system

Abstract

The system according to the embodiment aims to quickly detect the appearance of cockroaches and take immediate action. [Solution] The system according to the embodiment includes a sensing unit, a spraying unit, and a notification unit. The sensing unit detects cockroaches using AI that recognizes the shape and movement of cockroaches. The spraying unit sprays the spray based on the cockroach detected by the sensing unit. The notification unit sends a notification based on the information sensed by the sensing unit.

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have had the problem of making it difficult to quickly detect the appearance of cockroaches and take immediate action.

[0005] The system according to the embodiment aims to quickly detect the appearance of cockroaches and take immediate action. [Means for solving the problem]

[0006] The system according to the embodiment includes a sensing unit, a spraying unit, and a notification unit. The sensing unit detects cockroaches using AI that recognizes the shape and movement of cockroaches. The spraying unit sprays the spray based on the cockroach detected by the sensing unit. The notification unit sends a notification based on the information sensed by the sensing unit. [Effects of the Invention]

[0007] The system according to the embodiment can quickly detect the appearance of cockroaches and take immediate action. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple 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), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A system according to an embodiment of the present invention uses sensors installed at each entrance to a home to detect cockroaches, spray them, and send notifications. This system effectively eliminates cockroaches by using AI to learn their shape and movement, reducing false positives. For example, when a sensor installed near a front door or window detects a cockroach, it immediately sprays the cockroach to eliminate it. Notifications are also sent to devices such as smartphones, allowing users to monitor cockroach intrusions in real time. Furthermore, by transmitting sensor-detected information to the cloud and accumulating and analyzing the data, more accurate cockroach detection becomes possible. The system also includes a function to adjust the amount and timing of spraying. The sensors are designed to detect pests other than cockroaches, and the notification function is compatible with smartphones and other devices. This allows the system to prevent cockroach intrusions and quickly address them.

[0029] The cockroach detection system according to the embodiment includes a sensing unit, an ejection unit, and a notification unit. The sensing unit detects cockroaches using AI that recognizes the shape and movement of the cockroach. For example, the sensing unit recognizes the shape of the cockroach using a camera sensor. The sensing unit can also detect the movement of the cockroach using a motion sensor. The sensing unit can also learn the shape and movement of the cockroach using AI to reduce false detections. For example, the sensing unit recognizes the shape of the cockroach in real time using a camera sensor. The sensing unit can also detect the movement of the cockroach using a motion sensor and input that data into AI for learning. The ejection unit ejects spray based on the cockroach detected by the sensing unit. For example, the ejection unit ejects spray directly onto the cockroach using a spray nozzle. The ejection unit can also adjust the amount and timing of the spray. For example, the ejection unit receives a signal from the sensing unit and controls the spray nozzle to eject the spray. The ejection unit can also adjust the amount of spray to eject only the required amount. The notification unit sends a notification based on the information sensed by the sensing unit. The notification unit sends a notification to, for example, a smartphone. The notification unit can also send notifications to other devices. For example, the notification unit receives a signal from the sensing unit and sends a notification to a smartphone. The notification unit can also send notifications to other devices such as a tablet or a PC. This allows the cockroach detection system according to the embodiment to automatically detect, exterminate, and notify cockroaches. For example, the sensing unit uses a camera sensor to recognize the shape of the cockroach in real time, and the spray unit controls the spray nozzle to spray. The notification unit can also send a notification to a smartphone to notify the user of a cockroach intrusion.

[0030] The sensing unit can transmit sensed information to a cloud, where the data is accumulated and analyzed. Examples of cloud services include, but are not limited to, AWS (registered trademark), Google (registered trademark), and Microsoft Azure (registered trademark). For example, the sensing unit can transmit sensed information to a cloud and accumulate it in a database. The sensing unit can also analyze the accumulated data to improve sensing accuracy. For example, the sensing unit can transmit sensed information to a cloud and accumulate it in a database. The sensing unit can also analyze the accumulated data to improve sensing accuracy. In this way, sensing accuracy is improved by accumulating and analyzing data in the cloud. Some or all of the above-described processing in the sensing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sensing unit can transmit sensed information to a cloud and analyze the data using an AI model on the cloud.

[0031] The injection unit can adjust the amount and timing of spray injection. The amount and timing of spray injection include, but are not limited to, units of spray injection and timing standards (time, detection results, etc.). For example, the injection unit adjusts the amount of spray injection to inject only the required amount. The injection unit can also adjust the timing of spray injection to inject at the optimal timing. For example, the injection unit receives a signal from the detection unit and controls the spray nozzle to inject the spray. The injection unit can also adjust the amount of spray injection to inject only the required amount. In this way, cockroaches can be effectively exterminated by adjusting the amount and timing of spray injection. Some or all of the above-described processing in the injection unit may be performed using, for example, AI, or may be performed without using AI. For example, the injection unit can receive a signal from the detection unit and adjust the amount and timing of spray injection using an AI model.

[0032] The sensing unit can detect cockroaches and other pests. Examples of other pests include, but are not limited to, ants, flies, and mites. The sensing unit, for example, uses a camera sensor to recognize the shape of the cockroach. The sensing unit can also use a motion sensor to detect the cockroach's movement. The sensing unit can also use AI to learn the shape and movement of the cockroach and reduce false positives. For example, the sensing unit can use a camera sensor to recognize the shape of the cockroach in real time. The sensing unit can also use a motion sensor to detect the cockroach's movement and input that data into AI for learning. This allows for detection of pests other than cockroaches, enabling a wide range of pest control measures. Some or all of the above-described processing in the sensing unit may be performed using AI, for example, or without AI. For example, the sensing unit can use a camera sensor to recognize the shape of the cockroach in real time and detect other pests using an AI model.

[0033] The notification unit can also send notifications to devices other than smartphones. Devices other than smartphones include, but are not limited to, tablets, personal computers, and smartwatches. The notification unit can, for example, send notifications to smartphones. The notification unit can also send notifications to other devices. For example, the notification unit receives a signal from the sensing unit and sends a notification to a smartphone. The notification unit can also send notifications to other devices such as tablets and personal computers. This allows notifications to be sent to devices other than smartphones, allowing users to receive notifications on a variety of devices. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can receive a signal from the sensing unit, generate notification content using an AI model, and send the content to a smartphone or other device.

[0034] The sensing unit can learn the movement patterns of the cockroach in real time and improve its detection accuracy. For example, the sensing unit tracks the movement of the cockroach in real time and learns the movement patterns. The sensing unit can also analyze the speed and direction of the cockroach's movement and improve its detection accuracy. The sensing unit can also learn the frequency and time of the cockroach's movement and optimize its detection accuracy. For example, the sensing unit tracks the movement of the cockroach in real time and learns the movement patterns. The sensing unit can also analyze the speed and direction of the cockroach's movement and improve its detection accuracy. In this way, by learning the movement patterns of the cockroach, the detection accuracy is improved. Some or all of the above-mentioned processing in the sensing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sensing unit can input cockroach movement data to the generation AI and cause the generation AI to learn the movement patterns.

[0035] The sensing unit can analyze the cockroach's habitat and optimize the sensing timing. For example, the sensing unit can analyze the cockroach's habitat and set the most effective sensing timing. The sensing unit can also learn the cockroach's active hours and improve sensing accuracy during those hours. The sensing unit can also identify the cockroach's habitat and focus sensing on that location. For example, the sensing unit can analyze the cockroach's habitat and set the most effective sensing timing. The sensing unit can also learn the cockroach's active hours and improve sensing accuracy during those hours. This makes it possible to analyze the cockroach's habitat and detect at the optimal timing. Some or all of the above-described processing in the sensing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sensing unit can input cockroach habitat data into the generation AI and cause the generation AI to optimize the sensing timing.

[0036] The sensing unit can identify the size and color of the cockroach and prioritize detecting specific cockroaches. For example, the sensing unit can identify the size of the cockroach and prioritize detecting large cockroaches. The sensing unit can also identify the color of the cockroach and prioritize detecting cockroaches of a specific color. The sensing unit can also identify the shape of the cockroach and prioritize detecting cockroaches of a specific shape. For example, the sensing unit can identify the size of the cockroach and prioritize detecting large cockroaches. The sensing unit can also identify the color of the cockroach and prioritize detecting cockroaches of a specific color. In this way, by identifying the size and color of the cockroach, specific cockroaches can be prioritized for detection. Some or all of the above-described processing in the sensing unit may be performed using, or without, AI. For example, the sensing unit can input cockroach size and color data into the generation AI and cause the generation AI to identify specific cockroaches.

[0037] The sensing unit can detect cockroach pheromones and improve sensing accuracy. The sensing unit can, for example, detect cockroach pheromones and improve sensing accuracy. The sensing unit can also analyze the concentration of cockroach pheromones and optimize sensing accuracy. The sensing unit can also learn the distribution of cockroach pheromones and improve sensing accuracy. For example, the sensing unit can detect cockroach pheromones and improve sensing accuracy. The sensing unit can also analyze the concentration of cockroach pheromones and optimize sensing accuracy. In this way, sensing accuracy is improved by detecting cockroach pheromones. Some or all of the above-described processing in the sensing unit may be performed, for example, using AI or without AI. For example, the sensing unit can input cockroach pheromone data to the generation AI and cause the generation AI to detect pheromones.

[0038] The sensing unit can detect the sound of a cockroach and improve the detection accuracy. The sensing unit can detect, for example, the sound of footsteps of a cockroach and improve the detection accuracy. The sensing unit can also detect the sound of wings of a cockroach and improve the detection accuracy. The sensing unit can also detect sounds caused by cockroach movement and improve the detection accuracy. For example, the sensing unit can detect the sound of footsteps of a cockroach and improve the detection accuracy. The sensing unit can also detect the sound of wings of a cockroach and improve the detection accuracy. In this way, by detecting the sound of a cockroach, the detection accuracy is improved. Some or all of the above-described processing in the sensing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sensing unit can input cockroach sound data to the generation AI and cause the generation AI to detect the sound.

[0039] The sensing unit can detect the cockroach's temperature and improve its sensing accuracy. The sensing unit can, for example, detect the cockroach's body temperature and improve its sensing accuracy. The sensing unit can also detect temperature changes around the cockroach and improve its sensing accuracy. The sensing unit can also learn the cockroach's temperature distribution and improve its sensing accuracy. For example, the sensing unit can detect the cockroach's body temperature and improve its sensing accuracy. The sensing unit can also detect temperature changes around the cockroach and improve its sensing accuracy. In this way, by detecting the cockroach's temperature, its sensing accuracy is improved. Some or all of the above-described processing in the sensing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sensing unit can input the cockroach's temperature data into the generation AI and cause the generation AI to detect the temperature.

[0040] The spraying unit can accurately identify the position of the cockroach and spray the spray at a pinpoint. The spraying unit, for example, can accurately identify the position of the cockroach and spray the spray at a pinpoint. The spraying unit can also track the movement of the cockroach and spray the spray in accordance with the movement. The spraying unit can also analyze the position information of the cockroach and set the optimal spray point. For example, the spraying unit can accurately identify the position of the cockroach and spray the spray at a pinpoint. The spraying unit can also track the movement of the cockroach and spray the spray in accordance with the movement. In this way, the position of the cockroach can be accurately identified and the spray can be sprayed effectively. Some or all of the above-mentioned processing in the spraying unit may be performed, for example, using AI or without AI. For example, the spraying unit can input the cockroach's position data to the generation AI and have the generation AI identify the position.

[0041] The spraying unit can track the movement of the cockroach and spray the spray in accordance with the movement. The spraying unit, for example, tracks the movement of the cockroach in real time and sprays the spray in accordance with the movement. The spraying unit can also analyze the speed and direction of the cockroach's movement and spray the spray at the optimal timing. The spraying unit can also learn the movement patterns of the cockroach and spray the spray effectively. For example, the spraying unit can track the movement of the cockroach in real time and spray the spray in accordance with the movement. The spraying unit can also analyze the speed and direction of the cockroach's movement and spray the spray at the optimal timing. In this way, the spray can be effectively sprayed by tracking the movement of the cockroach. Some or all of the above-mentioned processing in the spraying unit may be performed, for example, using AI or without AI. For example, the spraying unit can input cockroach movement data to the generation AI and cause the generation AI to track the movement.

[0042] The spraying unit can select and spray different sprays depending on the type of cockroach. For example, the spraying unit can identify the type of cockroach and select and spray the optimal spray. The spraying unit can also identify the size and color of the cockroach and select and spray the appropriate spray. The spraying unit can also analyze the movement pattern of the cockroach and select and spray the optimal spray. For example, the spraying unit can identify the type of cockroach and select and spray the optimal spray. The spraying unit can also identify the size and color of the cockroach and select and spray the appropriate spray. This allows for effective extermination by selecting the optimal spray depending on the type of cockroach. Some or all of the above-mentioned processing in the spraying unit may be performed using, for example, AI, or may be performed without using AI. For example, the spraying unit can input cockroach type data into the generation AI and have the generation AI select the spray.

[0043] The injection unit can detect cockroach pheromones and inject the spray in response to the pheromones. The injection unit, for example, detects cockroach pheromones and injects the spray in response to the pheromones. The injection unit can also analyze the concentration of cockroach pheromones and set optimal injection timing. The injection unit can also learn the distribution of cockroach pheromones and effectively inject the spray. For example, the injection unit can detect cockroach pheromones and inject the spray in response to the pheromones. The injection unit can also analyze the concentration of cockroach pheromones and set optimal injection timing. In this way, the spray can be effectively injected by detecting cockroach pheromones. Some or all of the above-described processing in the injection unit may be performed, for example, using AI or without AI. For example, the injection unit can input cockroach pheromone data to the generation AI and cause the generation AI to detect pheromones.

[0044] The ejection unit can detect the sound of a cockroach and eject the spray in response to the sound. The ejection unit can, for example, detect the sound of a cockroach's footsteps and eject the spray in response to the sound. The ejection unit can also detect the sound of a cockroach's wings and eject the spray in response to the sound. The ejection unit can also detect sounds made by a cockroach's movement and eject the spray in response to the sound. For example, the ejection unit can detect the sound of a cockroach's footsteps and eject the spray in response to the sound. The ejection unit can also detect the sound of a cockroach's wings and eject the spray in response to the sound. In this way, the spray can be effectively ejected by detecting the sound of a cockroach. Some or all of the above-described processing in the ejection unit may be performed, for example, using AI, or may be performed without using AI. For example, the ejection unit can input cockroach sound data to a generation AI and cause the generation AI to detect the sound.

[0045] The spray unit can detect the temperature of the cockroach and spray the spray in response to the temperature. The spray unit can, for example, detect the body temperature of the cockroach and spray the spray in response to the temperature. The spray unit can also detect temperature changes around the cockroach and spray the spray in response to the temperature. The spray unit can also learn the temperature distribution of the cockroach and spray the spray effectively. For example, the spray unit can detect the body temperature of the cockroach and spray the spray in response to the temperature. The spray unit can also detect temperature changes around the cockroach and spray the spray in response to the temperature. In this way, by detecting the cockroach's temperature, the spray can be sprayed effectively. Some or all of the above-mentioned processing in the spray unit may be performed, for example, using AI, or may be performed without using AI. For example, the spray unit can input the cockroach's temperature data to the generation AI and cause the generation AI to detect the temperature.

[0046] The notification unit can analyze the cockroach detection frequency and determine the priority of notifications according to the detection frequency. For example, the notification unit can analyze the cockroach detection frequency and prioritize sending notifications if the frequency is high. The notification unit can also analyze the cockroach detection frequency and send regular notifications if the frequency is low. The notification unit can also learn the cockroach detection frequency and set the optimal notification timing. For example, the notification unit can analyze the cockroach detection frequency and prioritize sending notifications if the frequency is high. The notification unit can also analyze the cockroach detection frequency and send regular notifications if the frequency is low. In this way, by determining the priority of notifications according to the cockroach detection frequency, important notifications can be sent preferentially. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input cockroach detection frequency data to the generation AI and have the generation AI determine the priority of notifications.

[0047] The notification unit can identify the location where a cockroach is detected and customize the content of the notification depending on the location. For example, the notification unit can identify the location where a cockroach is detected, and if it is detected at the entrance, send a notification related to the entrance. The notification unit can also analyze the location where a cockroach is detected, and if it is detected in the kitchen, send a notification related to the kitchen. The notification unit can also learn the location where a cockroach is detected and set optimal notification content. For example, the notification unit can identify the location where a cockroach is detected, and if it is detected at the entrance, send a notification related to the entrance. The notification unit can also analyze the location where a cockroach is detected, and if it is detected in the kitchen, send a notification related to the kitchen. This allows the content of the notification to be customized depending on the location where the cockroach is detected, thereby providing more appropriate information. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input cockroach detection location data to a generation AI and have the generation AI customize the notification content.

[0048] The notification unit can record the time when a cockroach is detected and adjust the content of the notification depending on the time. For example, the notification unit can record the time when a cockroach is detected and send an emergency notification if it is detected at night. The notification unit can also analyze the time when a cockroach is detected and send a regular notification if it is detected during the day. The notification unit can also learn the time when a cockroach is detected and set the optimal notification content. For example, the notification unit can record the time when a cockroach is detected and send an emergency notification if it is detected at night. The notification unit can also analyze the time when a cockroach is detected and send a regular notification if it is detected during the day. This allows for more appropriate notification by adjusting the content of the notification depending on the time when a cockroach is detected. Some or all of the above-mentioned processing in the notification unit can be performed using, for example, AI, or without AI. For example, the notification unit can input cockroach detection time data to a generation AI and have the generation AI adjust the notification content.

[0049] The notification unit can detect cockroach pheromones and send a notification in response to the pheromones. The notification unit can, for example, detect cockroach pheromones and send a notification in response to the pheromones. The notification unit can also analyze the concentration of cockroach pheromones and set an optimal notification timing. The notification unit can also learn the distribution of cockroach pheromones and send a notification effectively. For example, the notification unit can detect cockroach pheromones and send a notification in response to the pheromones. The notification unit can also analyze the concentration of cockroach pheromones and set an optimal notification timing. In this way, by detecting cockroach pheromones, a notification can be sent effectively. Some or all of the above-described processing in the notification unit may be performed, for example, using AI or without using AI. For example, the notification unit can input cockroach pheromone data to the generation AI and cause the generation AI to detect pheromones.

[0050] The notification unit can detect the sound of a cockroach and send a notification in response to the sound. The notification unit can, for example, detect the sound of a cockroach's footsteps and send a notification in response to the sound. The notification unit can also detect the sound of a cockroach's wings and send a notification in response to the sound. The notification unit can also detect sounds made by cockroach movement and send a notification in response to the sound. For example, the notification unit can detect the sound of a cockroach's footsteps and send a notification in response to the sound. The notification unit can also detect the sound of a cockroach's wings and send a notification in response to the sound. In this way, detecting the sound of a cockroach allows for effective notification transmission. Some or all of the above-described processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input cockroach sound data to a generation AI and cause the generation AI to detect the sound.

[0051] The notification unit can detect the cockroach's temperature and send a notification in response to the temperature. The notification unit can, for example, detect the cockroach's body temperature and send a notification in response to the temperature. The notification unit can also detect temperature changes around the cockroach and send a notification in response to the temperature. The notification unit can also learn the cockroach's temperature distribution and send a notification effectively. For example, the notification unit can detect the cockroach's body temperature and send a notification in response to the temperature. The notification unit can also detect temperature changes around the cockroach and send a notification in response to the temperature. In this way, by detecting the cockroach's temperature, a notification can be sent effectively. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input the cockroach's temperature data to the generation AI and cause the generation AI to detect the temperature.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The sensing unit can learn the user's lifestyle patterns and set the optimal sensing timing. For example, the sensing unit can learn the time periods when the user is at home and increase sensing accuracy during those times. The sensing unit can also learn the time periods when the user is out and set sensing accuracy to normal during those times. Furthermore, the sensing unit can optimize sensing timing based on the user's lifestyle patterns and reduce false detections. This enables optimal sensing according to the user's lifestyle patterns.

[0054] The spray unit can monitor the user's health condition and adjust the amount of spray based on the health condition. For example, the spray unit can reduce the amount of spray if the user has an allergy. Alternatively, the spray unit can set a normal amount of spray if the user is healthy. Furthermore, the spray unit can adjust the ingredients of the spray based on the user's health condition to prevent allergic reactions. This allows for safe spraying according to the user's health condition.

[0055] The notification unit can set optimal notification timing taking into account the user's schedule. For example, the notification unit can obtain the user's calendar information and avoid sending notifications before or after important meetings or events. The notification unit can also set notification priorities based on the user's schedule and send important notifications preferentially. Furthermore, the notification unit can customize the content of notifications according to the user's schedule and provide appropriate information. This enables effective notifications that take the user's schedule into consideration.

[0056] The sensor unit can collect environmental data and adjust its detection accuracy based on environmental conditions. For example, the sensor unit can collect temperature and humidity data and optimize its detection accuracy based on these conditions. The sensor unit can also monitor light intensity and sound levels and adjust its detection accuracy according to environmental conditions. Furthermore, the sensor unit can analyze the environmental data and set the optimal detection timing. This enables highly accurate cockroach detection according to environmental conditions.

[0057] The spray unit can use a combination of different types of sprays. For example, the spray unit can use a combination of a fast-acting spray and a long-lasting spray to effectively exterminate cockroaches. The spray unit can also use a combination of sprays with different ingredients to deal with multiple pests. Furthermore, the spray unit can customize the spray combination according to the user's preferences. This allows for effective and flexible cockroach extermination.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The sensing unit detects cockroaches using AI that recognizes their shape and movement. The sensing unit recognizes the cockroach's shape using a camera sensor and detects its movement using a motion sensor. The sensing unit also uses AI to learn the cockroach's shape and movement, reducing false positives. Step 2: The spraying unit sprays the spray based on the cockroach detected by the sensing unit. The spraying unit sprays the spray directly onto the cockroach using a spray nozzle and can adjust the amount and timing of spray. It receives a signal from the sensing unit and controls the spray nozzle to spray only the required amount. Step 3: The notification unit sends a notification based on the information detected by the detection unit. The notification unit sends a notification to a smartphone or other device to notify the user of the cockroach intrusion.

[0060] (Example 2) A system according to an embodiment of the present invention uses sensors installed at each entrance to a home to detect cockroaches, spray them, and send notifications. This system effectively eliminates cockroaches by using AI to learn their shape and movement, reducing false positives. For example, when a sensor installed near a front door or window detects a cockroach, it immediately sprays the cockroach to eliminate it. Notifications are also sent to devices such as smartphones, allowing users to monitor cockroach intrusions in real time. Furthermore, by transmitting sensor-detected information to the cloud and accumulating and analyzing the data, more accurate cockroach detection becomes possible. The system also includes a function to adjust the amount and timing of spraying. The sensors are designed to detect pests other than cockroaches, and the notification function is compatible with smartphones and other devices. This allows the system to prevent cockroach intrusions and quickly address them.

[0061] The cockroach detection system according to the embodiment includes a sensing unit, an ejection unit, and a notification unit. The sensing unit detects cockroaches using AI that recognizes the shape and movement of the cockroach. For example, the sensing unit recognizes the shape of the cockroach using a camera sensor. The sensing unit can also detect the movement of the cockroach using a motion sensor. The sensing unit can also learn the shape and movement of the cockroach using AI to reduce false detections. For example, the sensing unit recognizes the shape of the cockroach in real time using a camera sensor. The sensing unit can also detect the movement of the cockroach using a motion sensor and input that data into AI for learning. The ejection unit ejects spray based on the cockroach detected by the sensing unit. For example, the ejection unit ejects spray directly onto the cockroach using a spray nozzle. The ejection unit can also adjust the amount and timing of the spray. For example, the ejection unit receives a signal from the sensing unit and controls the spray nozzle to eject the spray. The ejection unit can also adjust the amount of spray to eject only the required amount. The notification unit sends a notification based on the information sensed by the sensing unit. The notification unit sends a notification to, for example, a smartphone. The notification unit can also send notifications to other devices. For example, the notification unit receives a signal from the sensing unit and sends a notification to a smartphone. The notification unit can also send notifications to other devices such as a tablet or a PC. This allows the cockroach detection system according to the embodiment to automatically detect, exterminate, and notify cockroaches. For example, the sensing unit uses a camera sensor to recognize the shape of the cockroach in real time, and the spray unit controls the spray nozzle to spray. The notification unit can also send a notification to a smartphone to notify the user of a cockroach intrusion.

[0062] The sensing unit can transmit sensed information to a cloud, where the data is accumulated and analyzed. Examples of cloud services include, but are not limited to, AWS, Google Cloud, and Microsoft Azure. For example, the sensing unit can transmit sensed information to a cloud and accumulate it in a database. The sensing unit can also analyze the accumulated data to improve sensing accuracy. For example, the sensing unit can transmit sensed information to a cloud and accumulate it in a database. The sensing unit can also analyze the accumulated data to improve sensing accuracy. In this way, sensing accuracy is improved by accumulating and analyzing data in the cloud. Some or all of the above-described processing in the sensing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sensing unit can transmit sensed information to a cloud and analyze the data using an AI model on the cloud.

[0063] The injection unit can adjust the amount and timing of spray injection. The amount and timing of spray injection include, but are not limited to, units of spray injection and timing standards (time, detection results, etc.). For example, the injection unit adjusts the amount of spray injection to inject only the required amount. The injection unit can also adjust the timing of spray injection to inject at the optimal timing. For example, the injection unit receives a signal from the detection unit and controls the spray nozzle to inject the spray. The injection unit can also adjust the amount of spray injection to inject only the required amount. In this way, cockroaches can be effectively exterminated by adjusting the amount and timing of spray injection. Some or all of the above-described processing in the injection unit may be performed using, for example, AI, or may be performed without using AI. For example, the injection unit can receive a signal from the detection unit and adjust the amount and timing of spray injection using an AI model.

[0064] The sensing unit can detect cockroaches and other pests. Examples of other pests include, but are not limited to, ants, flies, and mites. The sensing unit, for example, uses a camera sensor to recognize the shape of the cockroach. The sensing unit can also use a motion sensor to detect the cockroach's movement. The sensing unit can also use AI to learn the shape and movement of the cockroach and reduce false positives. For example, the sensing unit can use a camera sensor to recognize the shape of the cockroach in real time. The sensing unit can also use a motion sensor to detect the cockroach's movement and input that data into AI for learning. This allows for detection of pests other than cockroaches, enabling a wide range of pest control measures. Some or all of the above-described processing in the sensing unit may be performed using AI, for example, or without AI. For example, the sensing unit can use a camera sensor to recognize the shape of the cockroach in real time and detect other pests using an AI model.

[0065] The notification unit can also send notifications to devices other than smartphones. Devices other than smartphones include, but are not limited to, tablets, personal computers, and smartwatches. The notification unit can, for example, send notifications to smartphones. The notification unit can also send notifications to other devices. For example, the notification unit receives a signal from the sensing unit and sends a notification to a smartphone. The notification unit can also send notifications to other devices such as tablets and personal computers. This allows notifications to be sent to devices other than smartphones, allowing users to receive notifications on a variety of devices. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can receive a signal from the sensing unit, generate notification content using an AI model, and send the content to a smartphone or other device.

[0066] The sensing unit can estimate the user's emotions and adjust cockroach detection accuracy based on the estimated user emotions. For example, when the user is stressed, the sensing unit increases the detection accuracy to quickly detect cockroaches. Furthermore, when the user is relaxed, the sensing unit can set the detection accuracy to normal to reduce false positives. Furthermore, when the user is anxious, the sensing unit can maximize the detection accuracy to detect even subtle movements. For example, the sensing unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, the sensing unit can record the user's voice and estimate the emotion using voice analysis technology. This allows for more accurate cockroach detection by adjusting the detection accuracy according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, 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 sensing unit may be performed using, for example, AI, or without AI. For example, the sensing unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0067] The sensing unit can learn the movement patterns of the cockroach in real time and improve its detection accuracy. For example, the sensing unit tracks the movement of the cockroach in real time and learns the movement patterns. The sensing unit can also analyze the speed and direction of the cockroach's movement and improve its detection accuracy. The sensing unit can also learn the frequency and time of the cockroach's movement and optimize its detection accuracy. For example, the sensing unit tracks the movement of the cockroach in real time and learns the movement patterns. The sensing unit can also analyze the speed and direction of the cockroach's movement and improve its detection accuracy. In this way, by learning the movement patterns of the cockroach, the detection accuracy is improved. Some or all of the above-mentioned processing in the sensing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sensing unit can input cockroach movement data to the generation AI and cause the generation AI to learn the movement patterns.

[0068] The sensing unit can analyze the cockroach's habitat and optimize the sensing timing. For example, the sensing unit can analyze the cockroach's habitat and set the most effective sensing timing. The sensing unit can also learn the cockroach's active hours and improve sensing accuracy during those hours. The sensing unit can also identify the cockroach's habitat and focus sensing on that location. For example, the sensing unit can analyze the cockroach's habitat and set the most effective sensing timing. The sensing unit can also learn the cockroach's active hours and improve sensing accuracy during those hours. This makes it possible to analyze the cockroach's habitat and detect at the optimal timing. Some or all of the above-described processing in the sensing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sensing unit can input cockroach habitat data into the generation AI and cause the generation AI to optimize the sensing timing.

[0069] The sensing unit can identify the size and color of the cockroach and prioritize detecting specific cockroaches. For example, the sensing unit can identify the size of the cockroach and prioritize detecting large cockroaches. The sensing unit can also identify the color of the cockroach and prioritize detecting cockroaches of a specific color. The sensing unit can also identify the shape of the cockroach and prioritize detecting cockroaches of a specific shape. For example, the sensing unit can identify the size of the cockroach and prioritize detecting large cockroaches. The sensing unit can also identify the color of the cockroach and prioritize detecting cockroaches of a specific color. In this way, by identifying the size and color of the cockroach, specific cockroaches can be prioritized for detection. Some or all of the above-described processing in the sensing unit may be performed using, or without, AI. For example, the sensing unit can input cockroach size and color data into the generation AI and cause the generation AI to identify specific cockroaches.

[0070] The sensing unit can estimate the user's emotions and adjust the sensing range of the sensing unit based on the estimated user emotions. For example, if the user is stressed, the sensing unit can widen the sensing range to quickly detect cockroaches. Furthermore, if the user is relaxed, the sensing unit can set the sensing range to normal to reduce false positives. Furthermore, if the user is anxious, the sensing unit can maximize the sensing range to detect even subtle movements. For example, the sensing unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, the sensing unit can record the user's voice and estimate the emotion using voice analysis technology. This allows for more accurate cockroach detection by adjusting the sensing range according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, 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 sensing unit may be performed using, for example, AI, or without AI. For example, the sensing unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0071] The sensing unit can detect cockroach pheromones and improve sensing accuracy. The sensing unit can, for example, detect cockroach pheromones and improve sensing accuracy. The sensing unit can also analyze the concentration of cockroach pheromones and optimize sensing accuracy. The sensing unit can also learn the distribution of cockroach pheromones and improve sensing accuracy. For example, the sensing unit can detect cockroach pheromones and improve sensing accuracy. The sensing unit can also analyze the concentration of cockroach pheromones and optimize sensing accuracy. In this way, sensing accuracy is improved by detecting cockroach pheromones. Some or all of the above-described processing in the sensing unit may be performed, for example, using AI or without AI. For example, the sensing unit can input cockroach pheromone data to the generation AI and cause the generation AI to detect pheromones.

[0072] The sensing unit can detect the sound of a cockroach and improve the detection accuracy. The sensing unit can detect, for example, the sound of footsteps of a cockroach and improve the detection accuracy. The sensing unit can also detect the sound of wings of a cockroach and improve the detection accuracy. The sensing unit can also detect sounds caused by cockroach movement and improve the detection accuracy. For example, the sensing unit can detect the sound of footsteps of a cockroach and improve the detection accuracy. The sensing unit can also detect the sound of wings of a cockroach and improve the detection accuracy. In this way, by detecting the sound of a cockroach, the detection accuracy is improved. Some or all of the above-described processing in the sensing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sensing unit can input cockroach sound data to the generation AI and cause the generation AI to detect the sound.

[0073] The sensing unit can detect the cockroach's temperature and improve its sensing accuracy. The sensing unit can, for example, detect the cockroach's body temperature and improve its sensing accuracy. The sensing unit can also detect temperature changes around the cockroach and improve its sensing accuracy. The sensing unit can also learn the cockroach's temperature distribution and improve its sensing accuracy. For example, the sensing unit can detect the cockroach's body temperature and improve its sensing accuracy. The sensing unit can also detect temperature changes around the cockroach and improve its sensing accuracy. In this way, by detecting the cockroach's temperature, its sensing accuracy is improved. Some or all of the above-described processing in the sensing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sensing unit can input the cockroach's temperature data into the generation AI and cause the generation AI to detect the temperature.

[0074] The spraying unit can estimate the user's emotions and adjust the amount of spray based on the estimated user's emotions. For example, if the user is feeling stressed, the spraying unit increases the amount of spray to quickly eliminate cockroaches. Furthermore, if the user is relaxed, the spraying unit can set the amount of spray to normal to reduce unnecessary spraying. Furthermore, if the user is feeling anxious, the spraying unit can maximize the amount of spray to reliably eliminate cockroaches. For example, the spraying unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, the spraying unit can record the user's voice and estimate the emotion using voice analysis technology. Thus, by adjusting the amount of spray according to the user's emotions, cockroaches can be effectively eliminated. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, 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 spraying unit may be performed using, for example, AI, or without AI. For example, the ejection unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0075] The spraying unit can accurately identify the position of the cockroach and spray the spray at a pinpoint. The spraying unit, for example, can accurately identify the position of the cockroach and spray the spray at a pinpoint. The spraying unit can also track the movement of the cockroach and spray the spray in accordance with the movement. The spraying unit can also analyze the position information of the cockroach and set the optimal spray point. For example, the spraying unit can accurately identify the position of the cockroach and spray the spray at a pinpoint. The spraying unit can also track the movement of the cockroach and spray the spray in accordance with the movement. In this way, the position of the cockroach can be accurately identified and the spray can be sprayed effectively. Some or all of the above-mentioned processing in the spraying unit may be performed, for example, using AI or without AI. For example, the spraying unit can input the cockroach's position data to the generation AI and have the generation AI identify the position.

[0076] The spraying unit can track the movement of the cockroach and spray the spray in accordance with the movement. The spraying unit, for example, tracks the movement of the cockroach in real time and sprays the spray in accordance with the movement. The spraying unit can also analyze the speed and direction of the cockroach's movement and spray the spray at the optimal timing. The spraying unit can also learn the movement patterns of the cockroach and spray the spray effectively. For example, the spraying unit can track the movement of the cockroach in real time and spray the spray in accordance with the movement. The spraying unit can also analyze the speed and direction of the cockroach's movement and spray the spray at the optimal timing. In this way, the spray can be effectively sprayed by tracking the movement of the cockroach. Some or all of the above-mentioned processing in the spraying unit may be performed, for example, using AI or without AI. For example, the spraying unit can input cockroach movement data to the generation AI and cause the generation AI to track the movement.

[0077] The spraying unit can select and spray different sprays depending on the type of cockroach. For example, the spraying unit can identify the type of cockroach and select and spray the optimal spray. The spraying unit can also identify the size and color of the cockroach and select and spray the appropriate spray. The spraying unit can also analyze the movement pattern of the cockroach and select and spray the optimal spray. For example, the spraying unit can identify the type of cockroach and select and spray the optimal spray. The spraying unit can also identify the size and color of the cockroach and select and spray the appropriate spray. This allows for effective extermination by selecting the optimal spray depending on the type of cockroach. Some or all of the above-mentioned processing in the spraying unit may be performed using, for example, AI, or may be performed without using AI. For example, the spraying unit can input cockroach type data into the generation AI and have the generation AI select the spray.

[0078] The spraying unit can estimate the user's emotions and adjust the spray timing based on the estimated user emotions. For example, if the user is feeling stressed, the spraying unit can accelerate the spray timing to quickly eliminate cockroaches. Furthermore, if the user is relaxed, the spraying unit can set the spray timing to normal to reduce unnecessary spraying. Furthermore, if the user is feeling anxious, the spraying unit can maximize the spray timing to reliably eliminate cockroaches. For example, the spraying unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, the spraying unit can record the user's voice and estimate the emotion using voice analysis technology. Thus, by adjusting the spray timing according to the user's emotions, cockroaches can be effectively eliminated. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, 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 injection unit may be performed using, for example, AI, or may be performed without using AI. For example, the injection unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate the emotion.

[0079] The injection unit can detect cockroach pheromones and inject the spray in response to the pheromones. The injection unit, for example, detects cockroach pheromones and injects the spray in response to the pheromones. The injection unit can also analyze the concentration of cockroach pheromones and set optimal injection timing. The injection unit can also learn the distribution of cockroach pheromones and effectively inject the spray. For example, the injection unit can detect cockroach pheromones and inject the spray in response to the pheromones. The injection unit can also analyze the concentration of cockroach pheromones and set optimal injection timing. In this way, the spray can be effectively injected by detecting cockroach pheromones. Some or all of the above-described processing in the injection unit may be performed, for example, using AI or without AI. For example, the injection unit can input cockroach pheromone data to the generation AI and cause the generation AI to detect pheromones.

[0080] The ejection unit can detect the sound of a cockroach and eject the spray in response to the sound. The ejection unit can, for example, detect the sound of a cockroach's footsteps and eject the spray in response to the sound. The ejection unit can also detect the sound of a cockroach's wings and eject the spray in response to the sound. The ejection unit can also detect sounds made by a cockroach's movement and eject the spray in response to the sound. For example, the ejection unit can detect the sound of a cockroach's footsteps and eject the spray in response to the sound. The ejection unit can also detect the sound of a cockroach's wings and eject the spray in response to the sound. In this way, the spray can be effectively ejected by detecting the sound of a cockroach. Some or all of the above-described processing in the ejection unit may be performed, for example, using AI, or may be performed without using AI. For example, the ejection unit can input cockroach sound data to a generation AI and cause the generation AI to detect the sound.

[0081] The spray unit can detect the temperature of the cockroach and spray the spray in response to the temperature. The spray unit can, for example, detect the body temperature of the cockroach and spray the spray in response to the temperature. The spray unit can also detect temperature changes around the cockroach and spray the spray in response to the temperature. The spray unit can also learn the temperature distribution of the cockroach and spray the spray effectively. For example, the spray unit can detect the body temperature of the cockroach and spray the spray in response to the temperature. The spray unit can also detect temperature changes around the cockroach and spray the spray in response to the temperature. In this way, by detecting the cockroach's temperature, the spray can be sprayed effectively. Some or all of the above-mentioned processing in the spray unit may be performed, for example, using AI, or may be performed without using AI. For example, the spray unit can input the cockroach's temperature data to the generation AI and cause the generation AI to detect the temperature.

[0082] The notification unit can estimate the user's emotions and adjust the content of the notification based on the estimated user emotions. For example, if the user is feeling stressed, the notification unit can send a concise and quick notification. Furthermore, if the user is feeling relaxed, the notification unit can send a notification with detailed information. Furthermore, if the user is feeling anxious, the notification unit can send a notification with content that provides a sense of security. For example, the notification unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, the notification unit can record the user's voice and estimate the emotion using voice analysis technology. This enables more appropriate notifications by adjusting the content of the notification according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, 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 notification unit can be performed using AI, for example, or without AI. For example, the notification unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0083] The notification unit can analyze the cockroach detection frequency and determine the priority of notifications according to the detection frequency. For example, the notification unit can analyze the cockroach detection frequency and prioritize sending notifications if the frequency is high. The notification unit can also analyze the cockroach detection frequency and send regular notifications if the frequency is low. The notification unit can also learn the cockroach detection frequency and set the optimal notification timing. For example, the notification unit can analyze the cockroach detection frequency and prioritize sending notifications if the frequency is high. The notification unit can also analyze the cockroach detection frequency and send regular notifications if the frequency is low. In this way, by determining the priority of notifications according to the cockroach detection frequency, important notifications can be sent preferentially. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input cockroach detection frequency data to the generation AI and have the generation AI determine the priority of notifications.

[0084] The notification unit can identify the location where a cockroach is detected and customize the content of the notification depending on the location. For example, the notification unit can identify the location where a cockroach is detected, and if it is detected at the entrance, send a notification related to the entrance. The notification unit can also analyze the location where a cockroach is detected, and if it is detected in the kitchen, send a notification related to the kitchen. The notification unit can also learn the location where a cockroach is detected and set optimal notification content. For example, the notification unit can identify the location where a cockroach is detected, and if it is detected at the entrance, send a notification related to the entrance. The notification unit can also analyze the location where a cockroach is detected, and if it is detected in the kitchen, send a notification related to the kitchen. This allows the content of the notification to be customized depending on the location where the cockroach is detected, thereby providing more appropriate information. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input cockroach detection location data to a generation AI and have the generation AI customize the notification content.

[0085] The notification unit can record the time when a cockroach is detected and adjust the content of the notification depending on the time. For example, the notification unit can record the time when a cockroach is detected and send an emergency notification if it is detected at night. The notification unit can also analyze the time when a cockroach is detected and send a regular notification if it is detected during the day. The notification unit can also learn the time when a cockroach is detected and set the optimal notification content. For example, the notification unit can record the time when a cockroach is detected and send an emergency notification if it is detected at night. The notification unit can also analyze the time when a cockroach is detected and send a regular notification if it is detected during the day. This allows for more appropriate notification by adjusting the content of the notification depending on the time when a cockroach is detected. Some or all of the above-mentioned processing in the notification unit can be performed using, for example, AI, or without AI. For example, the notification unit can input cockroach detection time data to a generation AI and have the generation AI adjust the notification content.

[0086] The notification unit can estimate the user's emotion and adjust the timing of notification transmission based on the estimated user emotion. For example, if the user is feeling stressed, the notification unit can send a notification promptly. Furthermore, if the user is relaxed, the notification unit can also send a notification at a normal timing. Furthermore, if the user is feeling anxious, the notification unit can also send a notification immediately. For example, the notification unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, the notification unit can record the user's voice and estimate the emotion using voice analysis technology. This allows the notification to be sent at a more appropriate timing by adjusting the timing of notification transmission according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, 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 notification unit can be performed using, for example, an AI. For example, the notification unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0087] The notification unit can detect cockroach pheromones and send a notification in response to the pheromones. The notification unit can, for example, detect cockroach pheromones and send a notification in response to the pheromones. The notification unit can also analyze the concentration of cockroach pheromones and set an optimal notification timing. The notification unit can also learn the distribution of cockroach pheromones and send a notification effectively. For example, the notification unit can detect cockroach pheromones and send a notification in response to the pheromones. The notification unit can also analyze the concentration of cockroach pheromones and set an optimal notification timing. In this way, by detecting cockroach pheromones, a notification can be sent effectively. Some or all of the above-described processing in the notification unit may be performed, for example, using AI or without using AI. For example, the notification unit can input cockroach pheromone data to the generation AI and cause the generation AI to detect pheromones.

[0088] The notification unit can detect the sound of a cockroach and send a notification in response to the sound. The notification unit can, for example, detect the sound of a cockroach's footsteps and send a notification in response to the sound. The notification unit can also detect the sound of a cockroach's wings and send a notification in response to the sound. The notification unit can also detect sounds made by cockroach movement and send a notification in response to the sound. For example, the notification unit can detect the sound of a cockroach's footsteps and send a notification in response to the sound. The notification unit can also detect the sound of a cockroach's wings and send a notification in response to the sound. In this way, detecting the sound of a cockroach allows for effective notification transmission. Some or all of the above-described processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input cockroach sound data to a generation AI and cause the generation AI to detect the sound.

[0089] The notification unit can detect the cockroach's temperature and send a notification in response to the temperature. The notification unit can, for example, detect the cockroach's body temperature and send a notification in response to the temperature. The notification unit can also detect temperature changes around the cockroach and send a notification in response to the temperature. The notification unit can also learn the cockroach's temperature distribution and send a notification effectively. For example, the notification unit can detect the cockroach's body temperature and send a notification in response to the temperature. The notification unit can also detect temperature changes around the cockroach and send a notification in response to the temperature. In this way, by detecting the cockroach's temperature, a notification can be sent effectively. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input the cockroach's temperature data to the generation AI and cause the generation AI to detect the temperature. === Hard Collateral 1-1 === Each of the multiple elements including the sensing unit, spraying unit, and notification unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the sensing unit detects the shape and movement of the cockroach using the camera sensor and motion sensor of the smart device 14, and the control unit 46A reduces false detections using AI. The spraying unit controls the spray nozzle of the smart device 14 to spray. The notification unit sends a notification to a smartphone or other device via the communication I / F 44 of the smart device 14. The sensing unit, spraying unit, and notification unit may also be realized, for example, by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements, including the sensing unit, spraying unit, and notification unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the sensing unit detects the shape and movement of the cockroach using a camera sensor and a motion sensor of the smart glasses 214, and the control unit 46A reduces false detections using AI. The spraying unit controls the spray nozzle of the smart glasses 214 to spray the spray. The notification unit sends a notification to a smartphone or other device via the communication I / F 44 of the smart glasses 214. The sensing unit, spraying unit, and notification unit may also be realized, for example, by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned sensing unit, spraying unit, and notification unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the sensing unit detects the shape and movement of the cockroach using a camera sensor and a motion sensor of the headset-type terminal 314, and the control unit 46A reduces false detections using AI. The spraying unit controls the spray nozzle of the headset-type terminal 314 to spray. The notification unit sends a notification to a smartphone or other device via the communication I / F 44 of the headset-type terminal 314. The sensing unit, spraying unit, and notification unit may also be realized, for example, by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the sensing unit, spraying unit, and notification unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the sensing unit detects the shape and movement of the cockroach using a camera sensor and a motion sensor of the robot 414, and the control unit 46A reduces false detections using AI. The spraying unit controls the spray nozzle of the robot 414 to spray. The notification unit sends a notification to a smartphone or other device via the communication I / F 44 of the robot 414. The sensing unit, spraying unit, and notification unit may also be realized, for example, by the specific processing unit 290 of the data processing device 12.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The sensing unit can learn the user's lifestyle patterns and set the optimal sensing timing. For example, the sensing unit can learn the time periods when the user is at home and increase sensing accuracy during those times. The sensing unit can also learn the time periods when the user is out and set sensing accuracy to normal during those times. Furthermore, the sensing unit can optimize sensing timing based on the user's lifestyle patterns and reduce false detections. This enables optimal sensing according to the user's lifestyle patterns.

[0092] The spray unit can monitor the user's health condition and adjust the amount of spray based on the health condition. For example, the spray unit can reduce the amount of spray if the user has an allergy. Alternatively, the spray unit can set a normal amount of spray if the user is healthy. Furthermore, the spray unit can adjust the ingredients of the spray based on the user's health condition to prevent allergic reactions. This allows for safe spraying according to the user's health condition.

[0093] The notification unit can set optimal notification timing taking into account the user's schedule. For example, the notification unit can obtain the user's calendar information and avoid sending notifications before or after important meetings or events. The notification unit can also set notification priorities based on the user's schedule and send important notifications preferentially. Furthermore, the notification unit can customize the content of notifications according to the user's schedule and provide appropriate information. This enables effective notifications that take the user's schedule into consideration.

[0094] The sensor unit can collect environmental data and adjust its detection accuracy based on environmental conditions. For example, the sensor unit can collect temperature and humidity data and optimize its detection accuracy based on these conditions. The sensor unit can also monitor light intensity and sound levels and adjust its detection accuracy according to environmental conditions. Furthermore, the sensor unit can analyze the environmental data and set the optimal detection timing. This enables highly accurate cockroach detection according to environmental conditions.

[0095] The spray unit can use a combination of different types of sprays. For example, the spray unit can use a combination of a fast-acting spray and a long-lasting spray to effectively exterminate cockroaches. The spray unit can also use a combination of sprays with different ingredients to deal with multiple pests. Furthermore, the spray unit can customize the spray combination according to the user's preferences. This allows for effective and flexible cockroach extermination.

[0096] The sensing unit can estimate the user's emotions and adjust the sensing range of the sensing unit based on the estimated user's emotions. For example, if the user is feeling stressed, the sensing unit widens the sensing range to quickly detect cockroaches. Also, if the user is relaxed, the sensing unit can set the sensing range to normal to reduce false positives. Furthermore, if the user is feeling anxious, the sensing unit can maximize the sensing range to detect even the smallest movements. This makes it possible to optimally adjust the sensing range according to the user's emotions.

[0097] The spraying unit can estimate the user's emotions and adjust the amount of spray based on the estimated user's emotions. For example, if the user is feeling stressed, the spraying unit increases the amount of spray to quickly eliminate cockroaches. Also, if the user is relaxed, the spraying unit can set the amount of spray to normal to reduce unnecessary spraying. Furthermore, if the user is feeling anxious, the spraying unit can maximize the amount of spray to reliably eliminate cockroaches. This enables effective spraying according to the user's emotions.

[0098] The notification unit can estimate the user's emotions and adjust the content of the notification based on the estimated user's emotions. For example, if the user is feeling stressed, the notification unit can send a concise and quick notification. If the user is feeling relaxed, the notification unit can also send a notification with detailed information. Furthermore, if the user is feeling anxious, the notification unit can also send a notification with content that provides a sense of security. This makes it possible to adjust the content of the notification appropriately according to the user's emotions.

[0099] The notification unit can estimate the user's emotions and adjust the timing of sending notifications based on the estimated user emotions. For example, the notification unit can send notifications quickly if the user is feeling stressed. Alternatively, the notification unit can send notifications at a normal timing if the user is feeling relaxed. Furthermore, the notification unit can send notifications immediately if the user is feeling anxious. This makes it possible to adjust the optimal timing of sending notifications according to the user's emotions.

[0100] The sensing unit can estimate the user's emotions and adjust the cockroach detection accuracy based on the estimated user's emotions. For example, if the user is feeling stressed, the sensing unit can increase the detection accuracy to quickly detect cockroaches. Also, if the user is relaxed, the sensing unit can set the detection accuracy to normal to reduce false positives. Furthermore, if the user is feeling anxious, the sensing unit can maximize the detection accuracy to detect even the smallest movements. This makes it possible to adjust the detection accuracy optimally according to the user's emotions.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The sensing unit detects cockroaches using AI that recognizes their shape and movement. The sensing unit recognizes the cockroach's shape using a camera sensor and detects its movement using a motion sensor. The sensing unit also uses AI to learn the cockroach's shape and movement, reducing false positives. Step 2: The spraying unit sprays the spray based on the cockroach detected by the sensing unit. The spraying unit sprays the spray directly onto the cockroach using a spray nozzle and can adjust the amount and timing of spray. It receives a signal from the sensing unit and controls the spray nozzle to spray only the required amount. Step 3: The notification unit sends a notification based on the information detected by the detection unit. The notification unit sends a notification to a smartphone or other device to notify the user of the cockroach intrusion.

[0103] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0125] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0147] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0148] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0149] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0153] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0158] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0159] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0160] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0161] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0163] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0164] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0165] 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.

[0166] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0167] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0168] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0169] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0171] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0172] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0173] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A sensing unit that senses cockroaches using AI that recognizes the shape and movement of cockroaches; An injection unit that injects a spray based on the cockroach detected by the detection unit; a notification unit that transmits a notification based on the information sensed by the sensing unit. A system characterized by:

2. The sensing unit Sensed information is sent to the cloud, where it is accumulated and analyzed.

2. The system of claim 1.

3. The injection unit is Adjust the amount and timing of spray 2. The system of claim 1.

4. The sensing unit Also detects cockroaches and other pests 2. The system of claim 1.

5. The notification unit Send notifications to devices other than your smartphone 2. The system of claim 1.

6. The sensing unit A method for estimating a user's emotion and adjusting the cockroach's detection accuracy based on the estimated user's emotion is also included.

2. The system of claim 1.

7. The sensing unit Learn cockroach movement patterns in real time to improve detection accuracy 2. The system of claim 1.

8. The sensing unit Includes methods to analyze cockroach habitats and optimize detection timing 2. The system of claim 1.

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