system

Abstract

The system according to this embodiment aims to analyze information that comes into the user's field of vision and predict and provide information about future events. [Solution] The system according to the embodiment comprises an acquisition unit, an analysis unit, a prediction unit, a visualization unit, and a provision unit. The acquisition unit acquires information that comes into the user's field of view. The analysis unit analyzes the information acquired by the acquisition unit. The prediction unit predicts future events based on the information analyzed by the analysis unit. The visualization unit visualizes the future scenes predicted by the prediction unit. The provision unit provides the information visualized by the visualization unit to the user.

Description

Technical Field

[0003]

[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 as a 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, information that enters the user's field of vision has not been sufficiently analyzed in real time to predict future events, and there is room for improvement.

[0005] [[ID=​​​​​​​The system according to this embodiment comprises an acquisition unit, an analysis unit, a prediction unit, a visualization unit, and a provision unit. The acquisition unit acquires information that is within the user's field of vision. The analysis unit analyzes the information acquired by the acquisition unit. The prediction unit predicts future events based on the information analyzed by the analysis unit. The visualization unit visualizes the future scenes predicted by the prediction unit. The provision unit provides the information visualized by the visualization unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can analyze information that comes into the user's field of vision and predict and provide information about future events. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The smart glasses system according to an embodiment of the present invention is a system that analyzes information entering the user's field of view in real time, predicts events that may occur in the near future, and displays them as images and text. This smart glasses system acquires information entering the user's field of view using a camera, and a generating AI analyzes it in real time. Next, the generating AI comprehensively analyzes the visual information and environmental data to predict events that may occur in the near future. Furthermore, it uses an image generating AI to visualize the predicted future scene in real time. This service provides business people, students, the elderly, and various professionals with support for decision-making in daily life and business situations, safety assurance, and improved learning efficiency. For example, the smart glasses system can predict the flow of a meeting to optimize a presentation, or anticipate changes in traffic conditions to suggest the optimal route. As an innovative wearable device that extends human cognitive abilities and supports smarter judgment and action, the smart glasses system contributes to improving quality of life and productivity. Thus, the smart glasses system can analyze information entering the user's field of view in real time, predict and visualize future events, and provide them to the user.

[0029] The smart glasses system according to the embodiment comprises an acquisition unit, an analysis unit, a prediction unit, a visualization unit, and a provision unit. The acquisition unit acquires information that enters the user's field of view. The acquisition unit acquires information that enters the user's field of view, for example, using a camera. The camera includes, but is not limited to, a fixed camera or a wearable camera. The analysis unit analyzes the information acquired by the acquisition unit. The analysis unit comprehensively analyzes, for example, visual information and environmental data. Visual information includes, for example, image data and video data, and environmental data includes, but is not limited to, temperature data, humidity data, and location information. The prediction unit predicts future events based on the information analyzed by the analysis unit. The prediction unit predicts, for example, events that may occur in the immediate future. The immediate future includes, but is not limited to, a few seconds later, a few minutes later, or a few hours later. The visualization unit visualizes the future scene predicted by the prediction unit in real time. The visualization unit visualizes, for example, the predicted future scene in real time. Real-time includes, but is not limited to, an acceptable range of latency. The providing unit provides the user with the information visualized by the visualization unit. The providing unit provides the user with the visualized information, for example, as images or text. Images and text include, but are not limited to, JPEG, PNG, HTML, PDF, etc. As a result, the smart glasses system according to the embodiment can analyze information that enters the user's field of view in real time, predict and visualize future events, and provide them to the user.

[0030] The acquisition unit acquires information that comes into the user's field of view. For example, the acquisition unit uses a camera to acquire information that comes into the user's field of view. The camera includes, but is not limited to, fixed cameras and wearable cameras. Specifically, a wearable camera is built into the user's eyeglass frame and captures images in accordance with the user's line of sight. This allows for real-time acquisition of the scenery and objects the user is seeing. Fixed cameras monitor the user's surrounding environment over a wide area and play a role in supplementing the information that comes into their field of view. These cameras have high resolution and can capture even the smallest details clearly, enabling highly accurate analysis by the analysis unit. Furthermore, the acquisition unit can be equipped with microphones and environmental sensors in addition to cameras. Microphones acquire ambient sound information, and environmental sensors collect data such as temperature, humidity, and atmospheric pressure. This allows for the integrated acquisition of not only visual information but also sound and environmental data, enabling more multifaceted information collection. The acquisition unit transmits this data to a central database in real time, allowing the analysis and prediction units to access it quickly. This allows the data acquisition unit to collect information from multiple perspectives that comes into the user's field of view, thereby improving the overall performance of the system.

[0031] The analysis unit analyzes the information acquired by the acquisition unit. For example, the analysis unit comprehensively analyzes visual information and environmental data. Visual information includes image data and video data, and environmental data includes, but is not limited to, temperature data, humidity data, and location information. Specifically, the analysis unit uses AI to analyze image data and video data to perform object recognition and scene analysis. For example, it identifies objects that come into the user's field of vision and determines the type and location of those objects. Scene analysis allows the user to understand the situation and environment of their location. Furthermore, by analyzing environmental data, it can detect changes in ambient temperature and humidity and evaluate the user's comfort and safety. The analysis unit comprehensively analyzes this data to understand the user's current situation in real time. By using deep learning and machine learning technologies, high-precision analysis is possible. For example, image recognition technology using deep learning can identify objects and people from visual information with high precision, and environmental data analysis using machine learning can detect abnormal environmental changes at an early stage. This allows the analysis unit to quickly and accurately analyze acquired information and understand the user's situation in real time. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term trend analysis and risk assessment. As a result, the analysis unit can not only grasp the situation in real time but also respond to future risk management and countermeasure planning, improving the reliability and security of the entire system.

[0032] The prediction unit predicts future events based on information analyzed by the analysis unit. For example, the prediction unit predicts events that may occur in the immediate future. The immediate future includes, but is not limited to, a few seconds, a few minutes, or a few hours from now. Specifically, the prediction unit simulates future scenarios based on data analyzed using AI. For example, it can predict the possibility of an obstacle appearing ahead of a user while they are walking and suggest avoidance actions. It can also predict changes in weather and traffic conditions and prompt the user to take appropriate action. The prediction unit utilizes past data and statistical information to predict future events with high accuracy. For example, it can predict the probability of rainfall in a specific area based on past weather data and notify the user to carry an umbrella. It can also analyze traffic data to predict congestion and suggest alternative routes to the user. The prediction unit can update these prediction results in real time and respond to the latest situation. For example, if weather or traffic conditions change rapidly, the prediction unit immediately incorporates new data and corrects the prediction results. This allows the prediction unit to provide highly accurate predictions based on the latest information at all times, improving user safety and comfort. Furthermore, the prediction unit can learn user behavior patterns and preferences, enabling it to make individually optimized predictions. This allows the prediction unit to provide more personalized services to users and improve the user experience.

[0033] The visualization unit visualizes future scenes predicted by the prediction unit in real time. For example, the visualization unit visualizes predicted future scenes in real time. Real time includes, but is not limited to, a tolerance for delay time. Specifically, the visualization unit displays future scenes overlaid on the user's field of view, allowing the user to understand them intuitively. For example, if an obstacle appears in front of the user while they are walking, the unit visually displays the location of the obstacle and an avoidance route. It can also visualize changes in weather and traffic conditions, prompting the user to take appropriate action. The visualization unit uses AR (augmented reality) technology to overlay future scenes on the real field of view. This allows the user to visually confirm the real environment and the predicted future simultaneously. The visualization unit is equipped with a high-resolution display, enabling it to display clear images. Furthermore, the visualization unit can track the user's gaze and head movements and dynamically adjust the displayed content. This allows the user to enjoy a natural visual experience. In addition, the visualization unit can provide multi-sensory information to the user by combining it with feedback such as sound and vibration. For example, if a dangerous situation is anticipated, the system will attract the user's attention through visual warnings, audio alerts, and vibration notifications. This allows the visualization unit to provide intuitive and multi-sensory information to the user, improving user safety and comfort.

[0034] The information provider unit provides users with information visualized by the visualization unit. For example, the information provider unit provides visualized information to users in the form of images and text. These images and text include, but are not limited to, JPEG, PNG, HTML, and PDF. Specifically, the information provider unit displays information overlaid on the user's field of view, enabling intuitive understanding. For example, if an obstacle appears in front of the user while walking, the location of the obstacle and an avoidance route are visually displayed. It can also visualize changes in weather and traffic conditions, prompting the user to take appropriate action. The information provider unit uses AR (augmented reality) technology to overlay information onto the real-world field of view. This allows the user to visually confirm the real environment and the information simultaneously. The information provider unit is equipped with a high-resolution display, enabling the display of clear images. Furthermore, the information provider unit can track the user's gaze and head movements and dynamically adjust the displayed content. This allows the user to enjoy a natural visual experience. In addition, the information provider unit can provide users with multi-sensory information by combining feedback such as sound and vibration. For example, if a dangerous situation is anticipated, the system will attract the user's attention through visual warnings, as well as audio alerts and vibration notifications. This allows the system provider to deliver intuitive and multi-sensory information to the user, improving user safety and comfort.

[0035] The acquisition unit acquires information that enters the user's field of view using a camera. The acquisition unit acquires information that enters the user's field of view using a camera, for example. The camera includes, but is not limited to, fixed cameras and wearable cameras. This makes it possible to accurately acquire information that enters the user's field of view by using a camera. The camera includes, but is not limited to, fixed cameras and wearable cameras. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or not using AI. For example, the acquisition unit can input image data acquired by the camera into a generation AI and have the generation AI perform analysis of visual information from the image data.

[0036] The analysis unit can comprehensively analyze visual information and environmental data. For example, the analysis unit comprehensively analyzes visual information and environmental data. Visual information includes, but is not limited to, image data and video data, and environmental data includes, but is not limited to, temperature data, humidity data, and location information. By comprehensively analyzing visual information and environmental data, more accurate analysis results can be obtained. Visual information includes, but is not limited to, image data and video data. Environmental data includes, but is not limited to, temperature data, humidity data, and location information. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input visual information and environmental data into a generative AI and have the generative AI perform a comprehensive analysis.

[0037] The prediction unit can predict events that may occur in the near future. For example, the prediction unit predicts events that may occur in the near future. The near future includes, but is not limited to, events that may occur in a few seconds, a few minutes, or a few hours. By predicting events that may occur in the near future, it is possible to provide users with forward-looking information. The near future includes, but is not limited to, events that may occur in a few seconds, a few minutes, or a few hours. Some or all of the above processing in the prediction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the prediction unit can input information analyzed by the analysis unit into a generative AI and have the generative AI perform predictions of future events.

[0038] The visualization unit can visualize predicted future scenes in real time. For example, the visualization unit visualizes predicted future scenes in real time. Real time includes, but is not limited to, an acceptable range of delay time. By visualizing predicted future scenes in real time, information that is easy for the user to understand intuitively can be provided. Real time includes, but is not limited to, an acceptable range of delay time. Some or all of the above processing in the visualization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the visualization unit can input the future scenes predicted by the prediction unit into a generative AI and have the generative AI perform the visualization.

[0039] The service provider can provide visualized information to the user in the form of images and text. For example, the service provider can provide visualized information to the user in the form of images and text. Images and text include, but are not limited to, JPEG, PNG, HTML, and PDF. This makes it possible to convey information to the user in an easy-to-understand manner by providing visualized information in the form of images and text. Images and text include, but are not limited to, JPEG, PNG, HTML, and PDF. Some or all of the processing described above in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input visualized information into a generating AI and have the generating AI perform the generation of images and text.

[0040] The acquisition unit can analyze the user's past visual information and select the optimal acquisition method. For example, the acquisition unit can prioritize acquiring locations and objects that the user has frequently viewed in the past. The acquisition unit can also acquire information important for specific time periods from the user's past visual information. Furthermore, the acquisition unit can analyze the user's visual information and select the optimal camera angle. This allows for efficient information acquisition by selecting the optimal acquisition method through analysis of the user's past visual information. Past visual information includes, but is not limited to, the use of databases and the storage of log files. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's past visual information into a generating AI and have the generating AI select the optimal acquisition method.

[0041] The data acquisition unit can filter information based on the user's current activities and areas of interest when acquiring it. For example, if the user is in a meeting, the data acquisition unit will prioritize acquiring information related to the meeting. It can also prioritize acquiring traffic information if the user is traveling. Furthermore, if the user is studying, the data acquisition unit can prioritize acquiring information related to the learning content. This allows the system to prioritize acquiring information important to the user by filtering information based on their current activities and areas of interest. Current activities and areas of interest include, but are not limited to, the user's activity history and survey results. Some or all of the processing described above in the data acquisition unit may be performed using AI, or not. For example, the data acquisition unit can input the user's current activities and areas of interest into a generating AI and have the generating AI perform the information filtering.

[0042] The acquisition unit can prioritize the acquisition of highly relevant information by considering the user's geographical location when acquiring information. For example, if the user is in a specific region, the acquisition unit will prioritize the acquisition of information related to that region. Furthermore, if the user is traveling, the acquisition unit can prioritize the acquisition of tourist information and transportation information. Additionally, if the user is at home, the acquisition unit can prioritize the acquisition of information within the home. This allows for the prioritization of highly relevant information by considering the user's geographical location. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the processing described above in the acquisition unit may be performed using, for example, AI, or without AI. For example, the acquisition unit can input the user's geographical location information into a generating AI and have the generating AI acquire highly relevant information.

[0043] The acquisition unit can analyze the user's social media activity and acquire relevant information when acquiring information. For example, the acquisition unit can acquire information related to topics the user has shown interest in on social media. The acquisition unit can also prioritize acquiring information shared by the user's followers and friends. Furthermore, the acquisition unit can acquire information related to groups and communities the user participates in. This allows for the efficient acquisition of relevant information by analyzing the user's social media activity. Social media activity includes, but is not limited to, analysis of post content and follower analysis. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's social media activity into a generating AI and have the generating AI acquire the relevant information.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit can perform a detailed analysis for important information. It can also perform a simplified analysis for general information. Furthermore, the analysis unit can perform a rapid analysis for information that is of high urgency. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the information. The importance of the information includes, but is not limited to, scoring systems and user feedback. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input the importance of the information into the generative AI and have the generative AI adjust the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a traffic analysis algorithm to traffic information. It can also apply a business analysis algorithm to business information. Furthermore, it can apply a learning analysis algorithm to learning information. By applying different analysis algorithms depending on the category of information, more accurate analysis results can be obtained. The categories of information include, but are not limited to, text data, image data, and sensor data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the category of information into a generative AI and have the generative AI execute the application of the analysis algorithm.

[0046] The analysis unit can determine the priority of analysis based on when the information was acquired. For example, the analysis unit may prioritize the analysis of the most recent information. The analysis unit can also perform analysis while referring to past information. Furthermore, the analysis unit can prioritize the analysis of information acquired during a specific time period. This allows for the prioritization of analysis of the most recent information by determining the priority of analysis based on when the information was acquired. The information acquisition time includes, but is not limited to, timestamps and data freshness. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input the information acquisition time into the generating AI and have the generating AI determine the priority of analysis.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant information. It can also postpone the analysis of less relevant information. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of the information. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the information. The relevance of information includes, but is not limited to, co-occurrence networks and correlation analysis. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the relevance of the information into a generative AI and have the generative AI perform the adjustment of the order of analysis.

[0048] The prediction unit can improve the accuracy of its predictions by considering the interrelationships of information during the prediction process. For example, the prediction unit can integrate data from multiple information sources and make predictions while considering their interrelationships. It can also compare past data with current data and make predictions while considering their interrelationships. Furthermore, the prediction unit can integrate information from different categories and make predictions while considering their interrelationships. This allows for improved prediction accuracy by considering the interrelationships of information. Examples of interrelationships of information include, but are not limited to, correlation coefficients and causal relationship analysis. Some or all of the above-described processes in the prediction unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the prediction unit can input the interrelationships of information into a generative AI and have the generative AI perform the task of improving prediction accuracy.

[0049] The prediction unit can make predictions while considering the attribute information of the information provider. For example, the prediction unit can make predictions while considering the reliability of the information provider. The prediction unit can also make predictions while considering the expertise of the information provider. Furthermore, the prediction unit can make predictions while considering the past performance of the information provider. In this way, the accuracy of the prediction can be improved by considering the attribute information of the information provider. The attribute information of the information provider includes, but is not limited to, age, gender, occupation, etc. Some or all of the above processing in the prediction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the prediction unit can input the attribute information of the information provider into a generative AI and have the generative AI perform the prediction.

[0050] The prediction unit can make predictions while considering the geographical distribution of information. For example, the prediction unit may prioritize predicting geographically close information. It can also postpone predicting geographically distant information. Furthermore, the prediction unit can adjust the order of predictions based on geographical distribution. This allows for more accurate predictions by considering the geographical distribution of information. The geographical distribution of information includes, but is not limited to, map data and location information services. Some or all of the above processing in the prediction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the prediction unit can input the geographical distribution of information into a generative AI and have the generative AI perform the prediction.

[0051] The prediction unit can improve the accuracy of its predictions by referring to relevant literature during the prediction process. For example, the prediction unit can improve the accuracy of its predictions by referring to relevant literature. The prediction unit can also make predictions based on past research results. Furthermore, the prediction unit can make predictions based on the latest research results. This allows the accuracy of predictions to be improved by referring to relevant literature. Relevant literature includes, but is not limited to, academic papers and technical reports. Some or all of the above processing in the prediction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the prediction unit can input relevant literature into a generative AI and have the generative AI perform the prediction.

[0052] The visualization unit can adjust the level of detail of the visualization based on the importance of the information during visualization. For example, the visualization unit will perform detailed visualizations for important information. It can also perform simplified visualizations for general information. Furthermore, it can perform rapid visualizations for information of high urgency. This allows for efficient visualization by adjusting the level of detail of the visualization based on the importance of the information. The importance of information includes, but is not limited to, resolution and data granularity. Some or all of the above processing in the visualization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the visualization unit can input the importance of the information into the generative AI and have the generative AI adjust the level of detail of the visualization.

[0053] The visualization unit can apply different visualization algorithms depending on the category of information during visualization. For example, the visualization unit can apply a traffic visualization algorithm to traffic information. It can also apply a business visualization algorithm to business information. Furthermore, it can apply a learning visualization algorithm to learning information. By applying different visualization algorithms depending on the category of information, more accurate visualization results can be obtained. The categories of information include, but are not limited to, the selection of algorithms according to the type of data. Some or all of the above processing in the visualization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the visualization unit can input the categories of information into a generative AI and have the generative AI execute the application of the visualization algorithm.

[0054] The visualization unit can determine the visualization priority based on when the information was acquired. For example, the visualization unit prioritizes the visualization of the most recent information. The visualization unit can also perform visualization while referring to past information. Furthermore, the visualization unit can prioritize the visualization of information acquired during a specific time period. This allows for the prioritization of visualization of the most recent information by determining the visualization priority based on when the information was acquired. The information acquisition time includes, but is not limited to, examples such as by importance or chronological order. Some or all of the above processing in the visualization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the visualization unit can input the information acquisition time to the generative AI and have the generative AI determine the visualization priority.

[0055] The visualization unit can adjust the order of visualizations based on the relevance of the information during visualization. For example, the visualization unit can prioritize the visualization of highly relevant information. It can also postpone the visualization of less relevant information. Furthermore, the visualization unit can dynamically adjust the order of visualizations according to the relevance of the information. This allows for efficient visualization by adjusting the order of visualizations based on the relevance of the information. The relevance of information includes, but is not limited to, examples such as relevance order and importance order. Some or all of the above processing in the visualization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the visualization unit can input the relevance of the information into a generative AI and have the generative AI perform the adjustment of the visualization order.

[0056] The information delivery unit can adjust the level of detail provided based on the importance of the information at the time of delivery. For example, the delivery unit will provide detailed information for important information. It can also provide simplified information for general information. Furthermore, it can provide urgent information quickly. In this way, information can be delivered efficiently by adjusting the level of detail based on the importance of the information. The importance of information includes, but is not limited to, the granularity of the information and the resolution of the display. Some or all of the above processing in the delivery unit may be performed using, for example, AI, or not using AI. For example, the delivery unit can input the importance of the information into a generating AI and have the generating AI perform the adjustment of the level of detail of the delivery.

[0057] The information provider can apply different information provision algorithms depending on the information category at the time of provision. For example, the information provider can apply a traffic provision algorithm to traffic information. It can also apply a business provision algorithm to business information. Furthermore, it can apply a learning provision algorithm to learning information. By applying different information provision algorithms depending on the information category, more accurate information can be provided. Information categories include, but are not limited to, the selection of algorithms according to the type of data. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the information category into a generating AI and have the generating AI execute the application of the information provision algorithm.

[0058] The information delivery unit can determine the priority of information delivery based on when the information was acquired. For example, the delivery unit can prioritize the delivery of the latest information. The delivery unit can also provide information while referring to past information. Furthermore, the delivery unit can prioritize the delivery of information acquired during a specific time period. This allows for the priority of providing the latest information by determining the priority of information delivery based on when the information was acquired. The information acquisition time includes, but is not limited to, examples such as by importance or chronological order. Some or all of the above processing in the delivery unit may be performed using, for example, AI, or without AI. For example, the delivery unit can input the information acquisition time into a generating AI and have the generating AI determine the priority of delivery.

[0059] The information provider can adjust the order of information delivery based on the relevance of the information. For example, the provider can prioritize the delivery of highly relevant information. It can also postpone the delivery of less relevant information. Furthermore, the provider can dynamically adjust the order of delivery according to the relevance of the information. This allows for efficient information delivery by adjusting the order of delivery based on the relevance of the information. The relevance of the information includes, but is not limited to, examples such as order of relevance or order of importance. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the relevance of the information into a generating AI and have the generating AI perform the adjustment of the delivery order.

[0060] The service provider can select the optimal display method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the service provider can also provide a display method optimized for a larger screen. In addition, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. This allows the service provider to provide the optimal display method by considering the user's device information. Device information includes, but is not limited to, the device type, resolution, and operating system. Some or all of the processing described above in the service provider may be performed using, for example, AI, or without AI. For example, the service provider can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The data acquisition unit can acquire user eye-tracking data and dynamically adjust the range of information acquisition based on eye movements. For example, if a user is focusing their gaze on a specific object, it will prioritize acquiring information related to that object. If the user is frequently shifting their gaze, it can acquire information over a wider range. Furthermore, if the user is fixed on a single point of view, it can acquire detailed information. By dynamically adjusting the range of information acquisition based on the user's eye movements, the system can acquire the most optimal information for the user.

[0063] The analysis unit can analyze a user's past behavioral history and determine analysis priorities based on behavioral patterns. For example, it can prioritize the analysis of information related to places the user has frequently visited in the past. It can also perform analysis based on actions the user has taken during specific time periods. Furthermore, it can analyze information related to predicted future behavior based on the user's behavioral patterns. In this way, by analyzing a user's past behavioral history, analysis priorities can be determined based on behavioral patterns, enabling efficient analysis.

[0064] The prediction unit can predict future events by taking into account the user's health data. For example, it can predict changes in the user's health status based on their heart rate and blood pressure data. It can also predict the risk of lack of exercise or overwork based on the user's exercise data. Furthermore, it can predict the effects of sleep deprivation based on the user's sleep data. By considering the user's health data, it can more accurately predict future events and provide information useful for health management.

[0065] The visualization unit can customize the visualization style based on user preferences. For example, if a user prefers a simple design, a simple visualization can be provided. If a user prefers detailed information, a detailed visualization can be provided. Furthermore, if a user prefers a colorful design, a colorful visualization can be provided. This allows for customization of the visualization style based on user preferences, providing information that is easy for users to see and understand.

[0066] The information delivery system can adjust how information is delivered based on the user's device's battery level. For example, if the battery level is low, it will prioritize providing text-based information. If the battery level is sufficient, it can also provide information including images and videos. Furthermore, if the battery level is very low, it can provide only essential information. This allows for efficient information delivery and extends the device's usage time by considering the user's device's battery level.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The acquisition unit acquires information that comes into the user's field of view. The acquisition unit acquires information that comes into the user's field of view, for example, using a camera. The camera includes, but is not limited to, fixed cameras and wearable cameras. Step 2: The analysis unit analyzes the information acquired by the acquisition unit. The analysis unit comprehensively analyzes, for example, visual information and environmental data. Visual information includes, but is not limited to, image data and video data, and environmental data includes, but is not limited to, temperature data, humidity data, and location information. Step 3: The prediction unit predicts future events based on the information analyzed by the analysis unit. The prediction unit predicts events that may occur in the immediate future, for example. The immediate future includes, but is not limited to, a few seconds, a few minutes, or a few hours from now. Step 4: The visualization unit visualizes the future scene predicted by the prediction unit in real time. For example, the visualization unit visualizes the predicted future scene in real time. Real time includes, but is not limited to, a tolerance for delay time. Step 5: The provider unit provides the user with the information visualized by the visualization unit. The provider unit provides the user with the visualized information, for example, as images or text. Images and text include, but are not limited to, JPEG, PNG, HTML, PDF, etc.

[0069] (Example of form 2) The smart glasses system according to an embodiment of the present invention is a system that analyzes information entering the user's field of view in real time, predicts events that may occur in the near future, and displays them as images and text. This smart glasses system acquires information entering the user's field of view using a camera, and a generating AI analyzes it in real time. Next, the generating AI comprehensively analyzes the visual information and environmental data to predict events that may occur in the near future. Furthermore, it uses an image generating AI to visualize the predicted future scene in real time. This service provides business people, students, the elderly, and various professionals with support for decision-making in daily life and business situations, safety assurance, and improved learning efficiency. For example, the smart glasses system can predict the flow of a meeting to optimize a presentation, or anticipate changes in traffic conditions to suggest the optimal route. As an innovative wearable device that extends human cognitive abilities and supports smarter judgment and action, the smart glasses system contributes to improving quality of life and productivity. Thus, the smart glasses system can analyze information entering the user's field of view in real time, predict and visualize future events, and provide them to the user.

[0070] The smart glasses system according to the embodiment comprises an acquisition unit, an analysis unit, a prediction unit, a visualization unit, and a provision unit. The acquisition unit acquires information that enters the user's field of view. The acquisition unit acquires information that enters the user's field of view, for example, using a camera. The camera includes, but is not limited to, a fixed camera or a wearable camera. The analysis unit analyzes the information acquired by the acquisition unit. The analysis unit comprehensively analyzes, for example, visual information and environmental data. Visual information includes, for example, image data and video data, and environmental data includes, but is not limited to, temperature data, humidity data, and location information. The prediction unit predicts future events based on the information analyzed by the analysis unit. The prediction unit predicts, for example, events that may occur in the immediate future. The immediate future includes, but is not limited to, a few seconds later, a few minutes later, or a few hours later. The visualization unit visualizes the future scene predicted by the prediction unit in real time. The visualization unit visualizes, for example, the predicted future scene in real time. Real-time includes, but is not limited to, an acceptable range of latency. The providing unit provides the user with the information visualized by the visualization unit. The providing unit provides the user with the visualized information, for example, as images or text. Images and text include, but are not limited to, JPEG, PNG, HTML, PDF, etc. As a result, the smart glasses system according to the embodiment can analyze information that enters the user's field of view in real time, predict and visualize future events, and provide them to the user.

[0071] The acquisition unit acquires information that comes into the user's field of view. For example, the acquisition unit uses a camera to acquire information that comes into the user's field of view. The camera includes, but is not limited to, fixed cameras and wearable cameras. Specifically, a wearable camera is built into the user's eyeglass frame and captures images in accordance with the user's line of sight. This allows for real-time acquisition of the scenery and objects the user is seeing. Fixed cameras monitor the user's surrounding environment over a wide area and play a role in supplementing the information that comes into their field of view. These cameras have high resolution and can capture even the smallest details clearly, enabling highly accurate analysis by the analysis unit. Furthermore, the acquisition unit can be equipped with microphones and environmental sensors in addition to cameras. Microphones acquire ambient sound information, and environmental sensors collect data such as temperature, humidity, and atmospheric pressure. This allows for the integrated acquisition of not only visual information but also sound and environmental data, enabling more multifaceted information collection. The acquisition unit transmits this data to a central database in real time, allowing the analysis and prediction units to access it quickly. This allows the data acquisition unit to collect information from multiple perspectives that comes into the user's field of view, thereby improving the overall performance of the system.

[0072] The analysis unit analyzes the information acquired by the acquisition unit. For example, the analysis unit comprehensively analyzes visual information and environmental data. Visual information includes image data and video data, and environmental data includes, but is not limited to, temperature data, humidity data, and location information. Specifically, the analysis unit uses AI to analyze image data and video data to perform object recognition and scene analysis. For example, it identifies objects that come into the user's field of vision and determines the type and location of those objects. Scene analysis allows the user to understand the situation and environment of their location. Furthermore, by analyzing environmental data, it can detect changes in ambient temperature and humidity and evaluate the user's comfort and safety. The analysis unit comprehensively analyzes this data to understand the user's current situation in real time. By using deep learning and machine learning technologies, high-precision analysis is possible. For example, image recognition technology using deep learning can identify objects and people from visual information with high precision, and environmental data analysis using machine learning can detect abnormal environmental changes at an early stage. This allows the analysis unit to quickly and accurately analyze acquired information and understand the user's situation in real time. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term trend analysis and risk assessment. As a result, the analysis unit can not only grasp the situation in real time but also respond to future risk management and countermeasure planning, improving the reliability and security of the entire system.

[0073] The prediction unit predicts future events based on information analyzed by the analysis unit. For example, the prediction unit predicts events that may occur in the immediate future. The immediate future includes, but is not limited to, a few seconds, a few minutes, or a few hours from now. Specifically, the prediction unit simulates future scenarios based on data analyzed using AI. For example, it can predict the possibility of an obstacle appearing ahead of a user while they are walking and suggest avoidance actions. It can also predict changes in weather and traffic conditions and prompt the user to take appropriate action. The prediction unit utilizes past data and statistical information to predict future events with high accuracy. For example, it can predict the probability of rainfall in a specific area based on past weather data and notify the user to carry an umbrella. It can also analyze traffic data to predict congestion and suggest alternative routes to the user. The prediction unit can update these prediction results in real time and respond to the latest situation. For example, if weather or traffic conditions change rapidly, the prediction unit immediately incorporates new data and corrects the prediction results. This allows the prediction unit to provide highly accurate predictions based on the latest information at all times, improving user safety and comfort. Furthermore, the prediction unit can learn user behavior patterns and preferences, enabling it to make individually optimized predictions. This allows the prediction unit to provide more personalized services to users and improve the user experience.

[0074] The visualization unit visualizes future scenes predicted by the prediction unit in real time. For example, the visualization unit visualizes predicted future scenes in real time. Real time includes, but is not limited to, a tolerance for delay time. Specifically, the visualization unit displays future scenes overlaid on the user's field of view, allowing the user to understand them intuitively. For example, if an obstacle appears in front of the user while they are walking, the unit visually displays the location of the obstacle and an avoidance route. It can also visualize changes in weather and traffic conditions, prompting the user to take appropriate action. The visualization unit uses AR (augmented reality) technology to overlay future scenes on the real field of view. This allows the user to visually confirm the real environment and the predicted future simultaneously. The visualization unit is equipped with a high-resolution display, enabling it to display clear images. Furthermore, the visualization unit can track the user's gaze and head movements and dynamically adjust the displayed content. This allows the user to enjoy a natural visual experience. In addition, the visualization unit can provide multi-sensory information to the user by combining it with feedback such as sound and vibration. For example, if a dangerous situation is anticipated, the system will attract the user's attention through visual warnings, audio alerts, and vibration notifications. This allows the visualization unit to provide intuitive and multi-sensory information to the user, improving user safety and comfort.

[0075] The information provider unit provides users with information visualized by the visualization unit. For example, the information provider unit provides visualized information to users in the form of images and text. These images and text include, but are not limited to, JPEG, PNG, HTML, and PDF. Specifically, the information provider unit displays information overlaid on the user's field of view, enabling intuitive understanding. For example, if an obstacle appears in front of the user while walking, the location of the obstacle and an avoidance route are visually displayed. It can also visualize changes in weather and traffic conditions, prompting the user to take appropriate action. The information provider unit uses AR (augmented reality) technology to overlay information onto the real-world field of view. This allows the user to visually confirm the real environment and the information simultaneously. The information provider unit is equipped with a high-resolution display, enabling the display of clear images. Furthermore, the information provider unit can track the user's gaze and head movements and dynamically adjust the displayed content. This allows the user to enjoy a natural visual experience. In addition, the information provider unit can provide users with multi-sensory information by combining feedback such as sound and vibration. For example, if a dangerous situation is anticipated, the system will attract the user's attention through visual warnings, as well as audio alerts and vibration notifications. This allows the system provider to deliver intuitive and multi-sensory information to the user, improving user safety and comfort.

[0076] The acquisition unit acquires information that enters the user's field of view using a camera. The acquisition unit acquires information that enters the user's field of view using a camera, for example. The camera includes, but is not limited to, fixed cameras and wearable cameras. This makes it possible to accurately acquire information that enters the user's field of view by using a camera. The camera includes, but is not limited to, fixed cameras and wearable cameras. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or not using AI. For example, the acquisition unit can input image data acquired by the camera into a generation AI and have the generation AI perform analysis of visual information from the image data.

[0077] The analysis unit can comprehensively analyze visual information and environmental data. For example, the analysis unit comprehensively analyzes visual information and environmental data. Visual information includes, but is not limited to, image data and video data, and environmental data includes, but is not limited to, temperature data, humidity data, and location information. By comprehensively analyzing visual information and environmental data, more accurate analysis results can be obtained. Visual information includes, but is not limited to, image data and video data. Environmental data includes, but is not limited to, temperature data, humidity data, and location information. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input visual information and environmental data into a generative AI and have the generative AI perform a comprehensive analysis.

[0078] The prediction unit can predict events that may occur in the near future. For example, the prediction unit predicts events that may occur in the near future. The near future includes, but is not limited to, events that may occur in a few seconds, a few minutes, or a few hours. By predicting events that may occur in the near future, it is possible to provide users with forward-looking information. The near future includes, but is not limited to, events that may occur in a few seconds, a few minutes, or a few hours. Some or all of the above processing in the prediction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the prediction unit can input information analyzed by the analysis unit into a generative AI and have the generative AI perform predictions of future events.

[0079] The visualization unit can visualize predicted future scenes in real time. For example, the visualization unit visualizes predicted future scenes in real time. Real time includes, but is not limited to, an acceptable range of delay time. By visualizing predicted future scenes in real time, information that is easy for the user to understand intuitively can be provided. Real time includes, but is not limited to, an acceptable range of delay time. Some or all of the above processing in the visualization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the visualization unit can input the future scenes predicted by the prediction unit into a generative AI and have the generative AI perform the visualization.

[0080] The service provider can provide visualized information to the user in the form of images and text. For example, the service provider can provide visualized information to the user in the form of images and text. Images and text include, but are not limited to, JPEG, PNG, HTML, and PDF. This makes it possible to convey information to the user in an easy-to-understand manner by providing visualized information in the form of images and text. Images and text include, but are not limited to, JPEG, PNG, HTML, and PDF. Some or all of the processing described above in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input visualized information into a generating AI and have the generating AI perform the generation of images and text.

[0081] The data acquisition unit can estimate the user's emotions and adjust the timing of information acquisition based on the estimated emotions. For example, if the user is tense, the data acquisition unit can reduce the frequency of information acquisition to alleviate the user's burden. Conversely, if the user is relaxed, the data acquisition unit can increase the frequency of information acquisition and collect more detailed data. Furthermore, if the user is focused, the data acquisition unit can prioritize acquiring only important information. By adjusting the timing of information acquisition according to the user's emotions, the user's burden can be reduced and appropriate information can be acquired. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data acquisition unit may be performed using AI, or not using AI. For example, the data acquisition unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0082] The acquisition unit can analyze the user's past visual information and select the optimal acquisition method. For example, the acquisition unit can prioritize acquiring locations and objects that the user has frequently viewed in the past. The acquisition unit can also acquire information important for specific time periods from the user's past visual information. Furthermore, the acquisition unit can analyze the user's visual information and select the optimal camera angle. This allows for efficient information acquisition by selecting the optimal acquisition method through analysis of the user's past visual information. Past visual information includes, but is not limited to, the use of databases and the storage of log files. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's past visual information into a generating AI and have the generating AI select the optimal acquisition method.

[0083] The data acquisition unit can filter information based on the user's current activities and areas of interest when acquiring it. For example, if the user is in a meeting, the data acquisition unit will prioritize acquiring information related to the meeting. It can also prioritize acquiring traffic information if the user is traveling. Furthermore, if the user is studying, the data acquisition unit can prioritize acquiring information related to the learning content. This allows the system to prioritize acquiring information important to the user by filtering information based on their current activities and areas of interest. Current activities and areas of interest include, but are not limited to, the user's activity history and survey results. Some or all of the processing described above in the data acquisition unit may be performed using AI, or not. For example, the data acquisition unit can input the user's current activities and areas of interest into a generating AI and have the generating AI perform the information filtering.

[0084] The data acquisition unit can estimate the user's emotions and determine the priority of information to acquire based on the estimated emotions. For example, if the user is stressed, the data acquisition unit will prioritize acquiring information that promotes relaxation. Similarly, if the user is excited, the data acquisition unit can prioritize acquiring information that interests them. Furthermore, if the user is tired, the data acquisition unit can prioritize acquiring information related to rest. This allows the system to acquire the most relevant information for the user by prioritizing information according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the processing described above in the data acquisition unit may be performed using AI, or not. For example, the data acquisition unit can input user emotion data into a generative AI and have the generative AI determine the priority of information.

[0085] The acquisition unit can prioritize the acquisition of highly relevant information by considering the user's geographical location when acquiring information. For example, if the user is in a specific region, the acquisition unit will prioritize the acquisition of information related to that region. Furthermore, if the user is traveling, the acquisition unit can prioritize the acquisition of tourist information and transportation information. Additionally, if the user is at home, the acquisition unit can prioritize the acquisition of information within the home. This allows for the prioritization of highly relevant information by considering the user's geographical location. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the processing described above in the acquisition unit may be performed using, for example, AI, or without AI. For example, the acquisition unit can input the user's geographical location information into a generating AI and have the generating AI acquire highly relevant information.

[0086] The acquisition unit can analyze the user's social media activity and acquire relevant information when acquiring information. For example, the acquisition unit can acquire information related to topics the user has shown interest in on social media. The acquisition unit can also prioritize acquiring information shared by the user's followers and friends. Furthermore, the acquisition unit can acquire information related to groups and communities the user participates in. This allows for the efficient acquisition of relevant information by analyzing the user's social media activity. Social media activity includes, but is not limited to, analysis of post content and follower analysis. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's social media activity into a generating AI and have the generating AI acquire the relevant information.

[0087] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results. In this way, by adjusting the presentation of the analysis according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of the analysis.

[0088] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit can perform a detailed analysis for important information. It can also perform a simplified analysis for general information. Furthermore, the analysis unit can perform a rapid analysis for information that is of high urgency. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the information. The importance of the information includes, but is not limited to, scoring systems and user feedback. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input the importance of the information into the generative AI and have the generative AI adjust the level of detail of the analysis.

[0089] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a traffic analysis algorithm to traffic information. It can also apply a business analysis algorithm to business information. Furthermore, it can apply a learning analysis algorithm to learning information. By applying different analysis algorithms depending on the category of information, more accurate analysis results can be obtained. The categories of information include, but are not limited to, text data, image data, and sensor data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the category of information into a generative AI and have the generative AI execute the application of the analysis algorithm.

[0090] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can perform a short, concise analysis. If the user is relaxed, the analysis unit can perform a detailed analysis. Furthermore, if the user is excited, the analysis unit can perform a visually stimulating analysis. By adjusting the length of the analysis according to the user's emotions, the system can provide the user with the most optimal analysis results. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the length of the analysis.

[0091] The analysis unit can determine the priority of analysis based on when the information was acquired. For example, the analysis unit may prioritize the analysis of the most recent information. The analysis unit can also perform analysis while referring to past information. Furthermore, the analysis unit can prioritize the analysis of information acquired during a specific time period. This allows for the prioritization of analysis of the most recent information by determining the priority of analysis based on when the information was acquired. The information acquisition time includes, but is not limited to, timestamps and data freshness. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input the information acquisition time into the generating AI and have the generating AI determine the priority of analysis.

[0092] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant information. It can also postpone the analysis of less relevant information. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of the information. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the information. The relevance of information includes, but is not limited to, co-occurrence networks and correlation analysis. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the relevance of the information into a generative AI and have the generative AI perform the adjustment of the order of analysis.

[0093] The prediction unit can estimate the user's emotions and adjust the prediction criteria based on the estimated emotions. For example, if the user is nervous, the prediction unit can make a prediction that minimizes risk. It can also make a more detailed prediction if the user is relaxed. Furthermore, if the user is in a hurry, the prediction unit can make a quick prediction. This allows the system to provide the user with the best possible prediction result by adjusting the prediction criteria according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the prediction unit may be performed using or without a generative AI. For example, the prediction unit can input user emotion data into a generative AI and have the generative AI adjust the prediction criteria.

[0094] The prediction unit can improve the accuracy of its predictions by considering the interrelationships of information during the prediction process. For example, the prediction unit can integrate data from multiple information sources and make predictions while considering their interrelationships. It can also compare past data with current data and make predictions while considering their interrelationships. Furthermore, the prediction unit can integrate information from different categories and make predictions while considering their interrelationships. This allows for improved prediction accuracy by considering the interrelationships of information. Examples of interrelationships of information include, but are not limited to, correlation coefficients and causal relationship analysis. Some or all of the above-described processes in the prediction unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the prediction unit can input the interrelationships of information into a generative AI and have the generative AI perform the task of improving prediction accuracy.

[0095] The prediction unit can make predictions while considering the attribute information of the information provider. For example, the prediction unit can make predictions while considering the reliability of the information provider. The prediction unit can also make predictions while considering the expertise of the information provider. Furthermore, the prediction unit can make predictions while considering the past performance of the information provider. In this way, the accuracy of the prediction can be improved by considering the attribute information of the information provider. The attribute information of the information provider includes, but is not limited to, age, gender, occupation, etc. Some or all of the above processing in the prediction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the prediction unit can input the attribute information of the information provider into a generative AI and have the generative AI perform the prediction.

[0096] The prediction unit can estimate the user's emotions and adjust the order in which the prediction results are displayed based on the estimated emotions. For example, if the user is stressed, the prediction unit may prioritize displaying important results. If the user is relaxed, the prediction unit may also prioritize displaying detailed results in a sequential manner. Furthermore, if the user is in a hurry, the prediction unit may prioritize displaying results that summarize the key points. In this way, by adjusting the order in which the prediction results are displayed according to the user's emotions, the system can provide the user with the most relevant information. Emotion estimation is achieved using an emotion estimation function, for example, by using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the prediction unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the prediction unit can input user emotion data into a generative AI and have the generative AI adjust the display order of the prediction results.

[0097] The prediction unit can make predictions while considering the geographical distribution of information. For example, the prediction unit may prioritize predicting geographically close information. It can also postpone predicting geographically distant information. Furthermore, the prediction unit can adjust the order of predictions based on geographical distribution. This allows for more accurate predictions by considering the geographical distribution of information. The geographical distribution of information includes, but is not limited to, map data and location information services. Some or all of the above processing in the prediction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the prediction unit can input the geographical distribution of information into a generative AI and have the generative AI perform the prediction.

[0098] The prediction unit can improve the accuracy of its predictions by referring to relevant literature during the prediction process. For example, the prediction unit can improve the accuracy of its predictions by referring to relevant literature. The prediction unit can also make predictions based on past research results. Furthermore, the prediction unit can make predictions based on the latest research results. This allows the accuracy of predictions to be improved by referring to relevant literature. Relevant literature includes, but is not limited to, academic papers and technical reports. Some or all of the above processing in the prediction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the prediction unit can input relevant literature into a generative AI and have the generative AI perform the prediction.

[0099] The visualization unit can estimate the user's emotions and adjust the visualization method based on the estimated emotions. For example, if the user is tense, the visualization unit can provide a simple and highly visible visualization. If the user is relaxed, the visualization unit can also provide a detailed visualization. Furthermore, if the user is in a hurry, the visualization unit can provide a concise visualization. By adjusting the visualization method according to the user's emotions, it is possible to provide visualizations that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the visualization unit may be performed using a generative AI, or not. For example, the visualization unit can input user emotion data into a generative AI and have the generative AI adjust the visualization method.

[0100] The visualization unit can adjust the level of detail of the visualization based on the importance of the information during visualization. For example, the visualization unit will perform detailed visualizations for important information. It can also perform simplified visualizations for general information. Furthermore, it can perform rapid visualizations for information of high urgency. This allows for efficient visualization by adjusting the level of detail of the visualization based on the importance of the information. The importance of information includes, but is not limited to, resolution and data granularity. Some or all of the above processing in the visualization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the visualization unit can input the importance of the information into the generative AI and have the generative AI adjust the level of detail of the visualization.

[0101] The visualization unit can apply different visualization algorithms depending on the category of information during visualization. For example, the visualization unit can apply a traffic visualization algorithm to traffic information. It can also apply a business visualization algorithm to business information. Furthermore, it can apply a learning visualization algorithm to learning information. By applying different visualization algorithms depending on the category of information, more accurate visualization results can be obtained. The categories of information include, but are not limited to, the selection of algorithms according to the type of data. Some or all of the above processing in the visualization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the visualization unit can input the categories of information into a generative AI and have the generative AI execute the application of the visualization algorithm.

[0102] The visualization unit can estimate the user's emotions and adjust the length of the visualization based on the estimated emotions. For example, if the user is in a hurry, the visualization unit can create a short, concise visualization. If the user is relaxed, the visualization unit can create a detailed visualization. Furthermore, if the user is excited, the visualization unit can create a visually stimulating visualization. By adjusting the length of the visualization according to the user's emotions, the system can provide the user with the most optimal visualization result. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the visualization unit may be performed using a generative AI, or not. For example, the visualization unit can input user emotion data into a generative AI and have the generative AI adjust the length of the visualization.

[0103] The visualization unit can determine the visualization priority based on when the information was acquired. For example, the visualization unit prioritizes the visualization of the most recent information. The visualization unit can also perform visualization while referring to past information. Furthermore, the visualization unit can prioritize the visualization of information acquired during a specific time period. This allows for the prioritization of visualization of the most recent information by determining the visualization priority based on when the information was acquired. The information acquisition time includes, but is not limited to, examples such as by importance or chronological order. Some or all of the above processing in the visualization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the visualization unit can input the information acquisition time to the generative AI and have the generative AI determine the visualization priority.

[0104] The visualization unit can adjust the order of visualizations based on the relevance of the information during visualization. For example, the visualization unit can prioritize the visualization of highly relevant information. It can also postpone the visualization of less relevant information. Furthermore, the visualization unit can dynamically adjust the order of visualizations according to the relevance of the information. This allows for efficient visualization by adjusting the order of visualizations based on the relevance of the information. The relevance of information includes, but is not limited to, examples such as relevance order and importance order. Some or all of the above processing in the visualization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the visualization unit can input the relevance of the information into a generative AI and have the generative AI perform the adjustment of the visualization order.

[0105] The information delivery unit can estimate the user's emotions and adjust the delivery method based on the estimated emotions. For example, if the user is nervous, the information delivery unit can provide information in a simple and visually clear manner. If the user is relaxed, the information delivery unit can also provide detailed information. Furthermore, if the user is in a hurry, the information delivery unit can provide concise information. In this way, by adjusting the delivery method according to the user's emotions, information that is easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or not using AI. For example, the information delivery unit can input user emotion data into a generative AI and have the generative AI adjust the delivery method.

[0106] The information delivery unit can adjust the level of detail provided based on the importance of the information at the time of delivery. For example, the delivery unit will provide detailed information for important information. It can also provide simplified information for general information. Furthermore, it can provide urgent information quickly. In this way, information can be delivered efficiently by adjusting the level of detail based on the importance of the information. The importance of information includes, but is not limited to, the granularity of the information and the resolution of the display. Some or all of the above processing in the delivery unit may be performed using, for example, AI, or not using AI. For example, the delivery unit can input the importance of the information into a generating AI and have the generating AI perform the adjustment of the level of detail of the delivery.

[0107] The information provider can apply different information provision algorithms depending on the information category at the time of provision. For example, the information provider can apply a traffic provision algorithm to traffic information. It can also apply a business provision algorithm to business information. Furthermore, it can apply a learning provision algorithm to learning information. By applying different information provision algorithms depending on the information category, more accurate information can be provided. Information categories include, but are not limited to, the selection of algorithms according to the type of data. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the information category into a generating AI and have the generating AI execute the application of the information provision algorithm.

[0108] The delivery unit can estimate the user's emotions and adjust the length of the delivery based on the estimated emotions. For example, if the user is in a hurry, the delivery unit can provide a short, concise delivery. If the user is relaxed, the delivery unit can provide a detailed delivery. Furthermore, if the user is excited, the delivery unit can provide a visually stimulating delivery. By adjusting the length of the delivery according to the user's emotions, the system can provide the user with the most relevant information. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input user emotion data into a generative AI and have the generative AI adjust the length of the delivery.

[0109] The information delivery unit can determine the priority of information delivery based on when the information was acquired. For example, the delivery unit can prioritize the delivery of the latest information. The delivery unit can also provide information while referring to past information. Furthermore, the delivery unit can prioritize the delivery of information acquired during a specific time period. This allows for the priority of providing the latest information by determining the priority of information delivery based on when the information was acquired. The information acquisition time includes, but is not limited to, examples such as by importance or chronological order. Some or all of the above processing in the delivery unit may be performed using, for example, AI, or without AI. For example, the delivery unit can input the information acquisition time into a generating AI and have the generating AI determine the priority of delivery.

[0110] The information provider can adjust the order of information delivery based on the relevance of the information. For example, the provider can prioritize the delivery of highly relevant information. It can also postpone the delivery of less relevant information. Furthermore, the provider can dynamically adjust the order of delivery according to the relevance of the information. This allows for efficient information delivery by adjusting the order of delivery based on the relevance of the information. The relevance of the information includes, but is not limited to, examples such as order of relevance or order of importance. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the relevance of the information into a generating AI and have the generating AI perform the adjustment of the delivery order.

[0111] The service provider can select the optimal display method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the service provider can also provide a display method optimized for a larger screen. In addition, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. This allows the service provider to provide the optimal display method by considering the user's device information. Device information includes, but is not limited to, the device type, resolution, and operating system. Some or all of the processing described above in the service provider may be performed using, for example, AI, or without AI. For example, the service provider can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[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 data acquisition unit can acquire user eye-tracking data and dynamically adjust the range of information acquisition based on eye movements. For example, if a user is focusing their gaze on a specific object, it will prioritize acquiring information related to that object. If the user is frequently shifting their gaze, it can acquire information over a wider range. Furthermore, if the user is fixed on a single point of view, it can acquire detailed information. By dynamically adjusting the range of information acquisition based on the user's eye movements, the system can acquire the most optimal information for the user.

[0114] The analysis unit can analyze a user's past behavioral history and determine analysis priorities based on behavioral patterns. For example, it can prioritize the analysis of information related to places the user has frequently visited in the past. It can also perform analysis based on actions the user has taken during specific time periods. Furthermore, it can analyze information related to predicted future behavior based on the user's behavioral patterns. In this way, by analyzing a user's past behavioral history, analysis priorities can be determined based on behavioral patterns, enabling efficient analysis.

[0115] The prediction unit can predict future events by taking into account the user's health data. For example, it can predict changes in the user's health status based on their heart rate and blood pressure data. It can also predict the risk of lack of exercise or overwork based on the user's exercise data. Furthermore, it can predict the effects of sleep deprivation based on the user's sleep data. By considering the user's health data, it can more accurately predict future events and provide information useful for health management.

[0116] The visualization unit can customize the visualization style based on user preferences. For example, if a user prefers a simple design, a simple visualization can be provided. If a user prefers detailed information, a detailed visualization can be provided. Furthermore, if a user prefers a colorful design, a colorful visualization can be provided. This allows for customization of the visualization style based on user preferences, providing information that is easy for users to see and understand.

[0117] The information delivery system can adjust how information is delivered based on the user's device's battery level. For example, if the battery level is low, it will prioritize providing text-based information. If the battery level is sufficient, it can also provide information including images and videos. Furthermore, if the battery level is very low, it can provide only essential information. This allows for efficient information delivery and extends the device's usage time by considering the user's device's battery level.

[0118] The information acquisition unit can estimate the user's emotions and adjust the information acquisition method based on the estimated emotions. For example, if the user is stressed, it can prioritize acquiring information that promotes relaxation. If the user is excited, it can prioritize acquiring information that is of interest. Furthermore, if the user is tired, it can prioritize acquiring information related to rest. In this way, by adjusting the information acquisition method according to the user's emotions, the system can acquire the most optimal information for the user.

[0119] The analysis unit can estimate the user's emotions and adjust the analysis method based on those emotions. For example, if the user is nervous, it can provide simple and easy-to-understand analysis results. If the user is relaxed, it can provide detailed analysis results. Furthermore, if the user is in a hurry, it can provide concise analysis results. By adjusting the analysis method according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand.

[0120] The prediction unit can estimate the user's emotions and adjust the accuracy of the prediction based on those emotions. For example, if the user is nervous, it can make a prediction that minimizes risk. If the user is relaxed, it can make a more detailed prediction. Furthermore, if the user is in a hurry, it can make a quick prediction. By adjusting the accuracy of the prediction according to the user's emotions, it can provide the user with the most optimal prediction result.

[0121] The visualization unit can estimate the user's emotions and adjust the visualization method based on those emotions. For example, if the user is nervous, it can provide a simple and highly visible visualization. If the user is relaxed, it can provide a more detailed visualization. Furthermore, if the user is in a hurry, it can provide a concise visualization. By adjusting the visualization method according to the user's emotions, it can provide visualizations that are easy for the user to understand.

[0122] The information delivery system can estimate the user's emotions and adjust the delivery method based on those estimates. For example, if the user is nervous, information can be delivered in a simple and easily visible way. If the user is relaxed, detailed information can be provided. Furthermore, if the user is in a hurry, concise information can be provided. By adjusting the delivery method according to the user's emotions, information can be delivered in a way that is easy for the user to understand.

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

[0124] Step 1: The acquisition unit acquires information that comes into the user's field of view. The acquisition unit acquires information that comes into the user's field of view, for example, using a camera. The camera includes, but is not limited to, fixed cameras and wearable cameras. Step 2: The analysis unit analyzes the information acquired by the acquisition unit. The analysis unit comprehensively analyzes, for example, visual information and environmental data. Visual information includes, but is not limited to, image data and video data, and environmental data includes, but is not limited to, temperature data, humidity data, and location information. Step 3: The prediction unit predicts future events based on the information analyzed by the analysis unit. The prediction unit predicts events that may occur in the immediate future, for example. The immediate future includes, but is not limited to, a few seconds, a few minutes, or a few hours from now. Step 4: The visualization unit visualizes the future scene predicted by the prediction unit in real time. For example, the visualization unit visualizes the predicted future scene in real time. Real time includes, but is not limited to, a tolerance for delay time. Step 5: The provider unit provides the user with the information visualized by the visualization unit. The provider unit provides the user with the visualized information, for example, as images or text. Images and text include, but are not limited to, JPEG, PNG, HTML, PDF, etc.

[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 acquisition unit, analysis unit, prediction unit, visualization unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the acquisition unit acquires information that comes into the user's field of view using the camera 42 of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and comprehensively analyzes visual information and environmental data. The prediction unit is implemented in the specific processing unit 290 of the data processing unit 12 and predicts events that may occur in the near future. The visualization unit is implemented in the specific processing unit 46A of the smart device 14 and visualizes the predicted future scene in real time. The provision unit provides the visualized information to the user using the output device 40 of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0129] [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 acquisition unit, analysis unit, prediction unit, visualization unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit acquires information that enters the user's field of view using the camera 42 of the smart glasses 214. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and comprehensively analyzes visual information and environmental data. The prediction unit is implemented in the specific processing unit 290 of the data processing unit 12 and predicts events that may occur in the near future. The visualization unit is implemented in the specific processing unit 46A of the smart glasses 214 and visualizes the predicted future scene in real time. The provision unit provides the visualized information to the user using the speaker 240 of the smart glasses 214. 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 acquisition unit, analysis unit, prediction unit, visualization unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit acquires information that enters the user's field of view using the camera 42 of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and comprehensively analyzes visual information and environmental data. The prediction unit is implemented in the specific processing unit 290 of the data processing unit 12 and predicts events that may occur in the near future. The visualization unit is implemented in the control unit 46A of the headset terminal 314 and visualizes the predicted future scene in real time. The provision unit provides the visualized information to the user using the display 343 of the headset terminal 314. 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.

[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 acquisition unit, analysis unit, prediction unit, visualization unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit acquires information that enters the user's field of view using the camera 42 of the robot 414. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and comprehensively analyzes visual information and environmental data. The prediction unit is implemented in the specific processing unit 290 of the data processing unit 12 and predicts events that may occur in the near future. The visualization unit is implemented in the control unit 46A of the robot 414 and visualizes the predicted future scene in real time. The provision unit provides the visualized information to the user using the speaker 240 of the robot 414. 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.

[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) An acquisition unit that acquires information that comes into the user's field of view, An analysis unit analyzes the information acquired by the acquisition unit, A prediction unit predicts future events based on the information analyzed by the aforementioned analysis unit, A visualization unit that visualizes the future scenes predicted by the prediction unit, The system includes a providing unit that provides the user with the information visualized by the visualization unit. A system characterized by the following features. (Note 2) The acquisition unit is, The camera is used to acquire information that comes into the user's field of view. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Integrating and analyzing visual information and environmental data. The system described in Appendix 1, characterized by the features described herein. (Note 4) The prediction unit, Predicting events that may occur in the immediate future. The system described in Appendix 1, characterized by the features described herein. (Note 5) The visualization unit, Visualizing predicted future scenes in real time The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Provide users with visualized information using images and text. The system described in Appendix 1, characterized by the features described herein. (Note 7) The acquisition unit is, It estimates the user's emotions and adjusts the timing of information acquisition based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition unit is, Analyze the user's past visual information and select the optimal acquisition method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, When retrieving information, filtering is performed based on the user's current activities and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition unit is, It estimates the user's emotions and determines the priority of information to acquire based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The acquisition unit is, When retrieving information, the system prioritizes retrieving highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The acquisition unit is, When acquiring information, the system analyzes the user's social media activity and retrieves relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis 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, adjust the level of detail based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the information was acquired. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The prediction unit, It estimates the user's emotions and adjusts the prediction criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The prediction unit, When making predictions, consider the interrelationships of information to improve prediction accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 21) The prediction unit, When making predictions, the attribute information of the information provider is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The prediction unit, It estimates the user's sentiment and adjusts the order in which the prediction results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The prediction unit, When making predictions, the geographical distribution of information should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The prediction unit, When making predictions, refer to relevant literature to improve the accuracy of the predictions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The visualization unit, It estimates the user's emotions and adjusts the visualization method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The visualization unit, When creating visualizations, adjust the level of detail based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 27) The visualization unit, When visualizing, different visualization algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 28) The visualization unit, It estimates the user's emotions and adjusts the length of the visualization based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The visualization unit, When creating visualizations, prioritize visualizations based on when the information was acquired. The system described in Appendix 1, characterized by the features described herein. (Note 30) The visualization unit, When visualizing, adjust the order of visualizations based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, It estimates the user's emotions and adjusts the delivery method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, When providing information, adjust the level of detail based on its importance. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, When providing information, different delivery algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the service based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned supply unit is, When providing information, the priority of provision will be determined based on when the information was acquired. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned supply unit is, When providing information, the order of provision will be adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned supply unit is, When providing the service, the optimal display method is selected considering the user's device information. The system described in Appendix 1, 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. An acquisition unit that acquires information that comes into the user's field of view, An analysis unit analyzes the information acquired by the acquisition unit, A prediction unit predicts future events based on the information analyzed by the aforementioned analysis unit, A visualization unit that visualizes the future scenes predicted by the prediction unit, The system includes a providing unit that provides the user with the information visualized by the visualization unit. A system characterized by the following features.

2. The acquisition unit is, The camera is used to acquire information that comes into the user's field of view. The system according to feature 1.

3. The aforementioned analysis unit, Integrating and analyzing visual information and environmental data. The system according to feature 1.

4. The prediction unit, Predicting events that may occur in the immediate future. The system according to feature 1.

5. The visualization unit, Visualizing predicted future scenes in real time The system according to feature 1.

6. The aforementioned supply unit is, Provide users with visualized information using images and text. The system according to feature 1.

7. The acquisition unit is, It estimates the user's emotions and adjusts the timing of information acquisition based on the estimated user emotions. The system according to feature 1.

8. The acquisition unit is, Analyze the user's past visual information and select the optimal acquisition method. The system according to feature 1.

Images