system

Abstract

The system according to this embodiment aims to propose the optimal pest control measures based on the user's home layout and lifestyle. [Solution] The system according to the embodiment comprises a collection unit, a suggestion unit, a collaboration unit, and an analysis unit. The collection unit collects information such as the user's house layout, family structure, lifestyle, preferences, and daily habits. The suggestion unit analyzes the information collected by the collection unit and proposes the most suitable insect repellent items and measures. The collaboration unit works in conjunction with IoT devices to analyze the living patterns and insect appearance patterns of the entire house. The analysis unit analyzes photos and blueprints of the house uploaded by the user and proposes the most suitable insect repellent measures.

Description

Technical Field

[0004] ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, an optimal pest control measure has not been sufficiently proposed based on the floor plan and lifestyle of the user's house, and there is room for improvement.

[0005] The system according to the embodiment aims to propose an optimal pest control measure based on the floor plan and lifestyle of the user's house.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a suggestion unit, a collaboration unit, and an analysis unit. The collection unit collects information such as the user's house layout, family structure, lifestyle, preferences, and daily habits. The suggestion unit analyzes the information collected by the collection unit and proposes the most suitable insect repellent items and measures. The collaboration unit works with IoT devices to analyze the overall living patterns and insect appearance patterns of the house. The analysis unit analyzes photos and blueprints of the house uploaded by the user and proposes the most suitable insect repellent measures. [Effects of the Invention]

[0007] The system according to this embodiment can propose the optimal pest control measures based on the user's home 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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The insect repellent suggestion system according to an embodiment of the present invention is a system that suggests optimal insect repellent items and measures based on the user's house layout, family structure, and lifestyle. This insect repellent suggestion system collects information such as the user's house layout, family structure, lifestyle, preferences, and daily habits, and suggests optimal insect repellent items and measures. Furthermore, it works in conjunction with IoT devices to analyze the living patterns and insect appearance patterns of the entire house, minimizing the user's trouble with insects. It also suggests optimal insect repellent measures from photos of the house and blueprints uploaded by the user, and unifies the effectiveness of insect repellent measures learned from other users with similar information, such as the user's lifestyle and house layout, and suggests optimal insect repellent measures based on this. For example, the insect repellent suggestion system collects information through questionnaires regarding the user's house layout, family structure, and lifestyle. This information is input into the insect repellent suggestion system. Next, the insect repellent suggestion system suggests optimal insect repellent items and measures based on the collected information. For example, it suggests which insect repellent items would be most effective in which rooms, depending on the house layout and family structure. Furthermore, the system suggests the most effective timing for implementing pest control measures based on lifestyle and preferences. The pest control suggestion system also uses IoT devices to monitor temperature, humidity, and light conditions inside the home, analyzing insect appearance patterns. Based on this analysis, it proposes the optimal pest control measures. The system also analyzes user-uploaded photos and blueprints of the house to suggest the most suitable measures. For example, a user can upload photos and blueprints of their house to the system, which then analyzes them and proposes the optimal measures. Additionally, the system integrates information such as the user's lifestyle and house layout with the effectiveness of pest control measures learned from other users with similar information, and proposes the most suitable measures based on this integration. This allows the system to create a living environment where users can live comfortably without being troubled by insects. For example, by suggesting optimal pest control items and measures tailored to the house layout and lifestyle, the system minimizes insect appearances and creates a comfortable living environment.Furthermore, the pest control suggestion system can implement more effective pest control measures by working in conjunction with IoT devices to analyze the lifestyle patterns and insect appearance patterns of the entire house. In addition, the pest control suggestion system provides customized pest control measures tailored to the user's lifestyle and house layout by suggesting the optimal pest control measures based on photos and blueprints of the house uploaded by the user. As a result, the pest control suggestion system can propose the most suitable pest control measures based on the user's house information, minimizing insect appearances.

[0029] The insect control suggestion system according to this embodiment comprises a collection unit, a suggestion unit, a coordination unit, and an analysis unit. The collection unit collects information such as the user's house layout, family structure, lifestyle, preferences, and daily habits. The collection unit collects information, for example, through questionnaires regarding the house layout, family structure, and lifestyle. The suggestion unit analyzes the information collected by the collection unit and proposes the most suitable insect control items and measures. For example, the suggestion unit suggests which insect control items should be placed in which rooms to be most effective, depending on the house layout and family structure. The suggestion unit also suggests when it would be most effective to implement insect control measures, depending on the lifestyle and preferences. The coordination unit works in conjunction with IoT devices to analyze the living patterns and insect appearance patterns of the entire house. For example, the coordination unit uses IoT devices to monitor the temperature, humidity, and light conditions inside the house and analyzes the insect appearance patterns. The analysis unit analyzes photos and blueprints of the house uploaded by the user and proposes the most suitable insect control measures. The analysis unit uploads, for example, photos or blueprints of the house to the system, which then analyzes them and proposes the optimal pest control measures. As a result, the pest control proposal system according to this embodiment can propose the optimal pest control measures based on the user's house information, minimizing the appearance of insects.

[0030] The data collection unit collects information such as the user's home layout, family structure, lifestyle, preferences, and daily habits. Specifically, the unit provides users with detailed questionnaires to collect information on the home layout, family structure, and lifestyle. For example, the home layout is a detailed drawing showing the arrangement and size of each room, the location of windows and doors, etc., allowing for an accurate understanding of the house's structure. Regarding family structure, information such as the number and ages of people living in the house, and whether or not they have pets is collected. Regarding lifestyle, information such as family members' wake-up and bedtime, meal times, and frequency of going out is collected. Furthermore, information on preferences and daily habits is also collected, such as scents that family members like and dislike, cleaning frequency and methods, and how often windows are opened and closed. This information is provided by users by entering it into online forms or through a dedicated application. The data collection unit centrally manages this information and stores it in a database. The collected information is used for subsequent analysis and recommendations, so accurate and detailed information is required. The data collection unit takes security measures such as data encryption and access restrictions to protect user privacy. This allows the data collection unit to efficiently and securely collect information about the user's home, improving the overall accuracy and reliability of the system.

[0031] The Proposal Department analyzes the information collected by the Data Collection Department and proposes the most suitable insect repellent items and measures. Specifically, the Proposal Department suggests which insect repellent items should be placed in which rooms for optimal effect, based on the house layout and family structure. For example, it might suggest placing insect repellent spray in the living room, insect nets in the bedroom, and insect repellent sheets in the kitchen. The Proposal Department also suggests the most effective timing for implementing insect repellent measures, based on lifestyle and preferences. For example, it might suggest using insect repellent spray when family members are out, or installing insect nets when windows are open. The Proposal Department uses AI to analyze the collected information and automatically generate optimal insect repellent measures. The AI ​​learns effective insect repellent measures based on past data and statistical information, and provides customized suggestions for each user. Furthermore, the Proposal Department collects user feedback and continuously improves the accuracy and effectiveness of its suggestions. For example, by providing feedback on the results of users implementing the suggested insect repellent measures, the AI ​​can continue to learn from this data, enabling more effective suggestions. This allows the proposal department to provide optimal pest control measures tailored to the user's needs, minimizing the appearance of insects.

[0032] The integrated system works in conjunction with IoT devices to analyze the lifestyle patterns and insect appearance patterns of the entire house. Specifically, the integrated system uses IoT devices such as temperature sensors, humidity sensors, and light sensors installed in the house to monitor environmental data in real time. For example, temperature sensors measure the temperature of each room in the house, humidity sensors measure humidity, and light sensors measure the brightness of the room and the amount of sunlight entering. This data is collected by the integrated system and used to analyze insect appearance patterns. The integrated system uses AI to analyze this data and identify insect appearance patterns. For example, if it is found that specific temperature, humidity, or light conditions affect insect appearance, it can propose insect control measures based on those conditions. Furthermore, the integrated system analyzes the lifestyle patterns within the house and identifies times and locations with a high risk of insect appearance. For example, if it is found that insects are more likely to appear during meal times or when the family is in the bedroom, it can propose implementing insect control measures to coincide with those times. In this way, the integrated system can work in conjunction with IoT devices to analyze environmental data of the entire house and provide effective insect control measures to minimize the risk of insect appearance.

[0033] The analysis unit analyzes photos and blueprints of homes uploaded by users to propose optimal pest control measures. Specifically, the analysis unit uses AI to analyze photos and blueprints of homes uploaded by users to the system. The AI ​​uses image recognition technology to analyze the structure and layout of the house, identifying places where insects are likely to enter and where pest control items should be placed. For example, it analyzes the location of windows and doors, the location of vents, and the arrangement of furniture to identify routes through which insects are likely to enter. Furthermore, by analyzing the blueprint, it can gain a detailed understanding of the house's structure and layout and propose optimal pest control measures. Based on this information, the analysis unit proposes specific pest control measures to the user. For example, it may suggest installing insect nets around windows and doors, or attaching insect filters to vents. In addition, the analysis unit collects user feedback and continuously improves the accuracy and effectiveness of its suggestions. For example, by providing feedback on the results of users implementing the proposed pest control measures, the AI ​​can continue to learn from this data, enabling more effective suggestions. As a result, the analysis unit can propose optimal pest control measures based on the user's home information, minimizing insect infestations.

[0034] The data collection unit can collect information such as house floor plans, family structure, and lifestyle. For example, the data collection unit can collect house floor plans and obtain information such as the number, arrangement, and size of rooms. It can also collect information on family structure and obtain information such as the number of family members, their ages, and genders. Furthermore, it can collect information on lifestyle and obtain information such as daily activity patterns, hobbies, and work hours. By collecting information on house floor plans, family structure, and lifestyle, it is possible to propose more accurate pest control measures. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input house floor plans into AI and have the AI ​​analyze information such as the number, arrangement, and size of rooms.

[0035] The suggestion unit can suggest which insect repellent items would be most effective in which rooms based on the collected information. For example, depending on the layout of the house and the family structure, the suggestion unit might suggest placing insect repellent spray in the living room. It could also suggest placing insect nets in the bedroom. Furthermore, it could suggest placing insect traps in the kitchen. This allows the suggestion unit to suggest the most effective placement of insect repellent items based on the collected information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit could input the collected information into an AI and have the AI ​​suggest the optimal placement of insect repellent items.

[0036] The suggestion function can propose the most effective timing for implementing pest control measures based on the user's lifestyle and preferences. For example, if the user is active at night, the suggestion function might suggest using insect repellent spray at night. If the user is active during the day, the suggestion function might suggest installing insect nets during the day. Furthermore, if the user cleans on weekends, the suggestion function might suggest installing insect traps on weekends. This allows for the suggestion of pest control measures at a time that suits the user's lifestyle and preferences. Some or all of the above processing in the suggestion function may be performed using AI, for example, or not. For example, the suggestion function could input information about the user's lifestyle and preferences into the AI ​​and have the AI ​​suggest the optimal timing for pest control measures.

[0037] The integrated unit can use IoT devices to monitor temperature, humidity, and light conditions inside a house and analyze insect appearance patterns. For example, the integrated unit can use IoT devices to monitor the temperature inside the house and analyze that insect appearances increase when the temperature is high. It can also monitor humidity and analyze that insect appearances increase when the humidity is high. Furthermore, it can monitor light conditions and analyze how the intensity and type of light affect insect appearances. By monitoring the environment inside the house using IoT devices and analyzing insect appearance patterns, it is possible to propose effective pest control measures. Some or all of the above processing in the integrated unit may be performed using AI, for example, or without AI. For example, the integrated unit can input data acquired from IoT devices into AI and have the AI ​​perform the analysis of insect appearance patterns.

[0038] The analysis unit can analyze photos of a house and blueprints to propose the optimal pest control measures. For example, the analysis unit can upload photos of a house to the system, which will then analyze them and propose the optimal pest control measures. It can also upload blueprints to the system, which will then analyze them and propose the optimal pest control measures. Furthermore, the analysis unit can combine and analyze photos of the house and blueprints to propose even more accurate pest control measures. This allows the system to propose the optimal pest control measures by analyzing photos of the house and blueprints. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input photos of the house and blueprints into an AI and have the AI ​​propose the optimal pest control measures.

[0039] The data collection unit can analyze the user's past pest control history and select the optimal information collection method. For example, the data collection unit can analyze the effectiveness of pest control items the user has used in the past and prioritize collecting information on items that were highly effective. The data collection unit can also analyze the timing of pest control measures the user has implemented in the past and collect information at the optimal time. Furthermore, the data collection unit can collect information on effective pest control measures based on the success rate of pest control measures the user has tried in the past. In this way, the optimal information collection method can be selected by analyzing the user's past pest control history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past pest control history into AI and have the AI ​​select the optimal information collection method.

[0040] The data collection unit can filter information based on the user's current living situation and areas of interest. For example, if the user is currently raising children, the data collection unit will prioritize collecting information on insect repellent items that are safe for children. If the user has pets, the data collection unit can also collect information on insect repellent measures that are not harmful to pets. Furthermore, if the user enjoys outdoor activities, the data collection unit can collect information on insect repellent items that can be used outdoors. This allows for the collection of highly relevant information by filtering it based on the user's current living situation and areas of interest. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input information about the user's current living situation and areas of interest into an AI and have the AI ​​perform the information filtering.

[0041] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location. For example, if the user lives in an urban area, the data collection unit can collect information on effective pest control measures in urban areas. It can also collect information on effective pest control measures in rural areas if the user lives in a rural area. Furthermore, if the user is traveling, the data collection unit can collect information on pest control measures specific to the travel destination. This allows for the priority collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into an AI and have the AI ​​collect highly relevant information.

[0042] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, the data collection unit can collect information about pest control items that the user has shared on social media. The data collection unit can also analyze posts from pest control experts that the user follows and collect relevant information. Furthermore, the data collection unit can analyze posts from pest control communities that the user participates in and collect relevant information. In this way, relevant information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's social media activity into AI and have the AI ​​collect relevant information.

[0043] The suggestion function can adjust the level of detail in its suggestions based on the importance of the insect repellent items. For example, it can provide suggestions with detailed explanations for highly important insect repellent items, and suggestions with concise explanations for less important items. Furthermore, it can adjust the order of suggestions according to their importance, prioritizing the provision of important information. This allows the suggestion function to prioritize providing information important to the user by adjusting the level of detail in suggestions based on the importance of the insect repellent items. Some or all of the above processing in the suggestion function may be performed using AI, for example, or without AI. For example, the suggestion function can input the importance of the insect repellent items into the AI ​​and have the AI ​​adjust the level of detail in the suggestions.

[0044] The suggestion unit can apply different suggestion algorithms depending on the category of the insect repellent item when making suggestions. For example, for indoor insect repellent items, the suggestion unit can apply a suggestion algorithm specialized for the indoor environment. Similarly, for outdoor insect repellent items, the suggestion unit can apply a suggestion algorithm specialized for the outdoor environment. Furthermore, for pet insect repellent items, the suggestion unit can apply a suggestion algorithm that takes pet safety into consideration. This allows for more effective suggestions by applying different suggestion algorithms depending on the category of the insect repellent item. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the category of the insect repellent item into the AI ​​and have the AI ​​apply the suggestion algorithm.

[0045] The suggestion unit can determine the priority of suggestions based on the timing of use of the insect repellent items. For example, the suggestion unit will prioritize suggesting insect repellent items that are due to be used soon. It can also postpone suggesting insect repellent items that are due to be used far in the future. Furthermore, the suggestion unit can adjust the level of detail in the suggestions according to the timing of use and provide the necessary information. By prioritizing suggestions based on the timing of use of insect repellent items, the system can provide users with the necessary information at the appropriate time. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the timing of use of insect repellent items into the AI ​​and have the AI ​​determine the priority of suggestions.

[0046] The suggestion unit can adjust the order of suggestions based on the relevance of the insect repellent items. For example, the suggestion unit will prioritize suggesting highly relevant insect repellent items. It can also postpone suggesting less relevant insect repellent items. Furthermore, the suggestion unit can adjust the level of detail of the suggestions according to their relevance, providing only the necessary information. This allows the user to receive the most relevant information by adjusting the order of suggestions based on the relevance of the insect repellent items. Some or all of the above processing in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input the relevance of the insect repellent items into the AI ​​and have the AI ​​adjust the order of suggestions.

[0047] The integrated unit can monitor the temperature, humidity, and light conditions inside the house in real time during integration and analyze insect appearance patterns. For example, if the temperature is high, the integrated unit will suggest measures to lower the temperature, as insect appearances increase. Similarly, if the humidity is high, the integrated unit will suggest measures to lower the humidity, as insect appearances also increase. Furthermore, since light conditions affect insect appearances, the integrated unit can also suggest adjusting the light. In this way, by monitoring the environment inside the house in real time, insect appearance patterns can be analyzed and effective pest control measures can be proposed. Some or all of the above processing in the integrated unit may be performed using AI, for example, or without AI. For example, the integrated unit can input data acquired from IoT devices into AI and have the AI ​​perform the analysis of insect appearance patterns.

[0048] The integration unit can optimize the operation of IoT devices based on the user's lifestyle patterns during integration. For example, if the user is active at night, the integration unit can suggest effective insect control measures for the night. It can also suggest effective insect control measures for the daytime if the user is active during the day. Furthermore, the integration unit can adjust the timing of IoT device operation according to the user's lifestyle patterns. This allows for the provision of effective insect control measures by optimizing IoT device operation based on the user's lifestyle patterns. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input information about the user's lifestyle patterns into the AI ​​and have the AI ​​optimize the operation of the IoT devices.

[0049] The integration unit can, during integration, place different IoT devices in different rooms within the house and analyze insect appearance patterns in detail. For example, the integration unit can place an IoT device specifically for the living room and analyze insect appearance patterns in the living room. It can also place an IoT device specifically for the kitchen and analyze insect appearance patterns in the kitchen. Furthermore, it can place an IoT device specifically for the bedroom and analyze insect appearance patterns in the bedroom. In this way, by placing different IoT devices in different rooms within the house, it is possible to analyze insect appearance patterns in detail and provide effective pest control measures. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input data acquired from IoT devices placed in each room into the AI ​​and have the AI ​​perform the analysis of insect appearance patterns.

[0050] The integration unit can, upon integration, connect with the user's smartphone or tablet to notify them of insect sightings. For example, the integration unit can notify the user's smartphone of insect sightings in real time. It can also notify the user's tablet of insect sightings in real time. Furthermore, the integration unit can suggest pest control measures based on insect sighting patterns to the user's smartphone or tablet. In this way, by connecting with the user's smartphone or tablet, it is possible to notify them of insect sightings in real time and provide effective pest control measures. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input data acquired from the smartphone or tablet into the AI ​​and have the AI ​​execute the notification of insect sightings.

[0051] The analysis unit can improve the accuracy of its analysis based on the level of detail of the house photos and blueprints during the analysis. For example, the analysis unit can perform a detailed analysis using high-resolution house photos. It can also perform a highly accurate analysis using a detailed blueprint. Furthermore, the analysis unit can combine house photos and blueprints to perform an even more accurate analysis. This allows for the provision of more effective pest control measures by improving the accuracy of the analysis based on the level of detail of the house photos and blueprints. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input house photos and blueprints into AI and have the AI ​​perform the analysis accuracy improvement.

[0052] The analysis unit can optimize its analysis algorithm based on the user's lifestyle during analysis. For example, if the user is active at night, the analysis unit can apply an analysis algorithm specialized for nighttime lifestyles. Similarly, if the user is active during the day, the analysis unit can apply an analysis algorithm specialized for daytime lifestyles. Furthermore, the analysis unit can adjust the analysis algorithm according to the user's lifestyle and propose the most suitable pest control measures. This allows for the provision of more effective pest control measures by optimizing the analysis algorithm based on the user's lifestyle. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information about the user's lifestyle into the AI ​​and have the AI ​​optimize the analysis algorithm.

[0053] The analysis unit can apply different analysis methods to different parts of the house during analysis and propose the optimal pest control measures. For example, the analysis unit can apply an analysis method specifically for the living room and propose the optimal pest control measures for the living room. It can also apply an analysis method specifically for the kitchen and propose the optimal pest control measures for the kitchen. Furthermore, it can apply an analysis method specifically for the bedroom and propose the optimal pest control measures for the bedroom. In this way, by applying different analysis methods to different parts of the house, more effective pest control measures can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information about each part of the house into the AI ​​and have the AI ​​perform the application of different analysis methods.

[0054] The analysis unit can improve the accuracy of its analysis by referring to the effectiveness of the user's past pest control measures. For example, the analysis unit can refer to the effectiveness of pest control items the user has used in the past and perform the analysis based on the items that were most effective. The analysis unit can also refer to the success rate of pest control measures the user has implemented in the past and perform the analysis based on those measures with high success rates. Furthermore, the analysis unit can adjust its analysis algorithm based on the user's past pest control data to perform a more accurate analysis. This improves the accuracy of the analysis by referring to the effectiveness of the user's past pest control measures, thereby providing more effective pest control measures. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past pest control data into AI and have the AI ​​perform the analysis to improve accuracy.

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

[0056] The pest control suggestion system can analyze a user's past pest control history and suggest the most suitable pest control items. For example, it can analyze 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 pest control measures the user has taken in the past and suggest pest control items at the optimal time. Furthermore, it can suggest effective pest control items based on the success rate of pest control measures the user has tried in the past. In this way, by analyzing the user's past pest control history, the system can suggest the most suitable pest control items. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past pest control history into AI and have the AI ​​suggest the most suitable pest control items.

[0057] The insect repellent suggestion system can suggest insect repellent items while considering the user's geographical location. For example, if the user lives in an urban area, it can suggest insect repellent items that are effective in urban areas. If the user lives in a rural area, it can suggest insect repellent items that are effective in rural areas. Furthermore, if the user is traveling, it can suggest insect repellent items that are specific to the region they are traveling to. In this way, by considering the user's geographical location, it can suggest highly relevant insect repellent items. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's geographical location information into the AI ​​and have the AI ​​suggest highly relevant insect repellent items.

[0058] The pest control suggestion system can analyze a user's social media activity and suggest relevant pest control items. For example, it can collect information about pest control items shared by the user on social media and suggest items based on that information. It can also analyze posts from pest control experts the user follows and suggest relevant pest control items. Furthermore, it can analyze posts from pest control communities the user participates in and suggest relevant pest control items. In this way, by analyzing the user's social media activity, it can suggest relevant pest control items. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's social media activity into AI and have the AI ​​suggest relevant pest control items.

[0059] The insect repellent suggestion system can suggest insect repellent items based on the user's current lifestyle and areas of interest. For example, if the user is currently raising children, it can suggest insect repellent items that are safe for children. If the user has pets, it can also suggest insect repellent items that are harmless to pets. Furthermore, if the user enjoys outdoor activities, it can suggest insect repellent items that can be used outdoors. In this way, by suggesting insect repellent items based on the user's current lifestyle and areas of interest, it can provide highly relevant insect repellent items. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input information about the user's current lifestyle and areas of interest into the AI ​​and have the AI ​​suggest highly relevant insect repellent items.

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

[0061] Step 1: The data collection unit collects information such as the user's home layout, family structure, lifestyle, preferences, and daily habits. The data collection unit collects information, for example, through questionnaires regarding home layouts, family structure, and lifestyle. Step 2: The proposal department analyzes the information collected by the data collection department and proposes the most suitable insect repellent items and measures. For example, the proposal department suggests which insect repellent items should be placed in which rooms to be most effective, based on the house layout and family structure. It also suggests the most effective timing for implementing insect repellent measures, based on lifestyle and preferences. Step 3: The integration unit works with IoT devices to analyze the overall living patterns and insect appearance patterns of the house. For example, the integration unit uses IoT devices to monitor the temperature, humidity, and light conditions inside the house and analyzes insect appearance patterns. Step 4: The analysis unit analyzes the house photos and blueprints uploaded by the user and proposes the optimal pest control measures. For example, the user uploads house photos and blueprints to the system, which then analyzes them and proposes the optimal pest control measures.

[0062] (Example of form 2) The insect repellent suggestion system according to an embodiment of the present invention is a system that suggests optimal insect repellent items and measures based on the user's house layout, family structure, and lifestyle. This insect repellent suggestion system collects information such as the user's house layout, family structure, lifestyle, preferences, and daily habits, and suggests optimal insect repellent items and measures. Furthermore, it works in conjunction with IoT devices to analyze the living patterns and insect appearance patterns of the entire house, minimizing the user's trouble with insects. It also suggests optimal insect repellent measures from photos of the house and blueprints uploaded by the user, and unifies the effectiveness of insect repellent measures learned from other users with similar information, such as the user's lifestyle and house layout, and suggests optimal insect repellent measures based on this. For example, the insect repellent suggestion system collects information through questionnaires regarding the user's house layout, family structure, and lifestyle. This information is input into the insect repellent suggestion system. Next, the insect repellent suggestion system suggests optimal insect repellent items and measures based on the collected information. For example, it suggests which insect repellent items would be most effective in which rooms, depending on the house layout and family structure. Furthermore, the system suggests the most effective timing for implementing pest control measures based on lifestyle and preferences. The pest control suggestion system also uses IoT devices to monitor temperature, humidity, and light conditions inside the home, analyzing insect appearance patterns. Based on this analysis, it proposes the optimal pest control measures. The system also analyzes user-uploaded photos and blueprints of the house to suggest the most suitable measures. For example, a user can upload photos and blueprints of their house to the system, which then analyzes them and proposes the optimal measures. Additionally, the system integrates information such as the user's lifestyle and house layout with the effectiveness of pest control measures learned from other users with similar information, and proposes the most suitable measures based on this integration. This allows the system to create a living environment where users can live comfortably without being troubled by insects. For example, by suggesting optimal pest control items and measures tailored to the house layout and lifestyle, the system minimizes insect appearances and creates a comfortable living environment.Furthermore, the pest control suggestion system can implement more effective pest control measures by working in conjunction with IoT devices to analyze the lifestyle patterns and insect appearance patterns of the entire house. In addition, the pest control suggestion system provides customized pest control measures tailored to the user's lifestyle and house layout by suggesting the optimal pest control measures based on photos and blueprints of the house uploaded by the user. As a result, the pest control suggestion system can propose the most suitable pest control measures based on the user's house information, minimizing insect appearances.

[0063] The insect control suggestion system according to this embodiment comprises a collection unit, a suggestion unit, a coordination unit, and an analysis unit. The collection unit collects information such as the user's house layout, family structure, lifestyle, preferences, and daily habits. The collection unit collects information, for example, through questionnaires regarding the house layout, family structure, and lifestyle. The suggestion unit analyzes the information collected by the collection unit and proposes the most suitable insect control items and measures. For example, the suggestion unit suggests which insect control items should be placed in which rooms to be most effective, depending on the house layout and family structure. The suggestion unit also suggests when it would be most effective to implement insect control measures, depending on the lifestyle and preferences. The coordination unit works in conjunction with IoT devices to analyze the living patterns and insect appearance patterns of the entire house. For example, the coordination unit uses IoT devices to monitor the temperature, humidity, and light conditions inside the house and analyzes the insect appearance patterns. The analysis unit analyzes photos and blueprints of the house uploaded by the user and proposes the most suitable insect control measures. The analysis unit uploads, for example, photos or blueprints of the house to the system, which then analyzes them and proposes the optimal pest control measures. As a result, the pest control proposal system according to this embodiment can propose the optimal pest control measures based on the user's house information, minimizing the appearance of insects.

[0064] The data collection unit collects information such as the user's home layout, family structure, lifestyle, preferences, and daily habits. Specifically, the unit provides users with detailed questionnaires to collect information on the home layout, family structure, and lifestyle. For example, the home layout is a detailed drawing showing the arrangement and size of each room, the location of windows and doors, etc., allowing for an accurate understanding of the house's structure. Regarding family structure, information such as the number and ages of people living in the house, and whether or not they have pets is collected. Regarding lifestyle, information such as family members' wake-up and bedtime, meal times, and frequency of going out is collected. Furthermore, information on preferences and daily habits is also collected, such as scents that family members like and dislike, cleaning frequency and methods, and how often windows are opened and closed. This information is provided by users by entering it into online forms or through a dedicated application. The data collection unit centrally manages this information and stores it in a database. The collected information is used for subsequent analysis and recommendations, so accurate and detailed information is required. The data collection unit takes security measures such as data encryption and access restrictions to protect user privacy. This allows the data collection unit to efficiently and securely collect information about the user's home, improving the overall accuracy and reliability of the system.

[0065] The Proposal Department analyzes the information collected by the Data Collection Department and proposes the most suitable insect repellent items and measures. Specifically, the Proposal Department suggests which insect repellent items should be placed in which rooms for optimal effect, based on the house layout and family structure. For example, it might suggest placing insect repellent spray in the living room, insect nets in the bedroom, and insect repellent sheets in the kitchen. The Proposal Department also suggests the most effective timing for implementing insect repellent measures, based on lifestyle and preferences. For example, it might suggest using insect repellent spray when family members are out, or installing insect nets when windows are open. The Proposal Department uses AI to analyze the collected information and automatically generate optimal insect repellent measures. The AI ​​learns effective insect repellent measures based on past data and statistical information, and provides customized suggestions for each user. Furthermore, the Proposal Department collects user feedback and continuously improves the accuracy and effectiveness of its suggestions. For example, by providing feedback on the results of users implementing the suggested insect repellent measures, the AI ​​can continue to learn from this data, enabling more effective suggestions. This allows the proposal department to provide optimal pest control measures tailored to the user's needs, minimizing the appearance of insects.

[0066] The integrated system works in conjunction with IoT devices to analyze the lifestyle patterns and insect appearance patterns of the entire house. Specifically, the integrated system uses IoT devices such as temperature sensors, humidity sensors, and light sensors installed in the house to monitor environmental data in real time. For example, temperature sensors measure the temperature of each room in the house, humidity sensors measure humidity, and light sensors measure the brightness of the room and the amount of sunlight entering. This data is collected by the integrated system and used to analyze insect appearance patterns. The integrated system uses AI to analyze this data and identify insect appearance patterns. For example, if it is found that specific temperature, humidity, or light conditions affect insect appearance, it can propose insect control measures based on those conditions. Furthermore, the integrated system analyzes the lifestyle patterns within the house and identifies times and locations with a high risk of insect appearance. For example, if it is found that insects are more likely to appear during meal times or when the family is in the bedroom, it can propose implementing insect control measures to coincide with those times. In this way, the integrated system can work in conjunction with IoT devices to analyze environmental data of the entire house and provide effective insect control measures to minimize the risk of insect appearance.

[0067] The analysis unit analyzes photos and blueprints of homes uploaded by users to propose optimal pest control measures. Specifically, the analysis unit uses AI to analyze photos and blueprints of homes uploaded by users to the system. The AI ​​uses image recognition technology to analyze the structure and layout of the house, identifying places where insects are likely to enter and where pest control items should be placed. For example, it analyzes the location of windows and doors, the location of vents, and the arrangement of furniture to identify routes through which insects are likely to enter. Furthermore, by analyzing the blueprint, it can gain a detailed understanding of the house's structure and layout and propose optimal pest control measures. Based on this information, the analysis unit proposes specific pest control measures to the user. For example, it may suggest installing insect nets around windows and doors, or attaching insect filters to vents. In addition, the analysis unit collects user feedback and continuously improves the accuracy and effectiveness of its suggestions. For example, by providing feedback on the results of users implementing the proposed pest control measures, the AI ​​can continue to learn from this data, enabling more effective suggestions. As a result, the analysis unit can propose optimal pest control measures based on the user's home information, minimizing insect infestations.

[0068] The data collection unit can collect information such as house floor plans, family structure, and lifestyle. For example, the data collection unit can collect house floor plans and obtain information such as the number, arrangement, and size of rooms. It can also collect information on family structure and obtain information such as the number of family members, their ages, and genders. Furthermore, it can collect information on lifestyle and obtain information such as daily activity patterns, hobbies, and work hours. By collecting information on house floor plans, family structure, and lifestyle, it is possible to propose more accurate pest control measures. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input house floor plans into AI and have the AI ​​analyze information such as the number, arrangement, and size of rooms.

[0069] The suggestion unit can suggest which insect repellent items would be most effective in which rooms based on the collected information. For example, depending on the layout of the house and the family structure, the suggestion unit might suggest placing insect repellent spray in the living room. It could also suggest placing insect nets in the bedroom. Furthermore, it could suggest placing insect traps in the kitchen. This allows the suggestion unit to suggest the most effective placement of insect repellent items based on the collected information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit could input the collected information into an AI and have the AI ​​suggest the optimal placement of insect repellent items.

[0070] The suggestion function can propose the most effective timing for implementing pest control measures based on the user's lifestyle and preferences. For example, if the user is active at night, the suggestion function might suggest using insect repellent spray at night. If the user is active during the day, the suggestion function might suggest installing insect nets during the day. Furthermore, if the user cleans on weekends, the suggestion function might suggest installing insect traps on weekends. This allows for the suggestion of pest control measures at a time that suits the user's lifestyle and preferences. Some or all of the above processing in the suggestion function may be performed using AI, for example, or not. For example, the suggestion function could input information about the user's lifestyle and preferences into the AI ​​and have the AI ​​suggest the optimal timing for pest control measures.

[0071] The integrated unit can use IoT devices to monitor temperature, humidity, and light conditions inside a house and analyze insect appearance patterns. For example, the integrated unit can use IoT devices to monitor the temperature inside the house and analyze that insect appearances increase when the temperature is high. It can also monitor humidity and analyze that insect appearances increase when the humidity is high. Furthermore, it can monitor light conditions and analyze how the intensity and type of light affect insect appearances. By monitoring the environment inside the house using IoT devices and analyzing insect appearance patterns, it is possible to propose effective pest control measures. Some or all of the above processing in the integrated unit may be performed using AI, for example, or without AI. For example, the integrated unit can input data acquired from IoT devices into AI and have the AI ​​perform the analysis of insect appearance patterns.

[0072] The analysis unit can analyze photos of a house and blueprints to propose the optimal pest control measures. For example, the analysis unit can upload photos of a house to the system, which will then analyze them and propose the optimal pest control measures. It can also upload blueprints to the system, which will then analyze them and propose the optimal pest control measures. Furthermore, the analysis unit can combine and analyze photos of the house and blueprints to propose even more accurate pest control measures. This allows the system to propose the optimal pest control measures by analyzing photos of the house and blueprints. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input photos of the house and blueprints into an AI and have the AI ​​propose the optimal pest control measures.

[0073] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of information collection and collect information when the user is relaxed. Furthermore, if the user is relaxed, the data collection unit can collect detailed information and provide it at a time when the user is likely to be interested. Additionally, if the user is busy, the data collection unit can temporarily stop information collection and resume it when the user has calmed down. This allows for information collection at the optimal time for the user by adjusting the timing of information collection based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0074] The data collection unit can analyze the user's past pest control history and select the optimal information collection method. For example, the data collection unit can analyze the effectiveness of pest control items the user has used in the past and prioritize collecting information on items that were highly effective. The data collection unit can also analyze the timing of pest control measures the user has implemented in the past and collect information at the optimal time. Furthermore, the data collection unit can collect information on effective pest control measures based on the success rate of pest control measures the user has tried in the past. In this way, the optimal information collection method can be selected by analyzing the user's past pest control history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past pest control history into AI and have the AI ​​select the optimal information collection method.

[0075] The data collection unit can filter information based on the user's current living situation and areas of interest. For example, if the user is currently raising children, the data collection unit will prioritize collecting information on insect repellent items that are safe for children. If the user has pets, the data collection unit can also collect information on insect repellent measures that are not harmful to pets. Furthermore, if the user enjoys outdoor activities, the data collection unit can collect information on insect repellent items that can be used outdoors. This allows for the collection of highly relevant information by filtering it based on the user's current living situation and areas of interest. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input information about the user's current living situation and areas of interest into an AI and have the AI ​​perform the information filtering.

[0076] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit will prioritize collecting information that provides a sense of security. Similarly, if the user is excited, the data collection unit can prioritize collecting information that is of interest. Furthermore, if the user is relaxed, the data collection unit can prioritize collecting detailed information. This allows for the priority collection of information important to the user by prioritizing information based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, or not. For example, the data collection unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0077] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location. For example, if the user lives in an urban area, the data collection unit can collect information on effective pest control measures in urban areas. It can also collect information on effective pest control measures in rural areas if the user lives in a rural area. Furthermore, if the user is traveling, the data collection unit can collect information on pest control measures specific to the travel destination. This allows for the priority collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into an AI and have the AI ​​collect highly relevant information.

[0078] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, the data collection unit can collect information about pest control items that the user has shared on social media. The data collection unit can also analyze posts from pest control experts that the user follows and collect relevant information. Furthermore, the data collection unit can analyze posts from pest control communities that the user participates in and collect relevant information. In this way, relevant information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's social media activity into AI and have the AI ​​collect relevant information.

[0079] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is feeling anxious, the suggestion unit can present suggestions in a way that provides reassurance. If the user is excited, the suggestion unit can present suggestions in a way that attracts interest. Furthermore, if the user is relaxed, the suggestion unit can present suggestions in a way that includes detailed information. By adjusting the way suggestions are presented based on the user's emotions, the system can provide the most suitable suggestions for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0080] The suggestion function can adjust the level of detail in its suggestions based on the importance of the insect repellent items. For example, it can provide suggestions with detailed explanations for highly important insect repellent items, and suggestions with concise explanations for less important items. Furthermore, it can adjust the order of suggestions according to their importance, prioritizing the provision of important information. This allows the suggestion function to prioritize providing information important to the user by adjusting the level of detail in suggestions based on the importance of the insect repellent items. Some or all of the above processing in the suggestion function may be performed using AI, for example, or without AI. For example, the suggestion function can input the importance of the insect repellent items into the AI ​​and have the AI ​​adjust the level of detail in the suggestions.

[0081] The suggestion unit can apply different suggestion algorithms depending on the category of the insect repellent item when making suggestions. For example, for indoor insect repellent items, the suggestion unit can apply a suggestion algorithm specialized for the indoor environment. Similarly, for outdoor insect repellent items, the suggestion unit can apply a suggestion algorithm specialized for the outdoor environment. Furthermore, for pet insect repellent items, the suggestion unit can apply a suggestion algorithm that takes pet safety into consideration. This allows for more effective suggestions by applying different suggestion algorithms depending on the category of the insect repellent item. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the category of the insect repellent item into the AI ​​and have the AI ​​apply the suggestion algorithm.

[0082] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit will provide a short, concise suggestion. If the user is relaxed, the suggestion unit can provide a longer suggestion with detailed explanations. Furthermore, if the user is excited, the suggestion unit can provide a suggestion with visually stimulating effects. By adjusting the length of the suggestion based on the user's emotions, the system can provide the most suitable suggestion for the user. 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. For example, the suggestion unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0083] The suggestion unit can determine the priority of suggestions based on the timing of use of the insect repellent items. For example, the suggestion unit will prioritize suggesting insect repellent items that are due to be used soon. It can also postpone suggesting insect repellent items that are due to be used far in the future. Furthermore, the suggestion unit can adjust the level of detail in the suggestions according to the timing of use and provide the necessary information. By prioritizing suggestions based on the timing of use of insect repellent items, the system can provide users with the necessary information at the appropriate time. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the timing of use of insect repellent items into the AI ​​and have the AI ​​determine the priority of suggestions.

[0084] The suggestion unit can adjust the order of suggestions based on the relevance of the insect repellent items. For example, the suggestion unit will prioritize suggesting highly relevant insect repellent items. It can also postpone suggesting less relevant insect repellent items. Furthermore, the suggestion unit can adjust the level of detail of the suggestions according to their relevance, providing only the necessary information. This allows the user to receive the most relevant information by adjusting the order of suggestions based on the relevance of the insect repellent items. Some or all of the above processing in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input the relevance of the insect repellent items into the AI ​​and have the AI ​​adjust the order of suggestions.

[0085] The integration unit can estimate the user's emotions and select IoT devices to integrate with based on the estimated emotions. For example, if the user is feeling anxious, the integration unit can select IoT devices that provide a sense of security. It can also select IoT devices that pique the user's interest if the user is excited. Furthermore, if the user is relaxed, the integration unit can select IoT devices that provide detailed information. This allows for the selection of the optimal IoT device based on the user's emotions, thereby providing the user with the most effective pest control solution. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the integration unit may be performed using AI, or not. For example, the integration unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0086] The integrated unit can monitor the temperature, humidity, and light conditions inside the house in real time during integration and analyze insect appearance patterns. For example, if the temperature is high, the integrated unit will suggest measures to lower the temperature, as insect appearances increase. Similarly, if the humidity is high, the integrated unit will suggest measures to lower the humidity, as insect appearances also increase. Furthermore, since light conditions affect insect appearances, the integrated unit can also suggest adjusting the light. In this way, by monitoring the environment inside the house in real time, insect appearance patterns can be analyzed and effective pest control measures can be proposed. Some or all of the above processing in the integrated unit may be performed using AI, for example, or without AI. For example, the integrated unit can input data acquired from IoT devices into AI and have the AI ​​perform the analysis of insect appearance patterns.

[0087] The integration unit can optimize the operation of IoT devices based on the user's lifestyle patterns during integration. For example, if the user is active at night, the integration unit can suggest effective insect control measures for the night. It can also suggest effective insect control measures for the daytime if the user is active during the day. Furthermore, the integration unit can adjust the timing of IoT device operation according to the user's lifestyle patterns. This allows for the provision of effective insect control measures by optimizing IoT device operation based on the user's lifestyle patterns. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input information about the user's lifestyle patterns into the AI ​​and have the AI ​​optimize the operation of the IoT devices.

[0088] The integration unit can estimate the user's emotions and adjust the timing of the operation of the integrated IoT device based on the estimated user emotions. For example, if the user is feeling anxious, the integration unit can operate the IoT device at a time that provides reassurance. It can also operate the IoT device at a time that attracts the user's interest if the user is excited. Furthermore, if the user is relaxed, it can operate the IoT device at a time that provides detailed information. This allows for the provision of pest control measures at the optimal time for the user by adjusting the timing of IoT device operation based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the integration unit may be performed using AI, or not. For example, the integration unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0089] The integration unit can, during integration, place different IoT devices in different rooms within the house and analyze insect appearance patterns in detail. For example, the integration unit can place an IoT device specifically for the living room and analyze insect appearance patterns in the living room. It can also place an IoT device specifically for the kitchen and analyze insect appearance patterns in the kitchen. Furthermore, it can place an IoT device specifically for the bedroom and analyze insect appearance patterns in the bedroom. In this way, by placing different IoT devices in different rooms within the house, it is possible to analyze insect appearance patterns in detail and provide effective pest control measures. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input data acquired from IoT devices placed in each room into the AI ​​and have the AI ​​perform the analysis of insect appearance patterns.

[0090] The integration unit can, upon integration, connect with the user's smartphone or tablet to notify them of insect sightings. For example, the integration unit can notify the user's smartphone of insect sightings in real time. It can also notify the user's tablet of insect sightings in real time. Furthermore, the integration unit can suggest pest control measures based on insect sighting patterns to the user's smartphone or tablet. In this way, by connecting with the user's smartphone or tablet, it is possible to notify them of insect sightings in real time and provide effective pest control measures. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input data acquired from the smartphone or tablet into the AI ​​and have the AI ​​execute the notification of insect sightings.

[0091] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling anxious, the analysis unit can provide the analysis results in a display method that provides a sense of security. The analysis unit can also provide the analysis results in an engaging display method if the user is excited. Furthermore, if the user is relaxed, the analysis unit can provide the analysis results in a display method that includes detailed information. This allows the analysis results to be provided in the most optimal display method for the user by adjusting the display method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0092] The analysis unit can improve the accuracy of its analysis based on the level of detail of the house photos and blueprints during the analysis. For example, the analysis unit can perform a detailed analysis using high-resolution house photos. It can also perform a highly accurate analysis using a detailed blueprint. Furthermore, the analysis unit can combine house photos and blueprints to perform an even more accurate analysis. This allows for the provision of more effective pest control measures by improving the accuracy of the analysis based on the level of detail of the house photos and blueprints. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input house photos and blueprints into AI and have the AI ​​perform the analysis accuracy improvement.

[0093] The analysis unit can optimize its analysis algorithm based on the user's lifestyle during analysis. For example, if the user is active at night, the analysis unit can apply an analysis algorithm specialized for nighttime lifestyles. Similarly, if the user is active during the day, the analysis unit can apply an analysis algorithm specialized for daytime lifestyles. Furthermore, the analysis unit can adjust the analysis algorithm according to the user's lifestyle and propose the most suitable pest control measures. This allows for the provision of more effective pest control measures by optimizing the analysis algorithm based on the user's lifestyle. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information about the user's lifestyle into the AI ​​and have the AI ​​optimize the analysis algorithm.

[0094] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit can prioritize providing reassuring analysis results. It can also prioritize providing interesting analysis results if the user is excited. Furthermore, if the user is relaxed, the analysis unit can prioritize providing detailed analysis results. This allows the system to provide the user with the most optimal analysis results by prioritizing the results based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0095] The analysis unit can apply different analysis methods to different parts of the house during analysis and propose the optimal pest control measures. For example, the analysis unit can apply an analysis method specifically for the living room and propose the optimal pest control measures for the living room. It can also apply an analysis method specifically for the kitchen and propose the optimal pest control measures for the kitchen. Furthermore, it can apply an analysis method specifically for the bedroom and propose the optimal pest control measures for the bedroom. In this way, by applying different analysis methods to different parts of the house, more effective pest control measures can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information about each part of the house into the AI ​​and have the AI ​​perform the application of different analysis methods.

[0096] The analysis unit can improve the accuracy of its analysis by referring to the effectiveness of the user's past pest control measures. For example, the analysis unit can refer to the effectiveness of pest control items the user has used in the past and perform the analysis based on the items that were most effective. The analysis unit can also refer to the success rate of pest control measures the user has implemented in the past and perform the analysis based on those measures with high success rates. Furthermore, the analysis unit can adjust its analysis algorithm based on the user's past pest control data to perform a more accurate analysis. This improves the accuracy of the analysis by referring to the effectiveness of the user's past pest control measures, thereby providing more effective pest control measures. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past pest control data into AI and have the AI ​​perform the analysis to improve accuracy.

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

[0098] The insect repellent suggestion system can estimate the user's emotions and customize the suggested insect repellent items based on those emotions. For example, if the user is feeling anxious, it can suggest insect repellent items that provide a sense of security. If the user is excited, it can suggest insect repellent items that pique their interest. Furthermore, if the user is relaxed, it can suggest insect repellent items that include detailed explanations. By customizing the suggested insect repellent items based on the user's emotions, the system can provide the user with the most suitable insect repellent solution. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0099] The pest control suggestion system can analyze a user's past pest control history and suggest the most suitable pest control items. For example, it can analyze 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 pest control measures the user has taken in the past and suggest pest control items at the optimal time. Furthermore, it can suggest effective pest control items based on the success rate of pest control measures the user has tried in the past. In this way, by analyzing the user's past pest control history, the system can suggest the most suitable pest control items. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past pest control history into AI and have the AI ​​suggest the most suitable pest control items.

[0100] The insect repellent suggestion system can suggest insect repellent items while considering the user's geographical location. For example, if the user lives in an urban area, it can suggest insect repellent items that are effective in urban areas. If the user lives in a rural area, it can suggest insect repellent items that are effective in rural areas. Furthermore, if the user is traveling, it can suggest insect repellent items that are specific to the region they are traveling to. In this way, by considering the user's geographical location, it can suggest highly relevant insect repellent items. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's geographical location information into the AI ​​and have the AI ​​suggest highly relevant insect repellent items.

[0101] The pest control suggestion system can analyze a user's social media activity and suggest relevant pest control items. For example, it can collect information about pest control items shared by the user on social media and suggest items based on that information. It can also analyze posts from pest control experts the user follows and suggest relevant pest control items. Furthermore, it can analyze posts from pest control communities the user participates in and suggest relevant pest control items. In this way, by analyzing the user's social media activity, it can suggest relevant pest control items. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's social media activity into AI and have the AI ​​suggest relevant pest control items.

[0102] The insect repellent suggestion system can estimate the user's emotions and suggest how to use insect repellent items based on those emotions. For example, if the user is feeling anxious, it can suggest a method of use that provides reassurance. If the user is excited, it can suggest an interesting method of use. Furthermore, if the user is relaxed, it can suggest a method of use that includes detailed explanations. In this way, by suggesting how to use insect repellent items based on the user's emotions, the system can provide the user with the most suitable insect repellent solution. 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 suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0103] The insect repellent suggestion system can suggest insect repellent items based on the user's current lifestyle and areas of interest. For example, if the user is currently raising children, it can suggest insect repellent items that are safe for children. If the user has pets, it can also suggest insect repellent items that are harmless to pets. Furthermore, if the user enjoys outdoor activities, it can suggest insect repellent items that can be used outdoors. In this way, by suggesting insect repellent items based on the user's current lifestyle and areas of interest, it can provide highly relevant insect repellent items. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input information about the user's current lifestyle and areas of interest into the AI ​​and have the AI ​​suggest highly relevant insect repellent items.

[0104] The insect repellent suggestion system can estimate the user's emotions and evaluate the effectiveness of insect repellent items based on those emotions. For example, if the user is feeling anxious, the system may highly value the effectiveness of insect repellent items that provide a sense of security. Similarly, if the user is excited, the system may highly value the effectiveness of insect repellent items that pique their interest. Furthermore, if the user is relaxed, the system may highly value the effectiveness of insect repellent items that include detailed explanations. This allows the system to provide the user with the most suitable insect repellent solution by evaluating the effectiveness of items based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, 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. For example, the suggestion unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0105] The insect repellent suggestion system can estimate the user's emotions and select insect repellent items based on those emotions. For example, if the user is feeling anxious, it can select insect repellent items that provide a sense of security. If the user is excited, it can select insect repellent items that pique their interest. Furthermore, if the user is relaxed, it can select insect repellent items that include detailed explanations. By selecting insect repellent items based on the user's emotions, the system can provide the user with the most suitable insect repellent solution. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, 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. For example, the suggestion unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0106] The insect repellent suggestion system can estimate the user's emotions and suggest the timing of using insect repellent items based on those emotions. For example, if the user is feeling anxious, it can suggest using the insect repellent item at a time that provides reassurance. If the user is excited, it can suggest using the insect repellent item at a time that captures their interest. Furthermore, if the user is relaxed, it can suggest using the insect repellent item at a time that includes detailed explanations. By suggesting the timing of insect repellent item use based on the user's emotions, the system can provide the user with the most optimal insect repellent solution. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0107] The insect repellent suggestion system can estimate the user's emotions and suggest placement locations for insect repellent items based on those emotions. For example, if the user is feeling anxious, it can suggest a placement location that provides a sense of security. If the user is excited, it can suggest a placement location that will pique their interest. Furthermore, if the user is relaxed, it can suggest a placement location that includes detailed explanations. By suggesting placement locations for insect repellent items based on the user's emotions, the system can provide the user with the most optimal insect repellent solution. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

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

[0109] Step 1: The data collection unit collects information such as the user's home layout, family structure, lifestyle, preferences, and daily habits. The data collection unit collects information, for example, through questionnaires regarding home layouts, family structure, and lifestyle. Step 2: The proposal department analyzes the information collected by the data collection department and proposes the most suitable insect repellent items and measures. For example, the proposal department suggests which insect repellent items should be placed in which rooms to be most effective, based on the house layout and family structure. It also suggests the most effective timing for implementing insect repellent measures, based on lifestyle and preferences. Step 3: The integration unit works with IoT devices to analyze the overall living patterns and insect appearance patterns of the house. For example, the integration unit uses IoT devices to monitor the temperature, humidity, and light conditions inside the house and analyzes insect appearance patterns. Step 4: The analysis unit analyzes the house photos and blueprints uploaded by the user and proposes the optimal pest control measures. For example, the user uploads house photos and blueprints to the system, which then analyzes them and proposes the optimal pest control measures.

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0112] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0113] Each of the multiple elements described above, including the collection unit, proposal unit, collaboration unit, and analysis unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 38B of the smart device 14 to collect information such as the layout of the user's house, family structure, and lifestyle. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected information to propose the most suitable insect repellent items and measures. The collaboration unit collaborates with IoT devices via the communication I / F 44 of the smart device 14 to analyze the living patterns and insect appearance patterns of the entire house. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes photos and blueprints of the house uploaded by the user to propose the most suitable insect repellent measures. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0115] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0117] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0118] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0120] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0121] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0122] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0123] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0124] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0126] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0128] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements described above, including the collection unit, proposal unit, collaboration unit, and analysis unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the smart glasses 214 to collect information such as the layout of the user's house, family structure, and lifestyle. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected information to propose the most suitable insect repellent items and measures. The collaboration unit collaborates with IoT devices via the communication I / F 44 of the smart glasses 214 to analyze the living patterns and insect appearance patterns of the entire house. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes photos and blueprints of the house uploaded by the user to propose the most suitable insect repellent measures. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0131] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0133] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0137] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0138] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements described above, including the collection unit, proposal unit, collaboration unit, and analysis unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the headset terminal 314 to collect information such as the layout of the user's house, family structure, and lifestyle. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected information to propose the most suitable insect repellent items and measures. The collaboration unit collaborates with IoT devices via the communication I / F 44 of the headset terminal 314 to analyze the living patterns and insect appearance patterns of the entire house. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes photos and blueprints of the house uploaded by the user to propose the most suitable insect repellent measures. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0147] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0149] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0152] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0153] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0154] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0155] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0156] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0157] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0159] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0161] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0162] Each of the multiple elements described above, including the collection unit, proposal unit, collaboration unit, and analysis unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit uses the robot 414's camera 42 and microphone 238 to collect information such as the user's house layout, family structure, and lifestyle. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the collected information to propose the most suitable insect repellent items and measures. The collaboration unit, for example, collaborates with IoT devices via the robot 414's communication I / F 44 to analyze the living patterns and insect appearance patterns of the entire house. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes photos and blueprints of the house uploaded by the user to propose the most suitable insect repellent measures. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0163] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0164] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0165] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0166] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0167] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0168] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0170] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0171] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0172] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0173] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0174] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0175] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0176] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0177] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0178] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0179] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0180] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0181] (Note 1) A data collection unit that collects information such as the user's home layout, family structure, lifestyle, preferences, and daily habits, The information collected by the aforementioned collection unit is analyzed, and the proposal unit proposes the most suitable insect repellent items and measures. A collaboration unit that works with IoT devices to analyze the lifestyle patterns and insect appearance patterns of the entire house, It includes an analysis unit that analyzes user-uploaded photos and blueprints of the house and proposes the optimal pest control measures. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect information about the house layout, family structure, and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, Based on the collected information, we will suggest which insect repellent items would be most effective to place in which rooms. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We propose the most effective timing for implementing pest control measures, based on your lifestyle and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned linkage unit is, Using IoT devices, we monitor the temperature, humidity, and light conditions inside the house and analyze insect appearance patterns. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, We analyze photos and blueprints of your home to propose the most suitable pest control measures. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past pest control history and select the optimal information gathering method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When gathering information, 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 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When gathering information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) 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 14) 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 15) 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 16) 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 17) 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 18) 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 19) The aforementioned linkage unit is, It estimates the user's emotions and selects IoT devices to collaborate with based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned linkage unit is, During integration, the system monitors the temperature, humidity, and light conditions inside the house in real time and analyzes insect appearance patterns. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned linkage unit is, When connected, the operation of IoT devices is optimized based on the user's lifestyle patterns. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned linkage unit is, It estimates the user's emotions and adjusts the timing of the operation of connected IoT devices based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned linkage unit is, During integration, different IoT devices are placed in different rooms within the house to analyze insect appearance patterns in detail. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned linkage unit is, When connected, the system will notify users of insect sightings by linking with their smartphones or tablets. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned analysis unit, During analysis, the accuracy of the analysis is improved based on the level of detail in the house photos and blueprints. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned analysis unit, During analysis, the analysis algorithm is optimized based on the user's lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned analysis unit, During the analysis, different analytical methods are applied to different parts of the house to propose the most suitable pest control measures. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned analysis unit, During analysis, the accuracy of the analysis is improved by referencing the effectiveness of the user's past pest control measures. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects information such as the user's home layout, family structure, lifestyle, preferences, and daily habits, The information collected by the aforementioned collection unit is analyzed, and the proposal unit proposes the most suitable insect repellent items and measures. A collaboration unit that works with IoT devices to analyze the lifestyle patterns and insect appearance patterns of the entire house, It includes an analysis unit that analyzes user-uploaded photos and blueprints of the house and proposes the optimal pest control measures. A system characterized by the following features.

2. The aforementioned collection unit is Collect information about the house layout, family structure, and lifestyle. The system according to feature 1.

3. The aforementioned proposal section is, Based on the collected information, we will suggest which insect repellent items would be most effective to place in which rooms. The system according to feature 1.

4. The aforementioned proposal section is, We propose the most effective timing for implementing pest control measures, based on your lifestyle and preferences. The system according to feature 1.

5. The aforementioned linkage unit is, Using IoT devices, we monitor the temperature, humidity, and light conditions inside the house and analyze insect appearance patterns. The system according to feature 1.

6. The aforementioned analysis unit, We analyze photos and blueprints of your home to propose the most suitable pest control measures. The system according to feature 1.

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

8. The aforementioned collection unit is Analyze the user's past pest control history and select the optimal information gathering method. The system according to feature 1.

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