system

Abstract

The system according to this embodiment aims to extract information from sensor data and images, compare it with past survey data, and provide preliminary survey and preliminary information. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a comparison unit, and a provision unit. The collection unit collects sensor data or images. The analysis unit analyzes the data collected by the collection unit and extracts information. The comparison unit compares the information extracted by the analysis unit with past survey data. The provision unit provides preliminary surveys and preliminary information.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, information extraction and analysis from sensor data and images have not been sufficiently performed, and there is room for improvement in comparison with past survey data and provision of preliminary surveys.

[0005] The system according to the embodiment aims to extract information from sensor data and images, compare it with past survey data, and provide preliminary surveys and preliminary information.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a comparison unit, and a data provision unit. The data collection unit collects sensor data or images. The analysis unit analyzes the data collected by the data collection unit and extracts information. The comparison unit compares the information extracted by the analysis unit with past survey data. The data provision unit provides preliminary surveys and preliminary information. [Effects of the Invention]

[0007] The system according to this embodiment can extract information from sensor data and images, compare it with past survey data, and provide preliminary surveys and preliminary 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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The field survey support system according to an embodiment of the present invention is a system capable of extracting and analyzing information from sensor data and images, comparing it with past survey data, and providing preliminary surveys and background information. The field survey support system collects sensor data and images, and an AI analyzes this data to extract information. Next, the extracted information is compared with past survey data to evaluate the site conditions. Furthermore, by providing preliminary surveys and background information, the field survey can be carried out efficiently. For example, the field survey support system collects sensor data and images. In this case, data is acquired from sensors and cameras installed at the site. For example, data is collected from temperature sensors, humidity sensors, cameras, etc. This allows for a detailed understanding of the site conditions. Next, the AI ​​analyzes the collected data and extracts information. The AI ​​analyzes the sensor data and images to detect abnormal values ​​and specific patterns. For example, it can detect an abnormal temperature rise from temperature sensor data or recognize a specific object from an image. This allows for a detailed evaluation of the site conditions. Furthermore, the extracted information is compared with past survey data. The AI ​​compares past survey data with current data to detect changes and anomalies. For example, it can compare past temperature data with current temperature data to detect abnormal temperature changes. This allows for an accurate assessment of the site conditions. Finally, it provides preliminary surveys and background information. Based on past survey data and related information, the AI ​​provides the information necessary for the site survey. For example, by providing past survey results and related technical information, the site survey can be conducted more efficiently. This improves the accuracy of the site survey and allows for more efficient survey progress. As a result, the site survey support system can extract and analyze information from sensor data and images, compare it with past survey data, and provide preliminary surveys and background information.

[0029] The field survey support system according to the embodiment comprises a collection unit, an analysis unit, a comparison unit, and a provision unit. The collection unit collects sensor data or images. The collection unit collects data from sensors or cameras installed at the site, for example. The collection unit can collect data from temperature sensors, humidity sensors, cameras, etc. For example, the collection unit collects temperature data at the site using a temperature sensor. The collection unit can also collect humidity data at the site using a humidity sensor. Furthermore, the collection unit can also collect image data at the site using a camera. The analysis unit analyzes the data collected by the collection unit and extracts information. For example, the analysis unit analyzes sensor data and images to detect abnormal values ​​or specific patterns. The analysis unit can detect abnormal temperature increases from temperature sensor data. Furthermore, the analysis unit can recognize specific objects from images. Furthermore, the analysis unit can analyze sensor data and images to detect abnormal values ​​or specific patterns. The comparison unit compares the information extracted by the analysis unit with past survey data. For example, the comparison unit compares past survey data with current data to detect changes or anomalies. The comparison unit can compare past temperature data with current temperature data to detect abnormal temperature changes. The comparison unit can also compare past humidity data with current humidity data to detect abnormal humidity changes. Furthermore, the comparison unit can compare past image data with current image data to detect abnormal changes. The provision unit provides preliminary surveys and background information. For example, the provision unit provides information necessary for on-site surveys based on past survey data and related information. The provision unit can provide past survey results and related technical information. The provision unit can also provide information necessary for on-site surveys based on past survey data and related information. As a result, the on-site survey support system according to this embodiment enables information extraction and analysis from sensor data and images, comparison with past survey data, and provision of preliminary surveys and background information.

[0030] The data collection unit collects sensor data or images. For example, it collects data from sensors and cameras installed on-site. Specifically, it can collect data from temperature sensors, humidity sensors, cameras, etc. A temperature sensor measures the on-site temperature in real time and transmits that data to the data collection unit. A humidity sensor measures the on-site humidity and transmits that data to the data collection unit. A camera captures images of the on-site and transmits the image data to the data collection unit. These sensors and cameras are installed in various locations on-site, allowing for the collection of data across a wide range of areas. The data collection unit centrally manages this data and transmits it to the analysis unit. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. For example, if an abnormal temperature change is detected, the data collection unit can increase the frequency of data collection from the temperature sensor to collect more detailed data. The data collection unit can also filter and preprocess the data to ensure data quality. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes data collected by the collection unit and extracts information. Specifically, it analyzes sensor data and images to detect anomalies and specific patterns. For example, it can detect abnormal temperature increases from temperature sensor data. The analysis unit uses AI to process data in real time and detect anomalies and specific patterns. The AI ​​uses machine learning algorithms to learn from past data and can detect anomalies and specific patterns with high accuracy. For example, it can analyze temperature sensor data to detect abnormal temperature increases. It can also use image recognition technology to recognize specific objects from images. For example, it can analyze images of a site to detect specific objects or abnormal changes. Furthermore, the analysis unit can analyze sensor data and images to detect anomalies and specific patterns. As a result, the analysis unit can quickly and accurately analyze collected data and grasp the situation on site in real time. In addition, the analysis unit can utilize past data and statistical information to perform long-term risk assessments and trend analyses. For example, based on past temperature data, it can predict risk fluctuations in specific regions and time periods 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 risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The comparison unit compares the information extracted by the analysis unit with past survey data. Specifically, it compares past survey data with current data to detect changes and anomalies. For example, it can compare past temperature data with current temperature data to detect abnormal temperature changes. The comparison unit uses AI to analyze the data and compare past and current data with high accuracy. The AI ​​uses machine learning algorithms to learn from past data and detect abnormal changes with high accuracy. For example, it can compare past temperature data with current temperature data to detect abnormal temperature changes. It can also compare past humidity data with current humidity data to detect abnormal humidity changes. Furthermore, it can compare past image data with current image data to detect abnormal changes. As a result, the comparison unit can compare past and current data with high accuracy and grasp the situation on site in real time. In addition, the comparison unit can utilize past data and statistical information to perform long-term risk assessments and trend analyses. For example, based on past temperature data, it can predict risk fluctuations in specific areas or time periods and formulate future countermeasures. The comparison unit can also use anomaly detection algorithms to detect patterns that are different from the norm or abnormal data and issue warnings early. This allows the comparison unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0033] The service provider will provide preliminary surveys and background information. Specifically, they will provide information necessary for on-site surveys based on past survey data and related information. For example, they can provide past survey results and related technical information. The service provider can analyze data using AI and provide information necessary for on-site surveys with high accuracy. The AI ​​will use machine learning algorithms to learn from past data and provide information necessary for on-site surveys with high accuracy. For example, they will provide information necessary for on-site surveys based on past survey results and related technical information. The service provider can also provide information necessary for on-site surveys based on past survey data and related information. This allows the service provider to provide information necessary for on-site surveys quickly and accurately, improving the efficiency of on-site surveys. Furthermore, the service provider can utilize past data and statistical information to conduct long-term risk assessments and trend analyses. For example, they can predict risk fluctuations in specific regions and time periods based on past survey data and formulate future countermeasures. In addition, the service provider can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the service provider to provide information necessary for on-site surveys quickly and accurately, improving the efficiency of on-site surveys.

[0034] The data collection unit can collect data from sensors and cameras installed on-site. For example, the data collection unit can collect data from temperature sensors, humidity sensors, and cameras installed on-site. The data collection unit can collect temperature data from the site using a temperature sensor. The data collection unit can also collect humidity data from the site using a humidity sensor. Furthermore, the data collection unit can collect image data from the site using a camera. This allows for a detailed understanding of the site conditions. 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 data acquired from sensors and cameras installed on-site into a generating AI and have the generating AI perform data analysis.

[0035] The analysis unit can analyze sensor data or images to detect abnormal values ​​or specific patterns. For example, the analysis unit can analyze sensor data or images to detect abnormal values ​​or specific patterns. The analysis unit can detect abnormal temperature increases from temperature sensor data. The analysis unit can also recognize specific objects from images. Furthermore, the analysis unit can analyze sensor data or images to detect abnormal values ​​or specific patterns. This allows for a detailed evaluation of the situation on site. 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 sensor data or images into a generating AI and have the generating AI perform the detection of abnormal values ​​or specific patterns.

[0036] The comparison unit can compare past survey data with current data to detect changes and anomalies. For example, the comparison unit can compare past survey data with current data to detect changes and anomalies. The comparison unit can compare past temperature data with current temperature data to detect abnormal temperature changes. The comparison unit can also compare past humidity data with current humidity data to detect abnormal humidity changes. Furthermore, the comparison unit can compare past image data with current image data to detect abnormal changes. This allows for an accurate assessment of the situation on site. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input past survey data and current data into a generating AI and have the generating AI perform the detection of changes and anomalies.

[0037] The information provision unit can provide past survey results and related technical information. For example, the information provision unit can provide information necessary for field surveys based on past survey data and related information. The information provision unit can provide past survey results and related technical information. In addition, the information provision unit can provide information necessary for field surveys based on past survey data and related information. This makes it possible to proceed with field surveys efficiently. Some or all of the above processing in the information provision unit may be performed using AI, for example, or without using AI. For example, the information provision unit can input past survey data and related information into a generating AI and have the generating AI perform the task of providing information necessary for field surveys.

[0038] The data collection unit can determine the optimal placement of sensors and cameras based on the environmental conditions at the site. For example, in rainy weather, the data collection unit can place sensors and cameras with high waterproof performance. Furthermore, at night, the data collection unit can place infrared cameras to collect data even in dark places. In addition, in high-temperature environments, the data collection unit can place sensors with high heat resistance. This enables the optimal placement of sensors and cameras according to environmental conditions. 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 environmental condition data from the site into a generating AI and have the generating AI determine the optimal placement of sensors and cameras.

[0039] The data collection unit can initiate data collection triggered by specific events. For example, if it detects an abnormal sound, it can automatically activate a camera and record video footage of the site. It can also initiate sensor data collection to identify the cause of an anomaly if it detects vibration. Furthermore, if it detects a rapid change in temperature, it can initiate data collection from a temperature sensor. This enables data collection in response to specific events. 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 specific event data into a generating AI and have the generating AI initiate data collection.

[0040] The data collection unit can prioritize the collection of highly relevant data based on the geographical location information of the site. For example, if the site is in an urban area, the data collection unit will prioritize the collection of traffic volume and noise level data. If the site is in a rural area, the data collection unit can also prioritize the collection of soil moisture and temperature data. Furthermore, if the site is in an industrial area, the data collection unit can also prioritize the collection of airborne hazardous substances data. This enables the collection of highly relevant data based on 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 the geographical location information of the site into a generating AI and have the generating AI perform the collection of highly relevant data.

[0041] The data collection unit can analyze on-site social media activity and collect relevant data. For example, the data collection unit can analyze social media posts around the site and collect data if unusual activity is reported. The data collection unit can also collect relevant data if a particular hashtag is frequently used on social media. Furthermore, the data collection unit can monitor activity around the site in real time based on social media location information and collect relevant data. This enables the collection of relevant data based on social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on important data to identify the cause of anomalies. It can also perform a simplified analysis on less important data to grasp the overall trend. Furthermore, the analysis unit can optimally allocate analysis resources according to the importance of the data. This enables analysis tailored to the importance of the data. 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 data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit can apply an algorithm to detect abnormal temperature changes to temperature data. It can also apply an object recognition algorithm to image data to detect specific objects. Furthermore, the analysis unit can apply an algorithm to detect abnormal vibration patterns to vibration data. This enables analysis according to the data category. 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 data category into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0044] The analysis unit can determine the priority of analysis based on the data collection timing. For example, the analysis unit can prioritize the analysis of the latest data to understand the current situation on site. The analysis unit can also refer to past data to understand long-term trends. Furthermore, the analysis unit can prioritize the analysis of data collected during a specific period to identify anomalies during that period. This makes it possible to determine the priority of analysis based on the data collection timing. 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 data collection timing into a generating AI and have the generating AI determine the priority of analysis.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit can prioritize the analysis of highly relevant data to grasp the overall trend. Alternatively, the analysis unit can postpone the analysis of less relevant data and prioritize the analysis of important data. Furthermore, the analysis unit can optimally allocate analysis resources according to the relevance of the data. This allows the order of analysis to be adjusted based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI adjust the order of analysis.

[0046] The comparison unit can improve the accuracy of the comparison by considering the interrelationships between the data. For example, the comparison unit can identify abnormal weather conditions by considering the interrelationships between temperature data and humidity data. The comparison unit can also identify abnormal phenomena by considering the interrelationships between image data and sensor data. Furthermore, the comparison unit can identify abnormal behavior by considering the interrelationships between vibration data and sound data. This improves the accuracy of the comparison by considering the interrelationships of the data. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input the interrelationships of the data into a generating AI and have the generating AI perform the improvement of the comparison accuracy.

[0047] The comparison unit can perform comparisons while considering the attribute information of the data providers. For example, the comparison unit can consider the expertise of the data providers and prioritize comparing highly reliable data. It can also consider the past performance of the data providers and prioritize comparing highly reliable data. Furthermore, the comparison unit can evaluate the reliability of the data based on the attribute information of the data providers and improve the accuracy of the comparison. This makes it possible to perform comparisons while considering the attribute information of the data providers. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input the attribute information of the data providers into a generating AI and have the generating AI perform the comparison.

[0048] The comparison unit can perform comparisons while considering the geographical distribution of the data. For example, the comparison unit can prioritize comparing geographically close data to understand regional trends. It can also prioritize comparing data from important regions, while delaying the comparison of geographically distant data. Furthermore, the comparison unit can evaluate the relevance of the data based on the geographical distribution to improve the accuracy of the comparison. This makes it possible to compare data based on geographical distribution. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input the geographical distribution of the data into a generating AI and have the generating AI perform the comparison.

[0049] The comparison unit can improve the accuracy of the comparison by referring to relevant literature for the data. For example, the comparison unit can evaluate the reliability of the data based on the relevant literature and improve the accuracy of the comparison. The comparison unit can also refer to relevant literature to supplement the interpretation of the data. Furthermore, the comparison unit can identify anomalies in the data based on the relevant literature and improve the accuracy of the comparison. This improves the accuracy of the comparison when referring to relevant literature. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input relevant literature into a generating AI and have the generating AI perform the comparison.

[0050] The information provider can adjust the level of detail provided based on the importance of the information. For example, the provider can provide detailed explanations for important information. It can also provide simplified explanations for less important information. Furthermore, the provider can optimally allocate resources according to the importance of the information. This enables the provision of detailed information according to its importance. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the importance of the information into a generating AI and have the generating AI adjust the level of detail provided.

[0051] The information delivery unit can apply different delivery algorithms depending on the category of information. For example, the delivery unit can provide detailed technical explanations for technical information. It can also provide concise and easy-to-understand explanations for general information. Furthermore, it can provide concise explanations that allow for quick response to urgent information. This enables the provision of optimal information according to the category of information. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the category of information into a generating AI and have the generating AI execute the application of different delivery algorithms.

[0052] The information provision unit can determine the priority of information provision based on when the information was collected. For example, the provision unit can prioritize providing the latest information to grasp the current situation on site. The provision unit can also refer to past information to grasp long-term trends. Furthermore, the provision unit can prioritize providing information collected during a specific period to identify anomalies during that period. This makes it possible to determine priorities based on when the information was collected. Some or all of the above processing in the information provision unit may be performed using AI, for example, or not using AI. For example, the provision unit can input the information collection period into a generating AI and have the generating AI perform the determination of the provision priority.

[0053] The information delivery unit can adjust the order of delivery based on the relevance of the information. For example, the delivery unit can prioritize the delivery of highly relevant information to grasp the overall trend. Alternatively, the delivery unit can postpone the delivery of less relevant information and prioritize the delivery of important information. Furthermore, the delivery unit can optimally allocate resources for delivery according to the relevance of the information. This makes it possible to adjust the order of delivery based on the relevance of the information. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input the relevance of the information into a generating AI and have the generating AI perform the adjustment of the delivery order.

[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 field survey support system can also be equipped with a voice recognition unit. The voice recognition unit collects voice data at the site and transmits it to the analysis unit. For example, it can collect conversations and ambient sounds at the site, and the analysis unit can analyze this voice data to detect abnormal sounds or specific keywords. This allows for a more detailed understanding of the site situation. The voice recognition unit can also receive instructions and reports from field workers via voice, and the analysis unit can analyze the content to extract necessary information. Furthermore, the voice recognition unit can monitor the noise level at the site and issue an alert if abnormal noise occurs. This can improve safety at the site.

[0056] The field survey support system can also be equipped with a drone control unit. The drone control unit remotely operates the drone and collects aerial data from the site. For example, the drone can be used to collect data from high places or dangerous locations, and the analysis unit can analyze this data to detect anomalies. This allows for a broader understanding of the site conditions. Furthermore, the drone control unit can automatically set the drone's flight path to efficiently collect data. In addition, the drone control unit can monitor the drone's battery level and flight status, providing information for safe operation. This improves the efficiency and safety of field surveys.

[0057] The field survey support system can also be equipped with an environmental adaptation unit. This unit optimizes the system's operation based on the environmental conditions of the site. For example, it adjusts the sensor sensitivity to cope with extreme temperature and humidity changes. The environmental adaptation unit can also automatically adjust the camera's exposure settings according to the lighting conditions at the site. Furthermore, it can perform filtering to improve the accuracy of voice recognition according to the noise level at the site. This enables optimal data collection and analysis tailored to the environmental conditions of the site.

[0058] The field survey support system can also include a data sharing unit. This unit shares collected data with other systems and devices in real time. For example, it can upload field data to a cloud server, allowing remote experts to view the data in real time. The data sharing unit can also transmit field data to mobile devices, enabling field workers to view the data immediately. Furthermore, the data sharing unit can integrate with other systems, enabling data interoperability. This allows for the rapid and efficient sharing of field data, improving the accuracy and efficiency of surveys.

[0059] The field survey support system can also be equipped with a prediction unit. The prediction unit predicts future field conditions based on collected data. For example, it compares past and present data to predict the likelihood of future anomalies. Furthermore, the prediction unit can predict future field conditions based on changes in environmental conditions. In addition, the prediction unit can predict the completion date of work based on the progress of work at the site. This makes it possible to understand future field conditions in advance and take appropriate countermeasures.

[0060] The field survey support system can also be equipped with a data filtering unit. The data filtering unit filters the collected data, extracting only the important data. For example, it prioritizes the extraction of data containing outliers or specific patterns and sends it to the analysis unit. The data filtering unit can also remove noisy data, improving the accuracy of the analysis. Furthermore, the data filtering unit can extract highly relevant data from the collected data, supporting efficient data analysis. This allows important data to be analyzed quickly and efficiently, enabling an accurate understanding of the field situation.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The collection unit collects sensor data or images. The collection unit can collect data from sensors and cameras installed on-site, such as temperature sensors, humidity sensors, and cameras. For example, it can collect temperature data from the site using a temperature sensor, humidity data from the site using a humidity sensor, and image data from the site using a camera. Step 2: The analysis unit analyzes the data collected by the collection unit and extracts information. The analysis unit analyzes sensor data and images to detect anomalies and specific patterns. For example, it can detect an abnormal temperature rise from temperature sensor data and recognize specific objects from images. Step 3: The comparison unit compares the information extracted by the analysis unit with past survey data. The comparison unit compares past survey data with current data to detect changes or anomalies. For example, it can compare past temperature data with current temperature data to detect abnormal temperature changes, and compare past humidity data with current humidity data to detect abnormal humidity changes. It can also compare past image data with current image data to detect abnormal changes. Step 4: The service provider provides preliminary research and information. Based on past research data and related information, the service provider provides the information necessary for the on-site investigation. For example, they can provide past research results and related technical information to provide the information necessary for the on-site investigation.

[0063] (Example of form 2) The field survey support system according to an embodiment of the present invention is a system capable of extracting and analyzing information from sensor data and images, comparing it with past survey data, and providing preliminary surveys and background information. The field survey support system collects sensor data and images, and an AI analyzes this data to extract information. Next, the extracted information is compared with past survey data to evaluate the site conditions. Furthermore, by providing preliminary surveys and background information, the field survey can be carried out efficiently. For example, the field survey support system collects sensor data and images. In this case, data is acquired from sensors and cameras installed at the site. For example, data is collected from temperature sensors, humidity sensors, cameras, etc. This allows for a detailed understanding of the site conditions. Next, the AI ​​analyzes the collected data and extracts information. The AI ​​analyzes the sensor data and images to detect abnormal values ​​and specific patterns. For example, it can detect an abnormal temperature rise from temperature sensor data or recognize a specific object from an image. This allows for a detailed evaluation of the site conditions. Furthermore, the extracted information is compared with past survey data. The AI ​​compares past survey data with current data to detect changes and anomalies. For example, it can compare past temperature data with current temperature data to detect abnormal temperature changes. This allows for an accurate assessment of the site conditions. Finally, it provides preliminary surveys and background information. Based on past survey data and related information, the AI ​​provides the information necessary for the site survey. For example, by providing past survey results and related technical information, the site survey can be conducted more efficiently. This improves the accuracy of the site survey and allows for more efficient survey progress. As a result, the site survey support system can extract and analyze information from sensor data and images, compare it with past survey data, and provide preliminary surveys and background information.

[0064] The field survey support system according to the embodiment comprises a collection unit, an analysis unit, a comparison unit, and a provision unit. The collection unit collects sensor data or images. The collection unit collects data from sensors or cameras installed at the site, for example. The collection unit can collect data from temperature sensors, humidity sensors, cameras, etc. For example, the collection unit collects temperature data at the site using a temperature sensor. The collection unit can also collect humidity data at the site using a humidity sensor. Furthermore, the collection unit can also collect image data at the site using a camera. The analysis unit analyzes the data collected by the collection unit and extracts information. For example, the analysis unit analyzes sensor data and images to detect abnormal values ​​or specific patterns. The analysis unit can detect abnormal temperature increases from temperature sensor data. Furthermore, the analysis unit can recognize specific objects from images. Furthermore, the analysis unit can analyze sensor data and images to detect abnormal values ​​or specific patterns. The comparison unit compares the information extracted by the analysis unit with past survey data. For example, the comparison unit compares past survey data with current data to detect changes or anomalies. The comparison unit can compare past temperature data with current temperature data to detect abnormal temperature changes. The comparison unit can also compare past humidity data with current humidity data to detect abnormal humidity changes. Furthermore, the comparison unit can compare past image data with current image data to detect abnormal changes. The provision unit provides preliminary surveys and background information. For example, the provision unit provides information necessary for on-site surveys based on past survey data and related information. The provision unit can provide past survey results and related technical information. The provision unit can also provide information necessary for on-site surveys based on past survey data and related information. As a result, the on-site survey support system according to this embodiment enables information extraction and analysis from sensor data and images, comparison with past survey data, and provision of preliminary surveys and background information.

[0065] The data collection unit collects sensor data or images. For example, it collects data from sensors and cameras installed on-site. Specifically, it can collect data from temperature sensors, humidity sensors, cameras, etc. A temperature sensor measures the on-site temperature in real time and transmits that data to the data collection unit. A humidity sensor measures the on-site humidity and transmits that data to the data collection unit. A camera captures images of the on-site and transmits the image data to the data collection unit. These sensors and cameras are installed in various locations on-site, allowing for the collection of data across a wide range of areas. The data collection unit centrally manages this data and transmits it to the analysis unit. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. For example, if an abnormal temperature change is detected, the data collection unit can increase the frequency of data collection from the temperature sensor to collect more detailed data. The data collection unit can also filter and preprocess the data to ensure data quality. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0066] The analysis unit analyzes data collected by the collection unit and extracts information. Specifically, it analyzes sensor data and images to detect anomalies and specific patterns. For example, it can detect abnormal temperature increases from temperature sensor data. The analysis unit uses AI to process data in real time and detect anomalies and specific patterns. The AI ​​uses machine learning algorithms to learn from past data and can detect anomalies and specific patterns with high accuracy. For example, it can analyze temperature sensor data to detect abnormal temperature increases. It can also use image recognition technology to recognize specific objects from images. For example, it can analyze images of a site to detect specific objects or abnormal changes. Furthermore, the analysis unit can analyze sensor data and images to detect anomalies and specific patterns. As a result, the analysis unit can quickly and accurately analyze collected data and grasp the situation on site in real time. In addition, the analysis unit can utilize past data and statistical information to perform long-term risk assessments and trend analyses. For example, based on past temperature data, it can predict risk fluctuations in specific regions and time periods 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 risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0067] The comparison unit compares the information extracted by the analysis unit with past survey data. Specifically, it compares past survey data with current data to detect changes and anomalies. For example, it can compare past temperature data with current temperature data to detect abnormal temperature changes. The comparison unit uses AI to analyze the data and compare past and current data with high accuracy. The AI ​​uses machine learning algorithms to learn from past data and detect abnormal changes with high accuracy. For example, it can compare past temperature data with current temperature data to detect abnormal temperature changes. It can also compare past humidity data with current humidity data to detect abnormal humidity changes. Furthermore, it can compare past image data with current image data to detect abnormal changes. As a result, the comparison unit can compare past and current data with high accuracy and grasp the situation on site in real time. In addition, the comparison unit can utilize past data and statistical information to perform long-term risk assessments and trend analyses. For example, based on past temperature data, it can predict risk fluctuations in specific areas or time periods and formulate future countermeasures. The comparison unit can also use anomaly detection algorithms to detect patterns that are different from the norm or abnormal data and issue warnings early. This allows the comparison unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0068] The service provider will provide preliminary surveys and background information. Specifically, they will provide information necessary for on-site surveys based on past survey data and related information. For example, they can provide past survey results and related technical information. The service provider can analyze data using AI and provide information necessary for on-site surveys with high accuracy. The AI ​​will use machine learning algorithms to learn from past data and provide information necessary for on-site surveys with high accuracy. For example, they will provide information necessary for on-site surveys based on past survey results and related technical information. The service provider can also provide information necessary for on-site surveys based on past survey data and related information. This allows the service provider to provide information necessary for on-site surveys quickly and accurately, improving the efficiency of on-site surveys. Furthermore, the service provider can utilize past data and statistical information to conduct long-term risk assessments and trend analyses. For example, they can predict risk fluctuations in specific regions and time periods based on past survey data and formulate future countermeasures. In addition, the service provider can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the service provider to provide information necessary for on-site surveys quickly and accurately, improving the efficiency of on-site surveys.

[0069] The data collection unit can collect data from sensors and cameras installed on-site. For example, the data collection unit can collect data from temperature sensors, humidity sensors, and cameras installed on-site. The data collection unit can collect temperature data from the site using a temperature sensor. The data collection unit can also collect humidity data from the site using a humidity sensor. Furthermore, the data collection unit can collect image data from the site using a camera. This allows for a detailed understanding of the site conditions. 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 data acquired from sensors and cameras installed on-site into a generating AI and have the generating AI perform data analysis.

[0070] The analysis unit can analyze sensor data or images to detect abnormal values ​​or specific patterns. For example, the analysis unit can analyze sensor data or images to detect abnormal values ​​or specific patterns. The analysis unit can detect abnormal temperature increases from temperature sensor data. The analysis unit can also recognize specific objects from images. Furthermore, the analysis unit can analyze sensor data or images to detect abnormal values ​​or specific patterns. This allows for a detailed evaluation of the situation on site. 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 sensor data or images into a generating AI and have the generating AI perform the detection of abnormal values ​​or specific patterns.

[0071] The comparison unit can compare past survey data with current data to detect changes and anomalies. For example, the comparison unit can compare past survey data with current data to detect changes and anomalies. The comparison unit can compare past temperature data with current temperature data to detect abnormal temperature changes. The comparison unit can also compare past humidity data with current humidity data to detect abnormal humidity changes. Furthermore, the comparison unit can compare past image data with current image data to detect abnormal changes. This allows for an accurate assessment of the situation on site. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input past survey data and current data into a generating AI and have the generating AI perform the detection of changes and anomalies.

[0072] The information provision unit can provide past survey results and related technical information. For example, the information provision unit can provide information necessary for field surveys based on past survey data and related information. The information provision unit can provide past survey results and related technical information. In addition, the information provision unit can provide information necessary for field surveys based on past survey data and related information. This makes it possible to proceed with field surveys efficiently. Some or all of the above processing in the information provision unit may be performed using AI, for example, or without using AI. For example, the information provision unit can input past survey data and related information into a generating AI and have the generating AI perform the task of providing information necessary for field surveys.

[0073] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the user's burden. Conversely, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. Furthermore, if the user is in a hurry, the data collection unit can prioritize the collection of only the most important data. This allows for more appropriate data collection by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the timing of data collection.

[0074] The data collection unit can determine the optimal placement of sensors and cameras based on the environmental conditions at the site. For example, in rainy weather, the data collection unit can place sensors and cameras with high waterproof performance. Furthermore, at night, the data collection unit can place infrared cameras to collect data even in dark places. In addition, in high-temperature environments, the data collection unit can place sensors with high heat resistance. This enables the optimal placement of sensors and cameras according to environmental conditions. 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 environmental condition data from the site into a generating AI and have the generating AI determine the optimal placement of sensors and cameras.

[0075] The data collection unit can initiate data collection triggered by specific events. For example, if it detects an abnormal sound, it can automatically activate a camera and record video footage of the site. It can also initiate sensor data collection to identify the cause of an anomaly if it detects vibration. Furthermore, if it detects a rapid change in temperature, it can initiate data collection from a temperature sensor. This enables data collection in response to specific events. 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 specific event data into a generating AI and have the generating AI initiate data collection.

[0076] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting only important data. If the user is relaxed, the data collection unit can also prioritize collecting detailed data. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting data that can be collected quickly. This allows for more appropriate data collection by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of the data.

[0077] The data collection unit can prioritize the collection of highly relevant data based on the geographical location information of the site. For example, if the site is in an urban area, the data collection unit will prioritize the collection of traffic volume and noise level data. If the site is in a rural area, the data collection unit can also prioritize the collection of soil moisture and temperature data. Furthermore, if the site is in an industrial area, the data collection unit can also prioritize the collection of airborne hazardous substances data. This enables the collection of highly relevant data based on 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 the geographical location information of the site into a generating AI and have the generating AI perform the collection of highly relevant data.

[0078] The data collection unit can analyze on-site social media activity and collect relevant data. For example, the data collection unit can analyze social media posts around the site and collect data if unusual activity is reported. The data collection unit can also collect relevant data if a particular hashtag is frequently used on social media. Furthermore, the data collection unit can monitor activity around the site in real time based on social media location information and collect relevant data. This enables the collection of relevant data based on social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0079] 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 stressed, the analysis unit can provide a simple and visually easy-to-understand analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a concise analysis result that gets straight to the point. This makes it possible to express the analysis results in a way that suits the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0080] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on important data to identify the cause of anomalies. It can also perform a simplified analysis on less important data to grasp the overall trend. Furthermore, the analysis unit can optimally allocate analysis resources according to the importance of the data. This enables analysis tailored to the importance of the data. 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 data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0081] The analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit can apply an algorithm to detect abnormal temperature changes to temperature data. It can also apply an object recognition algorithm to image data to detect specific objects. Furthermore, the analysis unit can apply an algorithm to detect abnormal vibration patterns to vibration data. This enables analysis according to the data category. 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 data category into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0082] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a short, concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide the analysis result quickly. This allows the length of the analysis result to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the user's emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0083] The analysis unit can determine the priority of analysis based on the data collection timing. For example, the analysis unit can prioritize the analysis of the latest data to understand the current situation on site. The analysis unit can also refer to past data to understand long-term trends. Furthermore, the analysis unit can prioritize the analysis of data collected during a specific period to identify anomalies during that period. This makes it possible to determine the priority of analysis based on the data collection timing. 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 data collection timing into a generating AI and have the generating AI determine the priority of analysis.

[0084] The analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit can prioritize the analysis of highly relevant data to grasp the overall trend. Alternatively, the analysis unit can postpone the analysis of less relevant data and prioritize the analysis of important data. Furthermore, the analysis unit can optimally allocate analysis resources according to the relevance of the data. This allows the order of analysis to be adjusted based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI adjust the order of analysis.

[0085] The comparison unit can estimate the user's emotions and adjust the comparison criteria based on the estimated emotions. For example, if the user is stressed, the comparison unit can use simple comparison criteria to provide easy-to-understand results. Alternatively, if the user is relaxed, the comparison unit can use detailed comparison criteria to provide more accurate results. Furthermore, if the user is in a hurry, the comparison unit can provide comparison results quickly. This allows for adjustment of comparison criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the comparison unit may be performed using AI or not. For example, the comparison unit can input user emotion data into the generative AI and have the generative AI adjust the comparison criteria.

[0086] The comparison unit can improve the accuracy of the comparison by considering the interrelationships between the data. For example, the comparison unit can identify abnormal weather conditions by considering the interrelationships between temperature data and humidity data. The comparison unit can also identify abnormal phenomena by considering the interrelationships between image data and sensor data. Furthermore, the comparison unit can identify abnormal behavior by considering the interrelationships between vibration data and sound data. This improves the accuracy of the comparison by considering the interrelationships of the data. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input the interrelationships of the data into a generating AI and have the generating AI perform the improvement of the comparison accuracy.

[0087] The comparison unit can perform comparisons while considering the attribute information of the data providers. For example, the comparison unit can consider the expertise of the data providers and prioritize comparing highly reliable data. It can also consider the past performance of the data providers and prioritize comparing highly reliable data. Furthermore, the comparison unit can evaluate the reliability of the data based on the attribute information of the data providers and improve the accuracy of the comparison. This makes it possible to perform comparisons while considering the attribute information of the data providers. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input the attribute information of the data providers into a generating AI and have the generating AI perform the comparison.

[0088] The comparison unit can estimate the user's emotions and adjust the order in which the comparison results are displayed based on the estimated emotions. For example, if the user is stressed, the comparison unit can prioritize displaying important results. If the user is relaxed, the comparison unit can also display detailed results in a sequential manner. Furthermore, if the user is in a hurry, the comparison unit can quickly display results that summarize the key points. This allows the display order of comparison results to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the comparison unit may be performed using AI, for example, or not using AI. For example, the comparison unit can input user emotion data into the generative AI and have the generative AI adjust the display order of the comparison results.

[0089] The comparison unit can perform comparisons while considering the geographical distribution of the data. For example, the comparison unit can prioritize comparing geographically close data to understand regional trends. It can also prioritize comparing data from important regions, while delaying the comparison of geographically distant data. Furthermore, the comparison unit can evaluate the relevance of the data based on the geographical distribution to improve the accuracy of the comparison. This makes it possible to compare data based on geographical distribution. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input the geographical distribution of the data into a generating AI and have the generating AI perform the comparison.

[0090] The comparison unit can improve the accuracy of the comparison by referring to relevant literature for the data. For example, the comparison unit can evaluate the reliability of the data based on the relevant literature and improve the accuracy of the comparison. The comparison unit can also refer to relevant literature to supplement the interpretation of the data. Furthermore, the comparison unit can identify anomalies in the data based on the relevant literature and improve the accuracy of the comparison. This improves the accuracy of the comparison when referring to relevant literature. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input relevant literature into a generating AI and have the generating AI perform the comparison.

[0091] 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 stressed, the service provider can provide simple and visually easy-to-understand information. If the user is relaxed, the service provider can also provide detailed information. Furthermore, if the user is in a hurry, the service provider can provide concise information that gets straight to the point. This allows the presentation of information to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the way the information is presented.

[0092] The information provider can adjust the level of detail provided based on the importance of the information. For example, the provider can provide detailed explanations for important information. It can also provide simplified explanations for less important information. Furthermore, the provider can optimally allocate resources according to the importance of the information. This enables the provision of detailed information according to its importance. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the importance of the information into a generating AI and have the generating AI adjust the level of detail provided.

[0093] The information delivery unit can apply different delivery algorithms depending on the category of information. For example, the delivery unit can provide detailed technical explanations for technical information. It can also provide concise and easy-to-understand explanations for general information. Furthermore, it can provide concise explanations that allow for quick response for urgent information. This enables the provision of optimal information according to the category of information. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the category of information into a generating AI and have the generating AI execute the application of different delivery algorithms.

[0094] The information provider can estimate the user's emotions and determine the priority of the information to be provided based on the estimated emotions. For example, if the user is stressed, the information provider may prioritize providing important information. It may also prioritize providing detailed information if the user is relaxed. Furthermore, if the user is in a hurry, the information provider may prioritize providing information that can be delivered quickly. This allows for the prioritization of information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI, or not. For example, the information provider can input user emotion data into a generative AI and have the generative AI determine the priority of information.

[0095] The information provision unit can determine the priority of information provision based on when the information was collected. For example, the provision unit can prioritize providing the latest information to grasp the current situation on site. The provision unit can also refer to past information to grasp long-term trends. Furthermore, the provision unit can prioritize providing information collected during a specific period to identify anomalies during that period. This makes it possible to determine priorities based on when the information was collected. Some or all of the above processing in the information provision unit may be performed using AI, for example, or not using AI. For example, the provision unit can input the information collection period into a generating AI and have the generating AI perform the determination of the provision priority.

[0096] The information delivery unit can adjust the order of delivery based on the relevance of the information. For example, the delivery unit can prioritize the delivery of highly relevant information to grasp the overall trend. Alternatively, the delivery unit can postpone the delivery of less relevant information and prioritize the delivery of important information. Furthermore, the delivery unit can optimally allocate resources for delivery according to the relevance of the information. This makes it possible to adjust the order of delivery based on the relevance of the information. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input the relevance of the information into a generating AI and have the generating AI perform the adjustment of the delivery order.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The field survey support system can also be equipped with a voice recognition unit. The voice recognition unit collects voice data at the site and transmits it to the analysis unit. For example, it can collect conversations and ambient sounds at the site, and the analysis unit can analyze this voice data to detect abnormal sounds or specific keywords. This allows for a more detailed understanding of the site situation. The voice recognition unit can also receive instructions and reports from field workers via voice, and the analysis unit can analyze the content to extract necessary information. Furthermore, the voice recognition unit can monitor the noise level at the site and issue an alert if abnormal noise occurs. This can improve safety at the site.

[0099] The field survey support system can also be equipped with a drone control unit. The drone control unit remotely operates the drone and collects aerial data from the site. For example, the drone can be used to collect data from high places or dangerous locations, and the analysis unit can analyze this data to detect anomalies. This allows for a broader understanding of the site conditions. Furthermore, the drone control unit can automatically set the drone's flight path to efficiently collect data. In addition, the drone control unit can monitor the drone's battery level and flight status, providing information for safe operation. This improves the efficiency and safety of field surveys.

[0100] The field survey support system can further adjust the reporting method of survey results based on the user's emotions using an emotion estimation function. For example, if the user is stressed, the reporting unit can generate a simple and visually easy-to-understand report. If the user is relaxed, it can also generate a detailed report. Furthermore, if the user is in a hurry, it can generate a concise report that gets straight to the point. This provides the optimal reporting method according to the user's emotions, facilitating understanding of the survey results. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0101] The field survey support system can also be equipped with an environmental adaptation unit. This unit optimizes the system's operation based on the environmental conditions of the site. For example, it adjusts the sensor sensitivity to cope with extreme temperature and humidity changes. The environmental adaptation unit can also automatically adjust the camera's exposure settings according to the lighting conditions at the site. Furthermore, it can perform filtering to improve the accuracy of voice recognition according to the noise level at the site. This enables optimal data collection and analysis tailored to the environmental conditions of the site.

[0102] The field survey support system can further use emotion estimation to provide real-time feedback on the survey progress based on the user's emotions. For example, if the user is stressed, it can provide a concise progress report to reduce the user's burden. If the user is relaxed, it can also provide a detailed progress report. Furthermore, if the user is in a hurry, it can prioritize reporting only the most important progress. This provides optimal feedback tailored to the user's emotions, improving the efficiency of the survey. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The field survey support system can also include a data sharing unit. This unit shares collected data with other systems and devices in real time. For example, it can upload field data to a cloud server, allowing remote experts to view the data in real time. The data sharing unit can also transmit field data to mobile devices, enabling field workers to view the data immediately. Furthermore, the data sharing unit can integrate with other systems, enabling data interoperability. This allows for the rapid and efficient sharing of field data, improving the accuracy and efficiency of surveys.

[0104] The field survey support system can further adjust survey priorities based on the user's emotions using an emotion estimation function. For example, if the user is stressed, important survey items can be prioritized to reduce the user's burden. If the user is relaxed, detailed survey items can be prioritized. Furthermore, if the user is in a hurry, survey items that can be completed quickly can be prioritized. This enables the survey to proceed optimally according to the user's emotions, improving survey efficiency. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The field survey support system can also be equipped with a prediction unit. The prediction unit predicts future field conditions based on collected data. For example, it compares past and present data to predict the likelihood of future anomalies. Furthermore, the prediction unit can predict future field conditions based on changes in environmental conditions. In addition, the prediction unit can predict the completion date of work based on the progress of work at the site. This makes it possible to understand future field conditions in advance and take appropriate countermeasures.

[0106] The field survey support system can further adjust how survey results are communicated based on the user's emotions using an emotion estimation function. For example, if the user is stressed, the notification unit will provide a simple and visually easy-to-understand notification. If the user is relaxed, a more detailed notification can be provided. Furthermore, if the user is in a hurry, a concise notification that gets straight to the point can be provided. This provides the optimal notification method according to the user's emotions, facilitating understanding of the survey results. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0107] The field survey support system can also be equipped with a data filtering unit. The data filtering unit filters the collected data, extracting only the important data. For example, it prioritizes the extraction of data containing outliers or specific patterns and sends it to the analysis unit. The data filtering unit can also remove noisy data, improving the accuracy of the analysis. Furthermore, the data filtering unit can extract highly relevant data from the collected data, supporting efficient data analysis. This allows important data to be analyzed quickly and efficiently, enabling an accurate understanding of the field situation.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The collection unit collects sensor data or images. The collection unit can collect data from sensors and cameras installed on-site, such as temperature sensors, humidity sensors, and cameras. For example, it can collect temperature data from the site using a temperature sensor, humidity data from the site using a humidity sensor, and image data from the site using a camera. Step 2: The analysis unit analyzes the data collected by the collection unit and extracts information. The analysis unit analyzes sensor data and images to detect anomalies and specific patterns. For example, it can detect an abnormal temperature rise from temperature sensor data and recognize specific objects from images. Step 3: The comparison unit compares the information extracted by the analysis unit with past survey data. The comparison unit compares past survey data with current data to detect changes or anomalies. For example, it can compare past temperature data with current temperature data to detect abnormal temperature changes, and compare past humidity data with current humidity data to detect abnormal humidity changes. It can also compare past image data with current image data to detect abnormal changes. Step 4: The service provider provides preliminary research and information. Based on past research data and related information, the service provider provides the information necessary for the on-site investigation. For example, they can provide past research results and related technical information to provide the information necessary for the on-site investigation.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] For example, the data collection unit can collect on-site data using the camera 42, temperature sensor, and humidity sensor of the smart device 14. The data collection unit can also be implemented by the specific processing unit 290 of the data processing device 12. The analysis unit can analyze the data collected by the control unit 46A of the smart device 14 and detect abnormal values ​​or specific patterns. The analysis unit can also be implemented by the specific processing unit 290 of the data processing device 12. The comparison unit can compare past survey data with current data by the specific processing unit 290 of the data processing device 12 and detect changes or anomalies. The provision unit can provide information necessary for on-site surveys based on past survey data and related information by the control unit 46A of the smart device 14. The provision unit can also be implemented by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] For example, the data collection unit can collect on-site data using the camera 42, temperature sensor, and humidity sensor of the smart glasses 214. The data collection unit can also be implemented by the specific processing unit 290 of the data processing device 12. The analysis unit can analyze the data collected by the control unit 46A of the smart glasses 214 and detect abnormal values ​​or specific patterns. The analysis unit can also be implemented by the specific processing unit 290 of the data processing device 12. The comparison unit can compare past survey data with current data using the specific processing unit 290 of the data processing device 12 and detect changes or anomalies. The information provision unit can provide information necessary for on-site surveys based on past survey data and related information using the control unit 46A of the smart glasses 214. The information provision unit can also be implemented by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] For example, the data collection unit can collect field data using the camera 42, temperature sensor, and humidity sensor of the headset terminal 314. The data collection unit can also be implemented by the specific processing unit 290 of the data processing device 12. The analysis unit can analyze the data collected by the control unit 46A of the headset terminal 314 and detect abnormal values ​​or specific patterns. The analysis unit can also be implemented by the specific processing unit 290 of the data processing device 12. The comparison unit can compare past survey data with current data by the specific processing unit 290 of the data processing device 12 and detect changes or anomalies. The information provision unit can provide information necessary for field surveys based on past survey data and related information by the control unit 46A of the headset terminal 314. The information provision unit can also be implemented by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] For example, the data collection unit can collect on-site data using the camera 42, temperature sensor, and humidity sensor of the robot 414. The data collection unit can also be implemented by the specific processing unit 290 of the data processing device 12. The analysis unit can analyze the data collected by the control unit 46A of the robot 414 and detect abnormal values ​​or specific patterns. The analysis unit can also be implemented by the specific processing unit 290 of the data processing device 12. The comparison unit can compare past survey data with current data using the specific processing unit 290 of the data processing device 12 and detect changes or anomalies. The information provision unit can provide information necessary for on-site surveys based on past survey data and related information using the control unit 46A of the robot 414. The information provision unit can also be implemented by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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."

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] (Note 1) A collection unit that collects sensor data or images, An analysis unit analyzes the data collected by the aforementioned collection unit and extracts information, A comparison unit compares the information extracted by the analysis unit with past survey data, It comprises a provision unit that provides preliminary surveys and preliminary information, A system characterized by the following features. (Note 2) The aforementioned collection unit is Data is collected from sensors and cameras installed on site. The system described in Appendix 1, characterized by the features described herein. (Note 3) The system according to Appendix 1, characterized in that the analysis unit analyzes sensor data or images and detects abnormal values ​​or specific patterns. (Note 4) The comparison unit is, Compare past survey data with current data to detect changes and anomalies. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, We provide past research results and related technical information. The system described in Appendix 1, characterized by the features described herein. (Note 6) The system according to Appendix 1, characterized in that the collection unit estimates the user's emotions and adjusts the timing of data collection based on the estimated user emotions. (Note 7) The aforementioned collection unit is The optimal placement of sensors and cameras is determined based on the environmental conditions at the site. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Start data collection triggered by a specific event. 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 prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The system described in Appendix 1 is characterized in that the collection unit prioritizes the collection of highly relevant data based on the geographical location information of the site. (Note 11) The aforementioned collection unit is Analyze on-site social media activity and collect relevant data. 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, Adjust the level of detail in the analysis based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, Apply different analysis algorithms depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, Prioritize analysis based on the data collection period. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, Adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The comparison unit is, It estimates the user's emotions and adjusts the comparison criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The comparison unit is, Improve the accuracy of comparisons by considering the interrelationships between data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The comparison unit is, The comparison will take into account the attribute information of the data provider. The system described in Appendix 1, characterized by the features described herein. (Note 21) The comparison unit is, It estimates the user's sentiment and adjusts the order in which comparison results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The comparison unit is, The comparison will take into account the geographical distribution of the data. The system described in Appendix 1, characterized by the features described herein. (Note 23) The comparison unit is, Referencing relevant literature on the data improves the accuracy of comparisons. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, It estimates the user's emotions and 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 25) The aforementioned supply unit is, Adjust the level of detail provided based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, Apply different information delivery algorithms depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 27) 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 28) The aforementioned supply unit is, Prioritizing information provision based on when the information was collected. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, The order in which information is provided will be adjusted based on its relevance. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0182] 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 sensor data or images, An analysis unit analyzes the data collected by the aforementioned collection unit and extracts information, A comparison unit compares the information extracted by the analysis unit with past survey data, It comprises a provision unit that provides preliminary surveys and preliminary information, A system characterized by the following features.

2. The aforementioned collection unit is Data is collected from sensors and cameras installed on site. The system according to feature 1.

3. The system according to claim 1, characterized in that the analysis unit analyzes sensor data or images and detects abnormal values ​​or specific patterns.

4. The comparison unit is, Compare past survey data with current data to detect changes and anomalies. The system according to feature 1.

5. The aforementioned supply unit is, We provide past research results and related technical information. The system according to feature 1.

6. The system according to claim 1, characterized in that the collection unit estimates the user's emotions and adjusts the timing of data collection based on the estimated user's emotions.

7. The aforementioned collection unit is The optimal placement of sensors and cameras is determined based on the environmental conditions at the site. The system according to feature 1.

8. The aforementioned collection unit is Start data collection triggered by a specific event. The system according to feature 1.

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