system

Abstract

The system according to this embodiment aims to propose the optimal pest control method according to the type and number of pests that appear indoors. [Solution] The system according to the embodiment comprises a registration unit, an observation unit, a measurement unit, and a proposal unit. The registration unit registers the floor plan of the room. The observation unit performs fixed-point observations of the presence or absence of insects in the room using a camera or insect trap. The measurement unit measures the presence, type, and number of insects based on the data observed by the observation unit. The proposal unit proposes the optimal extermination method based on the data measured by the measurement unit.

Description

Technical Field

[0004] ,

[0006] , , ,

[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, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult to find an optimal extermination method according to the types and numbers of pests generated indoors.

[0005] The system according to the embodiment aims to propose an optimal extermination method according to the types and numbers of pests generated indoors.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a registration unit, an observation unit, a measurement unit, and a proposal unit. The registration unit registers the floor plan of the room. The observation unit performs fixed-point observations of the presence or absence of insects in the room using a camera or insect trap. The measurement unit measures the presence, type, and number of insects based on the data observed by the observation unit. The proposal unit proposes the optimal extermination method based on the data measured by the measurement unit. [Effects of the Invention]

[0007] The system according to this embodiment can suggest the optimal pest control method depending on the type and number of pests that appear indoors. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The pest control suggestion system according to an embodiment of the present invention is a system that uses AI to suggest the optimal pest control method according to the type and number of pests that occur in a room in a typical household. The pest control suggestion system registers the room's floor plan and uses cameras and insect traps to perform fixed-point observations of the presence or absence of insects in the room. From the observation results, it measures the presence, type, and number of insects and suggests an appropriate pest control method. For example, the pest control suggestion system registers the room's floor plan. If map data from a robot vacuum cleaner is available, it can also be used in conjunction with this. Next, the pest control suggestion system uses cameras and insect traps to perform fixed-point observations of the presence or absence of insects in the room. The insect traps are equipped with cameras and sensors to measure the presence, type, and number of insects. Based on this data, the AI ​​suggests the optimal pest control method. For example, it uses cameras to perform fixed-point observations of the room's condition and installs insect sensors in the room to check for the presence or absence of insects. If insects are detected, it measures their type and number, and the AI ​​suggests an appropriate pest control method. For example, if a particular type of insect is present in large numbers, it suggests an effective pesticide and method for that insect. Furthermore, the system can identify locations and times when insects are likely to appear and suggest preventative measures. This system is extremely useful for people who dislike insects and for parents with newborns who want to avoid the effects of pests. For example, preventative measures can be taken in advance, tailored to the seasons and times when insects are most likely to appear. Even if an insect infestation occurs, the system can minimize the impact of pests by quickly suggesting appropriate extermination methods. In addition, this system can be used in public and commercial facilities. For example, by attaching cameras and sensors to insect traps used for pest surveys in public facilities, the pest situation can be monitored in real time, and appropriate extermination methods can be suggested. This helps maintain the hygiene of the facility and improves user comfort. By utilizing AI, this system can exterminate pests more efficiently and effectively than conventional pest control methods. For example, because the AI ​​suggests the optimal extermination method based on past data, the use of unnecessary pesticides can be reduced, lessening the burden on the environment. Also, because the AI ​​analyzes data in real time, it can respond quickly, minimizing pest damage.Thus, AI-powered pest control systems can be used in a variety of locations, including private homes, public facilities, and commercial establishments, making them extremely useful tools for people who want to avoid the effects of pests. This allows the pest control suggestion system to measure the presence, type, and number of insects indoors and propose the most suitable extermination method.

[0029] The pest control suggestion system according to this embodiment comprises a registration unit, an observation unit, a measurement unit, and a suggestion unit. The registration unit registers a floor plan of the room. The floor plan of the room includes, but is not limited to, the arrangement of rooms, the arrangement of furniture, and the area. The registration unit can also register the floor plan using, for example, map data from a robot vacuum cleaner. The observation unit performs fixed-point observations of the presence or absence of insects in the room using a camera or insect trap. The observation unit can, for example, perform fixed-point observations of the room using a camera. The observation unit can also perform observations by attaching a camera or sensor to an insect trap. For example, the observation unit can check for the presence or absence of insects using a camera attached to an insect trap. The observation unit can also check for the presence or absence of insects by installing an insect sensor in the room. The measurement unit measures the presence, type, and number of insects based on the data observed by the observation unit. The measurement unit can, for example, identify the type of insect by analyzing image data captured by a camera. The measurement unit can also measure the number of insects based on data detected by a sensor. For example, the measurement unit uses image analysis technology to identify the type of insect and measure its number. The measurement unit can also measure the number of insects based on sensor data. The suggestion unit proposes the optimal extermination method based on the data measured by the measurement unit. The suggestion unit proposes, for example, effective exterminators and methods for specific types of insects. The suggestion unit can also identify places and times when insects are likely to appear and propose preventive measures. For example, if a particular type of insect is appearing in large numbers, the suggestion unit proposes an effective exterminator for that insect. The suggestion unit can also identify places and times when insects are likely to appear and propose preventive measures. As a result, the pest control suggestion system according to this embodiment can measure the presence, type, and number of insects in a room and propose the optimal extermination method. Some or all of the above-described processes in the measurement unit may be performed using, for example, AI, or without AI. For example, the measurement unit can input image data captured by a camera into a generation AI and have the generation AI identify the type of insect from the image data. Some or all of the above-described processes in the suggestion unit may be performed using, for example, AI, or without AI.For example, the proposal unit can input data measured by the measurement unit into the generation AI, and have the generation AI generate a proposal for the optimal extermination method.

[0030] The registration unit registers interior floor plans. These floor plans include, but are not limited to, room layouts, furniture placement, and area. The registration unit can also register floor plans in conjunction with, for example, map data from a robotic vacuum cleaner. Specifically, this can be done by the user manually entering the floor plan or by importing map data automatically generated by the robotic vacuum cleaner. In the case of manual entry, the user can use a dedicated application to input detailed information about the shape of the rooms and the placement of furniture. In the case of automatic generation, an accurate floor plan is generated based on data collected by the robotic vacuum cleaner as it moves through the rooms. This floor plan forms the foundation of the entire system and is an important source of information for other departments to function efficiently. Furthermore, the registration unit is designed to allow for easy updating and modification of floor plans. For example, if the furniture placement changes or a new room is added, the user can easily update the floor plan. This ensures that pest control suggestions are always based on the latest information. The registration unit also has the function to manage multiple floor plans, allowing for centralized management of information from different rooms and floors. This allows for efficient pest control proposals to be made to large facilities and homes with multiple rooms.

[0031] The observation unit uses cameras and insect traps to perform fixed-point observations to check for the presence of insects in a room. For example, the observation unit can use a camera to observe the room's environment at a fixed point. The observation unit can also attach cameras and sensors to insect traps for observation. For example, the observation unit can check for the presence of insects using a camera attached to an insect trap. The observation unit can also install insect sensors in the room to check for the presence of insects. Specifically, the camera can cover a wide area with high resolution and monitor insect movements in real time. The camera attached to the insect trap records the type and number of captured insects in detail, and the sensor detects minute movements and vibrations to confirm the presence of insects. This allows the observation unit to accurately understand the movements of insects in the room. Furthermore, the observation unit can set a regular observation schedule and perform observations at specific times and under specific conditions. For example, a night mode can be set to observe insects that are active at night. The observation unit also has a function to issue an alert when abnormal movement is detected, enabling a quick response. In this way, the observation unit can efficiently monitor the presence of insects in a room and support a quick response.

[0032] The measurement unit measures the presence, type, and number of insects based on data observed by the observation unit. For example, the measurement unit identifies insect types by analyzing image data captured by a camera. The measurement unit can also measure the number of insects based on data detected by sensors. For example, the measurement unit uses image analysis technology to identify insect types and count them. The measurement unit can also measure the number of insects based on sensor data. Specifically, the measurement unit uses AI to analyze image data and identify insect types with high accuracy. Based on a pre-trained database, the AI ​​analyzes characteristics such as the shape, color, and movement of insects to identify their types. It can also understand the number and movement patterns of insects by analyzing sensor data. As a result, the measurement unit can understand the situation of insects indoors in detail and provide basic data for taking appropriate countermeasures. Furthermore, the measurement unit can accumulate past data and perform long-term trend analysis. This allows it to predict insect occurrence patterns in specific seasons and conditions and formulate preventive measures. The measurement unit also has a function to issue alerts when abnormal data is detected, enabling a quick response. This allows the measurement unit to accurately grasp the insect situation indoors and support a quick and appropriate response.

[0033] The proposal department proposes the optimal pest control method based on data measured by the measurement department. For example, the proposal department can suggest effective pesticides and methods for specific types of insects. It can also identify locations and times when insects are likely to appear and propose preventative measures. Specifically, the proposal department uses AI to analyze measurement data and propose the optimal pest control method. Based on past data and expertise, the AI ​​selects the most effective pesticides and methods for specific insects. It can also analyze insect infestation patterns and develop preventative measures. This allows the proposal department to provide users with effective pest control methods and preventative measures, minimizing insect infestations. Furthermore, the proposal department can collect user feedback and continuously improve the accuracy and effectiveness of its proposals. For example, it can evaluate the effectiveness of pest control methods and revise proposals as needed. The proposal department can also propose comprehensive measures combining multiple pest control methods. This allows the proposal department to provide users with the optimal extermination method and preventative measures, effectively suppressing the occurrence of insects indoors.

[0034] The identification unit can identify locations and times when insects are likely to appear. For example, the identification unit can identify locations and times when insects are likely to appear by analyzing past data. The identification unit can also identify locations and times when insects are likely to appear based on environmental conditions. For example, the identification unit can identify locations and times when insects are likely to appear based on environmental conditions such as temperature and humidity. The identification unit can also identify locations and times when insects are likely to appear by analyzing insect appearance patterns. For example, the identification unit can analyze insect appearance patterns and identify locations and times when insects are likely to appear. This makes effective extermination possible by identifying locations and times when insects are likely to appear. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input past data into a generating AI and have the generating AI perform the identification of locations and times when insects are likely to appear.

[0035] The prevention department can propose preventive measures. For example, the prevention department can propose environmental improvement measures. The prevention department can also propose regular maintenance. For example, the prevention department can propose indoor cleaning and ventilation. The prevention department can also propose preventive measures for places where insects are likely to appear. For example, the prevention department can propose the installation of insect nets in places where insects are likely to appear. In this way, by proposing preventive measures, insect infestations can be prevented. Some or all of the above processes in the prevention department may be carried out using AI, for example, or without AI. For example, the prevention department can have a generation AI execute the proposal of environmental improvement measures.

[0036] The Facility Support Division can provide solutions tailored to use in public and commercial facilities. For example, it can attach cameras and sensors to insect traps used for pest surveys in public facilities to monitor pest outbreaks in real time. Similarly, it can attach cameras and sensors to insect traps used for pest surveys in commercial facilities to monitor pest outbreaks in real time. For instance, it can attach cameras and sensors to insect traps used for pest surveys in public facilities to monitor pest outbreaks in real time and propose appropriate control methods. This adaptability to use in public and commercial facilities makes the system applicable to a wide range of uses.

[0037] The observation unit can perform observations by attaching cameras and sensors to insect traps. For example, the observation unit can attach a camera to an insect trap and perform observations. The observation unit can also attach sensors to an insect trap and perform observations. For example, the observation unit can check for the presence or absence of insects using a camera attached to the insect trap. The observation unit can also check for the presence or absence of insects using a sensor attached to the insect trap. By attaching cameras and sensors to insect traps, more accurate observations become possible. Some or all of the above processing in the observation unit may be performed using AI, for example, or without using AI. For example, the observation unit can input image data captured by a camera attached to an insect trap into a generating AI and have the generating AI check for the presence or absence of insects from the image data.

[0038] The suggestion unit can propose effective insecticides and methods for controlling specific types of insects. For example, the suggestion unit can propose an effective insecticide for cockroaches. It can also propose an effective method for controlling mosquitoes. For example, the suggestion unit can propose an effective insecticide for cockroaches. It can also propose an effective method for controlling mosquitoes. This improves the effectiveness of control by proposing effective methods for controlling specific types of insects. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can have a generation AI perform the task of proposing effective methods for controlling specific types of insects.

[0039] The registration unit can select the optimal registration method by referring to past floor plan data during registration. For example, the registration unit can suggest the optimal registration method based on floor plan data previously registered by the user. The registration unit can also prioritize suggesting registration methods previously used by the user (manual, voice, etc.). For example, the registration unit can predict and suggest a registration method to be used during a specific time period based on the user's past registration history. This allows the optimal registration method to be selected by referring to past data. Some or all of the above processing in the registration unit may be performed using AI, for example, or without AI. For example, the registration unit can input past floor plan data into a generating AI and have the generating AI select the optimal registration method.

[0040] The registration unit can adjust the level of detail in the floor plan based on the intended use of the room during registration. For example, in the case of a living room, the registration unit can register detailed information such as the arrangement of furniture and the location of power outlets. In the case of a kitchen, the registration unit can also register the location of cooking utensils and storage spaces in detail. For example, in the case of a bedroom, the registration unit can register the location of the bed and closet in detail. By adjusting the level of detail in the floor plan according to the intended use of the room, more accurate registration becomes possible. Some or all of the above processing in the registration unit may be performed using AI, for example, or without AI. For example, the registration unit can have a generating AI perform the adjustment of the level of detail based on the intended use of the room.

[0041] The observation unit can select the optimal observation method by referring to past observation data during observation. For example, the observation unit can propose the optimal camera position based on past observation data. The observation unit can also propose the optimal observation time based on past observation data. For example, the observation unit can propose the optimal observation frequency based on past observation data. In this way, the optimal observation method can be selected by referring to past data. Some or all of the above processing in the observation unit may be performed using AI, for example, or without using AI. For example, the observation unit can input past observation data into a generating AI and have the generating AI perform the selection of the optimal observation method.

[0042] The observation unit can adjust the level of detail of its observations based on how the room is being used. For example, in a living room, the observation unit can perform detailed observations to accurately measure the type and number of insects. In a kitchen, the observation unit can focus its observations on areas near food. In a bedroom, for example, the observation unit can focus its observations on the area around the bed. By adjusting the level of detail of the observations according to how the room is being used, more accurate observations become possible. Some or all of the above processing in the observation unit may be performed using AI, for example, or without AI. For example, the observation unit can have a generating AI perform the adjustment of the level of detail of the observations based on how the room is being used.

[0043] The measurement unit can select the optimal measurement method by referring to past measurement data during measurement. For example, the measurement unit can propose the optimal camera position based on past measurement data. The measurement unit can also propose the optimal measurement time based on past measurement data. For example, the measurement unit can propose the optimal measurement frequency based on past measurement data. In this way, the optimal measurement method can be selected by referring to past data. 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 past measurement data into a generating AI and have the generating AI perform the selection of the optimal measurement method.

[0044] The measurement unit can apply different measurement algorithms depending on the type of insect during measurement. For example, in the case of a cockroach, the measurement unit applies a measurement algorithm that takes into account the speed of its movement. Similarly, in the case of a mosquito, the measurement unit can apply a measurement algorithm that takes into account its small size. For example, in the case of a fly, the measurement unit applies a measurement algorithm that takes into account its flight pattern. This improves the accuracy of the measurement by applying a measurement algorithm appropriate to the type of insect. 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 have a generating AI perform the application of a measurement algorithm appropriate to the type of insect.

[0045] The suggestion unit can adjust the level of detail in its suggestions based on the importance of the insect. For example, in the case of cockroaches, the suggestion unit can suggest detailed extermination methods and preventative measures. In the case of mosquitoes, it can suggest simple extermination methods and preventative measures. For example, in the case of flies, the suggestion unit can suggest basic extermination methods and preventative measures. By adjusting the level of detail in suggestions according to the importance of the insect, more effective suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can have a generating AI perform the adjustment of the level of detail in suggestions based on the importance of the insect.

[0046] The proposal unit can apply different proposal algorithms depending on the type of insect during the proposal process. For example, in the case of a cockroach, the proposal unit applies a proposal algorithm that takes into account its speed of movement. Similarly, in the case of a mosquito, it can apply a proposal algorithm that takes into account its small size. For example, in the case of a fly, the proposal unit applies a proposal algorithm that takes into account its flight pattern. This improves the accuracy of the proposal by applying a proposal algorithm tailored to the type of insect. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can have a generating AI perform the application of a proposal algorithm tailored to the type of insect.

[0047] The identification unit can select the optimal identification method by referring to past identification data at the time of identification. For example, the identification unit can propose the optimal camera position based on past identification data. The identification unit can also propose the optimal identification time based on past identification data. For example, the identification unit can propose the optimal identification frequency based on past identification data. In this way, the optimal identification method can be selected by referring to past data. Some or all of the above processing in the identification unit may be performed using AI, for example, or without using AI. For example, the identification unit can input past identification data into a generating AI and have the generating AI perform the selection of the optimal identification method.

[0048] The identification unit can perform specific weighting based on the timing of insect emergence during the identification process. For example, the identification unit may prioritize identifying mosquitoes in the summer. It may also prioritize identifying cockroaches in the winter. For example, the identification unit may prioritize identifying flies in the spring. This allows for more effective identification by performing specific weighting based on the timing of insect emergence. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit may have a generating AI perform specific weighting based on the timing of insect emergence.

[0049] The prevention unit can select the optimal preventive measure by referring to past preventive data when proposing preventive measures. For example, the prevention unit can propose the optimal preventive measure based on past preventive data. The prevention unit can also propose the optimal timing for prevention based on past preventive data. For example, the prevention unit can propose the optimal frequency of prevention based on past preventive data. In this way, the optimal preventive measure can be selected by referring to past data. Some or all of the above processes in the prevention unit may be performed using AI, for example, or without using AI. For example, the prevention unit can input past preventive data into a generating AI and have the generating AI select the optimal preventive measure.

[0050] The prevention unit can weight preventive measures based on the timing of insect outbreaks when proposing preventive measures. For example, the prevention unit might prioritize mosquito prevention measures in the summer. It could also prioritize cockroach prevention measures in the winter. For example, it might prioritize fly prevention measures in the spring. By weighting preventive measures based on the timing of insect outbreaks, more effective preventive measures can be implemented. Some or all of the above processing in the prevention unit may be performed using AI, for example, or without AI. For example, the prevention unit could have a generating AI perform the weighting of preventive measures based on the timing of insect outbreaks.

[0051] The facility response unit can analyze the usage status of public and commercial facilities and select the optimal response method. For example, the facility response unit can monitor the usage status of public facilities in real time and propose the optimal response method. The facility response unit can also analyze the usage status of commercial facilities and propose the optimal response method. For example, the facility response unit selects the optimal response method based on the usage status of facilities. In this way, the optimal response method can be selected by analyzing the usage status of public and commercial facilities. Some or all of the above processing in the facility response unit may be performed using AI, for example, or without AI. For example, the facility response unit can input usage data of public and commercial facilities into a generating AI and have the generating AI select the optimal response method.

[0052] The facility response unit can optimize its response methods by referring to past facility data when responding to facility issues. For example, the facility response unit can propose the optimal response method based on past facility data. The facility response unit can also propose the optimal timing for response based on past facility data. For example, the facility response unit can propose the optimal frequency of response based on past facility data. In this way, the response method can be optimized by referring to past data. Some or all of the above processing in the facility response unit may be performed using AI, for example, or without using AI. For example, the facility response unit can input past facility data into a generating AI and have the generating AI perform the optimization of the response method.

[0053] The facility response unit can apply different response methods depending on the type of facility. For example, in the case of public facilities, the facility response unit will apply a response method that prioritizes user safety. In the case of commercial facilities, the facility response unit can also apply a response method that prioritizes user comfort. For example, in the case of a specific facility, the facility response unit will apply a response method that is appropriate to the characteristics of that facility. By applying a response method appropriate to the type of facility, more effective responses become possible. Some or all of the above processing in the facility response unit may be performed using AI, for example, or without AI. For example, the facility response unit can have a generating AI execute the application of a response method appropriate to the type of facility.

[0054] The facility response unit can adjust its response methods by considering the geographical location information of the facility. For example, in the case of a facility in an urban area, the facility response unit can propose methods to address problems specific to urban areas. Similarly, in the case of a facility in a suburban area, the facility response unit can propose methods to address problems specific to suburban areas. For example, in the case of a facility in a specific region, the facility response unit can propose methods to address problems specific to that region. This allows for the selection of a more appropriate response method by considering the geographical location information of the facility. Some or all of the above processing in the facility response unit may be performed using AI, for example, or without AI. For example, the facility response unit can input the geographical location information of the facility into a generating AI and have the generating AI perform the adjustment of the response method.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The pest control suggestion system can also be equipped with a learning unit. This unit accumulates past control data and learns the effectiveness of different control methods. For example, if a particular pesticide proves effective, this information is stored and reflected in future suggestions. The learning unit can also receive user feedback and use it to improve control methods. For instance, if a user provides feedback stating that a particular method was ineffective, the system adjusts the next suggestion based on that information. This allows the system to continuously improve and suggest more effective control methods.

[0057] The pest control suggestion system can also be equipped with an environmental monitoring unit. This unit monitors environmental conditions such as indoor temperature, humidity, and illuminance, and assesses the risk of insect infestation. For example, if humidity is high, the risk of mold and mite infestation increases, and appropriate preventative measures are suggested. Furthermore, the environmental monitoring unit can predict the risk of insect infestation in accordance with seasonal and weather changes, and take preventative measures in advance. This allows for the prevention of insect infestations and the maintenance of a comfortable indoor environment.

[0058] The pest control suggestion system can also include a user profile section. This section suggests individually optimized pest control methods based on the user's preferences and past pest control history. For example, for a user with an allergy to a specific pesticide, it would suggest a method that avoids that pesticide. The user profile section can also suggest pest control methods tailored to the user's lifestyle. For instance, for a household with pets, it would suggest a pet-safe pest control method. This allows for the provision of pest control methods that meet the individual needs of each user.

[0059] The pest control suggestion system can also include a prevention education section. This section educates users on pest prevention and control methods. For example, it explains the importance of regular cleaning and ventilation and encourages users to implement preventative measures. The prevention education section can also introduce simple pest control methods that users can easily perform at home. This allows users to acquire the knowledge to prevent pest infestations and maximize the system's effectiveness.

[0060] The pest control suggestion system can also include a community collaboration section. This section allows neighboring users to share information about pest outbreaks and jointly implement countermeasures. For example, if there is a high cockroach infestation in a particular area, this information can be shared, and pest control measures can be taken throughout the entire region. The community collaboration section can also support collaboration with professional pest control companies and arrange professional extermination services as needed. This enables effective pest control throughout the entire region.

[0061] The pest control suggestion system can also include a health management section. This section assesses the health risks posed by pests and proposes appropriate countermeasures. For example, if there is a large number of mites, it will suggest an appropriate extermination method due to the risk of allergic reactions. The health management section can also suggest extermination methods tailored to the user's health condition. For instance, it would suggest a less irritating method for a user with asthma. This minimizes the health risks associated with pests.

[0062] The pest control suggestion system can also be equipped with an energy management unit. This unit optimizes the energy consumption of the pest control method, reducing the environmental impact. For example, it avoids power-intensive methods and suggests more energy-efficient ones. The energy management unit can also adjust the timing of pest control execution to reduce peak energy consumption. This allows for the provision of environmentally friendly pest control methods, supporting sustainable living.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The registration unit registers the interior floor plan. The interior floor plan includes the room layout, furniture placement, and area. For example, the floor plan can also be registered using map data from a robot vacuum cleaner. Step 2: The observation unit performs fixed-point observations of the presence or absence of insects in the room using cameras and insect traps. For example, a camera can be used to observe the room's condition at a fixed point. Alternatively, cameras or sensors can be attached to insect traps for observation. Insect sensors can also be installed in the room to check for the presence or absence of insects. Step 3: The measurement unit measures the presence, type, and number of insects based on the data observed by the observation unit. For example, it can analyze image data captured by a camera to identify the type of insect and measure the number of insects based on data detected by sensors. Measurement is performed using image analysis technology and sensor data. Step 4: The proposal unit proposes the optimal pest control method based on the data measured by the measurement unit. For example, it can propose effective pesticides and methods for specific types of insects, and can also identify places and times when insects are likely to appear and propose preventative measures.

[0065] (Example of form 2) The pest control suggestion system according to an embodiment of the present invention is a system that uses AI to suggest the optimal pest control method according to the type and number of pests that occur in a room in a typical household. The pest control suggestion system registers the room's floor plan and uses cameras and insect traps to perform fixed-point observations of the presence or absence of insects in the room. From the observation results, it measures the presence, type, and number of insects and suggests an appropriate pest control method. For example, the pest control suggestion system registers the room's floor plan. If map data from a robot vacuum cleaner is available, it can also be used in conjunction with this. Next, the pest control suggestion system uses cameras and insect traps to perform fixed-point observations of the presence or absence of insects in the room. The insect traps are equipped with cameras and sensors to measure the presence, type, and number of insects. Based on this data, the AI ​​suggests the optimal pest control method. For example, it uses cameras to perform fixed-point observations of the room's condition and installs insect sensors in the room to check for the presence or absence of insects. If insects are detected, it measures their type and number, and the AI ​​suggests an appropriate pest control method. For example, if a particular type of insect is present in large numbers, it suggests an effective pesticide and method for that insect. Furthermore, the system can identify locations and times when insects are likely to appear and suggest preventative measures. This system is extremely useful for people who dislike insects and for parents with newborns who want to avoid the effects of pests. For example, preventative measures can be taken in advance, tailored to the seasons and times when insects are most likely to appear. Even if an insect infestation occurs, the system can minimize the impact of pests by quickly suggesting appropriate extermination methods. In addition, this system can be used in public and commercial facilities. For example, by attaching cameras and sensors to insect traps used for pest surveys in public facilities, the pest situation can be monitored in real time, and appropriate extermination methods can be suggested. This helps maintain the hygiene of the facility and improves user comfort. By utilizing AI, this system can exterminate pests more efficiently and effectively than conventional pest control methods. For example, because the AI ​​suggests the optimal extermination method based on past data, the use of unnecessary pesticides can be reduced, lessening the burden on the environment. Also, because the AI ​​analyzes data in real time, it can respond quickly, minimizing pest damage.Thus, AI-powered pest control systems can be used in a variety of locations, including private homes, public facilities, and commercial establishments, making them extremely useful tools for people who want to avoid the effects of pests. This allows the pest control suggestion system to measure the presence, type, and number of insects indoors and propose the most suitable extermination method.

[0066] The pest control suggestion system according to this embodiment comprises a registration unit, an observation unit, a measurement unit, and a suggestion unit. The registration unit registers a floor plan of the room. The floor plan of the room includes, but is not limited to, the arrangement of rooms, the arrangement of furniture, and the area. The registration unit can also register the floor plan using, for example, map data from a robot vacuum cleaner. The observation unit performs fixed-point observations of the presence or absence of insects in the room using a camera or insect trap. The observation unit can, for example, perform fixed-point observations of the room using a camera. The observation unit can also perform observations by attaching a camera or sensor to an insect trap. For example, the observation unit can check for the presence or absence of insects using a camera attached to an insect trap. The observation unit can also check for the presence or absence of insects by installing an insect sensor in the room. The measurement unit measures the presence, type, and number of insects based on the data observed by the observation unit. The measurement unit can, for example, identify the type of insect by analyzing image data captured by a camera. The measurement unit can also measure the number of insects based on data detected by a sensor. For example, the measurement unit uses image analysis technology to identify the type of insect and measure its number. The measurement unit can also measure the number of insects based on sensor data. The suggestion unit proposes the optimal extermination method based on the data measured by the measurement unit. The suggestion unit proposes, for example, effective exterminators and methods for specific types of insects. The suggestion unit can also identify places and times when insects are likely to appear and propose preventive measures. For example, if a particular type of insect is appearing in large numbers, the suggestion unit proposes an effective exterminator for that insect. The suggestion unit can also identify places and times when insects are likely to appear and propose preventive measures. As a result, the pest control suggestion system according to this embodiment can measure the presence, type, and number of insects in a room and propose the optimal extermination method. Some or all of the above-described processes in the measurement unit may be performed using, for example, AI, or without AI. For example, the measurement unit can input image data captured by a camera into a generation AI and have the generation AI identify the type of insect from the image data. Some or all of the above-described processes in the suggestion unit may be performed using, for example, AI, or without AI.For example, the proposal unit can input data measured by the measurement unit into the generation AI, and have the generation AI generate a proposal for the optimal extermination method.

[0067] The registration unit registers interior floor plans. These floor plans include, but are not limited to, room layouts, furniture placement, and area. The registration unit can also register floor plans in conjunction with, for example, map data from a robotic vacuum cleaner. Specifically, this can be done by the user manually entering the floor plan or by importing map data automatically generated by the robotic vacuum cleaner. In the case of manual entry, the user can use a dedicated application to input detailed information about the shape of the rooms and the placement of furniture. In the case of automatic generation, an accurate floor plan is generated based on data collected by the robotic vacuum cleaner as it moves through the rooms. This floor plan forms the foundation of the entire system and is an important source of information for other departments to function efficiently. Furthermore, the registration unit is designed to allow for easy updating and modification of floor plans. For example, if the furniture placement changes or a new room is added, the user can easily update the floor plan. This ensures that pest control suggestions are always based on the latest information. The registration unit also has the function to manage multiple floor plans, allowing for centralized management of information from different rooms and floors. This allows for efficient pest control proposals to be made to large facilities and homes with multiple rooms.

[0068] The observation unit uses cameras and insect traps to perform fixed-point observations to check for the presence of insects in a room. For example, the observation unit can use a camera to observe the room's environment at a fixed point. The observation unit can also attach cameras and sensors to insect traps for observation. For example, the observation unit can check for the presence of insects using a camera attached to an insect trap. The observation unit can also install insect sensors in the room to check for the presence of insects. Specifically, the camera can cover a wide area with high resolution and monitor insect movements in real time. The camera attached to the insect trap records the type and number of captured insects in detail, and the sensor detects minute movements and vibrations to confirm the presence of insects. This allows the observation unit to accurately understand the movements of insects in the room. Furthermore, the observation unit can set a regular observation schedule and perform observations at specific times and under specific conditions. For example, a night mode can be set to observe insects that are active at night. The observation unit also has a function to issue an alert when abnormal movement is detected, enabling a quick response. In this way, the observation unit can efficiently monitor the presence of insects in a room and support a quick response.

[0069] The measurement unit measures the presence, type, and number of insects based on data observed by the observation unit. For example, the measurement unit identifies insect types by analyzing image data captured by a camera. The measurement unit can also measure the number of insects based on data detected by sensors. For example, the measurement unit uses image analysis technology to identify insect types and count them. The measurement unit can also measure the number of insects based on sensor data. Specifically, the measurement unit uses AI to analyze image data and identify insect types with high accuracy. Based on a pre-trained database, the AI ​​analyzes characteristics such as the shape, color, and movement of insects to identify their types. It can also understand the number and movement patterns of insects by analyzing sensor data. As a result, the measurement unit can understand the situation of insects indoors in detail and provide basic data for taking appropriate countermeasures. Furthermore, the measurement unit can accumulate past data and perform long-term trend analysis. This allows it to predict insect occurrence patterns in specific seasons and conditions and formulate preventive measures. The measurement unit also has a function to issue alerts when abnormal data is detected, enabling a quick response. This allows the measurement unit to accurately grasp the insect situation indoors and support a quick and appropriate response.

[0070] The proposal department proposes the optimal pest control method based on data measured by the measurement department. For example, the proposal department can suggest effective pesticides and methods for specific types of insects. It can also identify locations and times when insects are likely to appear and propose preventative measures. Specifically, the proposal department uses AI to analyze measurement data and propose the optimal pest control method. Based on past data and expertise, the AI ​​selects the most effective pesticides and methods for specific insects. It can also analyze insect infestation patterns and develop preventative measures. This allows the proposal department to provide users with effective pest control methods and preventative measures, minimizing insect infestations. Furthermore, the proposal department can collect user feedback and continuously improve the accuracy and effectiveness of its proposals. For example, it can evaluate the effectiveness of pest control methods and revise proposals as needed. The proposal department can also propose comprehensive measures combining multiple pest control methods. This allows the proposal department to provide users with the optimal extermination method and preventative measures, effectively suppressing the occurrence of insects indoors.

[0071] The identification unit can identify locations and times when insects are likely to appear. For example, the identification unit can identify locations and times when insects are likely to appear by analyzing past data. The identification unit can also identify locations and times when insects are likely to appear based on environmental conditions. For example, the identification unit can identify locations and times when insects are likely to appear based on environmental conditions such as temperature and humidity. The identification unit can also identify locations and times when insects are likely to appear by analyzing insect appearance patterns. For example, the identification unit can analyze insect appearance patterns and identify locations and times when insects are likely to appear. This makes effective extermination possible by identifying locations and times when insects are likely to appear. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input past data into a generating AI and have the generating AI perform the identification of locations and times when insects are likely to appear.

[0072] The prevention department can propose preventive measures. For example, the prevention department can propose environmental improvement measures. The prevention department can also propose regular maintenance. For example, the prevention department can propose indoor cleaning and ventilation. The prevention department can also propose preventive measures for places where insects are likely to appear. For example, the prevention department can propose the installation of insect nets in places where insects are likely to appear. In this way, by proposing preventive measures, insect infestations can be prevented. Some or all of the above processes in the prevention department may be carried out using AI, for example, or without AI. For example, the prevention department can have a generation AI execute the proposal of environmental improvement measures.

[0073] The Facility Support Division can provide solutions tailored to use in public and commercial facilities. For example, it can attach cameras and sensors to insect traps used for pest surveys in public facilities to monitor pest outbreaks in real time. Similarly, it can attach cameras and sensors to insect traps used for pest surveys in commercial facilities to monitor pest outbreaks in real time. For instance, it can attach cameras and sensors to insect traps used for pest surveys in public facilities to monitor pest outbreaks in real time and propose appropriate control methods. This adaptability to use in public and commercial facilities makes the system applicable to a wide range of uses.

[0074] The observation unit can perform observations by attaching cameras and sensors to insect traps. For example, the observation unit can attach a camera to an insect trap and perform observations. The observation unit can also attach sensors to an insect trap and perform observations. For example, the observation unit can check for the presence or absence of insects using a camera attached to the insect trap. The observation unit can also check for the presence or absence of insects using a sensor attached to the insect trap. By attaching cameras and sensors to insect traps, more accurate observations become possible. Some or all of the above processing in the observation unit may be performed using AI, for example, or without using AI. For example, the observation unit can input image data captured by a camera attached to an insect trap into a generating AI and have the generating AI check for the presence or absence of insects from the image data.

[0075] The suggestion unit can propose effective insecticides and methods for controlling specific types of insects. For example, the suggestion unit can propose an effective insecticide for cockroaches. It can also propose an effective method for controlling mosquitoes. For example, the suggestion unit can propose an effective insecticide for cockroaches. It can also propose an effective method for controlling mosquitoes. This improves the effectiveness of control by proposing effective methods for controlling specific types of insects. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can have a generation AI perform the task of proposing effective methods for controlling specific types of insects.

[0076] The registration unit can estimate the user's emotions and adjust the floor plan registration method based on the estimated emotions. For example, if the user is stressed, the registration unit can provide a simple interface and minimize the registration procedure. If the user is relaxed, the registration unit can also provide detailed registration options and suggest a customizable registration method. For example, if the user is in a hurry, the registration unit can prioritize voice input to allow for quick floor plan registration. This improves user convenience by adjusting the floor plan registration method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the registration unit may be performed using AI or not. For example, the registration unit can input user emotion data into a generative AI and have the generative AI perform emotion-based adjustments to the registration method.

[0077] The registration unit can select the optimal registration method by referring to past floor plan data during registration. For example, the registration unit can suggest the optimal registration method based on floor plan data previously registered by the user. The registration unit can also prioritize suggesting registration methods previously used by the user (manual, voice, etc.). For example, the registration unit can predict and suggest a registration method to be used during a specific time period based on the user's past registration history. This allows the optimal registration method to be selected by referring to past data. Some or all of the above processing in the registration unit may be performed using AI, for example, or without AI. For example, the registration unit can input past floor plan data into a generating AI and have the generating AI select the optimal registration method.

[0078] The registration unit can adjust the level of detail in the floor plan based on the intended use of the room during registration. For example, in the case of a living room, the registration unit can register detailed information such as the arrangement of furniture and the location of power outlets. In the case of a kitchen, the registration unit can also register the location of cooking utensils and storage spaces in detail. For example, in the case of a bedroom, the registration unit can register the location of the bed and closet in detail. By adjusting the level of detail in the floor plan according to the intended use of the room, more accurate registration becomes possible. Some or all of the above processing in the registration unit may be performed using AI, for example, or without AI. For example, the registration unit can have a generating AI perform the adjustment of the level of detail based on the intended use of the room.

[0079] The observation unit can estimate the user's emotions and adjust the frequency of observations based on the estimated emotions. For example, if the user is stressed, the observation unit can set the observation frequency low and minimize notifications. Conversely, if the user is relaxed, the observation unit can set the observation frequency high and provide detailed data. For example, if the user is in a hurry, the observation unit can set the observation frequency to a moderate level and provide only the necessary information. This reduces the user's burden by adjusting the observation frequency according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the observation unit may be performed using AI or not. For example, the observation unit can input user emotion data into the generative AI and have the generative AI adjust the observation frequency based on emotions.

[0080] The observation unit can select the optimal observation method by referring to past observation data during observation. For example, the observation unit can propose the optimal camera position based on past observation data. The observation unit can also propose the optimal observation time based on past observation data. For example, the observation unit can propose the optimal observation frequency based on past observation data. In this way, the optimal observation method can be selected by referring to past data. Some or all of the above processing in the observation unit may be performed using AI, for example, or without using AI. For example, the observation unit can input past observation data into a generating AI and have the generating AI perform the selection of the optimal observation method.

[0081] The observation unit can adjust the level of detail of its observations based on how the room is being used. For example, in a living room, the observation unit can perform detailed observations to accurately measure the type and number of insects. In a kitchen, the observation unit can focus its observations on areas near food. In a bedroom, for example, the observation unit can focus its observations on the area around the bed. By adjusting the level of detail of the observations according to how the room is being used, more accurate observations become possible. Some or all of the above processing in the observation unit may be performed using AI, for example, or without AI. For example, the observation unit can have a generating AI perform the adjustment of the level of detail of the observations based on how the room is being used.

[0082] The measurement unit can estimate the user's emotions and adjust the measurement accuracy based on the estimated emotions. For example, if the user is stressed, the measurement unit can set the measurement accuracy low and minimize notifications. Conversely, if the user is relaxed, the measurement unit can set the measurement accuracy high and provide detailed data. For example, if the user is in a hurry, the measurement unit can set the measurement accuracy to a medium level and provide only the necessary information. This reduces the burden on the user by adjusting the measurement accuracy according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the measurement unit may be performed using AI, for example, or not using AI. For example, the measurement unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of measurement accuracy based on emotions.

[0083] The measurement unit can select the optimal measurement method by referring to past measurement data during measurement. For example, the measurement unit can propose the optimal camera position based on past measurement data. The measurement unit can also propose the optimal measurement time based on past measurement data. For example, the measurement unit can propose the optimal measurement frequency based on past measurement data. In this way, the optimal measurement method can be selected by referring to past data. 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 past measurement data into a generating AI and have the generating AI perform the selection of the optimal measurement method.

[0084] The measurement unit can apply different measurement algorithms depending on the type of insect during measurement. For example, in the case of a cockroach, the measurement unit applies a measurement algorithm that takes into account the speed of its movement. Similarly, in the case of a mosquito, the measurement unit can apply a measurement algorithm that takes into account its small size. For example, in the case of a fly, the measurement unit applies a measurement algorithm that takes into account its flight pattern. This improves the accuracy of the measurement by applying a measurement algorithm appropriate to the type of insect. 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 have a generating AI perform the application of a measurement algorithm appropriate to the type of insect.

[0085] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is nervous, the suggestion unit can provide a simple and easily understandable suggestion. If the user is relaxed, the suggestion unit can also provide a suggestion that includes detailed information. For example, if the user is in a hurry, the suggestion unit can provide a concise suggestion. This improves user convenience by adjusting the way suggestions are presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the way suggestions are presented based on those emotions.

[0086] The suggestion unit can adjust the level of detail in its suggestions based on the importance of the insect. For example, in the case of cockroaches, the suggestion unit can suggest detailed extermination methods and preventative measures. In the case of mosquitoes, it can suggest simple extermination methods and preventative measures. For example, in the case of flies, the suggestion unit can suggest basic extermination methods and preventative measures. By adjusting the level of detail in suggestions according to the importance of the insect, more effective suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can have a generating AI perform the adjustment of the level of detail in suggestions based on the importance of the insect.

[0087] The proposal unit can apply different proposal algorithms depending on the type of insect during the proposal process. For example, in the case of a cockroach, the proposal unit applies a proposal algorithm that takes into account its speed of movement. Similarly, in the case of a mosquito, it can apply a proposal algorithm that takes into account its small size. For example, in the case of a fly, the proposal unit applies a proposal algorithm that takes into account its flight pattern. This improves the accuracy of the proposal by applying a proposal algorithm tailored to the type of insect. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can have a generating AI perform the application of a proposal algorithm tailored to the type of insect.

[0088] The identification unit can estimate the user's emotions and adjust the identification method based on the estimated user emotions. For example, if the user is nervous, the identification unit can provide a simple and highly visible identification method. If the user is relaxed, the identification unit can also provide an identification method that includes detailed information. For example, if the user is in a hurry, the identification unit can provide a concise identification method. This improves user convenience by adjusting the identification method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the identification unit may be performed using AI, for example, or not using AI. For example, the identification unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the identification method based on the emotions.

[0089] The identification unit can select the optimal identification method by referring to past identification data at the time of identification. For example, the identification unit can propose the optimal camera position based on past identification data. The identification unit can also propose the optimal identification time based on past identification data. For example, the identification unit can propose the optimal identification frequency based on past identification data. In this way, the optimal identification method can be selected by referring to past data. Some or all of the above processing in the identification unit may be performed using AI, for example, or without using AI. For example, the identification unit can input past identification data into a generating AI and have the generating AI perform the selection of the optimal identification method.

[0090] The identification unit can perform specific weighting based on the timing of insect emergence during the identification process. For example, the identification unit may prioritize identifying mosquitoes in the summer. It may also prioritize identifying cockroaches in the winter. For example, the identification unit may prioritize identifying flies in the spring. This allows for more effective identification by performing specific weighting based on the timing of insect emergence. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit may have a generating AI perform specific weighting based on the timing of insect emergence.

[0091] The prevention unit can estimate the user's emotions and adjust the method of suggesting preventive measures based on the estimated user emotions. For example, if the user is tense, the prevention unit can provide simple and highly visible preventive measures. If the user is relaxed, the prevention unit can also provide preventive measures that include detailed information. For example, if the user is in a hurry, the prevention unit can provide concise preventive measures. This improves user convenience by adjusting the method of suggesting preventive measures according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prevention unit may be performed using AI or not using AI. For example, the prevention unit can input user emotion data into the generative AI and have the generative AI adjust the method of suggesting preventive measures based on emotions.

[0092] The prevention unit can select the optimal preventive measure by referring to past preventive data when proposing preventive measures. For example, the prevention unit can propose the optimal preventive measure based on past preventive data. The prevention unit can also propose the optimal timing for prevention based on past preventive data. For example, the prevention unit can propose the optimal frequency of prevention based on past preventive data. In this way, the optimal preventive measure can be selected by referring to past data. Some or all of the above processes in the prevention unit may be performed using AI, for example, or without using AI. For example, the prevention unit can input past preventive data into a generating AI and have the generating AI select the optimal preventive measure.

[0093] The prevention unit can weight preventive measures based on the timing of insect outbreaks when proposing preventive measures. For example, the prevention unit might prioritize mosquito prevention measures in the summer. It could also prioritize cockroach prevention measures in the winter. For example, it might prioritize fly prevention measures in the spring. By weighting preventive measures based on the timing of insect outbreaks, more effective preventive measures can be implemented. Some or all of the above processing in the prevention unit may be performed using AI, for example, or without AI. For example, the prevention unit could have a generating AI perform the weighting of preventive measures based on the timing of insect outbreaks.

[0094] The facility response unit can analyze the usage status of public and commercial facilities and select the optimal response method. For example, the facility response unit can monitor the usage status of public facilities in real time and propose the optimal response method. The facility response unit can also analyze the usage status of commercial facilities and propose the optimal response method. For example, the facility response unit selects the optimal response method based on the usage status of facilities. In this way, the optimal response method can be selected by analyzing the usage status of public and commercial facilities. Some or all of the above processing in the facility response unit may be performed using AI, for example, or without AI. For example, the facility response unit can input usage data of public and commercial facilities into a generating AI and have the generating AI select the optimal response method.

[0095] The facility response unit can optimize its response methods by referring to past facility data when responding to facility issues. For example, the facility response unit can propose the optimal response method based on past facility data. The facility response unit can also propose the optimal timing for response based on past facility data. For example, the facility response unit can propose the optimal frequency of response based on past facility data. In this way, the response method can be optimized by referring to past data. Some or all of the above processing in the facility response unit may be performed using AI, for example, or without using AI. For example, the facility response unit can input past facility data into a generating AI and have the generating AI perform the optimization of the response method.

[0096] The facility response unit can apply different response methods depending on the type of facility. For example, in the case of public facilities, the facility response unit will apply a response method that prioritizes user safety. In the case of commercial facilities, the facility response unit can also apply a response method that prioritizes user comfort. For example, in the case of a specific facility, the facility response unit will apply a response method that is appropriate to the characteristics of that facility. By applying a response method appropriate to the type of facility, more effective responses become possible. Some or all of the above processing in the facility response unit may be performed using AI, for example, or without AI. For example, the facility response unit can have a generating AI execute the application of a response method appropriate to the type of facility.

[0097] The facility response unit can adjust its response methods by considering the geographical location information of the facility. For example, in the case of a facility in an urban area, the facility response unit can propose methods to address problems specific to urban areas. Similarly, in the case of a facility in a suburban area, the facility response unit can propose methods to address problems specific to suburban areas. For example, in the case of a facility in a specific region, the facility response unit can propose methods to address problems specific to that region. This allows for the selection of a more appropriate response method by considering the geographical location information of the facility. Some or all of the above processing in the facility response unit may be performed using AI, for example, or without AI. For example, the facility response unit can input the geographical location information of the facility into a generating AI and have the generating AI perform the adjustment of the response method.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The pest control suggestion system can also be equipped with a voice recognition unit. The voice recognition unit receives voice commands from the user and allows the system to be operated by voice. For example, if the user says "There's a cockroach," the voice recognition unit analyzes the command and immediately suggests a suitable method for cockroach extermination. Furthermore, the voice recognition unit improves convenience because the user can operate the system without using their hands. In addition, the voice recognition unit can estimate the user's emotions from their voice and respond quickly if they are feeling stressed. This improves user convenience and satisfaction.

[0100] The pest control suggestion system can also be equipped with a learning unit. This unit accumulates past control data and learns the effectiveness of different control methods. For example, if a particular pesticide proves effective, this information is stored and reflected in future suggestions. The learning unit can also receive user feedback and use it to improve control methods. For instance, if a user provides feedback stating that a particular method was ineffective, the system adjusts the next suggestion based on that information. This allows the system to continuously improve and suggest more effective control methods.

[0101] The pest control suggestion system can also be equipped with a notification unit. This unit notifies the user of suggestions for control methods and observation results. For example, if insects are detected, it immediately notifies the user and suggests appropriate control methods. The notification unit can also report periodic observation results to the user, allowing them to understand the insect infestation situation. Furthermore, the notification unit can estimate the user's emotions and adjust the notification frequency if they are experiencing stress. This allows the user to receive necessary information at the right time, reducing stress.

[0102] The pest control suggestion system can also be equipped with an environmental monitoring unit. This unit monitors environmental conditions such as indoor temperature, humidity, and illuminance, and assesses the risk of insect infestation. For example, if humidity is high, the risk of mold and mite infestation increases, and appropriate preventative measures are suggested. Furthermore, the environmental monitoring unit can predict the risk of insect infestation in accordance with seasonal and weather changes, and take preventative measures in advance. This allows for the prevention of insect infestations and the maintenance of a comfortable indoor environment.

[0103] The pest control suggestion system can also include a user profile section. This section suggests individually optimized pest control methods based on the user's preferences and past pest control history. For example, for a user with an allergy to a specific pesticide, it would suggest a method that avoids that pesticide. The user profile section can also suggest pest control methods tailored to the user's lifestyle. For instance, for a household with pets, it would suggest a pet-safe pest control method. This allows for the provision of pest control methods that meet the individual needs of each user.

[0104] The pest control suggestion system can also be equipped with an emotion estimation unit. This unit estimates the user's emotions from their facial expressions and voice, and adjusts the system's operation accordingly. For example, if the user is surprised, it will quickly suggest a pest control method to reassure them. Conversely, if the user is relaxed, the emotion estimation unit can provide more detailed information and increase the options for pest control. This allows for flexible responses tailored to the user's emotions, improving user satisfaction.

[0105] The pest control suggestion system can also include a prevention education section. This section educates users on pest prevention and control methods. For example, it explains the importance of regular cleaning and ventilation and encourages users to implement preventative measures. The prevention education section can also introduce simple pest control methods that users can easily perform at home. This allows users to acquire the knowledge to prevent pest infestations and maximize the system's effectiveness.

[0106] The pest control suggestion system can also include a community collaboration section. This section allows neighboring users to share information about pest outbreaks and jointly implement countermeasures. For example, if there is a high cockroach infestation in a particular area, this information can be shared, and pest control measures can be taken throughout the entire region. The community collaboration section can also support collaboration with professional pest control companies and arrange professional extermination services as needed. This enables effective pest control throughout the entire region.

[0107] The pest control suggestion system can also include a health management section. This section assesses the health risks posed by pests and proposes appropriate countermeasures. For example, if there is a large number of mites, it will suggest an appropriate extermination method due to the risk of allergic reactions. The health management section can also suggest extermination methods tailored to the user's health condition. For instance, it would suggest a less irritating method for a user with asthma. This minimizes the health risks associated with pests.

[0108] The pest control suggestion system can also be equipped with an energy management unit. This unit optimizes the energy consumption of the pest control method, reducing the environmental impact. For example, it avoids power-intensive methods and suggests more energy-efficient ones. The energy management unit can also adjust the timing of pest control execution to reduce peak energy consumption. This allows for the provision of environmentally friendly pest control methods, supporting sustainable living.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The registration unit registers the interior floor plan. The interior floor plan includes the room layout, furniture placement, and area. For example, the floor plan can also be registered using map data from a robot vacuum cleaner. Step 2: The observation unit performs fixed-point observations of the presence or absence of insects in the room using cameras and insect traps. For example, a camera can be used to observe the room's condition at a fixed point. Alternatively, cameras or sensors can be attached to insect traps for observation. Insect sensors can also be installed in the room to check for the presence or absence of insects. Step 3: The measurement unit measures the presence, type, and number of insects based on the data observed by the observation unit. For example, it can analyze image data captured by a camera to identify the type of insect and measure the number of insects based on data detected by sensors. Measurement is performed using image analysis technology and sensor data. Step 4: The proposal unit proposes the optimal pest control method based on the data measured by the measurement unit. For example, it can propose effective pesticides and methods for specific types of insects, and can also identify places and times when insects are likely to appear and propose preventative measures.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] Each of the multiple elements described above, including the registration unit, observation unit, measurement unit, proposal unit, identification unit, prevention unit, facility response unit, and emotion estimation function, is implemented by at least one of the smart device 14 and the data processing device 12. For example, the registration unit is implemented by the control unit 46A of the smart device 14 and registers the floor plan of the room. The observation unit uses the camera 42 and insect traps of the smart device 14 to perform fixed-point observations of the presence or absence of insects in the room. The measurement unit is implemented by the control unit 46A of the smart device 14 and measures the presence, type, and number of insects based on the observed data. The proposal unit is implemented by the identification processing unit 290 of the data processing device 12 and proposes the optimal extermination method based on the measured data. The identification unit is implemented by the identification processing unit 290 of the data processing device 12 and identifies places and times when insects are likely to appear. The prevention unit is implemented by the identification processing unit 290 of the data processing device 12 and proposes preventive measures. The facility response unit is implemented by the control unit 46A of the smart device 14 and is responsible for pest surveys in public and commercial facilities. The emotion estimation function is implemented by the identification processing unit 290 of the data processing device 12, which estimates the user's emotions and adjusts the registration method. 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.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the registration unit, observation unit, measurement unit, proposal unit, identification unit, prevention unit, facility response unit, and emotion estimation function, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the registration unit is implemented by the control unit 46A of the smart glasses 214 and registers the floor plan of the room. The observation unit uses the camera 42 of the smart glasses 214 and an insect trap to perform fixed-point observations of the presence or absence of insects in the room. The measurement unit is implemented by the control unit 46A of the smart glasses 214 and measures the presence, type, and number of insects based on the observed data. The proposal unit is implemented by the identification processing unit 290 of the data processing device 12 and proposes the optimal extermination method based on the measured data. The identification unit is implemented by the identification processing unit 290 of the data processing device 12 and identifies places and times when insects are likely to appear. The prevention unit is implemented by the identification processing unit 290 of the data processing device 12 and proposes preventive measures. The facility response unit is implemented by the control unit 46A of the smart glasses 214 and is designed to handle pest surveys in public and commercial facilities. The emotion estimation function is implemented by the identification processing unit 290 of the data processing device 12, which estimates the user's emotions and adjusts the registration method. 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.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the registration unit, observation unit, measurement unit, proposal unit, identification unit, prevention unit, facility response unit, and emotion estimation function, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the registration unit is implemented by the control unit 46A of the headset terminal 314 and registers the floor plan of the room. The observation unit uses the camera 42 of the headset terminal 314 and an insect trap to perform fixed-point observations of the presence or absence of insects in the room. The measurement unit is implemented by the control unit 46A of the headset terminal 314 and measures the presence, type, and number of insects based on the observed data. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12 and proposes the optimal extermination method based on the measured data. The identification unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies places and times when insects are likely to appear. The prevention unit is implemented by the identification processing unit 290 of the data processing unit 12 and proposes preventive measures. The facility support unit is implemented by the control unit 46A of the headset terminal 314 and is designed to handle pest surveys in public and commercial facilities. The emotion estimation function is implemented by the specific processing unit 290 of the data processing device 12, which estimates the user's emotions and adjusts the registration method. 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.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] Each of the multiple elements described above, including the registration unit, observation unit, measurement unit, proposal unit, identification unit, prevention unit, facility response unit, and emotion estimation function, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the registration unit is implemented by the control unit 46A of the robot 414 and registers the floor plan of the room. The observation unit uses the camera 42 and insect traps of the robot 414 to perform fixed-point observations for the presence or absence of insects in the room. The measurement unit is implemented by the control unit 46A of the robot 414 and measures the presence, type, and number of insects based on the observed data. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12 and proposes the optimal extermination method based on the measured data. The identification unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies places and times when insects are likely to appear. The prevention unit is implemented by the identification processing unit 290 of the data processing unit 12 and proposes preventive measures. The facility response unit is implemented by the control unit 46A of the robot 414 and is responsible for pest surveys in public and commercial facilities. The emotion estimation function is implemented by the specific processing unit 290 of the data processing device 12, which estimates the user's emotions and adjusts the registration method. 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Note 1) A registration unit for registering interior floor plans, The observation unit uses cameras and insect traps to perform fixed-point observations to check for the presence of insects indoors, A measuring unit that measures the presence, type, and number of insects based on the data observed by the aforementioned observation unit, The system includes a proposal unit that proposes the optimal pest control method based on the data measured by the measurement unit. A system characterized by the following features. (Note 2) It is equipped with a unit that identifies locations and times when insects are likely to appear. The system described in Appendix 1, characterized by the features described herein. (Note 3) It has a prevention department that proposes preventive measures. The system described in Appendix 1, characterized by the features described herein. (Note 4) It includes a facility support section designed for use in public and commercial facilities. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned observation unit is Observations are conducted by attaching cameras and sensors to insect traps. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, We propose effective pest control agents and methods for specific types of insects. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned registration unit is The system estimates the user's emotions and adjusts the floor plan registration method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned registration unit is During registration, the system will refer to past floor plan data to select the most suitable registration method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned registration unit is During registration, adjust the level of detail in the floor plan based on the intended use of the room. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned observation unit is It estimates the user's emotions and adjusts the frequency of observations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned observation unit is During observation, the optimal observation method is selected by referring to past observation data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned observation unit is During observation, the level of detail of the observation is adjusted based on the room's usage. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned measuring unit is It estimates the user's emotions and adjusts the accuracy of the measurement based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned measuring unit is During measurement, the optimal measurement method is selected by referring to past measurement data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned measuring unit is During measurement, different measurement algorithms are applied depending on the type of insect. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the insects. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the type of insect. The system described in Appendix 1, characterized by the features described herein. (Note 19) The specified part is, It estimates the user's emotions and adjusts specific methods based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 20) The specified part is, At specific times, the optimal identification method is selected by referring to past identification data. The system described in Appendix 2, characterized by the features described herein. (Note 21) The specified part is, At specific times, a specific weighting is applied based on the timing of insect emergence. The system described in Appendix 2, characterized by the features described herein. (Note 22) The aforementioned prevention unit, It estimates the user's emotions and adjusts the method of suggesting preventative measures based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 23) The aforementioned prevention unit, When proposing preventive measures, the most suitable preventive measures are selected by referring to past preventive data. The system described in Appendix 3, characterized by the features described herein. (Note 24) The aforementioned prevention unit, When proposing preventive measures, weight the measures based on the timing of insect outbreaks. The system described in Appendix 3, characterized by the features described herein. (Note 25) The aforementioned facility response unit is: We analyze the usage patterns of public and commercial facilities and select the most appropriate response. The system described in Appendix 4, characterized by the features described herein. (Note 26) The aforementioned facility response unit is: When addressing a facility, we optimize the response method by referring to past facility data. The system described in Appendix 4, characterized by the features described herein. (Note 27) The aforementioned facility response unit is: Different approaches will be applied depending on the type of facility. The system described in Appendix 4, characterized by the features described herein. (Note 28) The aforementioned facility response unit is: We will adjust our response methods considering the geographical location of the facility. The system described in Appendix 4, characterized by the features described herein. [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A registration unit for registering interior floor plans, The observation unit uses cameras and insect traps to perform fixed-point observations to check for the presence of insects indoors, A measuring unit that measures the presence, type, and number of insects based on the data observed by the aforementioned observation unit, The system includes a proposal unit that proposes the optimal pest control method based on the data measured by the measurement unit. A system characterized by the following features.

2. It is equipped with a unit that identifies locations and times when insects are likely to appear. The system according to feature 1.

3. We have a prevention department that proposes preventative measures. The system according to feature 1.

4. It includes a facility support section designed for use in public and commercial facilities. The system according to feature 1.

5. The aforementioned observation unit is Observations are conducted by attaching cameras and sensors to insect traps. The system according to feature 1.

6. The aforementioned proposal section is, We propose effective pest control agents and methods for specific types of insects. The system according to feature 1.

7. The aforementioned registration unit is The system estimates the user's emotions and adjusts the floor plan registration method based on the estimated emotions. The system according to feature 1.

8. The aforementioned registration unit is During registration, the system will refer to past floor plan data to select the most suitable registration method. The system according to feature 1.

9. The aforementioned registration unit is During registration, adjust the level of detail in the floor plan based on the intended use of the room. The system according to feature 1.

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