system

Abstract

The system according to this embodiment aims to centrally collect, analyze, provide, and share environmental information. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a provision unit, and a communication unit. The collection unit collects environmental information. The analysis unit analyzes the information collected by the collection unit. The provision unit provides the information analyzed by the analysis unit. The communication unit shares the information provided by the provision unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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, the collection, analysis, provision, and sharing of environmental information are not performed in a unified manner, and there is room for improvement.

[0005] The system according to the embodiment aims to collect, analyze, provide, and share environmental information in a unified manner.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a provision unit, and a communication unit. The collection unit collects environmental information. The analysis unit analyzes the information collected by the collection unit. The provision unit provides the information analyzed by the analysis unit. The communication unit shares the information provided by the provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can centrally collect, analyze, provide, and share environmental information. [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 environmental information provision system according to an embodiment of the present invention is a new platform that seizes a major opportunity in the environmental business and outputs environmental information encompassing services unique to Lifestyle-Driven (LY). This environmental information provision system contributes to solving environmental problems and promotes corporate public relations by collecting, analyzing, providing, and sharing environmental information. For example, the environmental information provision system collects environmental information from sensors and external data sources. Next, the environmental information provision system analyzes the collected information using AI and provides information useful for solving environmental problems. For example, it provides disaster information and traffic congestion information in real time and suggests the optimal route. Furthermore, the environmental information provision system provides the analysis results to the user and provides corporate PR information and news related to the environment. Finally, the environmental information provision system promotes information sharing through SNS functions and facilitates communication among users. In this way, the environmental information provision system provides a new platform that contributes to solving environmental problems and promotes corporate public relations. As a result, the environmental information provision system can efficiently collect, analyze, provide, and share environmental information.

[0029] The environmental information provision system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, and a communication unit. The collection unit collects environmental information. The collection unit collects environmental information from, for example, sensors or external data sources. The collection unit can collect environmental information using, for example, temperature sensors, humidity sensors, air quality sensors, etc. The collection unit can also acquire information from external data sources such as weather databases or environmental monitoring systems. The analysis unit analyzes the information collected by the collection unit. The analysis unit can analyze the collected information using, for example, AI, and provide information that is useful for solving environmental problems. The analysis unit can analyze data using, for example, machine learning algorithms, and perform anomaly detection and prediction. The analysis unit can also analyze complex data patterns using deep learning technology. The provision unit provides the information analyzed by the analysis unit. The provision unit can, for example, provide the analysis results to the user and provide corporate PR information and environmental news. The provision unit can provide information through, for example, a web application or a mobile application. The provision unit can also provide information using email or push notifications. The Communication Department shares information provided by the Provision Department. The Communication Department facilitates information sharing and communication among users, for example, through SNS functions. The Communication Department can provide, for example, an SNS platform equipped with posting and commenting functions. The Communication Department also enables users to share information with other users through information sharing functions. As a result, the environmental information provision system according to the embodiment can efficiently collect, analyze, provide, and share environmental information.

[0030] The data collection unit collects environmental information. For example, it collects environmental information from sensors and external data sources. Specifically, it can collect environmental information using temperature sensors, humidity sensors, air quality sensors, etc. Temperature sensors measure ambient temperature in real time and collect data. Humidity sensors measure humidity in the air and understand environmental humidity fluctuations. Air quality sensors measure the concentration of harmful substances such as carbon dioxide, carbon monoxide, and PM2.5 to evaluate air quality. These sensors are installed inside and outside buildings, and data is collected periodically and transmitted to a central database. The data collection unit can also obtain information from external data sources such as weather databases and environmental monitoring systems. For example, weather information such as rainfall, wind speed, and temperature can be obtained from weather databases, and data on local environmental conditions and pollution levels can be obtained from environmental monitoring systems. This allows the data collection unit to collect a wide range of environmental information from diverse data sources and understand environmental conditions in real time. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and provisioning departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit uses AI to analyze the collected information and provide information useful for solving environmental problems. Specifically, it can use machine learning algorithms to analyze data and perform anomaly detection and prediction. For example, it can analyze temperature and humidity data to detect abnormal weather patterns and environmental fluctuations. It can also analyze data from air quality sensors to detect increases in pollution levels in specific areas at an early stage. Furthermore, it can analyze complex data patterns using deep learning technology. Deep learning uses multi-layered neural networks to extract data features and perform advanced analysis. For example, it can predict future weather patterns based on past weather data and assess the risk of extreme weather events. It can also analyze data from environmental monitoring systems to assess regional environmental risks. This allows the analysis unit to quickly and accurately analyze collected data and provide information useful for the early detection and prevention of environmental problems. Furthermore, the analysis unit can utilize historical data and statistical information to conduct long-term environmental risk assessments and trend analyses. For example, it can predict fluctuations in environmental risks in specific areas or time periods based on historical data and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term environmental risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The information provision department provides information analyzed by the analysis department. For example, the information provision department provides users with analysis results, corporate PR information, and environmental news. Specifically, information can be provided through web applications and mobile applications. Through these applications, users can check environmental information in real time and view analysis results. The information provision department can also provide information using email and push notifications. For example, if an abnormal environmental change is detected in a particular area, a notification can be sent immediately to the user to draw their attention. Furthermore, the information provision department can also provide customized information. It selectively provides specific environmental information and analysis results according to the user's interests and needs. For example, users living in a particular area can be provided with a focus on information about the environmental risks in that area, and companies can be provided with environmental information related to their business activities. In this way, the information provision department can provide users with appropriate and useful information and support their understanding of and countermeasures against environmental problems. Furthermore, the information provision department also focuses on visualizing information. Analysis results are provided in visual formats such as graphs, charts, and maps so that users can intuitively understand the information. For example, weather data can be plotted on a map to visually show areas where abnormal weather occurs. Furthermore, air quality data is graphed to show changes over time. This allows the service provider to provide users with clear and effective information.

[0033] The Communications Department shares information provided by the Service Providers. For example, the Communications Department facilitates information sharing and user communication through social networking services (SNS) functions. Specifically, it can provide an SNS platform equipped with posting and commenting functions. Users can post analysis results and environmental information, sharing it with other users. They can also comment on other users' posts, offering opinions and feedback. This allows users to share information and raise awareness of environmental issues. Furthermore, the Communications Department enables users to share information with others through information sharing functions. For example, it allows for easy sharing of analysis results and environmental information via SNS, email, and messaging apps. This enables users to quickly spread important information. The Communications Department also collects user feedback and uses it to improve the system. For example, it adds new features and improves existing ones based on user opinions and requests. This allows the Communications Department to respond flexibly to user needs and improve the overall usability of the system. Finally, the Communications Department can support discussions and events related to environmental issues. For example, it can provide a platform for experts and users to discuss environmental issues and share knowledge through online forums and webinars. This will allow the communications department to promote interaction among users and deepen their understanding and awareness of environmental issues.

[0034] The data collection unit can collect environmental information from sensors and external data sources. For example, the data collection unit can collect environmental information using temperature sensors, humidity sensors, air quality sensors, etc. For example, the data collection unit can collect temperature data using a temperature sensor. It can also collect humidity data using a humidity sensor. Furthermore, the data collection unit can measure the concentration of harmful substances in the air using an air quality sensor. The data collection unit can also acquire information from external data sources such as weather databases and environmental monitoring systems. For example, the data collection unit can acquire weather information from a weather database. It can also acquire environmental data from an environmental monitoring system. This allows the data collection unit to collect environmental information from a variety of data sources. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from sensors into a generating AI and have the generating AI perform data preprocessing and filtering.

[0035] The analysis unit can analyze collected information using AI and provide information useful for solving environmental problems. For example, the analysis unit can analyze data using machine learning algorithms to perform anomaly detection and prediction. For example, the analysis unit can detect anomalous patterns from collected data using machine learning algorithms. The analysis unit can also analyze complex data patterns using deep learning technology. For example, it can predict future environmental changes from collected data using deep learning technology. Furthermore, the analysis unit can analyze text data using natural language processing technology and generate news and reports on the environment. For example, it can extract important information from collected text data and generate summaries using natural language processing technology. In this way, the analysis unit can provide information useful for solving environmental problems by using AI. 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 data into a generating AI and have the generating AI perform the data analysis.

[0036] The service provider can provide users with analysis results, corporate PR information, and environmental news. For example, the service provider can provide users with analysis results through web applications or mobile applications. For example, the service provider can display analysis results on a web application dashboard. The service provider can also notify users of analysis results through mobile applications. Furthermore, the service provider can provide users with analysis results via email or push notifications. For example, the service provider can send analysis results to users via email. The service provider can also notify users of analysis results in real time using push notifications. The service provider can also provide corporate PR information and environmental news. For example, the service provider can provide users with reports on companies' environmental activities and information on the environmental performance of their products. The service provider can also provide users with news on the latest environmental regulations and advancements in environmental technology. This allows the service provider to provide users with analysis results, corporate PR information, and environmental news. Some or all of the above-described processes in the service provider may be performed using AI, or not. For example, the service provider can input the analysis results into a generating AI and have the AI ​​generate the information to be provided to the user.

[0037] The Communication Department can facilitate information sharing and communication among users through SNS functions. For example, the Communication Department can provide an SNS platform equipped with posting and commenting functions. For example, the Communication Department can provide an SNS platform where users can post environmental information and other users can leave comments. The Communication Department can also enable users to share information with other users through information sharing functions. For example, the Communication Department can provide a function that allows users to share environmental information they are interested in. Furthermore, the Communication Department can also provide a chat function to facilitate communication among users. For example, the Communication Department can provide a chat function that allows users to communicate in real time. In this way, the Communication Department can facilitate information sharing and communication among users through SNS functions. Some or all of the above processing in the Communication Department may be performed using AI, for example, or without AI. For example, the Communication Department can input user posts into a generating AI and have the generating AI perform analysis of the posts and generate feedback.

[0038] The data collection unit can analyze past environmental data and select the optimal data collection method. For example, the data collection unit can select a method that is highly efficient for a specific time period based on past data. For example, the data collection unit can optimize the placement of specific sensors based on past data. The data collection unit can also analyze past data and select the optimal data collection method under specific environmental conditions. For example, the data collection unit can determine the optimal sensor placement under specific weather conditions based on past data. This allows the data collection unit to select the optimal data collection method based on past data. 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 environmental data into a generating AI and have the generating AI select the optimal data collection method.

[0039] The data collection unit can filter environmental information based on specific environmental conditions or events. For example, the unit can prioritize collecting data when air pollution is high. For example, the unit can prioritize collecting environmental information related to a disaster. The unit can also filter and collect relevant data based on specific events (e.g., large-scale traffic congestion). For example, when a specific event occurs, the unit can prioritize collecting environmental information related to that event. This allows the unit to filter and collect data based on specific conditions or events. 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 data about specific environmental conditions or events into a generating AI and have the generating AI perform the filtering process.

[0040] The data collection unit can prioritize the collection of highly relevant information by considering geographical location information when collecting environmental information. For example, the data collection unit can prioritize the collection of environmental information around the user's current location. For example, the data collection unit can obtain the user's current location using GPS data and collect environmental information around it. The data collection unit can also collect environmental information in advance for places the user plans to visit. For example, the data collection unit can obtain the user's schedule from a calendar application and collect environmental information for places the user plans to visit. Furthermore, the data collection unit can prioritize the collection of information for highly relevant places based on the user's past travel history. For example, the data collection unit can analyze the user's past travel history and prioritize the collection of environmental information for places the user frequently visits. This allows the data collection unit to prioritize the collection of highly relevant information by considering geographical location 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 geographical location information into a generating AI and have the generating AI perform the process of determining the priority of highly relevant information.

[0041] The data collection unit can analyze social media activity and collect relevant information when collecting environmental information. For example, the data collection unit can collect information on environmental issues that are trending on social media. For example, the data collection unit can analyze the content of social media posts and collect information on trending environmental issues. The data collection unit can also collect relevant environmental information based on the content of posts from accounts that the user follows. For example, the data collection unit can analyze the content of posts from accounts that the user follows and collect relevant environmental information. Furthermore, the data collection unit can analyze trends on social media and collect relevant environmental information. For example, the data collection unit can analyze trends on social media and collect relevant environmental information. In this way, the data collection unit can analyze social media activity and collect relevant 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 social media data into a generating AI and have the generating AI collect relevant information.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the environmental information during the analysis. For example, the analysis unit can perform a detailed analysis on information of high importance. The analysis unit can evaluate the importance of the environmental information and perform a detailed analysis on information of high importance. The analysis unit can also perform a simplified analysis on information of low importance. For example, the analysis unit can perform a simplified analysis on information of low importance. Furthermore, the analysis unit can determine the priority of the analysis according to its importance. For example, the analysis unit can prioritize the analysis of information of high importance. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the environmental information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the environmental information into a generating AI and have the generating AI perform the process of adjusting the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the category of environmental information during analysis. For example, the analysis unit can apply a dedicated analysis algorithm to air pollution information. For example, the analysis unit can apply a dedicated machine learning algorithm to analyze air pollution information. The analysis unit can also apply a traffic analysis algorithm to congestion information. For example, the analysis unit can apply a traffic analysis algorithm to analyze congestion information. Furthermore, the analysis unit can apply a disaster analysis algorithm to disaster information. For example, the analysis unit can apply a disaster analysis algorithm to analyze disaster information. This allows the analysis unit to apply an appropriate analysis algorithm depending on the category of environmental 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 the category of environmental information into a generating AI and have the generating AI select an appropriate analysis algorithm.

[0044] The analysis unit can determine the priority of analysis based on the timing of environmental information collection during the analysis. For example, the analysis unit can prioritize the analysis of the most recent information. For example, the analysis unit can evaluate the timing of the collection of collected environmental information and prioritize the analysis of the most recent information. The analysis unit can also analyze long-term trends based on past data. For example, the analysis unit can analyze long-term environmental change trends based on past data. Furthermore, the analysis unit can prioritize the analysis of data collected during specific time periods. For example, the analysis unit can prioritize the analysis of data collected during specific time periods. This allows the analysis unit to determine the priority of analysis based on the timing of environmental information collection. 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 timing of environmental information collection into a generating AI and have the generating AI execute the process of determining the priority of analysis.

[0045] The analysis unit can improve the accuracy of its analysis by referring to relevant literature and data during the analysis. For example, the analysis unit can improve the accuracy of its analysis by referring to the latest research papers. The analysis unit can supplement its analysis results by referring to the latest research papers. The analysis unit can also supplement its analysis results based on past data. For example, the analysis unit can supplement its analysis results based on past data. Furthermore, the analysis unit can improve the accuracy of its analysis by referring to information from other data sources. For example, the analysis unit can supplement its analysis results by referring to information from other data sources. This allows the analysis unit to improve the accuracy of its analysis by referring to relevant literature and data. 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 relevant literature and data into a generating AI and have the generating AI perform processes to improve the accuracy of the analysis.

[0046] The information provider can adjust the level of detail provided based on the importance of the information at the time of provision. For example, the information provider can provide a detailed explanation for information of high importance. The information provider can evaluate the importance of the information and provide a detailed explanation for information of high importance. The information provider can also provide a simplified explanation for information of low importance. For example, the information provider can provide a simplified explanation for information of low importance. Furthermore, the information provider can determine the priority of provision according to importance. For example, the information provider can provide information of high importance first. This allows the information provider to adjust the level of detail provided based on the importance of the information. 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 importance of the information into a generating AI and have the generating AI perform the process of adjusting the level of detail provided.

[0047] The information provider can apply different information provision algorithms depending on the information category at the time of provision. For example, the information provider can apply a rapid provision algorithm to disaster information. For example, the information provider can apply a dedicated provision algorithm to provide disaster information quickly. The information provider can also apply a traffic information provision algorithm to congestion information. For example, the information provider can apply a traffic information provision algorithm to provide congestion information. Furthermore, the information provider can apply a news provision algorithm to environmental news. For example, the information provider can apply a news provision algorithm to provide environmental news. This allows the information provider to apply an appropriate provision algorithm depending on the information category. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the information category into a generating AI and have the generating AI select an appropriate provision algorithm.

[0048] The information provider can determine the priority of information provision based on the timing of information collection. For example, the provider can prioritize providing the latest information. For example, the provider can evaluate the timing of information collection and prioritize providing the latest information. The provider can also provide long-term trends based on historical data. For example, the provider can provide long-term environmental change trends based on historical data. Furthermore, the provider can prioritize providing data collected during specific time periods. For example, the provider can prioritize providing data collected during specific time periods. This allows the provider to determine the priority of information provision based on the timing of information collection. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the provider can input the timing of information collection into a generating AI and have the generating AI perform the process of determining the priority of information provision.

[0049] The service provider can improve the accuracy of its service by referring to relevant market data at the time of service provision. For example, the service provider can improve the accuracy of its service by referring to the latest market data. The service provider can supplement the content of its service by referring to the latest market data. The service provider can also supplement the content of its service based on past market data. For example, the service provider can supplement the content of its service based on past market data. Furthermore, the service provider can improve the accuracy of its service by referring to information from other data sources. For example, the service provider can supplement the content of its service by referring to information from other data sources. This allows the service provider to improve the accuracy of its service by referring to relevant market data. 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 relevant market data into a generating AI and have the generating AI perform processing to improve the accuracy of its service.

[0050] The communication unit can select the optimal method of communication by referring to the user's past communication history. For example, the communication unit can prioritize suggesting communication methods that the user has preferred to use in the past. For example, the communication unit can analyze the user's past communication history and select the optimal method of communication. The communication unit can also send messages at the optimal timing based on the user's past communication history. For example, the communication unit can send messages at the optimal timing based on the user's past communication history. Furthermore, the communication unit can analyze the user's past communication history and suggest the optimal tone and content. For example, the communication unit can suggest the optimal tone and content based on the user's past communication history. This allows the communication unit to select the optimal method by referring to the user's past communication history. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can input the user's past communication history into a generating AI and have the generating AI select the optimal method of communication.

[0051] The communication unit can customize the content of communications based on specific environmental conditions or events. For example, the communication unit can quickly provide relevant information in the event of a disaster. For example, the communication unit can apply a dedicated communication algorithm to quickly provide relevant information in the event of a disaster. The communication unit can also provide relevant information based on specific events (e.g., large-scale traffic congestion). For example, the communication unit can provide information related to a specific event when it occurs. Furthermore, the communication unit can also provide relevant information based on specific environmental conditions (e.g., high levels of air pollution). For example, the communication unit can provide relevant information when air pollution is high. This allows the communication unit to customize the content of communications based on specific environmental conditions or events. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can input data on specific environmental conditions or events into a generating AI and have the generating AI perform the processing to customize the content of communications.

[0052] The communication unit can select the optimal method of communication by considering the user's geographical location information. For example, if the user is in a specific location, the communication unit can provide information related to that location. For example, the communication unit can obtain the user's current location using GPS data and provide information related to that location. The communication unit can also provide information related to movement if the user is on the move. For example, the communication unit can analyze the user's travel route and provide information related to movement. Furthermore, if the communication unit is in a specific region, it can provide information related to that region. For example, the communication unit can provide information related to that region based on the user's past travel history. This allows the communication unit to select the optimal method of communication by considering the user's geographical location information. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal communication method.

[0053] The communications department can analyze social media activity and propose communication content during communication. For example, the communications department can provide relevant information based on topics that users are interested in on social media. For example, the communications department can analyze social media posts and provide relevant information based on topics that users are interested in. The communications department can also provide relevant information based on posts from accounts that users follow. For example, the communications department can analyze posts from accounts that users follow and provide relevant information. Furthermore, the communications department can analyze trends on social media and provide relevant information. For example, the communications department can analyze trends on social media and provide relevant information. This allows the communications department to analyze social media activity and propose communication content. Some or all of the above processing in the communications department may be performed using AI, for example, or without AI. For example, the communications department can input social media data into a generating AI and have the generating AI perform the process of proposing communication content.

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

[0055] The environmental information provision system can further collect energy consumption data and analyze it in combination with environmental information. For example, the collection unit collects energy consumption data from households and businesses and integrates it with environmental data. The analysis unit can analyze this data and identify patterns in energy consumption. Based on the analysis results, the provision unit can provide advice to improve energy efficiency. For example, the provision unit can identify times of day when energy consumption is high and suggest ways to reduce energy consumption during those times. In this way, the environmental information provision system can also contribute to improving energy efficiency.

[0056] The environmental information provision system can further collect user behavior data and analyze it in combination with environmental information. For example, the collection unit collects the user's travel history and activity patterns and integrates them with environmental data. The analysis unit analyzes this data and can identify environmental factors that influence the user's behavior patterns. Based on the analysis results, the provision unit can provide the user with advice on improving their behavior. For example, if a user frequently gets caught in traffic jams, the provision unit can suggest an optimal travel route. In this way, the environmental information provision system can also contribute to improving user behavior.

[0057] The environmental information provision system can further collect user purchase data and analyze it in combination with environmental information. For example, the collection unit collects user purchase history and integrates it with environmental data. The analysis unit analyzes this data and can identify environmental factors that influence user purchasing behavior. Based on the analysis results, the provision unit can provide users with advice on improving their purchasing behavior. For example, the provision unit can provide information that encourages users to purchase environmentally friendly products. In this way, the environmental information provision system can also contribute to improving user purchasing behavior.

[0058] The environmental information provision system can further collect user hobbies and interests and analyze them in combination with environmental information. For example, the collection unit collects data on users' hobbies and interests and integrates it with environmental data. The analysis unit analyzes this data and can identify environmental factors that influence users' hobbies and interests. Based on the analysis results, the provision unit can provide users with environmental information related to their hobbies and interests. For example, if a user enjoys outdoor activities, the provision unit can provide information on optimal outdoor spots. In this way, the environmental information provision system can also contribute to providing information tailored to users' hobbies and interests.

[0059] The environmental information provision system can further collect users' social media activities and analyze them in combination with environmental information. For example, the collection unit collects the content of users' social media posts and integrates it with environmental data. The analysis unit analyzes this data and can identify environmental factors that influence users' interests. Based on the analysis results, the provision unit can provide users with environmental information related to their interests. For example, the provision unit can provide relevant environmental information based on topics that users have shown interest in on social media. In this way, the environmental information provision system can also contribute to providing information tailored to users' social media activities.

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

[0061] Step 1: The collection unit collects environmental information. The collection unit can collect environmental information using, for example, temperature sensors, humidity sensors, and air quality sensors. It can also acquire information from external data sources such as weather databases and environmental monitoring systems. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit uses AI to analyze the collected information and provide information that can help solve environmental problems. The analysis unit can use machine learning algorithms and deep learning technologies to analyze the data and perform anomaly detection and prediction. Step 3: The service provider provides the information analyzed by the analysis provider. For example, the service provider provides users with analysis results, corporate PR information, and environmental news. The service provider can provide information through web applications and mobile applications. It can also provide information using email and push notifications. Step 4: The Communications Department shares the information provided by the Information Provider Department. The Communications Department facilitates information sharing and communication among users, for example, through social networking services (SNS) functions. The Communications Department can provide an SNS platform equipped with posting and commenting functions. It can also enable users to share information with other users through information sharing functions.

[0062] (Example of form 2) The environmental information provision system according to an embodiment of the present invention is a new platform that seizes a major opportunity in the environmental business and outputs environmental information encompassing services unique to Lifestyle-Driven (LY). This environmental information provision system contributes to solving environmental problems and promotes corporate public relations by collecting, analyzing, providing, and sharing environmental information. For example, the environmental information provision system collects environmental information from sensors and external data sources. Next, the environmental information provision system analyzes the collected information using AI and provides information useful for solving environmental problems. For example, it provides disaster information and traffic congestion information in real time and suggests the optimal route. Furthermore, the environmental information provision system provides the analysis results to the user and provides corporate PR information and news related to the environment. Finally, the environmental information provision system promotes information sharing through SNS functions and facilitates communication among users. In this way, the environmental information provision system provides a new platform that contributes to solving environmental problems and promotes corporate public relations. As a result, the environmental information provision system can efficiently collect, analyze, provide, and share environmental information.

[0063] The environmental information provision system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, and a communication unit. The collection unit collects environmental information. The collection unit collects environmental information from, for example, sensors or external data sources. The collection unit can collect environmental information using, for example, temperature sensors, humidity sensors, air quality sensors, etc. The collection unit can also acquire information from external data sources such as weather databases or environmental monitoring systems. The analysis unit analyzes the information collected by the collection unit. The analysis unit can analyze the collected information using, for example, AI, and provide information that is useful for solving environmental problems. The analysis unit can analyze data using, for example, machine learning algorithms, and perform anomaly detection and prediction. The analysis unit can also analyze complex data patterns using deep learning technology. The provision unit provides the information analyzed by the analysis unit. The provision unit can, for example, provide the analysis results to the user and provide corporate PR information and environmental news. The provision unit can provide information through, for example, a web application or a mobile application. The provision unit can also provide information using email or push notifications. The Communication Department shares information provided by the Provision Department. The Communication Department facilitates information sharing and communication among users, for example, through SNS functions. The Communication Department can provide, for example, an SNS platform equipped with posting and commenting functions. The Communication Department also enables users to share information with other users through information sharing functions. As a result, the environmental information provision system according to the embodiment can efficiently collect, analyze, provide, and share environmental information.

[0064] The data collection unit collects environmental information. For example, it collects environmental information from sensors and external data sources. Specifically, it can collect environmental information using temperature sensors, humidity sensors, air quality sensors, etc. Temperature sensors measure ambient temperature in real time and collect data. Humidity sensors measure humidity in the air and understand environmental humidity fluctuations. Air quality sensors measure the concentration of harmful substances such as carbon dioxide, carbon monoxide, and PM2.5 to evaluate air quality. These sensors are installed inside and outside buildings, and data is collected periodically and transmitted to a central database. The data collection unit can also obtain information from external data sources such as weather databases and environmental monitoring systems. For example, weather information such as rainfall, wind speed, and temperature can be obtained from weather databases, and data on local environmental conditions and pollution levels can be obtained from environmental monitoring systems. This allows the data collection unit to collect a wide range of environmental information from diverse data sources and understand environmental conditions in real time. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and provisioning departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0065] The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit uses AI to analyze the collected information and provide information useful for solving environmental problems. Specifically, it can use machine learning algorithms to analyze data and perform anomaly detection and prediction. For example, it can analyze temperature and humidity data to detect abnormal weather patterns and environmental fluctuations. It can also analyze data from air quality sensors to detect increases in pollution levels in specific areas at an early stage. Furthermore, it can analyze complex data patterns using deep learning technology. Deep learning uses multi-layered neural networks to extract data features and perform advanced analysis. For example, it can predict future weather patterns based on past weather data and assess the risk of extreme weather events. It can also analyze data from environmental monitoring systems to assess regional environmental risks. This allows the analysis unit to quickly and accurately analyze collected data and provide information useful for the early detection and prevention of environmental problems. Furthermore, the analysis unit can utilize historical data and statistical information to conduct long-term environmental risk assessments and trend analyses. For example, it can predict fluctuations in environmental risks in specific areas or time periods based on historical data and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term environmental risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0066] The information provision department provides information analyzed by the analysis department. For example, the information provision department provides users with analysis results, corporate PR information, and environmental news. Specifically, information can be provided through web applications and mobile applications. Through these applications, users can check environmental information in real time and view analysis results. The information provision department can also provide information using email and push notifications. For example, if an abnormal environmental change is detected in a particular area, a notification can be sent immediately to the user to draw their attention. Furthermore, the information provision department can also provide customized information. It selectively provides specific environmental information and analysis results according to the user's interests and needs. For example, users living in a particular area can be provided with a focus on information about the environmental risks in that area, and companies can be provided with environmental information related to their business activities. In this way, the information provision department can provide users with appropriate and useful information and support their understanding of and countermeasures against environmental problems. Furthermore, the information provision department also focuses on visualizing information. Analysis results are provided in visual formats such as graphs, charts, and maps so that users can intuitively understand the information. For example, weather data can be plotted on a map to visually show areas where abnormal weather occurs. Furthermore, air quality data is graphed to show changes over time. This allows the service provider to provide users with clear and effective information.

[0067] The Communications Department shares information provided by the Service Providers. For example, the Communications Department facilitates information sharing and user communication through social networking services (SNS) functions. Specifically, it can provide an SNS platform equipped with posting and commenting functions. Users can post analysis results and environmental information, sharing it with other users. They can also comment on other users' posts, offering opinions and feedback. This allows users to share information and raise awareness of environmental issues. Furthermore, the Communications Department enables users to share information with others through information sharing functions. For example, it allows for easy sharing of analysis results and environmental information via SNS, email, and messaging apps. This enables users to quickly spread important information. The Communications Department also collects user feedback and uses it to improve the system. For example, it adds new features and improves existing ones based on user opinions and requests. This allows the Communications Department to respond flexibly to user needs and improve the overall usability of the system. Finally, the Communications Department can support discussions and events related to environmental issues. For example, it can provide a platform for experts and users to discuss environmental issues and share knowledge through online forums and webinars. This will allow the communications department to promote interaction among users and deepen their understanding and awareness of environmental issues.

[0068] The data collection unit can collect environmental information from sensors and external data sources. For example, the data collection unit can collect environmental information using temperature sensors, humidity sensors, air quality sensors, etc. For example, the data collection unit can collect temperature data using a temperature sensor. It can also collect humidity data using a humidity sensor. Furthermore, the data collection unit can measure the concentration of harmful substances in the air using an air quality sensor. The data collection unit can also acquire information from external data sources such as weather databases and environmental monitoring systems. For example, the data collection unit can acquire weather information from a weather database. It can also acquire environmental data from an environmental monitoring system. This allows the data collection unit to collect environmental information from a variety of data sources. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from sensors into a generating AI and have the generating AI perform data preprocessing and filtering.

[0069] The analysis unit can analyze collected information using AI and provide information useful for solving environmental problems. For example, the analysis unit can analyze data using machine learning algorithms to perform anomaly detection and prediction. For example, the analysis unit can detect anomalous patterns from collected data using machine learning algorithms. The analysis unit can also analyze complex data patterns using deep learning technology. For example, it can predict future environmental changes from collected data using deep learning technology. Furthermore, the analysis unit can analyze text data using natural language processing technology and generate news and reports on the environment. For example, it can extract important information from collected text data and generate summaries using natural language processing technology. In this way, the analysis unit can provide information useful for solving environmental problems by using AI. 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 data into a generating AI and have the generating AI perform the data analysis.

[0070] The service provider can provide users with analysis results, corporate PR information, and environmental news. For example, the service provider can provide users with analysis results through web applications or mobile applications. For example, the service provider can display analysis results on a web application dashboard. The service provider can also notify users of analysis results through mobile applications. Furthermore, the service provider can provide users with analysis results via email or push notifications. For example, the service provider can send analysis results to users via email. The service provider can also notify users of analysis results in real time using push notifications. The service provider can also provide corporate PR information and environmental news. For example, the service provider can provide users with reports on companies' environmental activities and information on the environmental performance of their products. The service provider can also provide users with news on the latest environmental regulations and advancements in environmental technology. This allows the service provider to provide users with analysis results, corporate PR information, and environmental news. Some or all of the above-described processes in the service provider may be performed using AI, or not. For example, the service provider can input the analysis results into a generating AI and have the AI ​​generate the information to be provided to the user.

[0071] The Communication Department can facilitate information sharing and communication among users through SNS functions. For example, the Communication Department can provide an SNS platform equipped with posting and commenting functions. For example, the Communication Department can provide an SNS platform where users can post environmental information and other users can leave comments. The Communication Department can also enable users to share information with other users through information sharing functions. For example, the Communication Department can provide a function that allows users to share environmental information they are interested in. Furthermore, the Communication Department can also provide a chat function to facilitate communication among users. For example, the Communication Department can provide a chat function that allows users to communicate in real time. In this way, the Communication Department can facilitate information sharing and communication among users through SNS functions. Some or all of the above processing in the Communication Department may be performed using AI, for example, or without AI. For example, the Communication Department can input user posts into a generating AI and have the generating AI perform analysis of the posts and generate feedback.

[0072] The data collection unit can estimate the user's emotions and adjust the timing of environmental information collection based on the estimated emotions. For example, if the user is feeling stressed, the data collection unit can delay the collection timing to reduce the user's burden. For example, the data collection unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The data collection unit can also record the user's voice and estimate their emotions using voice analysis technology. Furthermore, the data collection unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the data collection unit can calculate an emotion score based on fluctuations in heart rate. This allows the data collection unit to reduce the user's burden by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes 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 generating AI, allowing the generating AI to perform emotion estimation.

[0073] The data collection unit can analyze past environmental data and select the optimal data collection method. For example, the data collection unit can select a method that is highly efficient for a specific time period based on past data. For example, the data collection unit can optimize the placement of specific sensors based on past data. The data collection unit can also analyze past data and select the optimal data collection method under specific environmental conditions. For example, the data collection unit can determine the optimal sensor placement under specific weather conditions based on past data. This allows the data collection unit to select the optimal data collection method based on past data. 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 environmental data into a generating AI and have the generating AI select the optimal data collection method.

[0074] The data collection unit can filter environmental information based on specific environmental conditions or events. For example, the unit can prioritize collecting data when air pollution is high. For example, the unit can prioritize collecting environmental information related to a disaster. The unit can also filter and collect relevant data based on specific events (e.g., large-scale traffic congestion). For example, when a specific event occurs, the unit can prioritize collecting environmental information related to that event. This allows the unit to filter and collect data based on specific conditions or events. 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 data about specific environmental conditions or events into a generating AI and have the generating AI perform the filtering process.

[0075] The data collection unit can estimate the user's emotions and determine the priority of environmental information to collect based on the estimated user emotions. For example, if the user is feeling anxious, the data collection unit will prioritize collecting information that provides a sense of security. For example, the data collection unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The data collection unit can also record the user's voice and estimate their emotions using voice analysis technology. Furthermore, the data collection unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the data collection unit can calculate an emotion score based on fluctuations in heart rate. This allows the data collection unit to determine the priority of information to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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-described 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 generating AI, allowing the generating AI to perform emotion estimation.

[0076] The data collection unit can prioritize the collection of highly relevant information by considering geographical location information when collecting environmental information. For example, the data collection unit can prioritize the collection of environmental information around the user's current location. For example, the data collection unit can obtain the user's current location using GPS data and collect environmental information around it. The data collection unit can also collect environmental information in advance for places the user plans to visit. For example, the data collection unit can obtain the user's schedule from a calendar application and collect environmental information for places the user plans to visit. Furthermore, the data collection unit can prioritize the collection of information for highly relevant places based on the user's past travel history. For example, the data collection unit can analyze the user's past travel history and prioritize the collection of environmental information for places the user frequently visits. This allows the data collection unit to prioritize the collection of highly relevant information by considering geographical location 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 geographical location information into a generating AI and have the generating AI perform the process of determining the priority of highly relevant information.

[0077] The data collection unit can analyze social media activity and collect relevant information when collecting environmental information. For example, the data collection unit can collect information on environmental issues that are trending on social media. For example, the data collection unit can analyze the content of social media posts and collect information on trending environmental issues. The data collection unit can also collect relevant environmental information based on the content of posts from accounts that the user follows. For example, the data collection unit can analyze the content of posts from accounts that the user follows and collect relevant environmental information. Furthermore, the data collection unit can analyze trends on social media and collect relevant environmental information. For example, the data collection unit can analyze trends on social media and collect relevant environmental information. In this way, the data collection unit can analyze social media activity and collect relevant 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 social media data into a generating AI and have the generating AI collect relevant information.

[0078] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is nervous, the analysis unit provides a simple and highly visual analysis result. For example, the analysis unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate their emotions using voice analysis technology. Furthermore, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on heart rate fluctuations. This allows the analysis unit to adjust the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes 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 a generating AI and have the generating AI perform emotion estimation.

[0079] The analysis unit can adjust the level of detail of the analysis based on the importance of the environmental information during the analysis. For example, the analysis unit can perform a detailed analysis on information of high importance. The analysis unit can evaluate the importance of the environmental information and perform a detailed analysis on information of high importance. The analysis unit can also perform a simplified analysis on information of low importance. For example, the analysis unit can perform a simplified analysis on information of low importance. Furthermore, the analysis unit can determine the priority of the analysis according to its importance. For example, the analysis unit can prioritize the analysis of information of high importance. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the environmental information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the environmental information into a generating AI and have the generating AI perform the process of adjusting the level of detail of the analysis.

[0080] The analysis unit can apply different analysis algorithms depending on the category of environmental information during analysis. For example, the analysis unit can apply a dedicated analysis algorithm to air pollution information. For example, the analysis unit can apply a dedicated machine learning algorithm to analyze air pollution information. The analysis unit can also apply a traffic analysis algorithm to congestion information. For example, the analysis unit can apply a traffic analysis algorithm to analyze congestion information. Furthermore, the analysis unit can apply a disaster analysis algorithm to disaster information. For example, the analysis unit can apply a disaster analysis algorithm to analyze disaster information. This allows the analysis unit to apply an appropriate analysis algorithm depending on the category of environmental 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 the category of environmental information into a generating AI and have the generating AI select an appropriate analysis algorithm.

[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, the analysis unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate emotions using voice analysis technology. Furthermore, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on heart rate fluctuations. This allows the analysis unit to adjust the display method of the analysis results 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-described processes 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 a generating AI and have the generating AI perform emotion estimation.

[0082] The analysis unit can determine the priority of analysis based on the timing of environmental information collection during the analysis. For example, the analysis unit can prioritize the analysis of the most recent information. For example, the analysis unit can evaluate the timing of the collection of collected environmental information and prioritize the analysis of the most recent information. The analysis unit can also analyze long-term trends based on past data. For example, the analysis unit can analyze long-term environmental change trends based on past data. Furthermore, the analysis unit can prioritize the analysis of data collected during specific time periods. For example, the analysis unit can prioritize the analysis of data collected during specific time periods. This allows the analysis unit to determine the priority of analysis based on the timing of environmental information collection. 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 timing of environmental information collection into a generating AI and have the generating AI execute the process of determining the priority of analysis.

[0083] The analysis unit can improve the accuracy of its analysis by referring to relevant literature and data during the analysis. For example, the analysis unit can improve the accuracy of its analysis by referring to the latest research papers. The analysis unit can supplement its analysis results by referring to the latest research papers. The analysis unit can also supplement its analysis results based on past data. For example, the analysis unit can supplement its analysis results based on past data. Furthermore, the analysis unit can improve the accuracy of its analysis by referring to information from other data sources. For example, the analysis unit can supplement its analysis results by referring to information from other data sources. This allows the analysis unit to improve the accuracy of its analysis by referring to relevant literature and data. 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 relevant literature and data into a generating AI and have the generating AI perform processes to improve the accuracy of the analysis.

[0084] The service provider can estimate the user's emotions and adjust the way the information is presented based on the estimated emotions. For example, if the user is nervous, the service provider can provide simple and easily visible information. For example, the service provider can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The service provider can also record the user's voice and estimate their emotions using voice analysis technology. Furthermore, the service provider can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the service provider can calculate an emotion score based on fluctuations in heart rate. This allows the service provider to adjust the way information is presented 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-described processes 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 generating AI and have the AI ​​perform emotion estimation.

[0085] The information provider can adjust the level of detail provided based on the importance of the information at the time of provision. For example, the information provider can provide a detailed explanation for information of high importance. The information provider can evaluate the importance of the information and provide a detailed explanation for information of high importance. The information provider can also provide a simplified explanation for information of low importance. For example, the information provider can provide a simplified explanation for information of low importance. Furthermore, the information provider can determine the priority of provision according to importance. For example, the information provider can provide information of high importance first. This allows the information provider to adjust the level of detail provided based on the importance of the information. 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 importance of the information into a generating AI and have the generating AI perform the process of adjusting the level of detail provided.

[0086] The information provider can apply different information provision algorithms depending on the information category at the time of provision. For example, the information provider can apply a rapid provision algorithm to disaster information. For example, the information provider can apply a dedicated provision algorithm to provide disaster information quickly. The information provider can also apply a traffic information provision algorithm to congestion information. For example, the information provider can apply a traffic information provision algorithm to provide congestion information. Furthermore, the information provider can apply a news provision algorithm to environmental news. For example, the information provider can apply a news provision algorithm to provide environmental news. This allows the information provider to apply an appropriate provision algorithm depending on the information category. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the information category into a generating AI and have the generating AI select an appropriate provision algorithm.

[0087] The service 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 feeling anxious, the service provider will prioritize providing information that provides a sense of security. For example, the service provider can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The service provider can also record the user's voice and estimate their emotions using voice analysis technology. Furthermore, the service provider can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the service provider can calculate an emotion score based on fluctuations in heart rate. This allows the service provider to determine the priority of 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-described processes 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 generating AI and have the AI ​​perform emotion estimation.

[0088] The information provider can determine the priority of information provision based on the timing of information collection. For example, the provider can prioritize providing the latest information. For example, the provider can evaluate the timing of information collection and prioritize providing the latest information. The provider can also provide long-term trends based on historical data. For example, the provider can provide long-term environmental change trends based on historical data. Furthermore, the provider can prioritize providing data collected during specific time periods. For example, the provider can prioritize providing data collected during specific time periods. This allows the provider to determine the priority of information provision based on the timing of information collection. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the provider can input the timing of information collection into a generating AI and have the generating AI perform the process of determining the priority of information provision.

[0089] The service provider can improve the accuracy of its service by referring to relevant market data at the time of service provision. For example, the service provider can improve the accuracy of its service by referring to the latest market data. The service provider can supplement the content of its service by referring to the latest market data. The service provider can also supplement the content of its service based on past market data. For example, the service provider can supplement the content of its service based on past market data. Furthermore, the service provider can improve the accuracy of its service by referring to information from other data sources. For example, the service provider can supplement the content of its service by referring to information from other data sources. This allows the service provider to improve the accuracy of its service by referring to relevant market data. 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 relevant market data into a generating AI and have the generating AI perform processing to improve the accuracy of its service.

[0090] The communication unit can estimate the user's emotions and adjust its communication method based on the estimated emotions. For example, if the user is nervous, the communication unit will communicate in a calm tone. For example, the communication unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The communication unit can also record the user's voice and estimate their emotions using voice analysis technology. Furthermore, the communication unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the communication unit can calculate an emotion score based on fluctuations in heart rate. This allows the communication unit to adjust its communication method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communications department can input user emotion data into a generative AI and have the AI ​​perform emotion estimation.

[0091] The communication unit can select the optimal method of communication by referring to the user's past communication history. For example, the communication unit can prioritize suggesting communication methods that the user has preferred to use in the past. For example, the communication unit can analyze the user's past communication history and select the optimal method of communication. The communication unit can also send messages at the optimal timing based on the user's past communication history. For example, the communication unit can send messages at the optimal timing based on the user's past communication history. Furthermore, the communication unit can analyze the user's past communication history and suggest the optimal tone and content. For example, the communication unit can suggest the optimal tone and content based on the user's past communication history. This allows the communication unit to select the optimal method by referring to the user's past communication history. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can input the user's past communication history into a generating AI and have the generating AI select the optimal method of communication.

[0092] The communication unit can customize the content of communications based on specific environmental conditions or events. For example, the communication unit can quickly provide relevant information in the event of a disaster. For example, the communication unit can apply a dedicated communication algorithm to quickly provide relevant information in the event of a disaster. The communication unit can also provide relevant information based on specific events (e.g., large-scale traffic congestion). For example, the communication unit can provide information related to a specific event when it occurs. Furthermore, the communication unit can also provide relevant information based on specific environmental conditions (e.g., high levels of air pollution). For example, the communication unit can provide relevant information when air pollution is high. This allows the communication unit to customize the content of communications based on specific environmental conditions or events. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can input data on specific environmental conditions or events into a generating AI and have the generating AI perform the processing to customize the content of communications.

[0093] The communication unit can estimate the user's emotions and determine communication priorities based on the estimated emotions. For example, if the user is feeling anxious, the communication unit will prioritize sending reassuring messages. The communication unit can, for example, capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. It can also record the user's voice and estimate their emotions using voice analysis technology. Furthermore, the communication unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the communication unit can calculate an emotion score based on fluctuations in heart rate. This allows the communication unit to determine communication priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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-described processes in the communication unit may be performed using AI, for example, or without AI. For example, the communications department can input user emotion data into a generative AI and have the AI ​​perform emotion estimation.

[0094] The communication unit can select the optimal method of communication by considering the user's geographical location information. For example, if the user is in a specific location, the communication unit can provide information related to that location. For example, the communication unit can obtain the user's current location using GPS data and provide information related to that location. The communication unit can also provide information related to movement if the user is on the move. For example, the communication unit can analyze the user's travel route and provide information related to movement. Furthermore, if the communication unit is in a specific region, it can provide information related to that region. For example, the communication unit can provide information related to that region based on the user's past travel history. This allows the communication unit to select the optimal method of communication by considering the user's geographical location information. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal communication method.

[0095] The communications department can analyze social media activity and propose communication content during communication. For example, the communications department can provide relevant information based on topics that users are interested in on social media. For example, the communications department can analyze social media posts and provide relevant information based on topics that users are interested in. The communications department can also provide relevant information based on posts from accounts that users follow. For example, the communications department can analyze posts from accounts that users follow and provide relevant information. Furthermore, the communications department can analyze trends on social media and provide relevant information. For example, the communications department can analyze trends on social media and provide relevant information. This allows the communications department to analyze social media activity and propose communication content. Some or all of the above processing in the communications department may be performed using AI, for example, or without AI. For example, the communications department can input social media data into a generating AI and have the generating AI perform the process of proposing communication content.

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

[0097] The environmental information provision system can also collect user health data and analyze it in combination with environmental information. For example, the collection unit collects user health data (e.g., heart rate, blood pressure, sleep patterns) and integrates it with environmental data. The analysis unit analyzes this data and can identify environmental factors that affect the user's health. Based on the analysis results, the provision unit can provide health advice to the user. For example, if the user is in a high-stress state, the provision unit can suggest a relaxing environment. In this way, the environmental information provision system can also contribute to the user's health management.

[0098] The environmental information provision system can further collect energy consumption data and analyze it in combination with environmental information. For example, the collection unit collects energy consumption data from households and businesses and integrates it with environmental data. The analysis unit can analyze this data and identify patterns in energy consumption. Based on the analysis results, the provision unit can provide advice to improve energy efficiency. For example, the provision unit can identify times of day when energy consumption is high and suggest ways to reduce energy consumption during those times. In this way, the environmental information provision system can also contribute to improving energy efficiency.

[0099] The environmental information provision system can further estimate the user's emotions and adjust the method of providing environmental information based on the estimated emotions. For example, if the user is feeling stressed, the system will prioritize providing relaxing environmental information. The system can, for instance, capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. It can also record the user's voice and estimate their emotions using voice analysis technology. Furthermore, the system can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the system to provide optimal environmental information according to the user's emotions.

[0100] The environmental information provision system can further collect user behavior data and analyze it in combination with environmental information. For example, the collection unit collects the user's travel history and activity patterns and integrates them with environmental data. The analysis unit analyzes this data and can identify environmental factors that influence the user's behavior patterns. Based on the analysis results, the provision unit can provide the user with advice on improving their behavior. For example, if a user frequently gets caught in traffic jams, the provision unit can suggest an optimal travel route. In this way, the environmental information provision system can also contribute to improving user behavior.

[0101] The environmental information provision system can further estimate the user's emotions and adjust its communication method based on those estimated emotions. For example, if the user is tense, the communication unit will communicate in a calm tone. The communication unit can, for instance, capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. It can also record the user's voice and estimate their emotions using voice analysis technology. Furthermore, the communication unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the communication unit to provide the optimal communication method according to the user's emotions.

[0102] The environmental information provision system can further collect user purchase data and analyze it in combination with environmental information. For example, the collection unit collects user purchase history and integrates it with environmental data. The analysis unit analyzes this data and can identify environmental factors that influence user purchasing behavior. Based on the analysis results, the provision unit can provide users with advice on improving their purchasing behavior. For example, the provision unit can provide information that encourages users to purchase environmentally friendly products. In this way, the environmental information provision system can also contribute to improving user purchasing behavior.

[0103] The environmental information provision system can further estimate the user's emotions and determine the priority of the information to be provided based on those estimated emotions. For example, if the user is feeling anxious, the system will prioritize providing information that provides a sense of security. The system can, for instance, capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. It can also record the user's voice and estimate their emotions using voice analysis technology. Furthermore, the system can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the system to provide optimal information according to the user's emotions.

[0104] The environmental information provision system can further collect user hobbies and interests and analyze them in combination with environmental information. For example, the collection unit collects data on users' hobbies and interests and integrates it with environmental data. The analysis unit analyzes this data and can identify environmental factors that influence users' hobbies and interests. Based on the analysis results, the provision unit can provide users with environmental information related to their hobbies and interests. For example, if a user enjoys outdoor activities, the provision unit can provide information on optimal outdoor spots. In this way, the environmental information provision system can also contribute to providing information tailored to users' hobbies and interests.

[0105] The environmental information provision system can further estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is feeling tense, the analysis unit will provide simple and highly visual analysis results. The analysis unit can, for instance, capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. It can also record the user's voice and estimate their emotions using voice analysis technology. Furthermore, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the analysis unit to provide optimal analysis results according to the user's emotions.

[0106] The environmental information provision system can further collect users' social media activities and analyze them in combination with environmental information. For example, the collection unit collects the content of users' social media posts and integrates it with environmental data. The analysis unit analyzes this data and can identify environmental factors that influence users' interests. Based on the analysis results, the provision unit can provide users with environmental information related to their interests. For example, the provision unit can provide relevant environmental information based on topics that users have shown interest in on social media. In this way, the environmental information provision system can also contribute to providing information tailored to users' social media activities.

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

[0108] Step 1: The collection unit collects environmental information. The collection unit can collect environmental information using, for example, temperature sensors, humidity sensors, and air quality sensors. It can also acquire information from external data sources such as weather databases and environmental monitoring systems. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit uses AI to analyze the collected information and provide information that can help solve environmental problems. The analysis unit can use machine learning algorithms and deep learning technologies to analyze the data and perform anomaly detection and prediction. Step 3: The service provider provides the information analyzed by the analysis provider. For example, the service provider provides users with analysis results, corporate PR information, and environmental news. The service provider can provide information through web applications and mobile applications. It can also provide information using email and push notifications. Step 4: The Communications Department shares the information provided by the Information Provider Department. The Communications Department facilitates information sharing and communication among users, for example, through social networking services (SNS) functions. The Communications Department can provide an SNS platform equipped with posting and commenting functions. It can also enable users to share information with other users through information sharing functions.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0111] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] Each of the multiple elements described above, including the data collection unit, analysis unit, provision unit, and communication unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects environmental information from sensors of the smart device 14 or from external data sources. The analysis unit is implemented in, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected information using AI. The provision unit is implemented in, for example, the control unit 46A of the smart device 14 and provides the analysis results to the user. The communication unit facilitates information sharing through, for example, the SNS function of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0114] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the data collection unit, analysis unit, provision unit, and communication unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects environmental information from sensors in the smart glasses 214 or from external data sources. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected information using AI. The provision unit is implemented in the control unit 46A of the smart glasses 214 and provides the analysis results to the user. The communication unit facilitates information sharing through the SNS function of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0130] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the data collection unit, analysis unit, provision unit, and communication unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects environmental information from sensors in the headset terminal 314 or from external data sources. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected information using AI. The provision unit is implemented in the control unit 46A of the headset terminal 314 and provides the analysis results to the user. The communication unit facilitates information sharing through the SNS function of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0146] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0153] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the data collection unit, analysis unit, provision unit, and communication unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects environmental information from the robot 414's sensors and external data sources. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the collected information using AI. The provision unit is implemented, for example, by the control unit 46A of the robot 414, and provides the analysis results to the user. The communication unit facilitates information sharing, for example, through the robot 414's SNS function. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0162] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0164] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0165] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0166] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0170] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0171] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0172] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0173] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0174] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0175] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0177] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0178] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0179] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0180] (Note 1) A collection unit that collects environmental information, An analysis unit analyzes the information collected by the aforementioned collection unit, A providing unit that provides the information analyzed by the aforementioned analysis unit, A communication unit that shares information provided by the aforementioned provisioning unit is included. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect environmental information from sensors and external data sources. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected information is analyzed using AI to provide information that can help solve environmental problems. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, We provide users with analysis results and offer corporate PR information and environmental news. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned communications department, Promote information sharing and facilitate communication among users through SNS features. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of collecting environmental information based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze past environmental data and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting environmental information, filtering is performed based on specific environmental conditions or events. 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 determines the priority of environmental information to collect 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 When collecting environmental information, prioritize the collection of highly relevant information, taking geographical location into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting environmental information, we analyze social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the importance of the environmental information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of environmental 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 how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the environmental information was collected. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, we refer to relevant literature and data to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the information provided is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing information, adjust the level of detail based on its importance. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing information, different delivery algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 21) 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 22) The aforementioned supply unit is, When providing information, the priority of provision will be determined based on when the information was collected. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing the service, we refer to relevant market data to improve the accuracy of the service. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned communications department, It estimates the user's emotions and adjusts the communication method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned communications department, During communication, the system selects the optimal method by referring to the user's past communication history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned communications department, During communication, customize the content of the communication based on specific environmental conditions or events. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned communications department, It estimates the user's emotions and determines communication priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned communications department, When communicating, the optimal method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned communications department, When communicating, we analyze social media activity and suggest content for communication. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

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

Claims

1. A collection unit that collects environmental information, An analysis unit analyzes the information collected by the aforementioned collection unit, A providing unit that provides the information analyzed by the aforementioned analysis unit, A communication unit that shares information provided by the aforementioned provisioning unit is included. A system characterized by the following features.

2. The aforementioned collection unit is Collect environmental information from sensors and external data sources. The system according to feature 1.

3. The aforementioned analysis unit, The collected information is analyzed using AI to provide information that can help solve environmental problems. The system according to feature 1.

4. The aforementioned supply unit is, We provide users with analysis results and offer corporate PR information and environmental news. The system according to feature 1.

5. The aforementioned communications department, Promote information sharing and facilitate communication among users through SNS features. The system according to feature 1.

6. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of collecting environmental information based on the estimated user emotions. The system according to feature 1.

7. The aforementioned collection unit is Analyze past environmental data and select the optimal collection method. The system according to feature 1.

8. The aforementioned collection unit is When collecting environmental information, filtering is performed based on specific environmental conditions or events. The system according to feature 1.

9. The aforementioned collection unit is It estimates the user's emotions and determines the priority of environmental information to collect based on the estimated user emotions. The system according to feature 1.

10. The aforementioned collection unit is When collecting environmental information, prioritize the collection of highly relevant information, taking geographical location into consideration. The system according to feature 1.

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