system

Abstract

The system according to this embodiment aims to provide a visual experience that allows seamless transitions between the real world and the digital world. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit collects information. The analysis unit analyzes the information collected by the collection unit. The provision unit provides information based on the analysis results obtained by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that a visual experience that seamlessly moves between the real world and the digital world has not been sufficiently provided.

[0005] The system according to the embodiment aims to provide a visual experience that can seamlessly move between the real world and the digital world.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects information. The analysis unit analyzes the information collected by the collection unit. The provision unit provides information based on the analysis result obtained by the analysis unit.

Effects of the Invention

[0007] The system according to this embodiment can provide a visual experience that allows seamless transitions between the real world and the digital world. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 next-generation AR technology and AI-integrated system according to an embodiment of the present invention is an AI glasses system that provides users with a new visual experience. This AI glasses system adds interactive information and future predictions to every moment of daily life through the user's field of vision, providing an innovative experience that allows users to seamlessly move between the real and digital worlds. For example, the AI ​​glasses system allows users to visually check information published by other AI glasses users and stores in real time through an AR information sharing platform. Next, the AI ​​glasses system provides real-time behavioral support, suggesting optimal travel routes, congestion levels, and schedule management in real time. Furthermore, the AI ​​glasses system streamlines networking by instantly displaying profiles and expertise of others in AR during business and social settings through real-time networking support. As a personal shopping assistant, the AI ​​analyzes the user's past purchase history, preferences, and current needs to support their shopping. Finally, the AI ​​glasses system provides smart travel and sightseeing support, displaying nearby tourist attractions and recommended activities in real time during travel. In this way, the AI ​​glasses system can provide users with a new visual experience, making daily life more convenient and enriching. This allows AI glasses systems to provide users with new visual experiences, making everyday life more convenient and enriching.

[0029] The AI ​​glasses system according to this embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit collects information. For example, the collection unit can collect information made public by other AI glasses users or stores. The collection unit can also collect, for example, the user's location information and weather information. The collection unit can also collect, for example, the user's past behavior history. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit can analyze the collected information using data mining techniques. For example, the analysis unit can also analyze the collected information using machine learning algorithms. For example, the analysis unit can also analyze the collected information using statistical analysis techniques. The provision unit provides information based on the analysis results obtained by the analysis unit. For example, the provision unit can provide the user with information visually in real time based on the collected information. For example, the provision unit can provide the user with the optimal travel route or evacuation route based on the collected location information and weather information. For example, the provision unit can also provide the user with the optimal products or services based on the analysis results. As a result, the AI ​​glasses system according to this embodiment can seamlessly collect, analyze, and provide information.

[0030] The data collection unit collects information. Specifically, it can collect information made public by other AI glasses users and stores. For example, it can obtain information such as reviews and ratings of places and stores visited by other users in real time. Furthermore, the data collection unit can also collect user location information and weather information. Location information is accurately obtained using GPS, and weather information is obtained in real time from a weather database. This allows users to understand the weather and surrounding conditions at their current location. The data collection unit can also collect the user's past behavior history. For example, it can collect data such as places the user has visited in the past, products purchased, and services used, and use this data to analyze the user's preferences and behavior patterns. The data collection unit centrally manages this diverse data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server and made accessible to the analysis and provision departments. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit analyzes the information collected by the data collection unit. Specifically, the analysis unit can analyze the collected information using data mining techniques. By using data mining techniques, it is possible to extract useful patterns and relationships from large amounts of data and predict user behavior and preferences. Furthermore, the analysis unit can also analyze the collected information using machine learning algorithms. By using machine learning algorithms, it is possible to automatically learn from the data and build predictive models. For example, based on a user's past behavior history, it is possible to predict places they are most likely to visit next or products they are most likely to purchase. In addition, the analysis unit can analyze the collected information using statistical analysis techniques. By using statistical analysis techniques, it is possible to understand the distribution and trends of the data and quantitatively evaluate user behavior patterns. As a result, the analysis unit can analyze the collected data from multiple angles and extract information that is useful to the user. Furthermore, the analysis unit can also utilize historical data and statistical information to perform long-term trend analysis and risk assessment. For example, based on historical weather data, it is possible to predict weather fluctuations in specific regions and time periods and provide appropriate advice to users. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and trend analysis, thereby improving the reliability and safety of the entire system.

[0032] The information provision unit provides information based on the analysis results obtained by the analysis unit. Specifically, the information provision unit can provide users with real-time, visual information based on the collected data. For example, it can visually display weather information for the user's current location and information on nearby stores, allowing users to quickly obtain the information they need. The information provision unit can also provide optimal travel routes and evacuation routes based on the collected location and weather information. For example, if the weather deteriorates, it can present users with safe evacuation routes to help them evacuate quickly. Furthermore, the information provision unit can also provide users with the most suitable products and services based on the analysis results. For example, it can recommend highly relevant products and services based on the user's past purchase history and preferences, thereby increasing the user's willingness to purchase. The information provision unit can provide this information seamlessly to users, improving user convenience. In addition, the information provision unit can collect user feedback and continuously improve the accuracy and effectiveness of the information provided. For example, it can analyze how users reacted to the information provided and optimize the content of future provision. The information provision unit can also reliably transmit information using multiple communication methods. For example, it can reliably convey important information to users by using not only visual displays but also voice guidance and vibration notifications. This allows the service provider to deliver information to users quickly and reliably, improving the overall convenience and reliability of the system.

[0033] The collection unit can collect information made public by other AI glasses users and stores. For example, the collection unit can collect reviews posted by other AI glasses users. For example, the collection unit can collect promotional information offered by stores. For example, the collection unit can collect location information of other AI glasses users. This improves the diversity of information by collecting information from other users and stores. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input reviews posted by other AI glasses users into AI and analyze the content of the reviews.

[0034] The information provider can provide users with information visually in real time based on the collected information. For example, the information provider can provide the collected information to users using AR display technology. For example, the information provider can provide the collected information to users using a HUD (Heads-Up Display). For example, the information provider can provide the collected information to users using text display technology. This improves user convenience by providing information visually in real time. 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 collected information into AI and have AI determine how to display the information.

[0035] The data collection unit can collect user location information and weather information. For example, the data collection unit can collect user location information using GPS data. For example, the data collection unit can also collect weather information using a weather API. For example, the data collection unit can also collect location information from the user's smartphone. By collecting location information and weather information, it becomes possible to provide information tailored to the environment. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input GPS data into AI and have AI perform location information analysis.

[0036] The service provider can provide optimal travel routes and evacuation routes based on collected location and weather information. For example, the service provider can suggest the optimal travel route based on collected location information. For example, the service provider can suggest the optimal evacuation route based on collected weather information. For example, the service provider can suggest the optimal travel route by combining collected location and weather information. This improves user safety by providing optimal travel and evacuation routes. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input collected location and weather information into AI and have the AI ​​suggest the optimal travel route.

[0037] The analysis unit can analyze a user's past purchase history, preferences, and current needs. For example, the analysis unit can analyze a user's purchase history data. For example, the analysis unit can also analyze a user profile. For example, the analysis unit can also analyze a user's search history. By analyzing a user's past data, it becomes possible to provide more personalized information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input a user's purchase history data into AI and have AI perform the purchase history analysis.

[0038] The service provider can provide users with the most suitable products and services based on the analysis results. For example, the service provider can suggest the most suitable products to users based on the analysis results. For example, the service provider can suggest the most suitable services to users based on the analysis results. For example, the service provider can suggest the most suitable locations to users based on the analysis results. By providing products and services based on the analysis results, user satisfaction is improved. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the analysis results into AI and have the AI ​​suggest the most suitable products and services.

[0039] The service provider can provide real-time information on nearby tourist attractions and recommended activities during a trip. For example, the service provider can suggest nearby tourist attractions based on the user's current location. For example, the service provider can suggest recommended activities based on the user's preferences. For example, the service provider can suggest the optimal sightseeing route based on the user's schedule. This improves the convenience of travel by providing information in real time during the trip. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's current location into the AI ​​and have the AI ​​suggest nearby tourist attractions.

[0040] The data collection unit can evaluate the reliability of information published by other AI glasses users and stores, and prioritize the collection of highly reliable information. For example, the data collection unit can evaluate reliability based on the past evaluations of the provider of the published information. For example, the data collection unit can evaluate reliability based on the degree of agreement between the content of the information and other sources. For example, the data collection unit can evaluate reliability based on the frequency of updates and recency of the information. This improves the accuracy of the information by prioritizing the collection of highly reliable information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past evaluation data of the provider of the published information into AI and have AI perform the reliability evaluation.

[0041] The data collection unit can analyze a user's past behavioral history and prioritize the collection of highly relevant information. For example, the data collection unit can prioritize the collection of information related to places the user has visited in the past. For example, the data collection unit can also collect relevant information based on a user's past search history. For example, the data collection unit can also collect relevant product information based on a user's past purchase history. This makes it possible to provide useful information to the user by collecting highly relevant information based on past behavioral history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavioral history data into AI and have the AI ​​perform the collection of highly relevant information.

[0042] The data collection unit collects user location and weather information and can respond to environmental changes in real time based on the collected information. For example, the data collection unit can collect information on nearby events based on the user's current location. For example, the data collection unit can also collect information on indoor and outdoor activities based on weather information. For example, the data collection unit can combine location and weather information to suggest the optimal travel route. This enables the provision of optimal information to the user by responding to environmental changes in real time. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user location and weather information into AI and have the AI ​​perform information collection to respond to environmental changes.

[0043] The data collection unit can analyze a user's social media activity and collect relevant information. For example, the data collection unit can collect relevant information based on the content of posts from accounts that the user follows. For example, the data collection unit can also analyze a user's posts and collect information related to their interests. For example, the data collection unit can collect relevant information based on the activity of the user's friends on social media. This makes it possible to provide information tailored to the user's interests by collecting relevant information based on social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into AI and have AI collect relevant information.

[0044] The analysis unit can improve the accuracy of the analysis by considering the interrelationships of the collected information. For example, the analysis unit can combine and analyze collected location information and weather information. For example, the analysis unit can combine and analyze collected social media information and behavioral history. For example, the analysis unit can combine and analyze collected purchase history and current needs. This improves the accuracy of the analysis by considering the interrelationships of the information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input interrelationship data of collected information into AI and have the AI ​​perform the analysis to improve accuracy.

[0045] The analysis unit can analyze a user's past purchase history, preferences, and current needs to predict future behavior. For example, based on a user's past purchase history, the analysis unit can predict the next product they are most likely to purchase. For example, based on a user's preferences, the analysis unit can predict the next place they are most likely to visit. For example, based on a user's current needs, the analysis unit can predict the next service they will need. This allows for more personalized information provision by predicting future behavior based on the user's past data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past purchase history data into AI and have the AI ​​perform predictions of future behavior.

[0046] The analysis unit can perform analysis while considering the geographical distribution of the collected information. For example, the analysis unit can analyze trends in a specific region based on collected location information. For example, the analysis unit can also analyze weather patterns for each region based on collected weather information. For example, the analysis unit can also analyze interests for each region based on collected social media information. This makes it possible to perform analysis tailored to the characteristics of each region by considering geographical distribution. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input collected geographical distribution data into AI and have the AI ​​perform region-specific analysis.

[0047] The analysis unit can improve the accuracy of its analysis by referring to relevant literature related to the collected information. For example, the analysis unit can perform its analysis by referring to academic papers related to the collected information. For example, the analysis unit can also perform its analysis by referring to patent documents related to the collected information. For example, the analysis unit can also perform its analysis by referring to industry reports related to the collected information. This improves the accuracy of the analysis by referring to relevant literature. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevant literature data of the collected information into the AI ​​and have the AI ​​perform the analysis to improve accuracy.

[0048] The service provider can provide users with the most suitable products and services based on the analysis results. For example, the service provider can suggest the most suitable products to users based on the analysis results. For example, the service provider can suggest the most suitable services to users based on the analysis results. For example, the service provider can suggest the most suitable locations to users based on the analysis results. By providing products and services based on the analysis results, user satisfaction is improved. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the analysis results into AI and have the AI ​​suggest the most suitable products and services.

[0049] The service provider can provide real-time information on nearby tourist attractions and recommended activities during a trip. For example, the service provider can suggest nearby tourist attractions based on the user's current location. For example, the service provider can suggest recommended activities based on the user's preferences. For example, the service provider can suggest the optimal sightseeing route based on the user's schedule. This improves the convenience of travel by providing information in real time during the trip. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's current location into the AI ​​and have the AI ​​suggest nearby tourist attractions.

[0050] The service provider can provide optimal travel routes and evacuation routes based on collected location and weather information. For example, the service provider can suggest the optimal travel route based on the user's current location and weather information. For example, the service provider can suggest the optimal evacuation route based on the user's current location and weather information. For example, the service provider can suggest the optimal mode of transportation based on the user's current location and weather information. This improves user safety by providing optimal travel and evacuation routes. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's current location and weather information into AI and have the AI ​​suggest the optimal travel route.

[0051] The service provider can analyze a user's social media activity and provide relevant information. For example, the service provider can provide relevant information based on the content of posts from accounts the user follows. For example, the service provider can analyze a user's posts and provide information related to their interests. For example, the service provider can provide relevant information based on the activity of the user's friends on social media. This makes it possible to provide information tailored to the user's interests by providing relevant information based on social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity data into AI and have the AI ​​perform the task of providing relevant information.

[0052] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0053] The data collection unit can collect user health data and support health management based on the collected data. For example, the data collection unit can collect the user's heart rate and blood pressure. For example, the data collection unit can collect the user's steps and exercise level. For example, the data collection unit can collect the user's sleep patterns. This makes it possible to understand the user's health status in real time and support appropriate health management. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's health data into AI and have AI perform an analysis of the health status.

[0054] The analysis unit can analyze the user's meal data and propose a healthy meal plan. For example, the analysis unit can analyze the user's meal content and evaluate its nutritional balance. For example, the analysis unit can also propose a meal plan that takes into account the user's allergy information. For example, the analysis unit can also propose calorie restrictions based on the user's weight management goals. This allows for more effective health management by providing a meal plan tailored to the user's health condition. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's meal data into AI and have AI propose a meal plan.

[0055] The data collection unit can collect the user's exercise data and propose an exercise plan based on the collected data. For example, the data collection unit can collect the user's exercise volume and exercise time. For example, the data collection unit can also collect the user's heart rate and calorie consumption. For example, the data collection unit can also collect the user's exercise history. This makes it possible to understand the user's exercise status in real time and propose an appropriate exercise plan. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's exercise data into AI and have AI propose an exercise plan.

[0056] The analysis unit can analyze user purchase data and identify purchasing patterns. For example, the analysis unit can analyze a user's purchase history and identify frequently purchased items. For example, the analysis unit can analyze a user's purchase history and identify items purchased during specific seasons. For example, the analysis unit can analyze a user's purchase history and identify items related to specific events. This allows for more personalized product recommendations by identifying user purchasing patterns. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user purchase data into AI and have AI perform the identification of purchasing patterns.

[0057] The data collection unit can collect data on the user's hobbies and interests and support their hobby activities based on the collected data. For example, the data collection unit can collect information on the user's hobbies. For example, the data collection unit can collect event information related to the user's interests. For example, the data collection unit can collect product information related to the user's hobbies. This makes it possible to provide information tailored to the user's hobbies and interests. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's hobby data into AI and have the AI ​​perform support for the hobby activities.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The collection unit collects information. The collection unit can collect information such as information made public by other AI glasses users and stores, the user's location information and weather information, and the user's past behavioral history. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit can analyze the collected information using, for example, data mining techniques, machine learning algorithms, and statistical analysis techniques. Step 3: The service provider provides information based on the analysis results obtained by the analysis unit. For example, the service provider can provide users with real-time visual information based on the collected data, or provide optimal travel routes, evacuation routes, or optimal products and services.

[0060] (Example of form 2) The next-generation AR technology and AI-integrated system according to an embodiment of the present invention is an AI glasses system that provides users with a new visual experience. This AI glasses system adds interactive information and future predictions to every moment of daily life through the user's field of vision, providing an innovative experience that allows users to seamlessly move between the real and digital worlds. For example, the AI ​​glasses system allows users to visually check information published by other AI glasses users and stores in real time through an AR information sharing platform. Next, the AI ​​glasses system provides real-time behavioral support, suggesting optimal travel routes, congestion levels, and schedule management in real time. Furthermore, the AI ​​glasses system streamlines networking by instantly displaying profiles and expertise of others in AR during business and social settings through real-time networking support. As a personal shopping assistant, the AI ​​analyzes the user's past purchase history, preferences, and current needs to support their shopping. Finally, the AI ​​glasses system provides smart travel and sightseeing support, displaying nearby tourist attractions and recommended activities in real time during travel. In this way, the AI ​​glasses system can provide users with a new visual experience, making daily life more convenient and enriching. This allows AI glasses systems to provide users with new visual experiences, making everyday life more convenient and enriching.

[0061] The AI ​​glasses system according to this embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit collects information. For example, the collection unit can collect information made public by other AI glasses users or stores. The collection unit can also collect, for example, the user's location information and weather information. The collection unit can also collect, for example, the user's past behavior history. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit can analyze the collected information using data mining techniques. For example, the analysis unit can also analyze the collected information using machine learning algorithms. For example, the analysis unit can also analyze the collected information using statistical analysis techniques. The provision unit provides information based on the analysis results obtained by the analysis unit. For example, the provision unit can provide the user with information visually in real time based on the collected information. For example, the provision unit can provide the user with the optimal travel route or evacuation route based on the collected location information and weather information. For example, the provision unit can also provide the user with the optimal products or services based on the analysis results. As a result, the AI ​​glasses system according to this embodiment can seamlessly collect, analyze, and provide information.

[0062] The data collection unit collects information. Specifically, it can collect information made public by other AI glasses users and stores. For example, it can obtain information such as reviews and ratings of places and stores visited by other users in real time. Furthermore, the data collection unit can also collect user location information and weather information. Location information is accurately obtained using GPS, and weather information is obtained in real time from a weather database. This allows users to understand the weather and surrounding conditions at their current location. The data collection unit can also collect the user's past behavior history. For example, it can collect data such as places the user has visited in the past, products purchased, and services used, and use this data to analyze the user's preferences and behavior patterns. The data collection unit centrally manages this diverse data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server and made accessible to the analysis and provision departments. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0063] The analysis unit analyzes the information collected by the data collection unit. Specifically, the analysis unit can analyze the collected information using data mining techniques. By using data mining techniques, it is possible to extract useful patterns and relationships from large amounts of data and predict user behavior and preferences. Furthermore, the analysis unit can also analyze the collected information using machine learning algorithms. By using machine learning algorithms, it is possible to automatically learn from the data and build predictive models. For example, based on a user's past behavior history, it is possible to predict places they are most likely to visit next or products they are most likely to purchase. In addition, the analysis unit can analyze the collected information using statistical analysis techniques. By using statistical analysis techniques, it is possible to understand the distribution and trends of the data and quantitatively evaluate user behavior patterns. As a result, the analysis unit can analyze the collected data from multiple angles and extract information that is useful to the user. Furthermore, the analysis unit can also utilize historical data and statistical information to perform long-term trend analysis and risk assessment. For example, based on historical weather data, it is possible to predict weather fluctuations in specific regions and time periods and provide appropriate advice to users. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and trend analysis, thereby improving the reliability and safety of the entire system.

[0064] The information provision unit provides information based on the analysis results obtained by the analysis unit. Specifically, the information provision unit can provide users with real-time, visual information based on the collected data. For example, it can visually display weather information for the user's current location and information on nearby stores, allowing users to quickly obtain the information they need. The information provision unit can also provide optimal travel routes and evacuation routes based on the collected location and weather information. For example, if the weather deteriorates, it can present users with safe evacuation routes to help them evacuate quickly. Furthermore, the information provision unit can also provide users with the most suitable products and services based on the analysis results. For example, it can recommend highly relevant products and services based on the user's past purchase history and preferences, thereby increasing the user's willingness to purchase. The information provision unit can provide this information seamlessly to users, improving user convenience. In addition, the information provision unit can collect user feedback and continuously improve the accuracy and effectiveness of the information provided. For example, it can analyze how users reacted to the information provided and optimize the content of future provision. The information provision unit can also reliably transmit information using multiple communication methods. For example, it can reliably convey important information to users by using not only visual displays but also voice guidance and vibration notifications. This allows the service provider to deliver information to users quickly and reliably, improving the overall convenience and reliability of the system.

[0065] The collection unit can collect information made public by other AI glasses users and stores. For example, the collection unit can collect reviews posted by other AI glasses users. For example, the collection unit can collect promotional information offered by stores. For example, the collection unit can collect location information of other AI glasses users. This improves the diversity of information by collecting information from other users and stores. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input reviews posted by other AI glasses users into AI and analyze the content of the reviews.

[0066] The information provider can provide users with information visually in real time based on the collected information. For example, the information provider can provide the collected information to users using AR display technology. For example, the information provider can provide the collected information to users using a HUD (Heads-Up Display). For example, the information provider can provide the collected information to users using text display technology. This improves user convenience by providing information visually in real time. 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 collected information into AI and have AI determine how to display the information.

[0067] The data collection unit can collect user location information and weather information. For example, the data collection unit can collect user location information using GPS data. For example, the data collection unit can also collect weather information using a weather API. For example, the data collection unit can also collect location information from the user's smartphone. By collecting location information and weather information, it becomes possible to provide information tailored to the environment. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input GPS data into AI and have AI perform location information analysis.

[0068] The service provider can provide optimal travel routes and evacuation routes based on collected location and weather information. For example, the service provider can suggest the optimal travel route based on collected location information. For example, the service provider can suggest the optimal evacuation route based on collected weather information. For example, the service provider can suggest the optimal travel route by combining collected location and weather information. This improves user safety by providing optimal travel and evacuation routes. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input collected location and weather information into AI and have the AI ​​suggest the optimal travel route.

[0069] The analysis unit can analyze a user's past purchase history, preferences, and current needs. For example, the analysis unit can analyze a user's purchase history data. For example, the analysis unit can also analyze a user profile. For example, the analysis unit can also analyze a user's search history. By analyzing a user's past data, it becomes possible to provide more personalized information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input a user's purchase history data into AI and have AI perform the purchase history analysis.

[0070] The service provider can provide users with the most suitable products and services based on the analysis results. For example, the service provider can suggest the most suitable products to users based on the analysis results. For example, the service provider can suggest the most suitable services to users based on the analysis results. For example, the service provider can suggest the most suitable locations to users based on the analysis results. By providing products and services based on the analysis results, user satisfaction is improved. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the analysis results into AI and have the AI ​​suggest the most suitable products and services.

[0071] The service provider can provide real-time information on nearby tourist attractions and recommended activities during a trip. For example, the service provider can suggest nearby tourist attractions based on the user's current location. For example, the service provider can suggest recommended activities based on the user's preferences. For example, the service provider can suggest the optimal sightseeing route based on the user's schedule. This improves the convenience of travel by providing information in real time during the trip. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's current location into the AI ​​and have the AI ​​suggest nearby tourist attractions.

[0072] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of information collection and collect only important information. For example, if the user is relaxed, the data collection unit can increase the frequency of information collection and collect more detailed information. For example, if the user is in a hurry, the data collection unit can prioritize the collection of necessary information in real time. This allows for more appropriate information collection by adjusting the timing of information collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0073] The data collection unit can evaluate the reliability of information published by other AI glasses users and stores, and prioritize the collection of highly reliable information. For example, the data collection unit can evaluate reliability based on the past evaluations of the provider of the published information. For example, the data collection unit can evaluate reliability based on the degree of agreement between the content of the information and other sources. For example, the data collection unit can evaluate reliability based on the frequency of updates and recency of the information. This improves the accuracy of the information by prioritizing the collection of highly reliable information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past evaluation data of the provider of the published information into AI and have AI perform the reliability evaluation.

[0074] The data collection unit can analyze a user's past behavioral history and prioritize the collection of highly relevant information. For example, the data collection unit can prioritize the collection of information related to places the user has visited in the past. For example, the data collection unit can also collect relevant information based on a user's past search history. For example, the data collection unit can also collect relevant product information based on a user's past purchase history. This makes it possible to provide useful information to the user by collecting highly relevant information based on past behavioral history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavioral history data into AI and have the AI ​​perform the collection of highly relevant information.

[0075] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit can prioritize collecting information that promotes relaxation. For example, if the user is excited, the data collection unit can prioritize collecting information that is of interest. For example, if the user is tired, the data collection unit can prioritize collecting information related to rest. This allows for the provision of more appropriate information by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of information.

[0076] The data collection unit collects user location and weather information and can respond to environmental changes in real time based on the collected information. For example, the data collection unit can collect information on nearby events based on the user's current location. For example, the data collection unit can also collect information on indoor and outdoor activities based on weather information. For example, the data collection unit can combine location and weather information to suggest the optimal travel route. This enables the provision of optimal information to the user by responding to environmental changes in real time. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user location and weather information into AI and have the AI ​​perform information collection to respond to environmental changes.

[0077] The data collection unit can analyze a user's social media activity and collect relevant information. For example, the data collection unit can collect relevant information based on the content of posts from accounts that the user follows. For example, the data collection unit can also analyze a user's posts and collect information related to their interests. For example, the data collection unit can collect relevant information based on the activity of the user's friends on social media. This makes it possible to provide information tailored to the user's interests by collecting relevant information based on social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into AI and have AI collect relevant information.

[0078] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide deep insights. For example, if the user is in a hurry, the analysis unit can perform a concise and rapid analysis. For example, if the user is excited, the analysis unit can provide visually appealing analysis results. By adjusting the analysis method according to the user's emotions, more appropriate analysis results can be obtained. 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 AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the analysis method.

[0079] The analysis unit can improve the accuracy of the analysis by considering the interrelationships of the collected information. For example, the analysis unit can combine and analyze collected location information and weather information. For example, the analysis unit can combine and analyze collected social media information and behavioral history. For example, the analysis unit can combine and analyze collected purchase history and current needs. This improves the accuracy of the analysis by considering the interrelationships of the information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input interrelationship data of collected information into AI and have the AI ​​perform the analysis to improve accuracy.

[0080] The analysis unit can analyze a user's past purchase history, preferences, and current needs to predict future behavior. For example, based on a user's past purchase history, the analysis unit can predict the next product they are most likely to purchase. For example, based on a user's preferences, the analysis unit can predict the next place they are most likely to visit. For example, based on a user's current needs, the analysis unit can predict the next service they will need. This allows for more personalized information provision by predicting future behavior based on the user's past data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past purchase history data into AI and have the AI ​​perform predictions of future behavior.

[0081] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can also provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can also provide a display method that gets straight to the point. By adjusting the display method according to the user's emotions, it becomes possible to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method.

[0082] The analysis unit can perform analysis while considering the geographical distribution of the collected information. For example, the analysis unit can analyze trends in a specific region based on collected location information. For example, the analysis unit can also analyze weather patterns for each region based on collected weather information. For example, the analysis unit can also analyze interests for each region based on collected social media information. This makes it possible to perform analysis tailored to the characteristics of each region by considering geographical distribution. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input collected geographical distribution data into AI and have the AI ​​perform region-specific analysis.

[0083] The analysis unit can improve the accuracy of its analysis by referring to relevant literature related to the collected information. For example, the analysis unit can perform its analysis by referring to academic papers related to the collected information. For example, the analysis unit can also perform its analysis by referring to patent documents related to the collected information. For example, the analysis unit can also perform its analysis by referring to industry reports related to the collected information. This improves the accuracy of the analysis by referring to relevant literature. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevant literature data of the collected information into the AI ​​and have the AI ​​perform the analysis to improve accuracy.

[0084] The information provider can estimate the user's emotions and adjust the way information is delivered based on the estimated emotions. For example, if the user is relaxed, the information provider can provide detailed information. For example, if the user is in a hurry, the information provider can provide concise information. For example, if the user is excited, the information provider can provide visually appealing information. By adjusting the way information is delivered according to the user's emotions, more appropriate information 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 provider may be performed using AI, for example, or not using AI. For example, the information provider can input user emotion data into a generative AI and have the generative AI adjust the way information is delivered.

[0085] The service provider can provide users with the most suitable products and services based on the analysis results. For example, the service provider can suggest the most suitable products to users based on the analysis results. For example, the service provider can suggest the most suitable services to users based on the analysis results. For example, the service provider can suggest the most suitable locations to users based on the analysis results. By providing products and services based on the analysis results, user satisfaction is improved. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the analysis results into AI and have the AI ​​suggest the most suitable products and services.

[0086] The service provider can provide real-time information on nearby tourist attractions and recommended activities during a trip. For example, the service provider can suggest nearby tourist attractions based on the user's current location. For example, the service provider can suggest recommended activities based on the user's preferences. For example, the service provider can suggest the optimal sightseeing route based on the user's schedule. This improves the convenience of travel by providing information in real time during the trip. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's current location into the AI ​​and have the AI ​​suggest nearby tourist attractions.

[0087] The information provider can estimate the user's emotions and determine the priority of the information to be provided based on the estimated emotions. For example, if the user is stressed, the information provider can prioritize providing information that helps them relax. For example, if the user is excited, the information provider can prioritize providing information that interests them. For example, if the user is tired, the information provider can prioritize providing information that helps them rest. This allows for more appropriate information to be provided by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input user emotion data into a generative AI and have the generative AI determine the priority of information.

[0088] The service provider can provide optimal travel routes and evacuation routes based on collected location and weather information. For example, the service provider can suggest the optimal travel route based on the user's current location and weather information. For example, the service provider can suggest the optimal evacuation route based on the user's current location and weather information. For example, the service provider can suggest the optimal mode of transportation based on the user's current location and weather information. This improves user safety by providing optimal travel and evacuation routes. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's current location and weather information into AI and have the AI ​​suggest the optimal travel route.

[0089] The service provider can analyze a user's social media activity and provide relevant information. For example, the service provider can provide relevant information based on the content of posts from accounts the user follows. For example, the service provider can analyze a user's posts and provide information related to their interests. For example, the service provider can provide relevant information based on the activity of the user's friends on social media. This makes it possible to provide information tailored to the user's interests by providing relevant information based on social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity data into AI and have the AI ​​perform the task of providing relevant information.

[0090] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0091] The data collection unit can collect user health data and support health management based on the collected data. For example, the data collection unit can collect the user's heart rate and blood pressure. For example, the data collection unit can collect the user's steps and exercise level. For example, the data collection unit can collect the user's sleep patterns. This makes it possible to understand the user's health status in real time and support appropriate health management. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's health data into AI and have AI perform an analysis of the health status.

[0092] The service provider can estimate the user's emotions and provide entertainment content based on those emotions. For example, if the user is stressed, the service provider can provide relaxing music or videos. If the user is excited, the service provider can provide action movies or sporting events. If the user is sad, the service provider can provide comedies or positive content to lift their spirits. By providing entertainment content that matches the user's emotions, user satisfaction is improved. 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 service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI select entertainment content.

[0093] The analysis unit can analyze the user's meal data and propose a healthy meal plan. For example, the analysis unit can analyze the user's meal content and evaluate its nutritional balance. For example, the analysis unit can also propose a meal plan that takes into account the user's allergy information. For example, the analysis unit can also propose calorie restrictions based on the user's weight management goals. This allows for more effective health management by providing a meal plan tailored to the user's health condition. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's meal data into AI and have AI propose a meal plan.

[0094] The service provider can estimate the user's emotions and provide learning content based on those emotions. For example, if the user is concentrating, the service provider can provide challenging learning content. For example, if the user is tired, the service provider can provide relaxing learning content. For example, if the user is excited, the service provider can provide engaging learning content. By providing learning content that matches the user's emotions, the learning effect is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 service provider may be performed using AI, for example, or without AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI select the learning content.

[0095] The data collection unit can collect the user's exercise data and propose an exercise plan based on the collected data. For example, the data collection unit can collect the user's exercise volume and exercise time. For example, the data collection unit can also collect the user's heart rate and calorie consumption. For example, the data collection unit can also collect the user's exercise history. This makes it possible to understand the user's exercise status in real time and propose an appropriate exercise plan. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's exercise data into AI and have AI propose an exercise plan.

[0096] The service provider can estimate the user's emotions and provide communication support based on the estimated emotions. For example, if the user is nervous, the service provider can offer relaxing topics. For example, if the user is excited, the service provider can offer interesting topics. For example, if the user is sad, the service provider can offer positive topics to lift their spirits. This improves the quality of communication by providing communication support tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI execute the content of the communication support.

[0097] The analysis unit can analyze user purchase data and identify purchasing patterns. For example, the analysis unit can analyze a user's purchase history and identify frequently purchased items. For example, the analysis unit can analyze a user's purchase history and identify items purchased during specific seasons. For example, the analysis unit can analyze a user's purchase history and identify items related to specific events. This allows for more personalized product recommendations by identifying user purchasing patterns. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user purchase data into AI and have AI perform the identification of purchasing patterns.

[0098] The service provider can estimate the user's emotions and provide feedback based on those emotions. For example, if the user is stressed, the service provider can provide relaxing feedback. For example, if the user is excited, the service provider can provide positive feedback. For example, if the user is sad, the service provider can provide encouraging feedback. By providing feedback that matches the user's emotions, user satisfaction is improved. 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 service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI execute the content of the feedback.

[0099] The data collection unit can collect data on the user's hobbies and interests and support their hobby activities based on the collected data. For example, the data collection unit can collect information on the user's hobbies. For example, the data collection unit can collect event information related to the user's interests. For example, the data collection unit can collect product information related to the user's hobbies. This makes it possible to provide information tailored to the user's hobbies and interests. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's hobby data into AI and have the AI ​​perform support for the hobby activities.

[0100] The service provider can estimate the user's emotions and provide reminders based on those emotions. For example, if the user is stressed, the service provider can provide a relaxing reminder. For example, if the user is excited, the service provider can also remind them of an important task. For example, if the user is tired, the service provider can provide a reminder to encourage rest. By providing reminders tailored to the user's emotions, the quality of life for the user is improved. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or 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 service provider may be performed using AI, for example, or without AI. For example, the service provider can input user emotion data into the generative AI and have the generative AI execute the content of the reminder.

[0101] The following briefly describes the processing flow for example form 2.

[0102] Step 1: The collection unit collects information. The collection unit can collect information such as information made public by other AI glasses users and stores, the user's location information and weather information, and the user's past behavioral history. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit can analyze the collected information using, for example, data mining techniques, machine learning algorithms, and statistical analysis techniques. Step 3: The service provider provides information based on the analysis results obtained by the analysis unit. For example, the service provider can provide users with real-time visual information based on the collected data, or provide optimal travel routes, evacuation routes, or optimal products and services.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] Each of the multiple elements described above, including the collection unit, analysis unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects information using the camera 42 and microphone 38B of the smart device 14, and the control unit 46A collects the user's location information and weather information. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and analyzes the collected information using data mining techniques and machine learning algorithms. The provision unit provides the user with information visually in real time based on the analysis results, for example, using the display 40A and speaker 40B of the smart device 14. The collection unit can also be implemented in the specific processing unit 290 of the data processing unit 12, and can collect the user's past behavior history. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0107] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] Each of the multiple elements described above, including the collection unit, analysis unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects information using the camera 42 and microphone 238 of the smart glasses 214, and the control unit 46A collects the user's location information and weather information. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and analyzes the collected information using data mining techniques and machine learning algorithms. The provision unit provides the user with information visually in real time based on the analysis results, for example, using the display and speaker 240 of the smart glasses 214. The collection unit can also be implemented, for example, in the specific processing unit 290 of the data processing unit 12, and can collect the user's past behavior history. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0123] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] Each of the multiple elements described above, including the collection unit, analysis unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects information using the camera 42 and microphone 238 of the headset terminal 314, and the control unit 46A collects the user's location information and weather information. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and analyzes the collected information using data mining techniques and machine learning algorithms. The provision unit provides the user with information visually in real time based on the analysis results, for example, using the display 343 and speaker 240 of the headset terminal 314. The collection unit can also be implemented in the specific processing unit 290 of the data processing unit 12, and can collect the user's past behavior history. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0139] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] Each of the multiple elements described above, including the collection unit, analysis unit, and provision unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects information using the camera 42 and microphone 238 of the robot 414, and the control unit 46A collects the user's location information and weather information. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and analyzes the collected information using data mining techniques and machine learning algorithms. The provision unit provides the user with information visually in real time based on the analysis results, for example, using the display and speaker 240 of the robot 414. The collection unit can also be implemented, for example, in the specific processing unit 290 of the data processing unit 12, and can collect the user's past behavior history. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] (Note 1) The information collection unit, An analysis unit analyzes the information collected by the aforementioned collection unit, The system includes a providing unit that provides information based on the analysis results obtained by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect information made public by other AI glasses users and stores. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, Based on the collected information, provide users with visual, real-time information. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is Collects user location and weather information. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Based on collected location and weather information, it provides optimal travel and evacuation routes. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, Analyze the user's past purchase history, preferences, and current needs. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, Based on the analysis results, we provide users with the most suitable products and services. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned supply unit is, We provide real-time information on nearby attractions and recommended activities during your trip. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It evaluates the reliability of information published by other AI glasses users and stores, and prioritizes collecting highly reliable information. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is Analyze the user's past behavior history and prioritize collecting highly relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is It collects user location and weather information and responds to environmental changes in real time based on the collected information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is Analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, Improve the accuracy of the analysis by considering the interrelationships of the collected information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, By analyzing users' past purchase history, preferences, and current needs, we predict their future behavior. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, The analysis will take into account the geographical distribution of the collected information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, Improve the accuracy of the analysis by referring to relevant literature on the collected information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, It estimates the user's emotions and adjusts how information is delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, Based on the analysis results, we provide users with the most suitable products and services. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, We provide real-time information on nearby attractions and recommended activities during your trip. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, Based on collected location and weather information, it provides optimal travel and evacuation routes. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, Analyze users' social media activity and provide relevant information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The information collection unit, An analysis unit analyzes the information collected by the aforementioned collection unit, The system includes a providing unit that provides information based on the analysis results obtained by the analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect information made public by other AI glasses users and stores. The system according to feature 1.

3. The aforementioned supply unit is, Based on the collected information, provide users with visual, real-time information. The system according to feature 1.

4. The aforementioned collection unit is Collects user location and weather information. The system according to feature 1.

5. The aforementioned supply unit is, Based on collected location and weather information, it provides optimal travel and evacuation routes. The system according to feature 1.

6. The aforementioned analysis unit, Analyze the user's past purchase history, preferences, and current needs. The system according to feature 1.

7. The aforementioned supply unit is, Based on the analysis results, we provide users with the most suitable products and services. The system according to feature 1.

8. The aforementioned supply unit is, We provide real-time information on nearby attractions and recommended activities during your trip. The system according to feature 1.

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