selects the optimal communication channel and restores the communication speed. Also, if latency increases, the communications department adjusts data prioritization and sends important data first to minimize latency. Furthermore, the communications department can use multiple communication methods in combination to ensure connection stability. For example, it can use Wi-Fi and mobile data communication simultaneously, and if one communication method becomes unstable, it will continue communication using the other method. In this way, the communications department can always provide users with a high-quality communication environment and smoothly realize real-time virtual events. In addition, the communications department can improve the overall system performance by continuously learning the state of the communication environment and automatically applying the optimal communication settings.

[0074] The generation unit includes a customization unit that performs customization according to user preferences. For example, if a user likes a particular character, the generation unit will generate content so that that character appears frequently. Using generation AI, the generation unit can identify user preferences based on the user's past selection history and survey results, and perform customization accordingly. For example, the generation unit can customize content based on characters and themes that the user has selected in the past. Furthermore, the generation unit can dynamically customize content based on the user's real-time reactions. For example, if a user shows a strong reaction to a particular character, the frequency of that character's appearance can be increased. This allows the generation unit to customize content according to user preferences.

[0075] The communications unit includes a monitoring unit that monitors the state of the communications environment and makes adjustments as needed. The communications unit evaluates the state of the communications environment based on criteria such as communications speed, latency, and connection stability. The communications unit can use AI to monitor the state of the communications environment in real time and make adjustments as needed. For example, if the communications speed decreases, the communications unit can adjust the bandwidth to improve the communications speed. Also, if the connection stability decreases, the communications unit can change the connection destination to provide a stable communications environment. In this way, the communications unit can provide a stable communications environment by monitoring the state of the communications environment and making adjustments as needed.

[0076] The data collection unit estimates the user's emotions and adjusts the frequency of collecting location and environmental data based on the estimated emotions. The data collection unit estimates the user's emotions using technologies such as facial recognition, voice analysis, and text analysis. The data collection unit is implemented using emotion estimation functions with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, if the user is excited, the data collection unit increases the collection frequency to acquire detailed environmental data. Also, if the user is relaxed, the data collection unit can lower the collection frequency to acquire only the minimum necessary data. Furthermore, if the user is stressed, the data collection unit can adjust the collection frequency to a moderate level to avoid excessive data collection. In this way, the data collection unit can collect more appropriate data by adjusting the collection frequency according to the user's emotions.

[0077] The data collection unit analyzes the user's past behavior history and selects the optimal data collection timing. For example, if the user was active during a specific time period in the past, the data collection unit will concentrate data collection during that time period. The data collection unit can use AI to analyze the user's past behavior history and select the optimal data collection timing. For example, if the user was frequently active in a specific location in the past, the data collection unit will prioritize data collection at that location. The data collection unit can also analyze the user's past behavior patterns and select the most efficient data collection timing. As a result, the data collection unit can efficiently collect data by selecting the optimal data collection timing based on the user's past behavior history.

[0078] The data collection unit filters data based on the user's current activity level during collection. For example, if the user is walking, the unit filters the data to be collected according to their movement speed. The data collection unit can use AI to filter data based on the user's current activity level. For example, if the user is sitting, the unit collects detailed environmental data. Furthermore, if the user is exercising, the unit can prioritize collecting data related to exercise. In this way, the data collection unit can efficiently collect the necessary data by filtering it based on the user's current activity level.

[0079] The data collection unit estimates the user's emotions and determines the priority of data to collect based on the estimated emotions. The data collection unit estimates the user's emotions using technologies such as facial recognition, speech analysis, and text analysis. The data collection unit is implemented using emotion estimation functions with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, if the user is excited, the data collection unit will prioritize collecting visually stimulating data. If the user is relaxed, the data collection unit can prioritize collecting calming environmental data. Furthermore, if the user is stressed, the data collection unit can prioritize collecting data that helps reduce stress. In this way, the data collection unit can collect more appropriate data by determining the priority of data to collect according to the user's emotions.

[0080] The data collection unit prioritizes collecting highly relevant data, taking into account the user's geographical location. For example, if the user is in a tourist area, the unit prioritizes collecting tourist information related to that location. The data collection unit can use AI to prioritize collecting highly relevant data, taking into account the user's geographical location. For example, if the user is in a commercial facility, the unit prioritizes collecting store information and sales information. Furthermore, if the user is using public transportation, the unit can prioritize collecting traffic information and service status. In this way, the data collection unit can obtain more useful data by prioritizing the collection of highly relevant data, taking into account the user's geographical location.

[0081] The data collection unit analyzes users' social media activity and collects relevant data during the collection process. For example, if a user posts about a specific event, the unit collects data related to that event. The data collection unit can also use AI to analyze users' social media activity and collect relevant data. For example, if a user checks in to a specific location, the unit collects data related to that location. Furthermore, if a user uses a specific hashtag, the unit can collect data related to that hashtag. This allows the data collection unit to efficiently collect relevant data by analyzing users' social media activity.

[0082] The analysis unit estimates the user's emotions and adjusts the analysis algorithm based on the estimated emotions. The analysis unit estimates the user's emotions using technologies such as facial recognition, voice analysis, and text analysis. The analysis unit is implemented using emotion estimation functions with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, if the user is excited, the analysis unit performs a detailed analysis and generates visually stimulating content. If the user is relaxed, the analysis unit can perform a simplified analysis and generate calming content. Furthermore, if the user is stressed, the analysis unit can perform an analysis that helps reduce stress and generate relaxing content. In this way, the analysis unit can provide more appropriate analysis results by adjusting the analysis algorithm according to the user's emotions.

[0083] The analysis unit adjusts the level of detail of the analysis based on the importance of the collected data during the analysis. For example, the analysis unit performs a detailed analysis on high-importance data to provide highly accurate information. The analysis unit can use AI to adjust the level of detail of the analysis based on the importance of the collected data. For example, the analysis unit performs a simplified analysis on low-importance data to provide information efficiently. Furthermore, the analysis unit can allocate analysis resources according to the importance of the data to provide optimal analysis results. In this way, the analysis unit can perform efficient analysis by adjusting the level of detail of the analysis based on the importance of the collected data.

[0084] The analysis unit applies different analysis methods depending on the data category during analysis. For example, it applies image analysis methods to image data to extract detailed information. The analysis unit can also use AI to apply different analysis methods depending on the data category. For example, it applies natural language processing methods to text data to extract meaningful information. Furthermore, it can apply speech analysis methods to speech data to perform speech recognition and sentiment analysis. As a result, the analysis unit can provide more accurate analysis results by applying different analysis methods depending on the data category.

[0085] The analysis unit estimates the user's emotions and adjusts the display method of the analysis results based on the estimated emotions. The analysis unit estimates the user's emotions using technologies such as facial recognition, voice analysis, and text analysis. The analysis unit is implemented using emotion estimation functions with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, if the user is excited, the analysis unit can provide a visually stimulating display method. If the user is relaxed, the analysis unit can provide a calm display method. Furthermore, if the user is stressed, the analysis unit can provide a simple and highly visible display method. In this way, the analysis unit can provide more appropriate information by adjusting the display method of the analysis results according to the user's emotions.

[0086] The analysis unit prioritizes analysis based on the data submission timing. For example, it prioritizes the analysis of the latest data and provides information in real time. The analysis unit can use AI to determine analysis priorities based on data submission timing. For example, it can postpone the analysis of older data, using resources efficiently. The analysis unit can also adjust the analysis schedule according to the data submission timing and provide information at the optimal time. As a result, the analysis unit can perform efficient analysis by prioritizing analysis based on data submission timing.

[0087] The analysis unit adjusts the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit prioritizes the analysis of highly relevant data, providing important information quickly. The analysis unit can use AI to adjust the order of analysis based on the relevance of the data. For example, the analysis unit postpones the analysis of less relevant data, using resources efficiently. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of the data to provide optimal analysis results. As a result, the analysis unit can quickly provide important information by adjusting the order of analysis based on the relevance of the data.

[0088] The generation unit estimates the user's emotions and adjusts the way the generated content is presented based on the estimated emotions. The generation unit estimates the user's emotions using technologies such as facial recognition, speech analysis, and text analysis. The generation unit is implemented using emotion estimation functions with an emotion engine or generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, if the user is excited, the generation unit can generate visually stimulating content. Also, if the user is relaxed, the generation unit can generate calming content. Furthermore, if the user is stressed, the generation unit can generate content that helps reduce stress. In this way, the generation unit can provide more appropriate content by adjusting the way the content is presented according to the user's emotions.

[0089] The generation unit adjusts the level of detail in the generation process based on the importance of the content. For example, the generation unit performs detailed generation for high-importance content to provide highly accurate information. The generation unit can use generation AI to adjust the level of detail in the generation process based on the importance of the content. For example, the generation unit performs simplified generation for low-importance content to provide information efficiently. Furthermore, the generation unit can allocate generation resources according to the importance of the content to provide optimal generation results. As a result, the generation unit can efficiently generate content by adjusting the level of detail in the generation process based on the importance of the content.

[0090] The generation unit applies different generation algorithms depending on the content category during generation. For example, for image content, the generation unit applies an image generation algorithm to generate detailed images. The generation unit can use generation AI to apply different generation algorithms depending on the content category. For example, for text content, the generation unit applies a text generation algorithm to generate meaningful text. Furthermore, the generation unit can apply a speech generation algorithm to audio content to generate natural-sounding audio. In this way, the generation unit can provide more accurate content by applying different generation algorithms depending on the content category.

[0091] The generation unit estimates the user's emotions and adjusts the length of the generated content based on the estimated emotions. The generation unit estimates the user's emotions using technologies such as facial recognition, speech analysis, and text analysis. The generation unit is implemented using emotion estimation functions with an emotion engine or generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, if the user is in a hurry, the generation unit will generate short, concise content. If the user is relaxed, the generation unit can generate longer content with detailed explanations. Furthermore, if the user is excited, the generation unit can generate content with visually stimulating effects. In this way, the generation unit can provide more appropriate content by adjusting the length of the content according to the user's emotions.

[0092] The generation unit determines the generation priority based on the content submission date during the generation process. For example, the generation unit prioritizes the generation of the latest content, providing information in real time. The generation unit can use generation AI to determine the generation priority based on the content submission date. For example, the generation unit postpones the generation of older content, using resources efficiently. The generation unit can also adjust the generation schedule according to the content submission date, providing information at the optimal time. As a result, the generation unit can efficiently generate content by determining the generation priority based on the content submission date.

[0093] The generation unit adjusts the generation order based on the relevance of the content during generation. For example, the generation unit prioritizes the generation of highly relevant content, providing important information quickly. The generation unit can use generation AI to adjust the generation order based on the relevance of the content. For example, the generation unit postpones the generation of less relevant content, using resources efficiently. Furthermore, the generation unit can adjust the generation order according to the relevance of the content to provide optimal generation results. As a result, the generation unit can provide important information quickly by adjusting the generation order based on the relevance of the content.

[0094] The display unit estimates the user's emotions and adjusts the display method based on the estimated emotions. The display unit estimates the user's emotions using technologies such as facial recognition, voice analysis, and text analysis. The display unit is implemented using emotion estimation functionality with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, if the user is excited, the display unit can provide a visually stimulating display method. If the user is relaxed, the display unit can provide a calm display method. Furthermore, if the user is stressed, the display unit can provide a simple and highly visible display method. In this way, the display unit can provide more appropriate information by adjusting the display method according to the user's emotions.

[0095] The display unit selects the optimal display method by referring to the user's past visual history when displaying information. For example, the display unit prioritizes providing display methods that the user has previously preferred. The display unit can use AI to select the optimal display method by referring to the user's past visual history. For example, the display unit selects the display method with the highest visibility from the user's past visual history. The display unit can also analyze the user's past visual history and suggest the optimal display method. In this way, the display unit can provide the optimal display method by referring to the user's past visual history.

[0096] The display unit estimates the user's emotions and determines the display priority based on the estimated emotions. The display unit estimates the user's emotions using technologies such as facial recognition, voice analysis, and text analysis. The display unit is implemented using emotion estimation functionality with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, if the user is excited, the display unit will prioritize displaying visually stimulating information. Also, if the user is relaxed, the display unit can prioritize displaying calming information. Furthermore, if the user is stressed, the display unit can prioritize displaying information that helps reduce stress. In this way, the display unit can provide more appropriate information by determining the display priority according to the user's emotions.

[0097] The display unit selects the optimal display method when displaying information, taking into account the user's device information. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. The display unit can use AI to select the optimal display method by considering the user's device information. For example, if the user is using a tablet, the display unit provides a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the display unit can provide a concise and highly visible display method. In this way, the display unit can provide the optimal display method by taking into account the user's device information.

[0098] The communication unit estimates the user's emotions and adjusts the communication environment based on the estimated emotions. The communication unit estimates the user's emotions using technologies such as facial recognition, voice analysis, and text analysis. The communication unit is implemented using emotion estimation functions with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, the communication unit can provide a high-speed communication environment when the user is excited. It can also provide a stable communication environment when the user is relaxed. Furthermore, it can minimize communication delays when the user is stressed. In this way, the communication unit can provide a more appropriate communication environment by adjusting it according to the user's emotions.

[0099] The communications unit selects the optimal communication method by referring to past communication data during communication. For example, the communications unit prioritizes providing communication methods previously used by the user. The communications unit can use AI to select the optimal communication method by referring to past communication data. For example, the communications unit can select the most efficient communication method from the user's past communication data. Furthermore, the communications unit can analyze the user's past communication data and propose the optimal communication method. In this way, the communications unit can provide the optimal communication method by referring to past communication data.

[0100] The communications unit estimates the user's emotions and prioritizes communications based on those emotions. The communications unit estimates user emotions using technologies such as facial recognition, voice analysis, and text analysis. The communications unit is implemented using emotion estimation functions, 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. For example, if the user is excited, the communications unit prioritizes important communications. If the user is relaxed, it prioritizes stable communications. Furthermore, if the user is stressed, the communications unit can minimize communication delays. This allows the communications unit to provide a more appropriate communication environment by prioritizing communications according to the user's emotions.

[0101] The communications unit selects the optimal communication method by considering the user's geographical location during communication. For example, if the user is in an urban area, the communications unit provides a high-speed communication environment. The communications unit can use AI to select the optimal communication method by considering the user's geographical location. For example, if the user is in a suburban area, the communications unit provides a stable communication environment. Furthermore, if the user is on the move, the communications unit can minimize communication interruptions. In this way, the communications unit can provide the optimal communication method by considering the user's geographical location.

[0102] The customization unit estimates the user's emotions and adjusts the customization method based on the estimated emotions. The customization unit estimates the user's emotions using technologies such as facial recognition, voice analysis, and text analysis. The customization unit is implemented using emotion estimation functions with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, if the user is excited, the customization unit can provide visually stimulating customization. If the user is relaxed, the customization unit can provide calming customization. Furthermore, if the user is stressed, the customization unit can provide customization that helps reduce stress. In this way, the customization unit can provide more appropriate customization by adjusting the customization method according to the user's emotions.

[0103] The customization unit selects the optimal customization method by referring to the user's past preference history during the customization process. For example, the customization unit prioritizes providing customization methods that the user has previously preferred. The customization unit can use generative AI to select the optimal customization method by referring to the user's past preference history. For example, the customization unit selects the most suitable customization method from the user's past preference history. Furthermore, the customization unit can analyze the user's past preference history and propose the optimal customization method. In this way, the customization unit can provide the optimal customization method by referring to the user's past preference history.

[0104] The customization unit estimates the user's emotions and determines the priority of customizations based on those emotions. The customization unit estimates the user's emotions using technologies such as facial recognition, voice analysis, and text analysis. The customization unit is implemented using emotion estimation functions, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, if the user is excited, the customization unit may prioritize visually stimulating customizations. If the user is relaxed, the customization unit may prioritize calming customizations. Furthermore, if the user is stressed, the customization unit may prioritize customizations that help reduce stress. This allows the customization unit to provide more appropriate customizations by determining the priority of customizations according to the user's emotions.

[0105] The customization unit selects the optimal customization method by considering the user's geographical location information during the customization process. For example, if the user is in an urban area, the customization unit will provide a customization suitable for urban areas. The customization unit can use generative AI to select the optimal customization method by considering the user's geographical location information. For example, if the user is in a suburban area, the customization unit will provide a customization suitable for suburban areas. Furthermore, if the user is on the move, the customization unit can provide a customization suitable for travel. In this way, the customization unit can provide the optimal customization method by considering the user's geographical location information.

[0106] The monitoring unit estimates the user's emotions and adjusts its monitoring methods based on the estimated emotions. The monitoring unit estimates the user's emotions using technologies such as facial recognition, voice analysis, and text analysis. The monitoring unit is implemented using emotion estimation functions with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, if the user is excited, the monitoring unit can perform detailed monitoring and provide information in real time. If the user is relaxed, the monitoring unit can perform simplified monitoring and provide only the necessary minimum information. Furthermore, if the user is stressed, the monitoring unit can perform monitoring that helps reduce stress and provide information that helps them relax. In this way, the monitoring unit can perform more appropriate monitoring by adjusting its monitoring methods according to the user's emotions.

[0107] The monitoring unit selects the optimal monitoring method by referring to past communication environment data during monitoring. For example, the monitoring unit selects the optimal monitoring method based on past communication environment data. The monitoring unit can also use AI to select the optimal monitoring method by referring to past communication environment data. For example, the monitoring unit selects the most efficient monitoring method from past communication environment data. Furthermore, the monitoring unit can analyze past communication environment data and propose the optimal monitoring method. Thus, the monitoring unit can provide the optimal monitoring method by referring to past communication environment data.

[0108] The monitoring unit estimates the user's emotions and determines monitoring priorities based on the estimated emotions. The monitoring unit estimates the user's emotions using technologies such as facial recognition, voice analysis, and text analysis. The monitoring unit is implemented using emotion estimation functions with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, if the user is excited, the monitoring unit will prioritize important monitoring. Also, if the user is relaxed, the monitoring unit will prioritize stable monitoring. Furthermore, if the user is stressed, the monitoring unit can minimize monitoring delays. As a result, the monitoring unit can perform more appropriate monitoring by determining monitoring priorities according to the user's emotions.

[0109] The monitoring unit selects the optimal monitoring method by considering the user's geographical location information during monitoring. For example, if the user is in an urban area, the monitoring unit provides monitoring suitable for urban areas. The monitoring unit can use AI to select the optimal monitoring method by considering the user's geographical location information. For example, if the user is in a suburban area, the monitoring unit provides monitoring suitable for suburban areas. Furthermore, if the user is on the move, the monitoring unit can provide monitoring suitable for movement. In this way, the monitoring unit can provide the optimal monitoring method by considering the user's geographical location information.

[0110] The monitoring unit monitors the user's current communication environment in real time during monitoring and makes adjustments as needed. For example, if the user senses a change in their communication environment, the monitoring unit will monitor it in real time and provide the optimal communication environment. The monitoring unit can use AI to monitor the user's current communication environment in real time and make adjustments as needed. For example, if the user reports a problem with their communication environment, the monitoring unit will monitor it in real time and resolve the problem. Furthermore, if the user requests an improvement in their communication environment, the monitoring unit can monitor it in real time and provide the optimal communication environment. In this way, the monitoring unit can provide the optimal communication environment by monitoring the user's current communication environment in real time.

[0111] The monitoring unit analyzes user communication data during monitoring and selects the optimal monitoring method. For example, the monitoring unit selects the optimal monitoring method based on user communication data. The monitoring unit can also use AI to analyze user communication data and select the optimal monitoring method. For example, the monitoring unit selects the most efficient monitoring method from user communication data. Furthermore, the monitoring unit can analyze user communication data and propose the optimal monitoring method. Thus, the monitoring unit can provide the optimal monitoring method by analyzing user communication data.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on those emotions. For example, if the user is excited, it can perform a detailed analysis and generate visually stimulating content. If the user is relaxed, it can perform a simplified analysis and generate calming content. Furthermore, if the user is stressed, it can perform an analysis that helps reduce stress and generate relaxing content. In this way, the analysis unit can provide more appropriate analysis results by adjusting the analysis algorithm according to the user's emotions.

[0114] The data collection unit can analyze the user's past behavior history and select the optimal data collection timing. For example, if the user was active during a specific time period in the past, data collection can be concentrated during that time period. Also, if the user was frequently active in a specific location in the past, data collection at that location can be prioritized. Furthermore, by analyzing the user's past behavior patterns, the unit can select the most efficient data collection timing. As a result, the data collection unit can efficiently collect data by selecting the optimal data collection timing based on the user's past behavior history.

[0115] The generation unit can estimate the user's emotions and adjust the way the generated content is presented based on those emotions. For example, if the user is excited, it can generate visually stimulating content. If the user is relaxed, it can generate calming content. Furthermore, if the user is stressed, it can generate content that helps reduce stress. In this way, the generation unit can provide more appropriate content by adjusting the way the content is presented according to the user's emotions.

[0116] The communications unit can select the optimal communication method by considering the user's geographical location during communication. For example, if the user is in an urban area, it can provide a high-speed communication environment. If the user is in a suburban area, it can provide a stable communication environment. Furthermore, if the user is on the move, it can minimize communication interruptions. In this way, the communications unit can provide the optimal communication method by considering the user's geographical location.

[0117] The display unit can estimate the user's emotions and adjust the display method based on those emotions. For example, if the user is excited, it can provide a visually stimulating display method. If the user is relaxed, it can provide a calming display method. Furthermore, if the user is stressed, it can provide a simple and highly visible display method. In this way, the display unit can provide more appropriate information by adjusting the display method according to the user's emotions.

[0118] The data collection unit can filter data based on the user's current activity level during collection. For example, if the user is walking, the data collected can be filtered according to their movement speed. If the user is sitting, detailed environmental data can be collected. Furthermore, if the user is exercising, data related to exercise can be prioritized for collection. In this way, the data collection unit can efficiently collect the necessary data by filtering it based on the user's current activity level.

[0119] The analysis unit can apply different analysis methods depending on the data category during analysis. For example, image analysis methods can be applied to image data to extract detailed information. Natural language processing methods can be applied to text data to extract meaningful information. Furthermore, speech analysis methods can be applied to speech data to perform speech recognition and sentiment analysis. As a result, the analysis unit can provide more accurate analysis results by applying different analysis methods depending on the data category.

[0120] The generation unit can apply different generation algorithms depending on the content category during generation. For example, it can apply an image generation algorithm to image content to generate detailed images, a text generation algorithm to text content to generate meaningful text, and a speech generation algorithm to audio content to generate natural-sounding audio. This allows the generation unit to provide more accurate content by applying different generation algorithms depending on the content category.

[0121] The communications unit can estimate the user's emotions and adjust the communication environment based on those emotions. For example, if the user is excited, it can provide a high-speed communication environment. If the user is relaxed, it can provide a stable communication environment. Furthermore, if the user is stressed, it can minimize communication delays. In this way, the communications unit can provide a more appropriate communication environment by adjusting it according to the user's emotions.

[0122] The customization unit can estimate the user's emotions and adjust the customization method based on those emotions. For example, if the user is excited, it can provide visually stimulating customization. If the user is relaxed, it can provide calming customization. Furthermore, if the user is stressed, it can provide customization that helps reduce stress. In this way, the customization unit can provide more appropriate customization by adjusting the customization method according to the user's emotions.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The data collection unit collects the user's location information and environmental data. The data collection unit can collect data such as GPS data, temperature, humidity, and noise levels. The data collection unit can also use AI to collect this data in real time. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses the collected data to understand the user's current situation and provides information to generate optimal content. The analysis unit can also use AI to analyze the collected data. Step 3: The generation unit generates content based on the information obtained by the analysis unit. The generation unit can also customize content according to user preferences using generation AI. For example, if a user likes a particular character, the generation unit will generate content that frequently features that character. Step 4: The display unit displays the content generated by the generation unit. The display unit can display the content according to the user's vision. The display unit can also use AI to display the generated content in the most optimal form. Step 5: The communications department provides a high-quality communication environment. The communications department provides the optimal communication environment based on criteria such as communication speed, latency, and connection stability. The communications department can also use AI to monitor the state of the communication environment and make adjustments as needed.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, display unit, and communication unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects user location information and environmental data using the camera 42 and microphone 38B of the smart device 14. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12. The generation unit generates content using the generation AI via the specific processing unit 290 of the data processing unit 12. The display unit displays the generated content using the display 40A of the smart device 14. The communication unit provides a high-quality communication environment using the communication I / F 44 of the smart device 14 and the communication I / F 26 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] 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.

[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 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.

[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 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.

[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 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.

[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 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.

[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 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.

[0144] Each of the multiple elements described above, including the data collection unit, analysis unit, generation unit, display unit, and communication unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects user location information and environmental data using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12. The generation unit generates content using the generation AI via the specific processing unit 290 of the data processing unit 12. The display unit displays the generated content using the display of the smart glasses 214. The communication unit provides a high-quality communication environment using the communication I / F 44 of the smart glasses 214 and the communication I / F 26 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] 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.

[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 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.

[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 (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).

[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] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, display unit, and communication unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects user location information and environmental data using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12. The generation unit generates content using a generation AI via the specific processing unit 290 of the data processing unit 12. The display unit displays the generated content using the display 343 of the headset terminal 314. The communication unit provides a high-quality communication environment using the communication I / F 44 of the headset terminal 314 and the communication I / F 26 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, display unit, and communication unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit collects user location information and environmental data using the camera 42 and microphone 238 of the robot 414. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12. The generation unit generates content using the generation AI via the specific processing unit 290 of the data processing unit 12. The display unit displays the generated content using the display of the robot 414. The communication unit provides a high-quality communication environment using the communication I / F 44 of the robot 414 and the communication I / F 26 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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."

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] (Note 1) A collection unit that collects user location information and environmental data, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit that generates content based on the information obtained by the analysis unit, A display unit that displays the content generated by the generation unit, It includes a communications unit that provides a high-quality communication environment. A system characterized by the following features. (Note 2) The generating unit is It features a customization section that allows users to customize the system according to their preferences. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned communications unit is It includes a monitoring unit that monitors the status of the communication environment and makes adjustments as needed. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is The system estimates the user's emotions and adjusts the frequency of collecting location and environmental data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is Analyze the user's past behavior history to select the optimal timing for data collection. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is During data collection, the data is filtered based on the user's current activity status. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is During data collection, the system analyzes the user's social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the collected data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, different analytical methods are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the priority of analyses is determined based on the timing of data submission. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates user emotions and adjusts how generated content is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is During generation, adjust the level of detail based on the importance of the content. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is During generation, different generation algorithms are applied depending on the content category. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is It estimates the user's emotions and adjusts the length of the generated content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During generation, the generation priority is determined based on the submission date of the content. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is During generation, the generation order is adjusted based on the relevance of the content. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned display unit is It estimates the user's emotions and adjusts the display method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned display unit is When displaying content, the system selects the optimal display method by referring to the user's past visual history. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned display unit is It estimates the user's emotions and determines the display priority based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned display unit is When displaying content, the system selects the optimal display method by considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned communications unit is It estimates the user's emotions and adjusts the communication environment based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned communications unit is During communication, the system selects the optimal communication method by referring to past communication data. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned communications unit is It estimates the user's emotions and determines communication priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned communications unit is During communication, the system selects the optimal communication method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned customization unit is It estimates the user's emotions and adjusts the customization method based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned customization unit is During customization, the system selects the optimal customization method by referring to the user's past preference history. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned customization unit is It estimates the user's emotions and determines the priority of customization based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned customization unit is During customization, the optimal customization method is selected by considering the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned monitoring unit, It estimates user sentiment and adjusts monitoring methods based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned monitoring unit, During monitoring, the optimal monitoring method is selected by referring to past communication environment data. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned monitoring unit, It estimates user sentiment and determines monitoring priorities based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned monitoring unit, During monitoring, the optimal monitoring method is selected considering the user's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned monitoring unit, During monitoring, the system monitors the user's current communication environment in real time and makes adjustments as needed. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned monitoring unit, During monitoring, the system analyzes user communication data and selects the optimal monitoring method. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0197] 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. A collection unit that collects user location information and environmental data, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit that generates content based on the information obtained by the analysis unit, A display unit that displays the content generated by the generation unit, It includes a communications unit that provides a high-quality communication environment. A system characterized by the following features.

2. The generating unit is It features a customization section that allows users to customize the system according to their preferences. The system according to feature 1.

3. The aforementioned communications unit is It includes a monitoring unit that monitors the status of the communication environment and makes adjustments as needed. The system according to feature 1.

4. The aforementioned collection unit is The system estimates the user's emotions and adjusts the frequency of collecting location and environmental data based on those estimated emotions. The system according to feature 1.

5. The aforementioned collection unit is Analyze the user's past behavior history to select the optimal timing for data collection. The system according to feature 1.

6. The aforementioned collection unit is During data collection, the data is filtered based on the user's current activity status. The system according to feature 1.

7. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant data, taking into account the user's geographical location. The system according to feature 1.

9. The aforementioned collection unit is During data collection, the system analyzes the user's social media activity and collects relevant data. The system according to feature 1.

10. The aforementioned analysis unit, The system estimates the user's emotions and adjusts the analysis algorithm based on those estimated emotions. The system according to feature 1.

Images