system

Abstract

The system according to this embodiment aims to propose optimal pest control measures based on the layout of the house and the lifestyle of the residents. [Solution] The system according to the embodiment comprises a collection unit, a floor plan identification unit, a proposal unit, an advice unit, and a planning unit. The collection unit collects visual images. The floor plan identification unit identifies the floor plan based on the images collected by the collection unit. The proposal unit proposes the optimal insect repellent items, insect repellent measures, and implementation timing based on the floor plan information identified by the floor plan identification unit. The advice unit provides specific advice for each room on the insect repellent measures proposed by the proposal unit. The planning unit provides a long-term insect repellent plan that takes into account the types of insects and their occurrence times for each season.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. <000**********

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to propose an optimal pest control measure based on the house layout and lifestyle, and there is room for improvement.

[0005] The system according to the embodiment aims to propose an optimal pest control measure based on the house layout and lifestyle.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a floor plan identification unit, a proposal unit, an advice unit, and a planning unit. The collection unit collects visual images. The floor plan identification unit identifies the floor plan based on the images collected by the collection unit. The proposal unit proposes the most suitable insect repellent items, insect repellent measures, and implementation timing based on the floor plan information identified by the floor plan identification unit. The advice unit provides specific advice for each room regarding the insect repellent measures proposed by the proposal unit. The planning unit provides a long-term insect repellent plan that takes into account the types of insects and their occurrence times for each season. [Effects of the Invention]

[0007] The system according to this embodiment can propose optimal pest control measures based on the house layout and lifestyle. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An embodiment of the present invention provides an insect control system that utilizes an AI agent and AI glasses to propose the optimal insect control measures for each household. This system automatically identifies the layout of the house from visual images as the user walks around inside the home wearing the AI ​​glasses, and provides specific advice on the optimal insect control measures for each room. Based on the house layout, family structure, and lifestyle, the system proposes the most suitable insect control items, measures, and implementation timing. For example, for households with small children or pets, it recommends safe insect control items and places the most suitable insect control measures in each room, such as the living room, bedroom, and kitchen. It also considers the types and timing of insect outbreaks for each season and notifies the user of the most effective implementation timing. Furthermore, the system provides a long-term insect control plan. For example, it suggests using mosquito coils or insect repellent sprays in the spring when mosquitoes are abundant, and suggests using cockroach exterminators in the fall when cockroaches are prevalent. In this way, it provides insect control measures that take into account the types and timing of insect outbreaks for each season. This allows users to implement optimal insect control measures without worrying about methods or item selection. Furthermore, even busy users can implement pest control measures at the appropriate time, as the AI ​​agent notifies them of the most effective implementation timing. Regular reminders further support implementation, enabling the continuous execution of long-term pest control plans. In this way, by utilizing the AI ​​agent and AI glasses, it's possible to propose optimal pest control measures for each household, ensuring families can live comfortably and healthily. In particular, it provides pest control measures that can be used safely even in homes with small children or pets, reducing stress caused by insects. Additionally, AI glasses enable more accurate personalized recommendations by identifying the floor plan. As a result, the pest control system proposes optimal pest control measures based on the user's home floor plan, family structure, and lifestyle, and notifies them of the optimal implementation timing, enabling effective pest control.

[0029] The insect control system according to this embodiment comprises a collection unit, a floor plan identification unit, a proposal unit, an advice unit, and a planning unit. The collection unit collects visual images. For example, the collection unit collects visual images when a user wears AI glasses and walks around the house. The collection unit collects still images and videos, and it is desirable that the resolution be high. The floor plan identification unit identifies the floor plan based on the images collected by the collection unit. For example, the floor plan identification unit uses AI to analyze the images and identifies each room, such as the living room, bedroom, and kitchen. The floor plan identification unit can identify the layout, type, and area of ​​the rooms. The proposal unit proposes the optimal insect control items, insect control measures, and implementation timing based on the floor plan information identified by the floor plan identification unit. For example, the proposal unit proposes effective insect control measures based on the house's floor plan, family structure, and lifestyle. The proposal unit uses AI to select the optimal insect control items and measures and determine the implementation timing. The advice unit provides specific advice on the insect control measures proposed by the proposal unit for each room. The advice department, for example, advises placing mosquito coils in the living room and using insect repellent spray in the bedroom. The advice department can provide specific advice on the optimal insect control measures for each room. The planning department provides a long-term insect control plan that takes into account the types of insects and their emergence periods for each season. For example, the planning department provides a plan to use mosquito coils and insect repellent spray in the spring when mosquitoes are abundant, and a plan to use cockroach exterminators in the fall when cockroaches are abundant. The planning department provides a long-term insect control plan and supports the implementation of insect control measures with regular reminders. As a result, the insect control system according to this embodiment can propose the optimal insect control measures based on the user's house layout, family structure, and lifestyle, and notify them of the timing of implementation, thereby enabling effective insect control.

[0030] The data collection unit collects visual images. For example, the data collection unit collects visual images when a user wears AI glasses and walks around their home. The data collection unit collects still images and videos, and it is desirable that the resolution be high. Specifically, the AI ​​glasses are equipped with a high-resolution camera that captures images of the surroundings in real time as the user walks around the home. This allows the data collection unit to obtain detailed video data of the interior of the house. Furthermore, the AI ​​glasses have a function to track the user's gaze and can identify areas that the user is focusing on. This allows the data collection unit to concentrate on collecting images of areas that require special attention, such as those requiring pest control. The data collection unit can also send the video data to a cloud server and store and manage the data in real time. This allows the data collection unit to collect the latest video data every time the user walks around the home and always provide the latest information. In addition, the data collection unit can flexibly adapt to the user's network environment and device performance by adjusting the resolution and frame rate of the video data. This allows the data collection unit to efficiently and effectively collect visual images and improve the overall performance of the system.

[0031] The floor plan identification unit identifies the floor plan based on the video collected by the data collection unit. For example, the floor plan identification unit uses AI to analyze the video and identify each room, such as the living room, bedroom, and kitchen. Specifically, the AI ​​analyzes the video data and recognizes the shape of the rooms, the placement of furniture, and the location of walls. This allows the floor plan identification unit to accurately identify the layout, type, and area of ​​each room. Furthermore, the AI ​​can extract room characteristics from the video data and automatically classify the type of room, such as the living room, bedroom, and kitchen. For example, living rooms often contain sofas and televisions, while bedrooms often contain beds. Based on these characteristics, the AI ​​identifies the type of room. The floor plan identification unit can also convert the collected video data into a 3D model and visually display the floor plan of the entire house. This allows users to intuitively understand the floor plan of their home. In addition, the floor plan identification unit can measure detailed information such as room area and ceiling height based on the collected video data. This allows the floor plan identification unit to provide detailed floor plan information of the user's home and help suggest effective pest control measures.

[0032] The Proposal Department proposes optimal insect repellent items, measures, and implementation timing based on the floor plan information identified by the Floor Plan Identification Department. For example, the Proposal Department proposes effective insect repellent measures based on the house's floor plan, family structure, and lifestyle. Specifically, the Proposal Department uses AI to select the optimal insect repellent items and measures based on the collected floor plan information and the user's lifestyle. For example, it might suggest placing mosquito coils in the living room and using insect repellent spray in the bedroom. The Proposal Department also determines the implementation timing considering the seasonal insect infestation and the user's lifestyle. For example, it might suggest using mosquito coils and insect repellent spray in the spring when mosquitoes are abundant, and suggest using cockroach exterminators in the fall when cockroaches are abundant. Furthermore, the Proposal Department can continuously improve its proposals based on user feedback. For example, by providing feedback on the results of the user's implementation of the proposed insect repellent measures, the Proposal Department can propose even more effective insect repellent measures. This allows the proposal department to suggest optimal pest control measures based on the user's home layout, family structure, and lifestyle, and to notify them of the timing of implementation, thereby enabling effective pest control.

[0033] The Advice Department provides specific advice on insect control measures for each room, based on the suggestions made by the Proposal Department. For example, the Advice Department might advise placing mosquito coils in the living room and using insect repellent spray in the bedroom. Specifically, the Advice Department proposes insect control measures tailored to the characteristics and usage of each room. For instance, the living room is where the family gathers, and placing mosquito coils can prevent mosquitoes from entering. Similarly, the bedroom is where people sleep, and using insect repellent spray can maintain a comfortable sleeping environment. Furthermore, the Advice Department provides specific advice on the optimal placement and usage of insect control items for each room. For example, mosquito coils are most effective when placed near windows or doorways. Insect repellent spray can be made more effective by spraying it on bedding and curtains. This allows the Advice Department to provide specific advice on the optimal insect control measures for each room, supporting users in effectively implementing insect control measures. Moreover, the Advice Department can continuously improve its advice based on user feedback. This allows the Advice Department to always provide effective insect control measures based on the latest information, thereby improving user satisfaction.

[0034] The planning department provides long-term pest control plans that take into account the types and timing of insect outbreaks in each season. For example, in spring, when mosquitoes are abundant, the planning department provides plans for using mosquito coils and insect repellent sprays, and in autumn, when cockroaches are abundant, it provides plans for using cockroach exterminators. Specifically, the planning department predicts the seasonal insect outbreaks based on past data and statistical information and develops long-term pest control plans. For example, in spring, when temperatures rise and mosquito outbreaks increase, it plans for using mosquito coils and insect repellent sprays. Similarly, in autumn, when temperatures fall and cockroach outbreaks increase, it plans for using cockroach exterminators. Furthermore, the planning department provides individually customized pest control plans that take into account the user's lifestyle and family structure. For example, for households with small children, it suggests pest control items that prioritize safety, and for households with pets, it suggests pest control measures that are harmless to pets. In addition, the planning department can encourage users to implement pest control measures through regular reminders. This ensures that users remember to implement pest control measures and can continue to maintain effective pest control. Furthermore, the planning department can continuously improve the pest control plan based on user feedback. This allows the planning department to always provide an effective pest control plan based on the latest information, thereby improving user satisfaction.

[0035] The suggestion department can propose optimal insect repellent items, measures, and implementation timing based on the house layout, family structure, and lifestyle. For example, based on the house layout, the suggestion department might suggest placing mosquito coils in the living room and using insect repellent spray in the bedroom. Considering the family structure, the suggestion department can recommend safe insect repellent items for households with small children or pets. Based on the lifestyle, the suggestion department can notify the homeowner of the most effective implementation timing. For example, considering the types and timing of insects that appear in each season, the suggestion department might suggest using mosquito coils in the spring and cockroach exterminators in the fall. This enables effective insect repellent measures by proposing optimal measures based on the house layout, family structure, and lifestyle. Some or all of the above processing in the suggestion department may be performed using AI or not.

[0036] The advice unit can provide specific advice on the most suitable pest control measures for each room. For example, the advice unit might advise placing mosquito coils in the living room and using insect repellent spray in the bedroom. It could also advise placing cockroach exterminators in the kitchen to implement effective pest control measures. By providing specific advice on the most suitable pest control measures for each room, the advice unit enables effective pest control. Some or all of the above processes in the advice unit may be performed using AI, or they may not be performed using AI.

[0037] The planning department can provide long-term pest control plans that take into account the types and timing of insect outbreaks in each season. For example, the planning department can provide plans to use mosquito coils and insect repellent sprays in the spring, when mosquito outbreaks are high. The planning department can provide plans to use cockroach exterminators in the fall, when cockroach outbreaks are high. The planning department can plan preventative pest control measures in the winter, when insect outbreaks are low. By providing long-term pest control plans that take into account the types and timing of insect outbreaks in each season, effective pest control measures become possible. Some or all of the above processes in the planning department may be performed using AI, or they may not be performed using AI.

[0038] The data collection unit can collect visual images as the user walks around their home wearing AI glasses. For example, the data collection unit can collect still images and videos as the user walks around their home wearing AI glasses. The data collection unit can save the collected images in high resolution and transmit them to the floor plan identification unit. This makes it possible for the user to collect visual images and identify the floor plan as they walk around their home wearing AI glasses. Some or all of the above processing in the data collection unit may be performed using AI or not.

[0039] The proposal department can recommend safe insect repellent items for households with small children or pets. For example, the proposal department can recommend insect repellent items with highly safe ingredients for households with small children or pets. The proposal department can select safe insect repellent items considering the method of use and the duration of effectiveness. This makes it possible to provide insect repellent measures that can be used with peace of mind even in households with small children or pets. Some or all of the above processing in the proposal department may be performed using AI or not.

[0040] The planning department can support the implementation of pest control measures with regular reminders. For example, the planning department can send regular reminders to users to encourage them to take pest control measures. The planning department can set the frequency and method of sending reminders and notify users at the appropriate time. This makes it possible to continuously implement long-term pest control plans by supporting the implementation of pest control measures with regular reminders. Some or all of the above processes in the planning department may be performed using AI or not using AI.

[0041] The collection unit can analyze the user's past behavior history and select the optimal collection method. For example, the collection unit can prioritize collecting video footage of rooms the user has frequently visited in the past. The collection unit can analyze the user's past behavior patterns and suggest the optimal collection route. The collection unit can prioritize suggesting collection methods the user has used in the past (manual, voice commands, etc.). This allows for efficient video collection by selecting the optimal collection method through analysis of the user's past behavior history. Some or all of the above processing in the collection unit may be performed using AI or not.

[0042] The collection unit can filter video footage based on the user's current living situation and areas of interest. For example, if the user is currently in the living room, the collection unit will prioritize collecting footage of the living room. If the user is interested in the kitchen, the collection unit can collect detailed footage of the kitchen. If the user is relaxing in the bedroom, the collection unit can quietly collect footage of the bedroom. This allows for more appropriate video collection by filtering based on the user's current living situation and areas of interest. Some or all of the processing described above in the collection unit may be performed using AI or not.

[0043] The collection unit can prioritize collecting highly relevant video footage by considering the user's geographical location information during video collection. For example, if the user is in the living room, the collection unit will prioritize collecting video footage of the living room. If the user is in the kitchen, the collection unit will prioritize collecting video footage of the kitchen. If the user is in the bedroom, the collection unit will prioritize collecting video footage of the bedroom. This allows for more appropriate video collection by prioritizing the collection of highly relevant video footage by considering the user's geographical location information. Some or all of the above processing in the collection unit may be performed using AI, or it may be performed without using AI.

[0044] The collection unit can analyze the user's social media activity and collect relevant videos when collecting video footage. For example, the collection unit can prioritize collecting videos of rooms that the user has shared on social media. The collection unit can collect videos of rooms that the user has shown interest in on social media. The collection unit can collect videos related to interior design that the user follows on social media. By analyzing the user's social media activity, it becomes possible to collect relevant videos and perform more appropriate video collection. Some or all of the above processing in the collection unit may be performed using AI or not.

[0045] The floor plan identification unit can optimize its identification algorithm by referring to past floor plan data when identifying a floor plan. For example, the floor plan identification unit can optimize the current floor plan identification based on previously identified floor plan data. The floor plan identification unit can analyze past floor plan data and improve its identification algorithm. The floor plan identification unit can improve the accuracy of identification by referring to past floor plan data. This makes it possible to optimize the identification algorithm and improve the accuracy of floor plan identification by referring to past floor plan data. Some or all of the above processing in the floor plan identification unit may be performed using AI or not.

[0046] The floor plan identification unit can adjust the level of detail based on the frequency and purpose of room use when identifying a floor plan. For example, the floor plan identification unit can identify the floor plan of frequently used rooms in detail. The floor plan identification unit can also simplify the identification of floor plans of infrequently used rooms. The floor plan identification unit can adjust the level of detail according to the purpose of the room. By adjusting the level of detail based on the frequency and purpose of room use, more appropriate floor plan identification becomes possible. Some or all of the above processing in the floor plan identification unit may be performed using AI or not.

[0047] The floor plan identification unit can improve the accuracy of floor plan identification by considering the user's lifestyle patterns. For example, the floor plan identification unit can analyze the user's lifestyle patterns to improve the accuracy of identification. The floor plan identification unit can adjust the level of detail of the identification based on the user's lifestyle patterns. The floor plan identification unit can determine the priority of the identification based on the user's lifestyle patterns. As a result, the accuracy of floor plan identification is improved by considering the user's lifestyle patterns. Some or all of the above processing in the floor plan identification unit may be performed using AI or not.

[0048] The floor plan identification unit can improve the accuracy of floor plan identification by referring to the user's family structure information. For example, the floor plan identification unit improves the accuracy of identification based on the user's family structure information. The floor plan identification unit can adjust the level of detail of identification by referring to the user's family structure information. The floor plan identification unit can determine the priority of identification by considering the user's family structure information. As a result, the accuracy of floor plan identification is improved by referring to the user's family structure information. Some or all of the above processing in the floor plan identification unit may be performed using AI or not.

[0049] The proposal unit can adjust the level of detail in its proposals based on the importance of the insect repellent items. For example, it can provide detailed proposals for important insect repellent items and concise proposals for less important items. The proposal unit can also determine the priority of proposals based on the importance of the insect repellent items. This allows for more appropriate proposals by adjusting the level of detail based on the importance of the insect repellent items. Some or all of the above processing in the proposal unit may be performed using AI or not.

[0050] The proposal unit can apply different proposal algorithms depending on the category of the insect repellent item when making a proposal. For example, for insect repellent sprays, the proposal unit can provide detailed suggestions on usage and effectiveness. For insect repellent nets, the proposal unit can provide detailed suggestions on installation location and effectiveness. For insect repellents, the proposal unit can provide detailed suggestions on timing of use and effectiveness. By applying different proposal algorithms depending on the category of the insect repellent item, more appropriate suggestions can be made. Some or all of the above processing in the proposal unit may be performed using AI or not.

[0051] The proposal unit can determine the priority of proposals based on the timing of use of the insect repellent items. For example, the proposal unit will prioritize proposals for insect repellent items that will be used soon. The proposal unit can postpone proposals for insect repellent items that will be used far in the future. The proposal unit can adjust the priority of proposals according to the timing of use of the insect repellent items. This allows for more appropriate proposals by determining the priority of proposals based on the timing of use of the insect repellent items. Some or all of the above processing in the proposal unit may be performed using AI or not.

[0052] The proposal unit can adjust the order of proposals based on the relevance of the insect repellent items. For example, the proposal unit will prioritize proposing insect repellent items that are highly relevant. The proposal unit can postpone proposing insect repellent items that are less relevant. The proposal unit can adjust the order of proposals according to the relevance of the insect repellent items. This allows for more appropriate proposals by adjusting the order of proposals based on the relevance of the insect repellent items. Some or all of the above processing in the proposal unit may be performed using AI or not.

[0053] The advice unit can adjust the level of detail in its advice based on the frequency and purpose of use of each room. For example, it can provide detailed advice for frequently used rooms such as the living room and kitchen, while providing concise advice for less frequently used rooms such as storage rooms and guest rooms. The advice unit can adjust the level of detail in its advice according to the purpose of each room. By adjusting the level of detail in the advice based on the frequency and purpose of use of each room, more appropriate advice can be provided. Some or all of the above processing in the advice unit may be performed using AI, or it may be performed without using AI.

[0054] The advice unit can optimize its advice algorithm by referring to past advice history when providing advice. For example, the advice unit can optimize the current advice based on past advice. The advice unit can analyze past advice history and improve the advice algorithm. The advice unit can improve the accuracy of advice by referring to past advice history. This makes it possible to optimize the advice algorithm and improve the accuracy of advice by referring to past advice history. Some or all of the above processes in the advice unit may be performed using AI or not.

[0055] The advice unit can provide optimal advice by considering the geographical location of each room when giving advice. For example, if the living room faces south, the advice unit can advise on pest control measures that take sunlight into account. If the kitchen faces north, the advice unit can advise on pest control measures that take humidity into account. If the bedroom faces west, the advice unit can advise on measures to prevent insects from entering in the evening. By considering the geographical location of each room, more appropriate advice becomes possible. Some or all of the above processing in the advice unit may be performed using AI, or it may be performed without using AI.

[0056] The advice unit can improve the accuracy of its advice by referring to relevant literature for each room when providing advice. For example, the advice unit can refer to literature on pest control measures for the living room to provide highly accurate advice. The advice unit can refer to literature on pest control measures for the kitchen to provide highly accurate advice. The advice unit can refer to literature on pest control measures for the bedroom to provide highly accurate advice. In this way, the accuracy of the advice is improved by referring to relevant literature for each room. Some or all of the above processing in the advice unit may be performed using AI or not.

[0057] The planning unit can optimize its planning algorithm by referring to past pest control data when creating a pest control plan. For example, the planning unit optimizes the current pest control plan based on past pest control data. The planning unit can analyze past pest control data and improve the planning algorithm. The planning unit can improve the accuracy of the plan by referring to past pest control data. This makes it possible to optimize the planning algorithm and improve the accuracy of the pest control plan by referring to past pest control data. Some or all of the above processes in the planning unit may be performed using AI or not.

[0058] The planning department can adjust the level of detail in its pest control plans based on the frequency and type of insects present. For example, it can create detailed pest control plans for insects that appear frequently, and simplified plans for insects that appear infrequently. The planning department can adjust the level of detail in its plans according to the type of insect. By adjusting the level of detail in the plans based on the frequency and type of insects present, a more appropriate pest control plan can be created. Some or all of the above processes in the planning department may be performed using AI, or they may not be performed using AI.

[0059] The planning department can analyze how to adjust its pest control plan based on the seasonal timing of insect outbreaks. For example, in spring, when mosquitoes are abundant, the planning department can create a plan to use mosquito coils and insect repellent sprays. In autumn, when cockroaches are abundant, the planning department can create a plan to use cockroach exterminators. In winter, when insects are less abundant, the planning department can plan preventative pest control measures. By analyzing how to adjust the plan based on the seasonal timing of insect outbreaks, a more appropriate pest control plan can be created. Some or all of the above-described processes in the planning department may be performed using AI, or they may not be performed using AI.

[0060] The planning department can analyze the plan by referring to relevant market data when creating the pest control plan. For example, the planning department can create a pest control plan based on popular pest control items in the market. The planning department can analyze market data and select the most effective pest control items. The planning department can refer to market data to improve the accuracy of the plan. This improves the accuracy of the plan by referring to relevant market data. Some or all of the above processes in the planning department may be performed using AI or not.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The pest control system can analyze a user's past pest control history and suggest the most effective measures. For example, it can suggest pest control items that were effective in the past. It can evaluate the effectiveness of pest control items the user has used in the past and prioritize suggesting items that were highly effective. It can also analyze the timing of past pest control measures and suggest measures at the optimal time. This makes it possible to propose more effective pest control measures by utilizing the user's past pest control history.

[0063] The pest control system can analyze the user's lifestyle patterns and suggest the most suitable pest control measures. For example, if the user is often active at night, it can suggest pest control items that are effective at night. If the user is often away from home during the day, it can suggest pest control measures that are effective during the day. Also, if the user is often at home on weekends, it can suggest pest control measures to implement on weekends. This makes it possible to suggest effective pest control measures based on the user's lifestyle patterns.

[0064] The pest control system can refer to the user's family structure information and suggest the most suitable pest control measures. For example, it can suggest highly safe pest control items for households with young children, pet-friendly pest control measures for households with pets, and easy-to-use pest control items for households with elderly members. This makes it possible to suggest appropriate pest control measures based on the user's family structure information.

[0065] The pest control system can propose the most suitable pest control measures by considering the user's geographical location. For example, if the user lives in a humid area, it can suggest pest control items that are resistant to humidity. If the user lives in a dry area, it can suggest pest control measures that are resistant to dryness. Furthermore, if the user lives in an urban area, it can suggest pest control measures suitable for urban areas. This makes it possible to propose appropriate pest control measures based on the user's geographical location.

[0066] The pest control system can analyze users' social media activity and suggest optimal pest control measures. For example, it can suggest similar items based on pest control items shared by users on social media. It can also suggest relevant pest control measures based on pest control measures users have shown interest in on social media. Furthermore, it can suggest pest control items recommended by influencers that users follow. This makes it possible to suggest appropriate pest control measures based on users' social media activity.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The collection unit collects visual images. For example, it collects visual images when the user wears AI glasses and walks around their home. The collection unit collects still images and videos, and it is desirable that the resolution is high. Step 2: The floor plan identification unit identifies the floor plan based on the video footage collected by the data collection unit. For example, it uses AI to analyze the video footage and identify each room, such as the living room, bedroom, and kitchen. The floor plan identification unit can identify the layout, type, and area of ​​each room. Step 3: The proposal department proposes the most suitable insect repellent items, measures, and implementation timing based on the floor plan information identified by the floor plan identification department. For example, it proposes effective insect repellent measures based on the house's floor plan, family structure, and lifestyle. The proposal department uses AI to select the most suitable insect repellent items and measures and determine the implementation timing. Step 4: The Advice Department provides specific advice on insect control measures for each room, based on the suggestions made by the Proposal Department. For example, they might advise placing mosquito coils in the living room and using insect repellent spray in the bedroom. The Advice Department can provide specific advice on the most suitable insect control measures for each room. Step 5: The planning department provides a long-term pest control plan that takes into account the types and timing of insect outbreaks in each season. For example, since mosquitoes are abundant in spring, they would provide a plan to use mosquito coils and insect repellent sprays, and since cockroaches are abundant in autumn, they would provide a plan to use cockroach exterminators. The planning department provides a long-term pest control plan and supports the implementation of pest control measures with regular reminders.

[0069] (Example of form 2) An embodiment of the present invention provides an insect control system that utilizes an AI agent and AI glasses to propose the optimal insect control measures for each household. This system automatically identifies the layout of the house from visual images as the user walks around inside the home wearing the AI ​​glasses, and provides specific advice on the optimal insect control measures for each room. Based on the house layout, family structure, and lifestyle, the system proposes the most suitable insect control items, measures, and implementation timing. For example, for households with small children or pets, it recommends safe insect control items and places the most suitable insect control measures in each room, such as the living room, bedroom, and kitchen. It also considers the types and timing of insect outbreaks for each season and notifies the user of the most effective implementation timing. Furthermore, the system provides a long-term insect control plan. For example, it suggests using mosquito coils or insect repellent sprays in the spring when mosquitoes are abundant, and suggests using cockroach exterminators in the fall when cockroaches are prevalent. In this way, it provides insect control measures that take into account the types and timing of insect outbreaks for each season. This allows users to implement optimal insect control measures without worrying about methods or item selection. Furthermore, even busy users can implement pest control measures at the appropriate time, as the AI ​​agent notifies them of the most effective implementation timing. Regular reminders further support implementation, enabling the continuous execution of long-term pest control plans. In this way, by utilizing the AI ​​agent and AI glasses, it's possible to propose optimal pest control measures for each household, ensuring families can live comfortably and healthily. In particular, it provides pest control measures that can be used safely even in homes with small children or pets, reducing stress caused by insects. Additionally, AI glasses enable more accurate personalized recommendations by identifying the floor plan. As a result, the pest control system proposes optimal pest control measures based on the user's home floor plan, family structure, and lifestyle, and notifies them of the optimal implementation timing, enabling effective pest control.

[0070] The insect control system according to this embodiment comprises a collection unit, a floor plan identification unit, a proposal unit, an advice unit, and a planning unit. The collection unit collects visual images. For example, the collection unit collects visual images when a user wears AI glasses and walks around the house. The collection unit collects still images and videos, and it is desirable that the resolution be high. The floor plan identification unit identifies the floor plan based on the images collected by the collection unit. For example, the floor plan identification unit uses AI to analyze the images and identifies each room, such as the living room, bedroom, and kitchen. The floor plan identification unit can identify the layout, type, and area of ​​the rooms. The proposal unit proposes the optimal insect control items, insect control measures, and implementation timing based on the floor plan information identified by the floor plan identification unit. For example, the proposal unit proposes effective insect control measures based on the house's floor plan, family structure, and lifestyle. The proposal unit uses AI to select the optimal insect control items and measures and determine the implementation timing. The advice unit provides specific advice on the insect control measures proposed by the proposal unit for each room. The advice department, for example, advises placing mosquito coils in the living room and using insect repellent spray in the bedroom. The advice department can provide specific advice on the optimal insect control measures for each room. The planning department provides a long-term insect control plan that takes into account the types of insects and their emergence periods for each season. For example, the planning department provides a plan to use mosquito coils and insect repellent spray in the spring when mosquitoes are abundant, and a plan to use cockroach exterminators in the fall when cockroaches are abundant. The planning department provides a long-term insect control plan and supports the implementation of insect control measures with regular reminders. As a result, the insect control system according to this embodiment can propose the optimal insect control measures based on the user's house layout, family structure, and lifestyle, and notify them of the timing of implementation, thereby enabling effective insect control.

[0071] The data collection unit collects visual images. For example, the data collection unit collects visual images when a user wears AI glasses and walks around their home. The data collection unit collects still images and videos, and it is desirable that the resolution be high. Specifically, the AI ​​glasses are equipped with a high-resolution camera that captures images of the surroundings in real time as the user walks around the home. This allows the data collection unit to obtain detailed video data of the interior of the house. Furthermore, the AI ​​glasses have a function to track the user's gaze and can identify areas that the user is focusing on. This allows the data collection unit to concentrate on collecting images of areas that require special attention, such as those requiring pest control. The data collection unit can also send the video data to a cloud server and store and manage the data in real time. This allows the data collection unit to collect the latest video data every time the user walks around the home and always provide the latest information. In addition, the data collection unit can flexibly adapt to the user's network environment and device performance by adjusting the resolution and frame rate of the video data. This allows the data collection unit to efficiently and effectively collect visual images and improve the overall performance of the system.

[0072] The floor plan identification unit identifies the floor plan based on the video collected by the data collection unit. For example, the floor plan identification unit uses AI to analyze the video and identify each room, such as the living room, bedroom, and kitchen. Specifically, the AI ​​analyzes the video data and recognizes the shape of the rooms, the placement of furniture, and the location of walls. This allows the floor plan identification unit to accurately identify the layout, type, and area of ​​each room. Furthermore, the AI ​​can extract room characteristics from the video data and automatically classify the type of room, such as the living room, bedroom, and kitchen. For example, living rooms often contain sofas and televisions, while bedrooms often contain beds. Based on these characteristics, the AI ​​identifies the type of room. The floor plan identification unit can also convert the collected video data into a 3D model and visually display the floor plan of the entire house. This allows users to intuitively understand the floor plan of their home. In addition, the floor plan identification unit can measure detailed information such as room area and ceiling height based on the collected video data. This allows the floor plan identification unit to provide detailed floor plan information of the user's home and help suggest effective pest control measures.

[0073] The Proposal Department proposes optimal insect repellent items, measures, and implementation timing based on the floor plan information identified by the Floor Plan Identification Department. For example, the Proposal Department proposes effective insect repellent measures based on the house's floor plan, family structure, and lifestyle. Specifically, the Proposal Department uses AI to select the optimal insect repellent items and measures based on the collected floor plan information and the user's lifestyle. For example, it might suggest placing mosquito coils in the living room and using insect repellent spray in the bedroom. The Proposal Department also determines the implementation timing considering the seasonal insect infestation and the user's lifestyle. For example, it might suggest using mosquito coils and insect repellent spray in the spring when mosquitoes are abundant, and suggest using cockroach exterminators in the fall when cockroaches are abundant. Furthermore, the Proposal Department can continuously improve its proposals based on user feedback. For example, by providing feedback on the results of the user's implementation of the proposed insect repellent measures, the Proposal Department can propose even more effective insect repellent measures. This allows the proposal department to suggest optimal pest control measures based on the user's home layout, family structure, and lifestyle, and to notify them of the timing of implementation, thereby enabling effective pest control.

[0074] The Advice Department provides specific advice on insect control measures for each room, based on the suggestions made by the Proposal Department. For example, the Advice Department might advise placing mosquito coils in the living room and using insect repellent spray in the bedroom. Specifically, the Advice Department proposes insect control measures tailored to the characteristics and usage of each room. For instance, the living room is where the family gathers, and placing mosquito coils can prevent mosquitoes from entering. Similarly, the bedroom is where people sleep, and using insect repellent spray can maintain a comfortable sleeping environment. Furthermore, the Advice Department provides specific advice on the optimal placement and usage of insect control items for each room. For example, mosquito coils are most effective when placed near windows or doorways. Insect repellent spray can be made more effective by spraying it on bedding and curtains. This allows the Advice Department to provide specific advice on the optimal insect control measures for each room, supporting users in effectively implementing insect control measures. Moreover, the Advice Department can continuously improve its advice based on user feedback. This allows the Advice Department to always provide effective insect control measures based on the latest information, thereby improving user satisfaction.

[0075] The planning department provides long-term pest control plans that take into account the types and timing of insect outbreaks in each season. For example, in spring, when mosquitoes are abundant, the planning department provides plans for using mosquito coils and insect repellent sprays, and in autumn, when cockroaches are abundant, it provides plans for using cockroach exterminators. Specifically, the planning department predicts the seasonal insect outbreaks based on past data and statistical information and develops long-term pest control plans. For example, in spring, when temperatures rise and mosquito outbreaks increase, it plans for using mosquito coils and insect repellent sprays. Similarly, in autumn, when temperatures fall and cockroach outbreaks increase, it plans for using cockroach exterminators. Furthermore, the planning department provides individually customized pest control plans that take into account the user's lifestyle and family structure. For example, for households with small children, it suggests pest control items that prioritize safety, and for households with pets, it suggests pest control measures that are harmless to pets. In addition, the planning department can encourage users to implement pest control measures through regular reminders. This ensures that users remember to implement pest control measures and can continue to maintain effective pest control. Furthermore, the planning department can continuously improve the pest control plan based on user feedback. This allows the planning department to always provide an effective pest control plan based on the latest information, thereby improving user satisfaction.

[0076] The suggestion department can propose optimal insect repellent items, measures, and implementation timing based on the house layout, family structure, and lifestyle. For example, based on the house layout, the suggestion department might suggest placing mosquito coils in the living room and using insect repellent spray in the bedroom. Considering the family structure, the suggestion department can recommend safe insect repellent items for households with small children or pets. Based on the lifestyle, the suggestion department can notify the homeowner of the most effective implementation timing. For example, considering the types and timing of insects that appear in each season, the suggestion department might suggest using mosquito coils in the spring and cockroach exterminators in the fall. This enables effective insect repellent measures by proposing optimal measures based on the house layout, family structure, and lifestyle. Some or all of the above processing in the suggestion department may be performed using AI or not.

[0077] The advice unit can provide specific advice on the most suitable pest control measures for each room. For example, the advice unit might advise placing mosquito coils in the living room and using insect repellent spray in the bedroom. It could also advise placing cockroach exterminators in the kitchen to implement effective pest control measures. By providing specific advice on the most suitable pest control measures for each room, the advice unit enables effective pest control. Some or all of the above processes in the advice unit may be performed using AI, or they may not be performed using AI.

[0078] The planning department can provide long-term pest control plans that take into account the types and timing of insect outbreaks in each season. For example, the planning department can provide plans to use mosquito coils and insect repellent sprays in the spring, when mosquito outbreaks are high. The planning department can provide plans to use cockroach exterminators in the fall, when cockroach outbreaks are high. The planning department can plan preventative pest control measures in the winter, when insect outbreaks are low. By providing long-term pest control plans that take into account the types and timing of insect outbreaks in each season, effective pest control measures become possible. Some or all of the above processes in the planning department may be performed using AI, or they may not be performed using AI.

[0079] The data collection unit can collect visual images as the user walks around their home wearing AI glasses. For example, the data collection unit can collect still images and videos as the user walks around their home wearing AI glasses. The data collection unit can save the collected images in high resolution and transmit them to the floor plan identification unit. This makes it possible for the user to collect visual images and identify the floor plan as they walk around their home wearing AI glasses. Some or all of the above processing in the data collection unit may be performed using AI or not.

[0080] The proposal department can recommend safe insect repellent items for households with small children or pets. For example, the proposal department can recommend insect repellent items with highly safe ingredients for households with small children or pets. The proposal department can select safe insect repellent items considering the method of use and the duration of effectiveness. This makes it possible to provide insect repellent measures that can be used with peace of mind even in households with small children or pets. Some or all of the above processing in the proposal department may be performed using AI or not.

[0081] The planning department can support the implementation of pest control measures with regular reminders. For example, the planning department can send regular reminders to users to encourage them to take pest control measures. The planning department can set the frequency and method of sending reminders and notify users at the appropriate time. This makes it possible to continuously implement long-term pest control plans by supporting the implementation of pest control measures with regular reminders. Some or all of the above processes in the planning department may be performed using AI or not using AI.

[0082] The collection unit can estimate the user's emotions and adjust the timing of video collection based on the estimated emotions. For example, if the user is relaxed, the collection unit can collect video slowly and gather detailed information. If the user is in a hurry, the collection unit can collect video quickly and gather only the minimum necessary information. If the user is stressed, the collection unit can interrupt video collection and wait until the user calms down. This allows for more appropriate video collection by adjusting the timing of video collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collection unit may be performed using AI or not.

[0083] The collection unit can analyze the user's past behavior history and select the optimal collection method. For example, the collection unit can prioritize collecting video footage of rooms the user has frequently visited in the past. The collection unit can analyze the user's past behavior patterns and suggest the optimal collection route. The collection unit can prioritize suggesting collection methods the user has used in the past (manual, voice commands, etc.). This allows for efficient video collection by selecting the optimal collection method through analysis of the user's past behavior history. Some or all of the above processing in the collection unit may be performed using AI or not.

[0084] The collection unit can filter video footage based on the user's current living situation and areas of interest. For example, if the user is currently in the living room, the collection unit will prioritize collecting footage of the living room. If the user is interested in the kitchen, the collection unit can collect detailed footage of the kitchen. If the user is relaxing in the bedroom, the collection unit can quietly collect footage of the bedroom. This allows for more appropriate video collection by filtering based on the user's current living situation and areas of interest. Some or all of the processing described above in the collection unit may be performed using AI or not.

[0085] The collection unit can estimate the user's emotions and determine the priority of the videos to collect based on the estimated emotions. For example, if the user is excited, the collection unit will prioritize collecting videos of important rooms. If the user is relaxed, the collection unit can collect a balanced mix of videos. If the user is tired, the collection unit can collect only the most important videos. This allows for more appropriate video collection by prioritizing the videos to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the collection unit may be performed using AI or not.

[0086] The collection unit can prioritize collecting highly relevant video footage by considering the user's geographical location information during video collection. For example, if the user is in the living room, the collection unit will prioritize collecting video footage of the living room. If the user is in the kitchen, the collection unit will prioritize collecting video footage of the kitchen. If the user is in the bedroom, the collection unit will prioritize collecting video footage of the bedroom. This allows for more appropriate video collection by prioritizing the collection of highly relevant video footage by considering the user's geographical location information. Some or all of the above processing in the collection unit may be performed using AI, or it may be performed without using AI.

[0087] The collection unit can analyze the user's social media activity and collect relevant videos when collecting video footage. For example, the collection unit can prioritize collecting videos of rooms that the user has shared on social media. The collection unit can collect videos of rooms that the user has shown interest in on social media. The collection unit can collect videos related to interior design that the user follows on social media. By analyzing the user's social media activity, it becomes possible to collect relevant videos and perform more appropriate video collection. Some or all of the above processing in the collection unit may be performed using AI or not.

[0088] The floor plan identification unit can estimate the user's emotions and adjust the accuracy of floor plan identification based on the estimated emotions. For example, if the user is relaxed, the floor plan identification unit can perform detailed floor plan identification. If the user is in a hurry, the floor plan identification unit can perform simplified floor plan identification. If the user is stressed, the floor plan identification unit can interrupt floor plan identification and wait until the user calms down. This allows for more appropriate floor plan identification by adjusting the accuracy of floor plan identification 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 floor plan identification unit may be performed using AI or not.

[0089] The floor plan identification unit can optimize its identification algorithm by referring to past floor plan data when identifying a floor plan. For example, the floor plan identification unit can optimize the current floor plan identification based on previously identified floor plan data. The floor plan identification unit can analyze past floor plan data and improve its identification algorithm. The floor plan identification unit can improve the accuracy of identification by referring to past floor plan data. This makes it possible to optimize the identification algorithm and improve the accuracy of floor plan identification by referring to past floor plan data. Some or all of the above processing in the floor plan identification unit may be performed using AI or not.

[0090] The floor plan identification unit can adjust the level of detail based on the frequency and purpose of room use when identifying a floor plan. For example, the floor plan identification unit can identify the floor plan of frequently used rooms in detail. The floor plan identification unit can also simplify the identification of floor plans of infrequently used rooms. The floor plan identification unit can adjust the level of detail according to the purpose of the room. By adjusting the level of detail based on the frequency and purpose of room use, more appropriate floor plan identification becomes possible. Some or all of the above processing in the floor plan identification unit may be performed using AI or not.

[0091] The floor plan identification unit can estimate the user's emotions and determine the priority of floor plan identification based on the estimated user emotions. For example, if the user is excited, the floor plan identification unit will prioritize identifying important rooms. If the user is relaxed, the floor plan identification unit can identify the overall floor plan in a balanced manner. If the user is tired, the floor plan identification unit can identify only the most important rooms. This allows for more appropriate floor plan identification by determining the priority of floor plan identification 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 floor plan identification unit may be performed using AI or not.

[0092] The floor plan identification unit can improve the accuracy of floor plan identification by considering the user's lifestyle patterns. For example, the floor plan identification unit can analyze the user's lifestyle patterns to improve the accuracy of identification. The floor plan identification unit can adjust the level of detail of the identification based on the user's lifestyle patterns. The floor plan identification unit can determine the priority of the identification based on the user's lifestyle patterns. As a result, the accuracy of floor plan identification is improved by considering the user's lifestyle patterns. Some or all of the above processing in the floor plan identification unit may be performed using AI or not.

[0093] The floor plan identification unit can improve the accuracy of floor plan identification by referring to the user's family structure information. For example, the floor plan identification unit improves the accuracy of identification based on the user's family structure information. The floor plan identification unit can adjust the level of detail of identification by referring to the user's family structure information. The floor plan identification unit can determine the priority of identification by considering the user's family structure information. As a result, the accuracy of floor plan identification is improved by referring to the user's family structure information. Some or all of the above processing in the floor plan identification unit may be performed using AI or not.

[0094] The suggestion unit can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide concise suggestions. If the user is stressed, the suggestion unit can interrupt its suggestions and wait until the user calms down. This allows for more appropriate suggestions by adjusting the way they 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 processing described above in the suggestion unit may be performed using AI or not.

[0095] The proposal unit can adjust the level of detail in its proposals based on the importance of the insect repellent items. For example, it can provide detailed proposals for important insect repellent items and concise proposals for less important items. The proposal unit can also determine the priority of proposals based on the importance of the insect repellent items. This allows for more appropriate proposals by adjusting the level of detail based on the importance of the insect repellent items. Some or all of the above processing in the proposal unit may be performed using AI or not.

[0096] The proposal unit can apply different proposal algorithms depending on the category of the insect repellent item when making a proposal. For example, for insect repellent sprays, the proposal unit can provide detailed suggestions on usage and effectiveness. For insect repellent nets, the proposal unit can provide detailed suggestions on installation location and effectiveness. For insect repellents, the proposal unit can provide detailed suggestions on timing of use and effectiveness. By applying different proposal algorithms depending on the category of the insect repellent item, more appropriate suggestions can be made. Some or all of the above processing in the proposal unit may be performed using AI or not.

[0097] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide concise suggestions. If the user is stressed, the suggestion unit can interrupt the suggestions and wait until the user calms down. This allows for more appropriate suggestions by adjusting the length of the suggestions 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 processing described above in the suggestion unit may be performed using AI or not.

[0098] The proposal unit can determine the priority of proposals based on the timing of use of the insect repellent items. For example, the proposal unit will prioritize proposals for insect repellent items that will be used soon. The proposal unit can postpone proposals for insect repellent items that will be used far in the future. The proposal unit can adjust the priority of proposals according to the timing of use of the insect repellent items. This allows for more appropriate proposals by determining the priority of proposals based on the timing of use of the insect repellent items. Some or all of the above processing in the proposal unit may be performed using AI or not.

[0099] The proposal unit can adjust the order of proposals based on the relevance of the insect repellent items. For example, the proposal unit will prioritize proposing insect repellent items that are highly relevant. The proposal unit can postpone proposing insect repellent items that are less relevant. The proposal unit can adjust the order of proposals according to the relevance of the insect repellent items. This allows for more appropriate proposals by adjusting the order of proposals based on the relevance of the insect repellent items. Some or all of the above processing in the proposal unit may be performed using AI or not.

[0100] The advice unit can estimate the user's emotions and adjust the way it expresses advice based on the estimated emotions. For example, if the user is relaxed, the advice unit can provide detailed advice. If the user is in a hurry, the advice unit can provide concise advice. If the user is stressed, the advice unit can interrupt the advice and wait until the user calms down. This allows for more appropriate advice by adjusting the way it expresses advice 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 advice unit may be performed using AI or not.

[0101] The advice unit can adjust the level of detail in its advice based on the frequency and purpose of use of each room. For example, it can provide detailed advice for frequently used rooms such as the living room and kitchen, while providing concise advice for less frequently used rooms such as storage rooms and guest rooms. The advice unit can adjust the level of detail in its advice according to the purpose of each room. By adjusting the level of detail in the advice based on the frequency and purpose of use of each room, more appropriate advice can be provided. Some or all of the above processing in the advice unit may be performed using AI, or it may be performed without using AI.

[0102] The advice unit can optimize its advice algorithm by referring to past advice history when providing advice. For example, the advice unit can optimize the current advice based on past advice. The advice unit can analyze past advice history and improve the advice algorithm. The advice unit can improve the accuracy of advice by referring to past advice history. This makes it possible to optimize the advice algorithm and improve the accuracy of advice by referring to past advice history. Some or all of the above processes in the advice unit may be performed using AI or not.

[0103] The advice unit can estimate the user's emotions and determine the priority of advice based on the estimated emotions. For example, if the user is excited, the advice unit will prioritize important advice. If the user is relaxed, the advice unit will provide a balanced range of advice. If the user is tired, the advice unit will provide only the most important advice. This allows for more appropriate advice by prioritizing advice 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice unit may be performed using AI or not.

[0104] The advice unit can provide optimal advice by considering the geographical location of each room when giving advice. For example, if the living room faces south, the advice unit can advise on pest control measures that take sunlight into account. If the kitchen faces north, the advice unit can advise on pest control measures that take humidity into account. If the bedroom faces west, the advice unit can advise on measures to prevent insects from entering in the evening. By considering the geographical location of each room, more appropriate advice becomes possible. Some or all of the above processing in the advice unit may be performed using AI, or it may be performed without using AI.

[0105] The advice unit can improve the accuracy of its advice by referring to relevant literature for each room when providing advice. For example, the advice unit can refer to literature on pest control measures for the living room to provide highly accurate advice. The advice unit can refer to literature on pest control measures for the kitchen to provide highly accurate advice. The advice unit can refer to literature on pest control measures for the bedroom to provide highly accurate advice. In this way, the accuracy of the advice is improved by referring to relevant literature for each room. Some or all of the above processing in the advice unit may be performed using AI or not.

[0106] The planning unit can estimate the user's emotions and adjust how the pest control plan is displayed based on the estimated emotions. For example, if the user is relaxed, the planning unit can display a detailed pest control plan. If the user is in a hurry, the planning unit can display a concise pest control plan. If the user is stressed, the planning unit can interrupt the display of the pest control plan and wait until the user calms down. This allows for a more appropriate pest control plan by adjusting how the pest control plan is displayed 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 planning unit may be performed using AI or not.

[0107] The planning unit can optimize its planning algorithm by referring to past pest control data when creating a pest control plan. For example, the planning unit optimizes the current pest control plan based on past pest control data. The planning unit can analyze past pest control data and improve the planning algorithm. The planning unit can improve the accuracy of the plan by referring to past pest control data. This makes it possible to optimize the planning algorithm and improve the accuracy of the pest control plan by referring to past pest control data. Some or all of the above processes in the planning unit may be performed using AI or not.

[0108] The planning department can adjust the level of detail in its pest control plans based on the frequency and type of insects present. For example, it can create detailed pest control plans for insects that appear frequently, and simplified plans for insects that appear infrequently. The planning department can adjust the level of detail in its plans according to the type of insect. By adjusting the level of detail in the plans based on the frequency and type of insects present, a more appropriate pest control plan can be created. Some or all of the above processes in the planning department may be performed using AI, or they may not be performed using AI.

[0109] The planning unit can estimate the user's emotions and determine the priority of pest control plans based on the estimated emotions. For example, if the user is excited, the planning unit will prioritize creating important pest control plans. If the user is relaxed, the planning unit can create a well-balanced overall pest control plan. If the user is tired, the planning unit can create only the most important pest control plans. This allows for more appropriate pest control plans by prioritizing them 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 planning unit may be performed using AI or not.

[0110] The planning department can analyze how to adjust its pest control plan based on the seasonal timing of insect outbreaks. For example, in spring, when mosquitoes are abundant, the planning department can create a plan to use mosquito coils and insect repellent sprays. In autumn, when cockroaches are abundant, the planning department can create a plan to use cockroach exterminators. In winter, when insects are less abundant, the planning department can plan preventative pest control measures. By analyzing how to adjust the plan based on the seasonal timing of insect outbreaks, a more appropriate pest control plan can be created. Some or all of the above-described processes in the planning department may be performed using AI, or they may not be performed using AI.

[0111] The planning department can analyze the plan by referring to relevant market data when creating the pest control plan. For example, the planning department can create a pest control plan based on popular pest control items in the market. The planning department can analyze market data and select the most effective pest control items. The planning department can refer to market data to improve the accuracy of the plan. This improves the accuracy of the plan by referring to relevant market data. Some or all of the above processes in the planning department may be performed using AI or not.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] The pest control system can estimate the user's emotions and adjust its pest control suggestions based on those emotions. For example, if the user is stressed, the system can suggest simple and easy-to-implement pest control measures. If the user is relaxed, it can suggest detailed pest control measures, explaining how to implement them and their effects in detail. If the user is excited, the system can prioritize suggesting pest control measures that can be implemented quickly. This allows for the suggestion of appropriate pest control measures tailored to the user's emotions.

[0114] The pest control system can analyze a user's past pest control history and suggest the most effective measures. For example, it can suggest pest control items that were effective in the past. It can evaluate the effectiveness of pest control items the user has used in the past and prioritize suggesting items that were highly effective. It can also analyze the timing of past pest control measures and suggest measures at the optimal time. This makes it possible to propose more effective pest control measures by utilizing the user's past pest control history.

[0115] The pest control system can estimate the user's emotions and adjust the timing of pest control based on those emotions. For example, if the user is relaxed, the system can send a notification prompting them to take action. If the user is busy, the system can postpone the action. Furthermore, if the user is stressed, the system can temporarily suspend the action and wait until the user calms down. This makes it possible to provide appropriate timing for pest control based on the user's emotions.

[0116] The pest control system can analyze the user's lifestyle patterns and suggest the most suitable pest control measures. For example, if the user is often active at night, it can suggest pest control items that are effective at night. If the user is often away from home during the day, it can suggest pest control measures that are effective during the day. Also, if the user is often at home on weekends, it can suggest pest control measures to implement on weekends. This makes it possible to suggest effective pest control measures based on the user's lifestyle patterns.

[0117] The pest control system can estimate the user's emotions and prioritize pest control measures based on those emotions. For example, if the user is excited, the system can prioritize suggesting important pest control measures. If the user is relaxed, it can suggest a balanced set of overall pest control measures. If the user is tired, it can suggest only the most important pest control measures. This makes it possible to provide appropriate pest control priority according to the user's emotions.

[0118] The pest control system can refer to the user's family structure information and suggest the most suitable pest control measures. For example, it can suggest highly safe pest control items for households with young children, pet-friendly pest control measures for households with pets, and easy-to-use pest control items for households with elderly members. This makes it possible to suggest appropriate pest control measures based on the user's family structure information.

[0119] The pest control system can estimate the user's emotions and adjust the way it explains pest control based on those emotions. For example, if the user is relaxed, it can provide a detailed explanation. If the user is in a hurry, it can provide a concise explanation. Also, if the user is stressed, it can interrupt the explanation and wait until the user calms down. This makes it possible to provide an appropriate explanation of pest control tailored to the user's emotions.

[0120] The pest control system can propose the most suitable pest control measures by considering the user's geographical location. For example, if the user lives in a humid area, it can suggest pest control items that are resistant to humidity. If the user lives in a dry area, it can suggest pest control measures that are resistant to dryness. Furthermore, if the user lives in an urban area, it can suggest pest control measures suitable for urban areas. This makes it possible to propose appropriate pest control measures based on the user's geographical location.

[0121] The pest control system can estimate the user's emotions and adjust pest control reminders based on those emotions. For example, if the user is relaxed, it can send a reminder to encourage them to take pest control measures. If the user is busy, sending the reminder can be postponed. Also, if the user is stressed, sending the reminder can be temporarily suspended, and the system can wait until the user calms down. This makes it possible to provide appropriate pest control reminders that are tailored to the user's emotions.

[0122] The pest control system can analyze users' social media activity and suggest optimal pest control measures. For example, it can suggest similar items based on pest control items shared by users on social media. It can also suggest relevant pest control measures based on pest control measures users have shown interest in on social media. Furthermore, it can suggest pest control items recommended by influencers that users follow. This makes it possible to suggest appropriate pest control measures based on users' social media activity.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The collection unit collects visual images. For example, it collects visual images when the user wears AI glasses and walks around their home. The collection unit collects still images and videos, and it is desirable that the resolution is high. Step 2: The floor plan identification unit identifies the floor plan based on the video footage collected by the data collection unit. For example, it uses AI to analyze the video footage and identify each room, such as the living room, bedroom, and kitchen. The floor plan identification unit can identify the layout, type, and area of ​​each room. Step 3: The proposal department proposes the most suitable insect repellent items, measures, and implementation timing based on the floor plan information identified by the floor plan identification department. For example, it proposes effective insect repellent measures based on the house's floor plan, family structure, and lifestyle. The proposal department uses AI to select the most suitable insect repellent items and measures and determine the implementation timing. Step 4: The Advice Department provides specific advice on insect control measures for each room, based on the suggestions made by the Proposal Department. For example, they might advise placing mosquito coils in the living room and using insect repellent spray in the bedroom. The Advice Department can provide specific advice on the most suitable insect control measures for each room. Step 5: The planning department provides a long-term pest control plan that takes into account the types and timing of insect outbreaks in each season. For example, since mosquitoes are abundant in spring, they would provide a plan to use mosquito coils and insect repellent sprays, and since cockroaches are abundant in autumn, they would provide a plan to use cockroach exterminators. The planning department provides a long-term pest control plan and supports the implementation of pest control measures with regular reminders.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the data collection unit, floor plan identification unit, proposal unit, advice unit, and planning unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects visual images using the camera 42 of the smart device 14 and processes them with the control unit 46A. The floor plan identification unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which analyzes the collected images to identify the floor plan. The proposal unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which proposes the most suitable insect repellent items and measures based on the floor plan information. The advice unit is implemented by, for example, the control unit 46A of the smart device 14, which provides specific insect repellent advice for each room. The planning unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which provides a long-term insect repellent plan that takes into account the types of insects and their occurrence times for each season. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] 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.

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

[0132] The 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.

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

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

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

[0136] Figure 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.

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

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

[0139] In the 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.

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

[0141] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0143] The data processing system 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.

[0144] Each of the multiple elements described above, including the data collection unit, floor plan identification unit, proposal unit, advice unit, and planning unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects visual images using the camera 42 of the smart glasses 214 and processes them with the control unit 46A. The floor plan identification unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected images to identify the floor plan. The proposal unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which proposes the most suitable insect repellent items and measures based on the floor plan information. The advice unit is implemented, for example, by the control unit 46A of the smart glasses 214, which provides specific insect repellent advice for each room. The planning unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which provides a long-term insect repellent plan that takes into account the types of insects and their occurrence times for each season. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] 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.

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

[0148] The 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.

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

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (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).

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

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] Each of the multiple elements described above, including the data collection unit, floor plan identification unit, proposal unit, advice unit, and planning unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects visual images using the camera 42 of the headset terminal 314 and processes them with the control unit 46A. The floor plan identification unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which analyzes the collected images to identify the floor plan. The proposal unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which proposes the most suitable insect repellent items and measures based on the floor plan information. The advice unit is implemented by, for example, the control unit 46A of the headset terminal 314, which provides specific insect repellent advice for each room. The planning unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which provides a long-term insect repellent plan that takes into account the types of insects and their occurrence times for each season. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] Each of the multiple elements described above, including the data collection unit, floor plan identification unit, proposal unit, advice unit, and planning unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects visual images using the camera 42 of the robot 414 and processes them with the control unit 46A. The floor plan identification unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which analyzes the collected images to identify the floor plan. The proposal unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which proposes the most suitable insect repellent items and measures based on the floor plan information. The advice unit is implemented by, for example, the control unit 46A of the robot 414, which advises on specific insect repellent measures for each room. The planning unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which provides a long-term insect repellent plan that takes into account the types of insects and their occurrence times for each season. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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."

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] (Note 1) A collection unit that collects visual images, A floor plan identification unit identifies the floor plan based on the video footage collected by the aforementioned collection unit, Based on the floor plan information identified by the aforementioned floor plan identification unit, the proposal unit proposes the most suitable insect repellent items, insect repellent measures, and implementation timing. The proposal section provides specific advice on pest control measures for each room, based on the proposed measures. It includes a planning department that provides a long-term pest control plan that takes into account the types of insects and their occurrence times for each season. A system characterized by the following features. (Note 2) The aforementioned proposal section is, Based on your home layout, family structure, and lifestyle, we will propose the most suitable insect repellent items, measures, and timing for implementation. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned advice section, We provide specific advice on the most suitable pest control measures for each room. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned planning department, We provide long-term pest control plans that take into account the types of insects and their emergence periods in each season. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is The system collects visual images as the user wears AI glasses and walks around their home. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, For households with small children or pets, we recommend safe insect repellent products. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned planning department, Regular reminders support the implementation of pest control measures. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of video collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze the user's past behavior history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting video footage, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and determines the priority of the videos to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting video footage, the system prioritizes collecting highly relevant footage by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting video footage, the system analyzes users' social media activity and collects relevant videos. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned floor plan specification section is, It estimates the user's emotions and adjusts the accuracy of floor plan selection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned floor plan specification section is, When identifying a floor plan, the identification algorithm is optimized by referring to past floor plan data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned floor plan specification section is, When specifying the floor plan, adjust the level of detail based on the frequency and purpose of use of each room. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned floor plan specification section is, The system estimates the user's emotions and determines the priority of floor plan selection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned floor plan specification section is, When identifying floor plans, we improve accuracy by considering the user's lifestyle patterns. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned floor plan specification section is, When identifying a floor plan, the system improves accuracy by referencing the user's family structure information. The system described in Appendix 1, characterized by the features described herein. (Note 20) 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 21) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the insect repellent items. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the category of the insect repellent item. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making proposals, prioritize them based on when the insect repellent items will be used. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on the relevance of the insect repellent items. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned advice section, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned advice section, When providing advice, we adjust the level of detail based on the frequency and purpose of use of each room. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned advice section, When providing advice, the advice algorithm is optimized by referring to past advice history. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned advice section, It estimates the user's emotions and prioritizes advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned advice section, When providing advice, we take into account the geographical location of each room to offer the most suitable advice. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned advice section, When giving advice, refer to relevant literature for each room to improve the accuracy of the advice. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned planning department, The system estimates the user's emotions and adjusts how the pest control plan is displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned planning department, When creating a pest control plan, we optimize the planning algorithm by referring to past pest control data. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned planning department, When creating an insect control plan, adjust the level of detail based on the frequency and type of insects that appear. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned planning department, The system estimates user emotions and prioritizes pest control plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned planning department, When creating an insect control plan, analyze how the plan will change based on the timing of insect outbreaks in each season. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned planning department, When creating an insect control plan, analyze the plan by referring to relevant market data. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0197] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects visual images, A floor plan identification unit identifies the floor plan based on the video footage collected by the aforementioned collection unit, Based on the floor plan information identified by the aforementioned floor plan identification unit, the proposal unit proposes the most suitable insect repellent items, insect repellent measures, and implementation timing. The proposal section provides specific advice on pest control measures for each room, based on the proposed measures. It includes a planning department that provides a long-term pest control plan that takes into account the types of insects and their occurrence times for each season. A system characterized by the following features.

2. The aforementioned proposal section is, Based on your home layout, family structure, and lifestyle, we will propose the most suitable insect repellent items, measures, and timing for implementation. The system according to feature 1.

3. The aforementioned advice section, We provide specific advice on the most suitable pest control measures for each room. The system according to feature 1.

4. The aforementioned planning department, We provide long-term pest control plans that take into account the types of insects and their emergence periods in each season. The system according to feature 1.

5. The aforementioned collection unit is The system collects visual images by having the user wear AI glasses and walk around their home. The system according to feature 1.

6. The aforementioned proposal section is, For households with small children or pets, we recommend safe insect repellent products. The system according to feature 1.

7. The aforementioned planning department, Regular reminders support the implementation of pest control measures. The system according to feature 1.

8. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of video collection based on those estimated emotions. The system according to feature 1.

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