system

Abstract

The system according to this embodiment aims to accurately grasp the situation of pest outbreaks and analyze their impact on real estate value. [Solution] The system according to the embodiment comprises a monitoring unit, a measurement unit, an extraction unit, a collection unit, a display unit, an analysis unit, and a generation unit. The monitoring unit monitors the number of captured pests. The measurement unit measures the frequency of pest sightings based on the number of captured pests monitored by the monitoring unit. The extraction unit extracts the user's preferences. The collection unit collects data. The display unit visually displays the pest outbreak status based on the data collected by the collection unit. The analysis unit analyzes the impact on real estate value based on the data displayed by the display unit. The generation unit automatically generates a report based on the results analyzed by the analysis unit.

Description

Technical Field

[0005]

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot 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 the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, the occurrence situation of pests has not been accurately grasped, and the impact on real estate value has not been sufficiently analyzed, leaving room for improvement.

[0005] The system according to the embodiment aims to accurately grasp the occurrence situation of pests and analyze the impact on real estate value.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a monitoring unit, a measurement unit, an extraction unit, a collection unit, a display unit, an analysis unit, and a generation unit. The monitoring unit monitors the number of captured pests. The measurement unit measures the frequency of pest sightings based on the number of captured pests monitored by the monitoring unit. The extraction unit extracts the user's preferences. The collection unit collects data. The display unit visually displays the pest outbreak status based on the data collected by the collection unit. The analysis unit analyzes the impact on real estate value based on the data displayed by the display unit. The generation unit automatically generates a report based on the results analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can accurately grasp the situation of pest outbreaks and analyze their impact on real estate value. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 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 pest infestation monitoring system according to an embodiment of the present invention is a system that improves the comfort of the living environment by monitoring and analyzing the occurrence of pests in rental and condominium apartments. This pest infestation monitoring system uses IoT sensors and cameras to trace the number of pests captured and the number of sightings, and analyzes the results to quantify and visualize the pest infestation rate in the apartments. This provides a new criterion, "pest infestation rate," when selecting real estate, and makes suggestions to service users using an AI agent. For example, the pest infestation monitoring system deploys IoT sensors in traps that capture pests and monitors the number of captured individuals. Next, the pest infestation monitoring system uses an AI camera to identify pests and measure the frequency of sightings. This allows for a detailed understanding of the pest infestation situation. Furthermore, the pest infestation monitoring system uses a communication tool to extract the "desired place to live" and "desired conditions" that users are looking for. For example, it uses the conversational UI of LINE (registered trademark) to collect user preferences. In addition, the pest infestation monitoring system collects map data, such as the distribution of restaurants, using an API, and collects weather information, such as precipitation and humidity, using a weather information API. By combining this data and using image generation AI, an interactive map is generated that allows users to visually understand the pest infestation situation. Users can visually check the level of contamination and the status of countermeasures implemented on the map, which can be used as a reference when choosing a property. With this system, an autonomous AI agent analyzes the impact on property value based on the risk of pest infestation and automatically generates reports on a regular basis. By proposing concrete action plans to property owners and developers, early countermeasures and value improvement can be promoted. In addition, the visual interactive map allows residents and property buyers to grasp the pest infestation situation at a glance, supporting quick decision-making. In this way, the pest infestation monitoring system can improve the comfort of the living environment.

[0029] The pest occurrence monitoring system according to the embodiment comprises a monitoring unit, a measurement unit, an extraction unit, a collection unit, a display unit, an analysis unit, and a generation unit. The monitoring unit monitors the number of captured pests. The monitoring unit can monitor the number of captured pests using, for example, an IoT sensor. The monitoring unit counts the number of captured pests in real time using, for example, a sensor attached to the capture device. The monitoring unit can also use different sensors depending on the type of capture device. For example, a combination of temperature sensors and humidity sensors can be used to monitor changes in the capture environment. The measurement unit measures the frequency of pest sightings based on the number of captured pests monitored by the monitoring unit. The measurement unit can measure the frequency of pest sightings using, for example, an AI camera. The measurement unit identifies the type and number of pests and measures the sighting frequency using, for example, image recognition technology from the camera. The measurement unit can also apply different measurement methods depending on the resolution and installation location of the camera. For example, a high-resolution camera can be used to obtain detailed sighting data. The extraction unit extracts the user's preferences. The extraction unit can extract user preferences using, for example, communication tools. The extraction unit can collect desired conditions through dialogue with users, for example, using a chatbot. The extraction unit can also use survey tools to gain a detailed understanding of user preferences. The collection unit collects data. The collection unit can collect, for example, map data and weather information. The collection unit can collect map data for specific areas using, for example, Geographic Information System (GIS) data. The collection unit can also collect weather data such as temperature, humidity, and precipitation using weather information APIs. The display unit visually displays the pest outbreak status based on the data collected by the collection unit. The display unit can visually display the pest outbreak status using, for example, image generation AI. The display unit can display the pest outbreak status as a heatmap or graph using, for example, deep learning technology. The display unit can also update the pest outbreak status in real time using image generation algorithms. The analysis unit analyzes the impact on real estate value based on the data displayed by the display unit.The analysis unit can, for example, analyze the impact on property value based on pest infestation risk. The analysis unit can, for example, use a risk assessment model to quantify pest infestation risk and evaluate its impact on property value. The analysis unit can also perform a factor analysis of price fluctuations and calculate a risk score. The generation unit automatically generates reports based on the results analyzed by the analysis unit. The generation unit can, for example, automatically generate reports on a regular basis. The generation unit can, for example, set the report format and generate reports at frequencies such as daily, weekly, or monthly. The generation unit can also deliver the generated reports via email or web application. This allows the pest infestation monitoring system according to the embodiment to improve the comfort of the living environment.

[0030] The monitoring unit monitors the number of captured pests. For example, the monitoring unit can monitor the number of captured pests using IoT sensors. Specifically, it uses sensors attached to the capture device to count the number of captured pests in real time. This allows for an accurate understanding of the pest infestation situation at the location where the capture device is installed. The monitoring unit can also use different sensors depending on the type of capture device. For example, it can combine temperature and humidity sensors to monitor changes in the capture environment. This allows for simultaneous monitoring of environmental factors affecting pest outbreaks, resulting in more accurate data. Furthermore, the monitoring unit can adjust the sensitivity and settings of the sensors according to the location of the capture device and the type of pest being captured. For example, if a particular pest is active under specific temperature and humidity conditions, the sensor settings can be optimized to match those conditions. This allows the monitoring unit to efficiently and effectively monitor the number of captured pests and respond quickly.

[0031] The measurement unit measures the frequency of pest sightings based on the number of captured insects monitored by the monitoring unit. The measurement unit can, for example, measure the frequency of pest sightings using an AI camera. Specifically, it uses image recognition technology from the camera to identify the type and number of pests and measure the frequency of sightings. The AI ​​camera can analyze video in real time and track the movement of pests. This allows for a detailed understanding of pest activity patterns and locations. The measurement unit can also apply different measurement methods depending on the camera's resolution and installation location. For example, a high-resolution camera can be used to obtain detailed sighting data. In addition, multiple cameras can be installed to cover a wide area, and the data can be integrated and analyzed. This allows the measurement unit to accurately measure the frequency of pest sightings in a wide area and respond quickly. Furthermore, the measurement unit can analyze fluctuations in sighting frequency by comparing them with past data and predict pest outbreak trends. This allows the measurement unit to understand the risk of pest outbreaks in advance and take appropriate measures.

[0032] The extraction unit extracts user preferences. For example, the extraction unit can extract user preferences using communication tools. Specifically, it can use a chatbot to collect desired conditions through dialogue with the user. The chatbot can analyze user input using natural language processing technology and automatically extract desired conditions. This allows users to easily communicate their desired conditions, and the extraction unit can respond quickly. The extraction unit can also use survey tools to understand user preferences in detail. Survey tools can present specific questions to users and collect answers to understand detailed desired conditions. This allows the extraction unit to provide customized services that meet user needs. Furthermore, the extraction unit can analyze users' past usage history and behavioral data to predict potential desired conditions. This allows the extraction unit to anticipate user needs and provide more satisfying services.

[0033] The data collection unit collects data. For example, the data collection unit can collect map data and weather information. Specifically, it can use Geographic Information System (GIS) data to collect map data for a specific area. GIS data provides detailed geographical information and helps to accurately understand the pest outbreak situation. The data collection unit can also use weather information APIs to collect weather data such as temperature, humidity, and precipitation. Weather data is an important factor that affects pest outbreaks, and by collecting this data, the risk of pest outbreaks can be predicted. Furthermore, the data collection unit can collect data from other relevant data sources. For example, by collecting and integrating data related to pest outbreaks, such as agricultural data and environmental data, more accurate predictions can be made. This allows the data collection unit to efficiently collect diverse data and improve the overall performance of the system.

[0034] The display unit visually displays the pest outbreak status based on data collected by the data collection unit. For example, the display unit can visually display the pest outbreak status using image generation AI. Specifically, it can use deep learning technology to display the pest outbreak status as a heatmap or graph. The heatmap shows the frequency of pest outbreaks using varying shades of color, making it visually easy to understand. The graph displays the pest outbreak status over time, helping to understand trends and fluctuations. Furthermore, the display unit can update the pest outbreak status in real time using image generation algorithms. This allows users to always have access to the latest information and respond quickly. The display unit can also provide customized display formats according to user needs. For example, it can provide displays that focus on specific regions or periods, or highlight the outbreak status of specific pests, offering flexible display options to meet user requirements. This allows the display unit to provide users with intuitive and easy-to-understand information, supporting quick decision-making.

[0035] The analysis department analyzes the impact on real estate value based on the data displayed by the display department. For example, the analysis department can analyze the impact on real estate value based on pest infestation risk. Specifically, it uses a risk assessment model to quantify pest infestation risk and evaluate its impact on real estate value. The risk assessment model calculates a risk score based on data such as the frequency, location, and timing of pest infestations. This allows for a quantitative evaluation of pest infestation risk in specific areas or properties. The analysis department can also perform a factor analysis of price fluctuations and calculate a risk score. The factor analysis of price fluctuations takes into account factors other than pest infestation risk and comprehensively evaluates the impact on real estate value. This allows for an accurate understanding of the impact of pest infestation risk on real estate value. Furthermore, the analysis department can analyze past data and market trends to perform future risk assessments and price forecasts. This allows the analysis department to evaluate the impact on real estate value from a long-term perspective and provide information for taking appropriate measures.

[0036] The generation unit automatically generates reports based on the results analyzed by the analysis unit. For example, the generation unit can automatically generate reports on a regular basis. Specifically, it can set the report format and generate reports at frequencies such as daily, weekly, or monthly. The reports include information such as pest outbreaks, risk assessments, and impacts on property values. The generation unit can also deliver the generated reports via email or web applications. This allows users to receive the latest information regularly and take quick action. Furthermore, the generation unit can generate customized reports according to user needs. For example, it can generate flexible reports tailored to user requests, such as reports focusing on specific regions or time periods, or reports highlighting specific pest outbreaks. This allows the generation unit to provide users with intuitive and easy-to-understand information, supporting rapid decision-making.

[0037] The monitoring unit can monitor the number of captured pests using IoT sensors. For example, the monitoring unit can attach a palm-sized IoT sensor to the capture device and count the number of captured pests in real time. The monitoring unit can also monitor changes in the capture environment by combining, for example, temperature sensors and humidity sensors. The monitoring unit can also use different sensors depending on the type of capture device. This allows for accurate monitoring of the number of captured pests using IoT sensors. Some or all of the above processing in the monitoring unit may be performed using, for example, AI, or not using AI. For example, the monitoring unit can input data from IoT sensors attached to the capture device into a generating AI and have the generating AI perform the monitoring of the number of captured pests.

[0038] The measurement unit can measure the frequency of pest sightings using an AI camera. The measurement unit can, for example, use image recognition technology from the camera to identify the type and number of pests and measure the sighting frequency. The measurement unit can also, for example, use a high-resolution camera to acquire detailed sighting data. The measurement unit can also apply different measurement methods depending on, for example, the camera's installation location. This allows for accurate measurement of pest sighting frequency using an AI camera. Some or all of the above-described processes in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can input image data captured by the camera into a generating AI and have the generating AI perform the measurement of sighting frequency.

[0039] The extraction unit can extract user preferences using communication tools. For example, the extraction unit can use a chatbot to collect desired conditions through dialogue with the user. The extraction unit can also use a survey tool to understand user preferences in detail. The extraction unit can also use a conversational UI like LINE to collect user preferences. This allows for accurate extraction of user preferences using communication tools. Some or all of the above-described processes in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input user dialogue data into a generating AI and have the generating AI perform the extraction of desired conditions.

[0040] The data collection unit can collect map data and weather information. For example, the data collection unit can collect map data for a specific area using Geographic Information System (GIS) data. The data collection unit can also collect weather data such as temperature, humidity, and precipitation using a weather information API. The data collection unit can also collect information such as the distribution of restaurants using an API. By collecting map data and weather information, it is possible to accurately understand the situation of pest outbreaks. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data obtained from an API into a generating AI and have the generating AI perform the data collection.

[0041] The display unit can visually display the pest infestation status using image generation AI. For example, the display unit can use deep learning technology to display the pest infestation status as a heatmap or graph. The display unit can also update the pest infestation status in real time using an image generation algorithm. The display unit can also generate an interactive map, allowing users to visually check the level of contamination and the status of countermeasures implemented on the map. This allows for the visual display of pest infestation status using image generation AI. Some or all of the above-described processes in the display unit may be performed using AI, or not. For example, the display unit can input collected data into a generation AI and have the generation AI perform the visual display.

[0042] The analysis unit can analyze the impact on property value based on pest infestation risk. For example, the analysis unit can use a risk assessment model to quantify pest infestation risk and evaluate its impact on property value. The analysis unit can also perform a factor analysis of price fluctuations and calculate a risk score. The analysis unit can also quantitatively evaluate the impact on property value based on pest infestation risk. This allows for appropriate countermeasures to be taken by analyzing the impact on property value based on pest infestation risk. 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 collected data into a generating AI and have the generating AI perform an analysis of the impact on property value.

[0043] The generation unit can automatically generate reports on a regular basis. For example, the generation unit can set the report format and generate reports at frequencies such as daily, weekly, or monthly. The generation unit can also distribute the generated reports via email or web applications. The generation unit can also automatically update the report content to provide the latest information. This allows for continuous monitoring of pest infestations by automatically generating reports on a regular basis. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input collected data into a generation AI and have the generation AI perform the automatic generation of reports.

[0044] The monitoring unit can analyze past capture data and strengthen monitoring during specific seasons or time periods. For example, the monitoring unit can strengthen monitoring during times when pest outbreaks are high in the summer. For example, the monitoring unit can strengthen nighttime monitoring for pests that are active at night. For example, the monitoring unit can strengthen spring monitoring for pests that increase in early spring. In this way, pest outbreaks can be effectively suppressed by strengthening monitoring during specific seasons or time periods. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input past capture data into a generating AI and have the generating AI perform enhanced monitoring during specific seasons or time periods.

[0045] The monitoring unit can apply different monitoring methods to each type of pest when monitoring the number of captured insects. For example, for cockroaches, the monitoring unit can place sensors in dark places for monitoring. For mosquitoes, for example, the monitoring unit can place sensors in humid places for monitoring. For flies, for example, the monitoring unit can place sensors near food for monitoring. This improves monitoring accuracy by applying different monitoring methods to each type of pest. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the number of captured insects monitoring data into a generating AI and cause the generating AI to execute different monitoring methods for each type of pest.

[0046] The monitoring unit can enhance monitoring by taking into account geographical information of the locations where pests are captured. For example, the monitoring unit can enhance monitoring of capture locations around the kitchen. For example, the monitoring unit can enhance monitoring of capture locations around the bathroom. For example, the monitoring unit can enhance monitoring of capture locations around the balcony. By enhancing monitoring while taking into account geographical information of the capture locations, pest outbreaks can be effectively suppressed. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input geographical information of the capture locations into a generating AI and have the generating AI perform enhanced monitoring.

[0047] The monitoring unit can automatically optimize the placement of traps based on the number of pests captured during monitoring. For example, the monitoring unit can place additional traps in areas with a high number of captures. For example, the monitoring unit can move traps away from areas with a low number of captures. For example, the monitoring unit can also adjust the placement of traps in real time in response to fluctuations in the number of captures. This improves the efficiency of pest capture by optimizing the placement of traps based on the number of captures. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the number of captures data into a generating AI and have the generating AI perform the trap placement optimization.

[0048] The measurement unit can improve measurement accuracy by analyzing the movement patterns of pests when measuring sighting frequency. For example, the measurement unit can improve measurement accuracy by analyzing the movement speed of pests. For example, the measurement unit can improve measurement accuracy by analyzing the movement paths of pests. For example, the measurement unit can also improve measurement accuracy by analyzing the activity periods of pests. In this way, measurement accuracy is improved by analyzing the movement patterns of pests. Some or all of the above processing in the measurement unit may be performed using AI, for example, or without using AI. For example, the measurement unit can input pest movement data into a generating AI and have the generating AI perform movement pattern analysis.

[0049] The measurement unit can apply different measurement algorithms depending on the size and shape of the pest when measuring the frequency of sightings. For example, the measurement unit can apply a high-precision measurement algorithm to small pests. For example, the measurement unit can apply a wide-area measurement algorithm to large pests. For example, the measurement unit can also apply a dedicated measurement algorithm to pests of a specific shape. By applying different measurement algorithms depending on the size and shape of the pest, the measurement accuracy is improved. Some or all of the above processing in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can input data on the size and shape of the pest into a generating AI and have the generating AI execute the application of the measurement algorithm.

[0050] The measurement unit can enhance measurements by taking into account geographical information of the locations where pests are sighted. For example, the measurement unit can enhance measurements for sighting locations around the kitchen. For example, the measurement unit can enhance measurements for sighting locations around the bathroom. For example, the measurement unit can enhance measurements for sighting locations around the balcony. By enhancing measurements while taking into account geographical information of the sighting locations, pest outbreaks can be effectively suppressed. Some or all of the above processing in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can input geographical information of the sighting locations into a generating AI and have the generating AI perform the measurement enhancement.

[0051] The measurement unit can automatically optimize camera placement based on the frequency of pest sightings during measurement. For example, the measurement unit can add cameras to locations with high sighting frequencies. For example, the measurement unit can move cameras away from locations with low sighting frequencies. For example, the measurement unit can adjust camera placement in real time according to fluctuations in sighting frequencies. This improves the accuracy of pest detection by optimizing camera placement based on sighting frequencies. Some or all of the above processing in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can input sighting frequency data into a generating AI and have the generating AI perform camera placement optimization.

[0052] The extraction unit can analyze past user preference data and improve the extraction accuracy for specific conditions. For example, the extraction unit can suggest optimal conditions based on conditions previously requested by the user. For example, the extraction unit can improve the extraction accuracy for specific conditions from the user's past preference data. For example, the extraction unit can analyze the user's past preference data and suggest the most suitable conditions. This improves extraction accuracy by analyzing past preference data. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input past user preference data into a generating AI and have the generating AI perform the extraction accuracy improvement.

[0053] The extraction unit can optimize desired conditions by considering the user's geographical information during the extraction process. For example, the extraction unit can suggest optimal conditions based on the user's current location. For example, the extraction unit can suggest optimal conditions based on the user's past travel history. For example, the extraction unit can analyze the user's geographical information and suggest the most suitable conditions. In this way, by optimizing desired conditions while considering geographical information, conditions that match the user's preferences can be suggested. Some or all of the above-described processes in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input the user's geographical information into a generating AI and have the generating AI perform the optimization of desired conditions.

[0054] The data collection unit can analyze past collected data and enhance data collection during specific seasons or time periods. For example, the data collection unit can enhance data collection during the summer. For example, the data collection unit can enhance data collection at night. For example, the data collection unit can enhance data collection during the spring. By enhancing data collection during specific seasons or time periods, the accuracy of data collection is improved. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past collected data into a generating AI and have the generating AI perform data collection enhancement during specific seasons or time periods.

[0055] The data collection unit can enhance data collection by considering the geographical information of the data during collection. For example, the data collection unit can enhance data collection in a specific region. For example, the data collection unit can enhance data collection in a specific building. For example, the data collection unit can enhance data collection in a specific room. By enhancing data collection while considering geographical information, the accuracy of data collection is improved. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical information into a generating AI and have the generating AI perform the data collection enhancement.

[0056] The display unit can analyze past display data and improve display accuracy for specific conditions. For example, the display unit can suggest the optimal display method based on data previously displayed by the user. For example, the display unit can improve display accuracy for specific conditions from the user's past display data. For example, the display unit can analyze the user's past display data and suggest the most suitable display method. This improves display accuracy by analyzing past display data. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input past display data into a generating AI and have the generating AI perform the display accuracy improvement.

[0057] The display unit can enhance the display by taking geographical information into account when displaying data. For example, the display unit can highlight data in a specific region. For example, the display unit can highlight data in a specific building. For example, the display unit can highlight data in a specific room. By enhancing the display by taking geographical information into account, the display accuracy is improved. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input geographical information into a generating AI and have the generating AI perform the display enhancement.

[0058] The analysis unit can optimize the analysis algorithm by referring to past analysis data. For example, the analysis unit can propose the optimal analysis algorithm based on past analysis data. For example, the analysis unit can improve the accuracy of analysis for specific conditions from past analysis data. For example, the analysis unit can analyze past analysis data and propose the most suitable analysis algorithm. In this way, the accuracy of the analysis algorithm is improved by referring to past analysis 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 past analysis data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0059] The analysis unit can enhance its analysis by considering the geographical information of the data during the analysis process. For example, the analysis unit can emphasize data from a specific region. For example, the analysis unit can emphasize data from a specific building. For example, the analysis unit can emphasize data from a specific room. By enhancing the analysis by considering geographical information, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input geographical information into a generating AI and have the generating AI perform the analysis enhancement.

[0060] The generation unit can optimize the report generation algorithm by referring to past generation data. For example, the generation unit can propose the optimal report generation algorithm based on past generation data. For example, the generation unit can improve the accuracy of report generation for specific conditions from past generation data. For example, the generation unit can analyze past generation data and propose the most suitable report generation algorithm. This improves the accuracy of the generation algorithm by referring to past generation data. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input past generation data into a generation AI and have the generation AI perform the optimization of the generation algorithm.

[0061] The generation unit can enhance report generation by considering the geographical information of the data during generation. For example, the generation unit can generate reports emphasizing data in a specific region. For example, the generation unit can generate reports emphasizing data in a specific building. For example, the generation unit can generate reports emphasizing data in a specific room. By enhancing report generation by considering geographical information, the accuracy of the report is improved. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input geographical information into a generation AI and have the generation AI perform report generation enhancement.

[0062] The generation unit can generate an optimal report by considering the user's health condition during the generation process. For example, if the user is tired, the generation unit can generate a concise report. For example, if the user is healthy, the generation unit can generate a detailed report. For example, if the user is unwell, the generation unit can generate a concise report. By generating reports that take the user's health condition into consideration, the generation unit can provide the user with the most relevant information. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's health condition data into a generation AI and have the generation AI perform report generation based on the health condition.

[0063] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0064] The monitoring unit not only monitors the number of pests captured but can also identify the types of pests captured. For example, the monitoring unit uses sensors attached to the capture devices to identify the types of captured pests in real time. This allows for a detailed understanding of the infestation situation of specific pests. Furthermore, the monitoring unit can propose different countermeasures depending on the type of pest captured. For example, if a large number of cockroaches are captured, it can propose strengthening cockroach control measures. In addition, the monitoring unit can optimize the placement of capture devices based on the types of pests captured. For example, by placing additional capture devices in areas where a particular pest is captured in large numbers, capture efficiency can be improved.

[0065] The measurement unit can not only measure the frequency of pest sightings but also analyze the behavioral patterns of the sighted pests. For example, the measurement unit uses camera image recognition technology to identify the movement routes of pests and analyze their behavioral patterns. This makes it possible to identify the source and movement routes of pests. Furthermore, the measurement unit can also propose effective countermeasures based on the pest behavioral patterns. For example, if pests are moving along a specific route, taking countermeasures along that route can prevent pest intrusion. In addition, the measurement unit can optimize the placement of cameras based on the pest behavioral patterns. For example, by placing cameras along the pest movement routes, the accuracy of measuring sighting frequency can be improved.

[0066] The extraction unit can not only extract user preferences but also propose the optimal living environment based on the user's lifestyle. For example, the extraction unit collects information about the user's lifestyle and proposes the optimal living environment based on that information. This allows it to provide a living environment that suits the user's lifestyle. Furthermore, the extraction unit can also propose pest control measures based on the user's lifestyle. For example, it can propose pest control measures that do not harm pets to users who own pets. In addition, the extraction unit can also propose improvements to the living environment based on the user's lifestyle. For example, it can propose improvements to the living environment that take health into consideration to health-conscious users.

[0067] The data collection unit can collect not only map data and weather information, but also local event information. For example, the unit can collect local event information and use it to analyze pest outbreaks. This allows for an understanding of the impact of local events on pest outbreaks. The unit can also make suggestions for pest control measures based on local event information. For example, in areas where large-scale events are held, it can suggest strengthening pest control measures in advance. Furthermore, the unit can also make suggestions for improving living environments based on local event information. For example, it can provide users participating in local events with suggestions for improving their living environment related to the event.

[0068] The display unit can not only visually show the pest infestation status but also visually show the effectiveness of pest control measures. For example, the display unit can compare and display the infestation status before and after the implementation of pest control measures. This allows users to grasp the effectiveness of pest control measures at a glance. The display unit can also suggest additional measures based on the effectiveness of the pest control measures. For example, if the effect of the measures is insufficient, it can suggest additional measures. Furthermore, the display unit can update the effectiveness of pest control measures in real time. For example, by displaying changes in the infestation status in real time according to the implementation status of the measures, it can support a quick response.

[0069] The following briefly describes the processing flow for example form 1.

[0070] Step 1: The monitoring unit monitors the number of captured pests. The monitoring unit can monitor the number of captured pests using, for example, IoT sensors. The monitoring unit counts the number of captured pests in real time using sensors attached to the capture device. Different sensors can also be used depending on the type of capture device. For example, temperature sensors and humidity sensors can be combined to monitor changes in the capture environment. Step 2: The measurement unit measures the frequency of pest sightings based on the number of captured insects monitored by the monitoring unit. The measurement unit can, for example, use an AI camera to measure the frequency of pest sightings. The measurement unit uses image recognition technology from the camera to identify the type and number of pests and measure the frequency of sightings. Different measurement methods can also be applied depending on the camera's resolution and installation location. Step 3: The extraction unit extracts user preferences. The extraction unit can extract user preferences using, for example, a communication tool. The extraction unit can use a chatbot to collect desired conditions through dialogue with the user. Alternatively, it can use a survey tool to gain a detailed understanding of user preferences. Step 4: The collection unit collects data. The collection unit can collect, for example, map data and weather information. The collection unit can use Geographic Information System (GIS) data to collect map data for a specific area. It can also use weather information APIs to collect weather data such as temperature, humidity, and precipitation. Step 5: The display unit visually displays the pest infestation status based on the data collected by the collection unit. The display unit can, for example, visually display the pest infestation status using image generation AI. The display unit can display the pest infestation status as a heatmap or graph using deep learning technology. It can also update the pest infestation status in real time using an image generation algorithm. Step 6: The analysis unit analyzes the impact on property value based on the data displayed by the display unit. For example, the analysis unit can analyze the impact on property value based on pest infestation risk. The analysis unit uses a risk assessment model to quantify pest infestation risk and evaluate its impact on property value. It can also perform a factor analysis of price fluctuations and calculate a risk score. Step 7: The generation unit automatically generates reports based on the results analyzed by the analysis unit. The generation unit can, for example, automatically generate reports on a regular basis. The generation unit sets the report format and generates reports at frequencies such as daily, weekly, or monthly. The generation unit can also deliver the generated reports via email or web applications.

[0071] (Example of form 2) The pest infestation monitoring system according to an embodiment of the present invention is a system that improves the comfort of the living environment by monitoring and analyzing the occurrence of pests in rental and condominium apartments. This pest infestation monitoring system uses IoT sensors and cameras to trace the number of captured pests and the number of sightings, and analyzes the results to quantify and visualize the pest infestation rate in the apartments. This provides a new criterion, "pest infestation rate," when selecting real estate, and makes suggestions to service users using an AI agent. For example, the pest infestation monitoring system deploys IoT sensors in traps that capture pests and monitors the number of captured individuals. Next, the pest infestation monitoring system uses an AI camera to identify pests and measure the frequency of sightings. This allows for a detailed understanding of the pest infestation situation. Furthermore, the pest infestation monitoring system uses a communication tool to extract the "desired place to live" and "desired conditions" that users are looking for. For example, it uses a LINE conversation-style UI to collect user preferences. In addition, the pest infestation monitoring system collects map data, such as the distribution of restaurants, using an API, and collects weather information, such as precipitation and humidity, using a weather information API. By combining this data and using image generation AI, an interactive map is generated that allows users to visually understand the pest infestation situation. Users can visually check the level of contamination and the status of countermeasures implemented on the map, which can be used as a reference when choosing a property. With this system, an autonomous AI agent analyzes the impact on property value based on the risk of pest infestation and automatically generates reports on a regular basis. By proposing concrete action plans to property owners and developers, early countermeasures and value improvement can be promoted. In addition, the visual interactive map allows residents and property buyers to grasp the pest infestation situation at a glance, supporting quick decision-making. In this way, the pest infestation monitoring system can improve the comfort of the living environment.

[0072] The pest occurrence monitoring system according to the embodiment comprises a monitoring unit, a measurement unit, an extraction unit, a collection unit, a display unit, an analysis unit, and a generation unit. The monitoring unit monitors the number of captured pests. The monitoring unit can monitor the number of captured pests using, for example, an IoT sensor. The monitoring unit counts the number of captured pests in real time using, for example, a sensor attached to the capture device. The monitoring unit can also use different sensors depending on the type of capture device. For example, a combination of temperature sensors and humidity sensors can be used to monitor changes in the capture environment. The measurement unit measures the frequency of pest sightings based on the number of captured pests monitored by the monitoring unit. The measurement unit can measure the frequency of pest sightings using, for example, an AI camera. The measurement unit identifies the type and number of pests and measures the sighting frequency using, for example, image recognition technology from the camera. The measurement unit can also apply different measurement methods depending on the resolution and installation location of the camera. For example, a high-resolution camera can be used to obtain detailed sighting data. The extraction unit extracts the user's preferences. The extraction unit can extract user preferences using, for example, communication tools. The extraction unit can collect desired conditions through dialogue with users, for example, using a chatbot. The extraction unit can also use survey tools to gain a detailed understanding of user preferences. The collection unit collects data. The collection unit can collect, for example, map data and weather information. The collection unit can collect map data for specific areas using, for example, Geographic Information System (GIS) data. The collection unit can also collect weather data such as temperature, humidity, and precipitation using weather information APIs. The display unit visually displays the pest outbreak status based on the data collected by the collection unit. The display unit can visually display the pest outbreak status using, for example, image generation AI. The display unit can display the pest outbreak status as a heatmap or graph using, for example, deep learning technology. The display unit can also update the pest outbreak status in real time using image generation algorithms. The analysis unit analyzes the impact on real estate value based on the data displayed by the display unit.The analysis unit can, for example, analyze the impact on property value based on pest infestation risk. The analysis unit can, for example, use a risk assessment model to quantify pest infestation risk and evaluate its impact on property value. The analysis unit can also perform a factor analysis of price fluctuations and calculate a risk score. The generation unit automatically generates reports based on the results analyzed by the analysis unit. The generation unit can, for example, automatically generate reports on a regular basis. The generation unit can, for example, set the report format and generate reports at frequencies such as daily, weekly, or monthly. The generation unit can also deliver the generated reports via email or web application. This allows the pest infestation monitoring system according to the embodiment to improve the comfort of the living environment.

[0073] The monitoring unit monitors the number of captured pests. For example, the monitoring unit can monitor the number of captured pests using IoT sensors. Specifically, it uses sensors attached to the capture device to count the number of captured pests in real time. This allows for an accurate understanding of the pest infestation situation at the location where the capture device is installed. The monitoring unit can also use different sensors depending on the type of capture device. For example, it can combine temperature and humidity sensors to monitor changes in the capture environment. This allows for simultaneous monitoring of environmental factors affecting pest outbreaks, resulting in more accurate data. Furthermore, the monitoring unit can adjust the sensitivity and settings of the sensors according to the location of the capture device and the type of pest being captured. For example, if a particular pest is active under specific temperature and humidity conditions, the sensor settings can be optimized to match those conditions. This allows the monitoring unit to efficiently and effectively monitor the number of captured pests and respond quickly.

[0074] The measurement unit measures the frequency of pest sightings based on the number of captured insects monitored by the monitoring unit. The measurement unit can, for example, measure the frequency of pest sightings using an AI camera. Specifically, it uses image recognition technology from the camera to identify the type and number of pests and measure the frequency of sightings. The AI ​​camera can analyze video in real time and track the movement of pests. This allows for a detailed understanding of pest activity patterns and locations. The measurement unit can also apply different measurement methods depending on the camera's resolution and installation location. For example, a high-resolution camera can be used to obtain detailed sighting data. In addition, multiple cameras can be installed to cover a wide area, and the data can be integrated and analyzed. This allows the measurement unit to accurately measure the frequency of pest sightings in a wide area and respond quickly. Furthermore, the measurement unit can analyze fluctuations in sighting frequency by comparing them with past data and predict pest outbreak trends. This allows the measurement unit to understand the risk of pest outbreaks in advance and take appropriate measures.

[0075] The extraction unit extracts user preferences. For example, the extraction unit can extract user preferences using communication tools. Specifically, it can use a chatbot to collect desired conditions through dialogue with the user. The chatbot can analyze user input using natural language processing technology and automatically extract desired conditions. This allows users to easily communicate their desired conditions, and the extraction unit can respond quickly. The extraction unit can also use survey tools to understand user preferences in detail. Survey tools can present specific questions to users and collect answers to understand detailed desired conditions. This allows the extraction unit to provide customized services that meet user needs. Furthermore, the extraction unit can analyze users' past usage history and behavioral data to predict potential desired conditions. This allows the extraction unit to anticipate user needs and provide more satisfying services.

[0076] The data collection unit collects data. For example, the data collection unit can collect map data and weather information. Specifically, it can use Geographic Information System (GIS) data to collect map data for a specific area. GIS data provides detailed geographical information and helps to accurately understand the pest outbreak situation. The data collection unit can also use weather information APIs to collect weather data such as temperature, humidity, and precipitation. Weather data is an important factor that affects pest outbreaks, and by collecting this data, the risk of pest outbreaks can be predicted. Furthermore, the data collection unit can collect data from other relevant data sources. For example, by collecting and integrating data related to pest outbreaks, such as agricultural data and environmental data, more accurate predictions can be made. This allows the data collection unit to efficiently collect diverse data and improve the overall performance of the system.

[0077] The display unit visually displays the pest outbreak status based on data collected by the data collection unit. For example, the display unit can visually display the pest outbreak status using image generation AI. Specifically, it can use deep learning technology to display the pest outbreak status as a heatmap or graph. The heatmap shows the frequency of pest outbreaks using varying shades of color, making it visually easy to understand. The graph displays the pest outbreak status over time, helping to understand trends and fluctuations. Furthermore, the display unit can update the pest outbreak status in real time using image generation algorithms. This allows users to always have access to the latest information and respond quickly. The display unit can also provide customized display formats according to user needs. For example, it can provide displays that focus on specific regions or periods, or highlight the outbreak status of specific pests, offering flexible display options to meet user requirements. This allows the display unit to provide users with intuitive and easy-to-understand information, supporting quick decision-making.

[0078] The analysis department analyzes the impact on real estate value based on the data displayed by the display department. For example, the analysis department can analyze the impact on real estate value based on pest infestation risk. Specifically, it uses a risk assessment model to quantify pest infestation risk and evaluate its impact on real estate value. The risk assessment model calculates a risk score based on data such as the frequency, location, and timing of pest infestations. This allows for a quantitative evaluation of pest infestation risk in specific areas or properties. The analysis department can also perform a factor analysis of price fluctuations and calculate a risk score. The factor analysis of price fluctuations takes into account factors other than pest infestation risk and comprehensively evaluates the impact on real estate value. This allows for an accurate understanding of the impact of pest infestation risk on real estate value. Furthermore, the analysis department can analyze past data and market trends to perform future risk assessments and price forecasts. This allows the analysis department to evaluate the impact on real estate value from a long-term perspective and provide information for taking appropriate measures.

[0079] The generation unit automatically generates reports based on the results analyzed by the analysis unit. For example, the generation unit can automatically generate reports on a regular basis. Specifically, it can set the report format and generate reports at frequencies such as daily, weekly, or monthly. The reports include information such as pest outbreaks, risk assessments, and impacts on property values. The generation unit can also deliver the generated reports via email or web applications. This allows users to receive the latest information regularly and take quick action. Furthermore, the generation unit can generate customized reports according to user needs. For example, it can generate flexible reports tailored to user requests, such as reports focusing on specific regions or time periods, or reports highlighting specific pest outbreaks. This allows the generation unit to provide users with intuitive and easy-to-understand information, supporting rapid decision-making.

[0080] The monitoring unit can monitor the number of captured pests using IoT sensors. For example, the monitoring unit can attach a palm-sized IoT sensor to the capture device and count the number of captured pests in real time. The monitoring unit can also monitor changes in the capture environment by combining, for example, temperature sensors and humidity sensors. The monitoring unit can also use different sensors depending on the type of capture device. This allows for accurate monitoring of the number of captured pests using IoT sensors. Some or all of the above processing in the monitoring unit may be performed using, for example, AI, or not using AI. For example, the monitoring unit can input data from IoT sensors attached to the capture device into a generating AI and have the generating AI perform the monitoring of the number of captured pests.

[0081] The measurement unit can measure the frequency of pest sightings using an AI camera. The measurement unit can, for example, use image recognition technology from the camera to identify the type and number of pests and measure the sighting frequency. The measurement unit can also, for example, use a high-resolution camera to acquire detailed sighting data. The measurement unit can also apply different measurement methods depending on, for example, the camera's installation location. This allows for accurate measurement of pest sighting frequency using an AI camera. Some or all of the above-described processes in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can input image data captured by the camera into a generating AI and have the generating AI perform the measurement of sighting frequency.

[0082] The extraction unit can extract user preferences using communication tools. For example, the extraction unit can use a chatbot to collect desired conditions through dialogue with the user. The extraction unit can also use a survey tool to understand user preferences in detail. The extraction unit can also use a conversational UI like LINE to collect user preferences. This allows for accurate extraction of user preferences using communication tools. Some or all of the above-described processes in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input user dialogue data into a generating AI and have the generating AI perform the extraction of desired conditions.

[0083] The data collection unit can collect map data and weather information. For example, the data collection unit can collect map data for a specific area using Geographic Information System (GIS) data. The data collection unit can also collect weather data such as temperature, humidity, and precipitation using a weather information API. The data collection unit can also collect information such as the distribution of restaurants using an API. By collecting map data and weather information, it is possible to accurately understand the situation of pest outbreaks. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data obtained from an API into a generating AI and have the generating AI perform the data collection.

[0084] The display unit can visually display the pest infestation status using image generation AI. For example, the display unit can use deep learning technology to display the pest infestation status as a heatmap or graph. The display unit can also update the pest infestation status in real time using an image generation algorithm. The display unit can also generate an interactive map, allowing users to visually check the level of contamination and the status of countermeasures implemented on the map. This allows for the visual display of pest infestation status using image generation AI. Some or all of the above-described processes in the display unit may be performed using AI, or not. For example, the display unit can input collected data into a generation AI and have the generation AI perform the visual display.

[0085] The analysis unit can analyze the impact on property value based on pest infestation risk. For example, the analysis unit can use a risk assessment model to quantify pest infestation risk and evaluate its impact on property value. The analysis unit can also perform a factor analysis of price fluctuations and calculate a risk score. The analysis unit can also quantitatively evaluate the impact on property value based on pest infestation risk. This allows for appropriate countermeasures to be taken by analyzing the impact on property value based on pest infestation risk. 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 collected data into a generating AI and have the generating AI perform an analysis of the impact on property value.

[0086] The generation unit can automatically generate reports on a regular basis. For example, the generation unit can set the report format and generate reports at frequencies such as daily, weekly, or monthly. The generation unit can also distribute the generated reports via email or web applications. The generation unit can also automatically update the report content to provide the latest information. This allows for continuous monitoring of pest infestations by automatically generating reports on a regular basis. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input collected data into a generation AI and have the generation AI perform the automatic generation of reports.

[0087] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency of pest captures based on the estimated user emotions. For example, if the user is stressed, the monitoring unit can increase the monitoring frequency for a quicker response. For example, if the user is relaxed, the monitoring unit can maintain a normal monitoring frequency. For example, if the user is in a hurry, the monitoring unit can temporarily lower the monitoring frequency. This reduces user stress by adjusting the monitoring frequency based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 monitoring unit may be performed using AI or not. For example, the monitoring unit can input user emotion data into a generative AI and have the generative AI perform emotion-based adjustments to the monitoring frequency.

[0088] The monitoring unit can analyze past capture data and strengthen monitoring during specific seasons or time periods. For example, the monitoring unit can strengthen monitoring during times when pest outbreaks are high in the summer. For example, the monitoring unit can strengthen nighttime monitoring for pests that are active at night. For example, the monitoring unit can strengthen spring monitoring for pests that increase in early spring. In this way, pest outbreaks can be effectively suppressed by strengthening monitoring during specific seasons or time periods. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input past capture data into a generating AI and have the generating AI perform enhanced monitoring during specific seasons or time periods.

[0089] The monitoring unit can apply different monitoring methods to each type of pest when monitoring the number of captured insects. For example, for cockroaches, the monitoring unit can place sensors in dark places for monitoring. For mosquitoes, for example, the monitoring unit can place sensors in humid places for monitoring. For flies, for example, the monitoring unit can place sensors near food for monitoring. This improves monitoring accuracy by applying different monitoring methods to each type of pest. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the number of captured insects monitoring data into a generating AI and cause the generating AI to execute different monitoring methods for each type of pest.

[0090] The monitoring unit can estimate the user's emotions and adjust the notification method of the monitoring results based on the estimated user emotions. For example, if the user is stressed, the monitoring unit can provide a concise notification. For example, if the user is relaxed, the monitoring unit can provide a detailed notification. For example, if the user is in a hurry, the monitoring unit can also provide an audio notification. This reduces user stress by adjusting the notification method based on 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 monitoring unit may be performed using AI or not using AI. For example, the monitoring unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the notification method based on emotions.

[0091] The monitoring unit can enhance monitoring by taking into account geographical information of the locations where pests are captured. For example, the monitoring unit can enhance monitoring of capture locations around the kitchen. For example, the monitoring unit can enhance monitoring of capture locations around the bathroom. For example, the monitoring unit can enhance monitoring of capture locations around the balcony. By enhancing monitoring while taking into account geographical information of the capture locations, pest outbreaks can be effectively suppressed. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input geographical information of the capture locations into a generating AI and have the generating AI perform enhanced monitoring.

[0092] The monitoring unit can automatically optimize the placement of traps based on the number of pests captured during monitoring. For example, the monitoring unit can place additional traps in areas with a high number of captures. For example, the monitoring unit can move traps away from areas with a low number of captures. For example, the monitoring unit can also adjust the placement of traps in real time in response to fluctuations in the number of captures. This improves the efficiency of pest capture by optimizing the placement of traps based on the number of captures. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the number of captures data into a generating AI and have the generating AI perform the trap placement optimization.

[0093] The measurement unit can estimate the user's emotions and adjust the measurement method for sighting frequency based on the estimated user emotions. For example, if the user is stressed, the measurement unit will measure sighting frequency more frequently. For example, if the user is relaxed, the measurement unit can measure sighting frequency as usual. For example, if the user is in a hurry, the measurement unit can temporarily reduce the measurement of sighting frequency. In this way, the user's stress can be reduced by adjusting the measurement method based on 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 measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the measurement method based on emotions.

[0094] The measurement unit can improve measurement accuracy by analyzing the movement patterns of pests when measuring sighting frequency. For example, the measurement unit can improve measurement accuracy by analyzing the movement speed of pests. For example, the measurement unit can improve measurement accuracy by analyzing the movement paths of pests. For example, the measurement unit can also improve measurement accuracy by analyzing the activity periods of pests. In this way, measurement accuracy is improved by analyzing the movement patterns of pests. Some or all of the above processing in the measurement unit may be performed using AI, for example, or without using AI. For example, the measurement unit can input pest movement data into a generating AI and have the generating AI perform movement pattern analysis.

[0095] The measurement unit can apply different measurement algorithms depending on the size and shape of the pest when measuring the frequency of sightings. For example, the measurement unit can apply a high-precision measurement algorithm to small pests. For example, the measurement unit can apply a wide-area measurement algorithm to large pests. For example, the measurement unit can also apply a dedicated measurement algorithm to pests of a specific shape. By applying different measurement algorithms depending on the size and shape of the pest, the measurement accuracy is improved. Some or all of the above processing in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can input data on the size and shape of the pest into a generating AI and have the generating AI execute the application of the measurement algorithm.

[0096] The measurement unit can estimate the user's emotions and adjust the display method of the measurement results based on the estimated user emotions. For example, if the user is stressed, the measurement unit can provide a concise display method. For example, if the user is relaxed, the measurement unit can provide a detailed display method. For example, if the user is in a hurry, the measurement unit can also provide a concise display method. In this way, by adjusting the display method based on the user's emotions, the user's stress can be reduced. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method based on the emotions.

[0097] The measurement unit can enhance measurements by taking into account geographical information of the locations where pests are sighted. For example, the measurement unit can enhance measurements for sighting locations around the kitchen. For example, the measurement unit can enhance measurements for sighting locations around the bathroom. For example, the measurement unit can enhance measurements for sighting locations around the balcony. By enhancing measurements while taking into account geographical information of the sighting locations, pest outbreaks can be effectively suppressed. Some or all of the above processing in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can input geographical information of the sighting locations into a generating AI and have the generating AI perform the measurement enhancement.

[0098] The measurement unit can automatically optimize camera placement based on the frequency of pest sightings during measurement. For example, the measurement unit can add cameras to locations with high sighting frequencies. For example, the measurement unit can move cameras away from locations with low sighting frequencies. For example, the measurement unit can adjust camera placement in real time according to fluctuations in sighting frequencies. This improves the accuracy of pest detection by optimizing camera placement based on sighting frequencies. Some or all of the above processing in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can input sighting frequency data into a generating AI and have the generating AI perform camera placement optimization.

[0099] The extraction unit can estimate the user's emotions and adjust the extraction method for desired conditions based on the estimated user emotions. For example, if the user is stressed, the extraction unit can extract desired conditions in the form of concise questions. For example, if the user is relaxed, the extraction unit can extract desired conditions in the form of detailed questions. For example, if the user is in a hurry, the extraction unit can prioritize voice input when extracting desired conditions. This reduces user stress by adjusting the extraction method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the extraction unit may be performed using AI or not. For example, the extraction unit can input user emotion data into the generative AI and have the generative AI perform adjustments to the extraction method based on emotions.

[0100] The extraction unit can analyze past user preference data and improve the extraction accuracy for specific conditions. For example, the extraction unit can suggest optimal conditions based on conditions previously requested by the user. For example, the extraction unit can improve the extraction accuracy for specific conditions from the user's past preference data. For example, the extraction unit can analyze the user's past preference data and suggest the most suitable conditions. This improves extraction accuracy by analyzing past preference data. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input past user preference data into a generating AI and have the generating AI perform the extraction accuracy improvement.

[0101] The extraction unit can estimate the user's emotions and adjust the display method of the extraction results based on the estimated user emotions. For example, if the user is stressed, the extraction unit can provide a concise display method. For example, if the user is relaxed, the extraction unit can provide a detailed display method. For example, if the user is in a hurry, the extraction unit can provide a concise display method. In this way, the user's stress can be reduced by adjusting the display method based on 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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the display method based on emotions.

[0102] The extraction unit can optimize desired conditions by considering the user's geographical information during the extraction process. For example, the extraction unit can suggest optimal conditions based on the user's current location. For example, the extraction unit can suggest optimal conditions based on the user's past travel history. For example, the extraction unit can analyze the user's geographical information and suggest the most suitable conditions. In this way, by optimizing desired conditions while considering geographical information, conditions that match the user's preferences can be suggested. Some or all of the above-described processes in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input the user's geographical information into a generating AI and have the generating AI perform the optimization of desired conditions.

[0103] The data collection unit can estimate the user's emotions and adjust the frequency of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can increase the frequency of data collection. For example, if the user is relaxed, the data collection unit can maintain a normal frequency of data collection. For example, if the user is in a hurry, the data collection unit can temporarily decrease the frequency of data collection. This reduces user stress by adjusting the collection frequency based on 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 using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the collection frequency based on emotions.

[0104] The data collection unit can analyze past collected data and enhance data collection during specific seasons or time periods. For example, the data collection unit can enhance data collection during the summer. For example, the data collection unit can enhance data collection at night. For example, the data collection unit can enhance data collection during the spring. By enhancing data collection during specific seasons or time periods, the accuracy of data collection is improved. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past collected data into a generating AI and have the generating AI perform data collection enhancement during specific seasons or time periods.

[0105] The data collection unit can estimate the user's emotions and adjust the display method of the collected results based on the estimated user emotions. For example, if the user is stressed, the data collection unit can provide a concise display method. For example, if the user is relaxed, the data collection unit can provide a detailed display method. For example, if the user is in a hurry, the data collection unit can provide a concise display method. In this way, the user's stress can be reduced by adjusting the display method based on 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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's emotion data into a generative AI and have the generative AI perform the adjustment of the display method based on the emotions.

[0106] The data collection unit can enhance data collection by considering the geographical information of the data during collection. For example, the data collection unit can enhance data collection in a specific region. For example, the data collection unit can enhance data collection in a specific building. For example, the data collection unit can enhance data collection in a specific room. By enhancing data collection while considering geographical information, the accuracy of data collection is improved. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical information into a generating AI and have the generating AI perform the data collection enhancement.

[0107] The display unit can estimate the user's emotions and adjust the display method based on the estimated emotions. For example, if the user is stressed, the display unit can provide a concise and highly visible display method. For example, if the user is relaxed, the display unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the display unit can provide a display method that gets straight to the point. In this way, by adjusting the display method based on the user's emotions, the user's stress can be reduced. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method based on the emotions.

[0108] The display unit can analyze past display data and improve display accuracy for specific conditions. For example, the display unit can suggest the optimal display method based on data previously displayed by the user. For example, the display unit can improve display accuracy for specific conditions from the user's past display data. For example, the display unit can analyze the user's past display data and suggest the most suitable display method. This improves display accuracy by analyzing past display data. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input past display data into a generating AI and have the generating AI perform the display accuracy improvement.

[0109] The display unit can estimate the user's emotions and adjust the order of the displayed results based on the estimated emotions. For example, if the user is stressed, the display unit can display important information first. For example, if the user is relaxed, the display unit can display detailed information in a sequential manner. For example, if the user is in a hurry, the display unit can also display concise information first. In this way, by adjusting the display order based on the user's emotions, the user's stress can be reduced. 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 display unit may be performed using AI, for example, or without AI. For example, the display unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display order based on emotions.

[0110] The display unit can enhance the display by taking geographical information into account when displaying data. For example, the display unit can highlight data in a specific region. For example, the display unit can highlight data in a specific building. For example, the display unit can highlight data in a specific room. By enhancing the display by taking geographical information into account, the display accuracy is improved. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input geographical information into a generating AI and have the generating AI perform the display enhancement.

[0111] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a concise analysis method. For example, if the user is relaxed, the analysis unit can provide a detailed analysis method. For example, if the user is in a hurry, the analysis unit can provide a concise analysis method. This reduces the user's stress by adjusting the analysis method based on their emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, 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 a generative AI and have the generative AI perform the adjustment of the analysis method based on the emotions.

[0112] The analysis unit can optimize the analysis algorithm by referring to past analysis data. For example, the analysis unit can propose the optimal analysis algorithm based on past analysis data. For example, the analysis unit can improve the accuracy of analysis for specific conditions from past analysis data. For example, the analysis unit can analyze past analysis data and propose the most suitable analysis algorithm. In this way, the accuracy of the analysis algorithm is improved by referring to past analysis 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 past analysis data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0113] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is stressed, the analysis unit can provide a concise display method. For example, if the user is relaxed, the analysis unit can provide a detailed display method. For example, if the user is in a hurry, the analysis unit can provide a concise display method. In this way, the user's stress can be reduced by adjusting the display method based on 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 analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the display method based on emotions.

[0114] The analysis unit can enhance its analysis by considering the geographical information of the data during the analysis process. For example, the analysis unit can emphasize data from a specific region. For example, the analysis unit can emphasize data from a specific building. For example, the analysis unit can emphasize data from a specific room. By enhancing the analysis by considering geographical information, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input geographical information into a generating AI and have the generating AI perform the analysis enhancement.

[0115] The generation unit can estimate the user's emotions and adjust the frequency of report generation based on the estimated emotions. For example, if the user is stressed, the generation unit can increase the frequency of report generation. For example, if the user is relaxed, the generation unit can maintain a normal frequency of report generation. For example, if the user is in a hurry, the generation unit can temporarily decrease the frequency of report generation. This reduces user stress by adjusting the generation frequency based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI or not using AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform the emotion-based adjustment of the generation frequency.

[0116] The generation unit can optimize the report generation algorithm by referring to past generation data. For example, the generation unit can propose the optimal report generation algorithm based on past generation data. For example, the generation unit can improve the accuracy of report generation for specific conditions from past generation data. For example, the generation unit can analyze past generation data and propose the most suitable report generation algorithm. This improves the accuracy of the generation algorithm by referring to past generation data. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input past generation data into a generation AI and have the generation AI perform the optimization of the generation algorithm.

[0117] The generation unit can estimate the user's emotions and adjust the report display method based on the estimated user emotions. For example, if the user is stressed, the generation unit can provide a concise display method. For example, if the user is relaxed, the generation unit can provide a detailed display method. For example, if the user is in a hurry, the generation unit can provide a concise display method. This reduces user stress by adjusting the display method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation 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 generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform the emotion-based adjustment of the display method.

[0118] The generation unit can enhance report generation by considering the geographical information of the data during generation. For example, the generation unit can generate reports emphasizing data in a specific region. For example, the generation unit can generate reports emphasizing data in a specific building. For example, the generation unit can generate reports emphasizing data in a specific room. By enhancing report generation by considering geographical information, the accuracy of the report is improved. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input geographical information into a generation AI and have the generation AI perform report generation enhancement.

[0119] The generation unit can generate an optimal report by considering the user's health condition during the generation process. For example, if the user is tired, the generation unit can generate a concise report. For example, if the user is healthy, the generation unit can generate a detailed report. For example, if the user is unwell, the generation unit can generate a concise report. By generating reports that take the user's health condition into consideration, the generation unit can provide the user with the most relevant information. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's health condition data into a generation AI and have the generation AI perform report generation based on the health condition.

[0120] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0121] The monitoring unit not only monitors the number of pests captured but can also identify the types of pests captured. For example, the monitoring unit uses sensors attached to the capture devices to identify the types of captured pests in real time. This allows for a detailed understanding of the infestation situation of specific pests. Furthermore, the monitoring unit can propose different countermeasures depending on the type of pest captured. For example, if a large number of cockroaches are captured, it can propose strengthening cockroach control measures. In addition, the monitoring unit can optimize the placement of capture devices based on the types of pests captured. For example, by placing additional capture devices in areas where a particular pest is captured in large numbers, capture efficiency can be improved.

[0122] The measurement unit can not only measure the frequency of pest sightings but also analyze the behavioral patterns of the sighted pests. For example, the measurement unit uses camera image recognition technology to identify the movement routes of pests and analyze their behavioral patterns. This makes it possible to identify the source and movement routes of pests. Furthermore, the measurement unit can also propose effective countermeasures based on the pest behavioral patterns. For example, if pests are moving along a specific route, taking countermeasures along that route can prevent pest intrusion. In addition, the measurement unit can optimize the placement of cameras based on the pest behavioral patterns. For example, by placing cameras along the pest movement routes, the accuracy of measuring sighting frequency can be improved.

[0123] The extraction unit can not only extract user preferences but also propose the optimal living environment based on the user's lifestyle. For example, the extraction unit collects information about the user's lifestyle and proposes the optimal living environment based on that information. This allows it to provide a living environment that suits the user's lifestyle. Furthermore, the extraction unit can also propose pest control measures based on the user's lifestyle. For example, it can propose pest control measures that do not harm pets to users who own pets. In addition, the extraction unit can also propose improvements to the living environment based on the user's lifestyle. For example, it can propose improvements to the living environment that take health into consideration to health-conscious users.

[0124] The data collection unit can collect not only map data and weather information, but also local event information. For example, the unit can collect local event information and use it to analyze pest outbreaks. This allows for an understanding of the impact of local events on pest outbreaks. The unit can also make suggestions for pest control measures based on local event information. For example, in areas where large-scale events are held, it can suggest strengthening pest control measures in advance. Furthermore, the unit can also make suggestions for improving living environments based on local event information. For example, it can provide users participating in local events with suggestions for improving their living environment related to the event.

[0125] The display unit can not only visually show the pest infestation status but also visually show the effectiveness of pest control measures. For example, the display unit can compare and display the infestation status before and after the implementation of pest control measures. This allows users to grasp the effectiveness of pest control measures at a glance. The display unit can also suggest additional measures based on the effectiveness of the pest control measures. For example, if the effect of the measures is insufficient, it can suggest additional measures. Furthermore, the display unit can update the effectiveness of pest control measures in real time. For example, by displaying changes in the infestation status in real time according to the implementation status of the measures, it can support a quick response.

[0126] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency of pest captures based on the estimated user emotions. For example, if the user is stressed, the monitoring frequency can be increased for a quicker response. If the user is relaxed, the monitoring frequency can be kept at a normal level. If the user is in a hurry, the monitoring frequency can be temporarily set lower. This reduces user stress by adjusting the monitoring frequency based on 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 monitoring unit may be performed using AI or not. For example, the monitoring unit can input user emotion data into a generative AI and have the generative AI perform emotion-based adjustments to the monitoring frequency.

[0127] The measurement unit can estimate the user's emotions and adjust the measurement method for sighting frequency based on the estimated user emotions. For example, if the user is stressed, sighting frequency will be measured more frequently. If the user is relaxed, sighting frequency can be measured as usual. If the user is in a hurry, sighting frequency can be temporarily reduced. In this way, adjusting the measurement method based on the user's emotions can reduce the user's stress. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the measurement unit may be performed using AI or not. For example, the measurement unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the measurement method based on emotions.

[0128] The extraction unit can estimate the user's emotions and adjust the extraction method for desired conditions based on the estimated user emotions. For example, if the user is stressed, desired conditions can be extracted using a concise question format. If the user is relaxed, desired conditions can be extracted using a detailed question format. If the user is in a hurry, voice input can be prioritized for extracting desired conditions. This reduces user stress by adjusting the extraction method based on 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 extraction unit may be performed using AI or not. For example, the extraction unit can input user emotion data into the generative AI and have the generative AI perform adjustments to the extraction method based on emotions.

[0129] The data collection unit can estimate the user's emotions and adjust the frequency of data collection based on the estimated emotions. For example, if the user is stressed, the frequency of data collection can be increased. If the user is relaxed, the frequency of data collection can be kept at a normal level. If the user is in a hurry, the frequency of data collection can be temporarily reduced. This reduces user stress by adjusting the collection frequency based on 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 collection frequency based on emotions.

[0130] The display unit can estimate the user's emotions and adjust the display method based on the estimated emotions. For example, if the user is stressed, it can provide a concise and highly visible display method. If the user is relaxed, it can provide a display method that includes detailed information. If the user is in a hurry, it can provide a display method that gets straight to the point. In this way, by adjusting the display method based on the user's emotions, the user's stress can be reduced. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input user emotion data into a generative AI and have the generative AI perform an adjustment of the display method based on the emotion.

[0131] The following briefly describes the processing flow for example form 2.

[0132] Step 1: The monitoring unit monitors the number of captured pests. The monitoring unit can monitor the number of captured pests using, for example, IoT sensors. The monitoring unit counts the number of captured pests in real time using sensors attached to the capture device. Different sensors can also be used depending on the type of capture device. For example, temperature sensors and humidity sensors can be combined to monitor changes in the capture environment. Step 2: The measurement unit measures the frequency of pest sightings based on the number of captured insects monitored by the monitoring unit. The measurement unit can, for example, use an AI camera to measure the frequency of pest sightings. The measurement unit uses image recognition technology from the camera to identify the type and number of pests and measure the frequency of sightings. Different measurement methods can also be applied depending on the camera's resolution and installation location. Step 3: The extraction unit extracts user preferences. The extraction unit can extract user preferences using, for example, a communication tool. The extraction unit can use a chatbot to collect desired conditions through dialogue with the user. Alternatively, it can use a survey tool to gain a detailed understanding of user preferences. Step 4: The collection unit collects data. The collection unit can collect, for example, map data and weather information. The collection unit can use Geographic Information System (GIS) data to collect map data for a specific area. It can also use weather information APIs to collect weather data such as temperature, humidity, and precipitation. Step 5: The display unit visually displays the pest infestation status based on the data collected by the collection unit. The display unit can, for example, visually display the pest infestation status using image generation AI. The display unit can display the pest infestation status as a heatmap or graph using deep learning technology. It can also update the pest infestation status in real time using an image generation algorithm. Step 6: The analysis unit analyzes the impact on property value based on the data displayed by the display unit. For example, the analysis unit can analyze the impact on property value based on pest infestation risk. The analysis unit uses a risk assessment model to quantify pest infestation risk and evaluate its impact on property value. It can also perform a factor analysis of price fluctuations and calculate a risk score. Step 7: The generation unit automatically generates reports based on the results analyzed by the analysis unit. The generation unit can, for example, automatically generate reports on a regular basis. The generation unit sets the report format and generates reports at frequencies such as daily, weekly, or monthly. The generation unit can also deliver the generated reports via email or web applications.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] Each of the multiple elements described above, including the monitoring unit, measurement unit, extraction unit, collection unit, display unit, analysis unit, and generation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the monitoring unit can monitor the number of pests captured using the IoT sensor of the smart device 14. The measurement unit can measure the frequency of pest sightings using the AI ​​camera of the smart device 14. The extraction unit can extract user preferences using the communication tool of the smart device 14. The collection unit can collect map data and weather information using the API of the smart device 14. The display unit can visually display the pest outbreak situation using the image generation AI of the smart device 14. The analysis unit can analyze the impact on real estate value based on pest outbreak risk using the specific processing unit 290 of the data processing unit 12. The generation unit can automatically generate reports using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0137] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] Each of the multiple elements described above, including the monitoring unit, measurement unit, extraction unit, collection unit, display unit, analysis unit, and generation unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the monitoring unit can monitor the number of pests captured using the IoT sensors of the smart glasses 214. The measurement unit can measure the frequency of pest sightings using the AI ​​camera of the smart glasses 214. The extraction unit can extract user preferences using the communication tools of the smart glasses 214. The collection unit can collect map data and weather information using the API of the smart glasses 214. The display unit can visually display the pest outbreak situation using the image generation AI of the smart glasses 214. The analysis unit can analyze the impact on real estate value based on pest outbreak risk using the specific processing unit 290 of the data processing unit 12. The generation unit can automatically generate reports using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0153] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] Each of the multiple elements described above, including the monitoring unit, measurement unit, extraction unit, collection unit, display unit, analysis unit, and generation unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the monitoring unit can monitor the number of captured pests using the IoT sensor of the headset terminal 314. The measurement unit can measure the frequency of pest sightings using the AI ​​camera of the headset terminal 314. The extraction unit can extract user preferences using the communication tool of the headset terminal 314. The collection unit can collect map data and weather information using the API of the headset terminal 314. The display unit can visually display the pest outbreak situation using the image generation AI of the headset terminal 314. The analysis unit can analyze the impact on real estate value based on pest outbreak risk using the specific processing unit 290 of the data processing unit 12. The generation unit can automatically generate reports using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0169] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.).

[0182] 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.

[0183] 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.

[0184] 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.

[0185] Each of the multiple elements described above, including the monitoring unit, measurement unit, extraction unit, collection unit, display unit, analysis unit, and generation unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the monitoring unit can monitor the number of pests captured using the IoT sensors of the robot 414. The measurement unit can measure the frequency of pest sightings using the AI ​​camera of the robot 414. The extraction unit can extract user preferences using the communication tools of the robot 414. The collection unit can collect map data and weather information using the API of the robot 414. The display unit can visually display the pest outbreak situation using the image generation AI of the robot 414. The analysis unit can analyze the impact on real estate value based on pest outbreak risk using the specific processing unit 290 of the data processing unit 12. The generation unit can automatically generate reports using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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."

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] (Note 1) The monitoring department monitors the number of pests captured, A measuring unit measures the frequency of pest sightings based on the number of captures monitored by the aforementioned monitoring unit, An extraction unit that extracts user preferences, A data collection unit that collects data, A display unit visually displays the pest outbreak status based on the data collected by the aforementioned collection unit, An analysis unit analyzes the impact on real estate value based on the data displayed by the display unit, The system comprises a generation unit that automatically generates a report based on the results analyzed by the aforementioned analysis unit. A system characterized by the following features. (Note 2) The aforementioned monitoring unit, Using IoT sensors to monitor the number of pests captured. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned measuring unit is We will use AI cameras to measure the frequency of pest sightings. The system described in Appendix 1, characterized by the features described herein. (Note 4) The extraction unit is Use communication tools to extract user preferences. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is Collect map data and weather information. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned display unit is Using image generation AI, the pest infestation situation is visually displayed. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit is Analyzing the impact on property value based on pest infestation risk. The system described in Appendix 1, characterized by the features described herein. (Note 8) The generating unit is Automatically generate reports on a regular basis. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned monitoring unit, The system estimates the user's emotions and adjusts the frequency of monitoring pest captures based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned monitoring unit, We will analyze past capture data and strengthen monitoring during specific seasons and time periods. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned monitoring unit, When monitoring the number of captured insects, different monitoring methods should be applied for each type of pest. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned monitoring unit, It estimates the user's emotions and adjusts the notification method of monitoring results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned monitoring unit, During monitoring, take into account geographical information about the locations where pests are captured to enhance monitoring efforts. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned monitoring unit, During monitoring, the trap placement is automatically optimized based on the number of pests captured. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned measuring unit is We estimate the user's emotions and adjust the method of measuring sighting frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned measuring unit is When measuring the frequency of sightings, we analyze the movement patterns of pests to improve measurement accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned measuring unit is When measuring the frequency of sightings, different measurement algorithms are applied depending on the size and shape of the pest. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned measuring unit is It estimates the user's emotions and adjusts how the measurement results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned measuring unit is During measurement, the geographical information of the locations where pests have been sighted will be taken into consideration to enhance the measurement process. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned measuring unit is During measurement, the camera placement is automatically optimized based on the frequency of pest sightings. The system described in Appendix 1, characterized by the features described herein. (Note 21) The extraction unit is We estimate the user's emotions and adjust the method of extracting desired conditions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The extraction unit is Analyze past user preference data to improve extraction accuracy for specific conditions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The extraction unit is It estimates the user's emotions and adjusts how the extraction results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The extraction unit is During the extraction process, the user's geographical information is taken into consideration to optimize the desired conditions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned collection unit is It estimates the user's emotions and adjusts the frequency of data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned collection unit is We analyze past collected data and enhance data collection during specific seasons and time periods. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned collection unit is It estimates the user's emotions and adjusts how the collected results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned collection unit is During data collection, enhance the collection process by considering the data's geographical information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned display unit is It estimates the user's emotions and adjusts the display method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned display unit is Analyze past display data to improve display accuracy for specific conditions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned display unit is It estimates the user's emotions and adjusts the order of displayed results based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned display unit is When displaying data, the display is enhanced by taking geographical information into account. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned analysis unit is We estimate the user's emotions and adjust the analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned analysis unit is Optimize the analysis algorithm by referring to past analysis data. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned analysis unit is During analysis, consider the geographical information of the data to enhance the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 37) The generating unit is It estimates user sentiment and adjusts the frequency of report generation based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 38) The generating unit is Optimize the report generation algorithm by referencing past generated data. The system described in Appendix 1, characterized by the features described herein. (Note 39) The generating unit is It estimates user sentiment and adjusts how reports are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 40) The generating unit is During generation, the report generation process will be enhanced by taking into account the geographical information of the data. The system described in Appendix 1, characterized by the features described herein. (Note 41) The generating unit is During generation, the system considers the user's health status to create the most suitable report. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0205] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The monitoring department monitors the number of pests captured, A measuring unit measures the frequency of pest sightings based on the number of captures monitored by the aforementioned monitoring unit, An extraction unit that extracts user preferences, A data collection unit that collects data, A display unit visually displays the pest outbreak status based on the data collected by the aforementioned collection unit, An analysis unit analyzes the impact on real estate value based on the data displayed by the display unit, The system comprises a generation unit that automatically generates a report based on the results analyzed by the aforementioned analysis unit. A system characterized by the following features.

2. The aforementioned monitoring unit, Using IoT sensors to monitor the number of pests captured. The system according to feature 1.

3. The aforementioned measuring unit is We will use AI cameras to measure the frequency of pest sightings. The system according to feature 1.

4. The extraction unit is Use communication tools to extract user preferences. The system according to feature 1.

5. The aforementioned collection unit is Collect map data and weather information. The system according to feature 1.

6. The aforementioned display unit is Using image generation AI, the pest infestation situation is visually displayed. The system according to feature 1.

7. The aforementioned analysis unit is Analyzing the impact on property value based on pest infestation risk. The system according to feature 1.

8. The generating unit is Automatically generate reports on a regular basis. The system according to feature 1.

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