system

Abstract

The system according to this embodiment aims to detect suspicious activity and respond quickly. [Solution] The system according to the embodiment comprises a detection unit, an alarm unit, a camera unit, and a notification unit. The detection unit detects suspicious activity. The alarm unit sounds an alarm in response to the suspicious activity detected by the detection unit. The camera unit takes pictures of the situation with a camera when the alarm unit sounds an alarm. The notification unit notifies the police based on the video captured by the camera unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to respond promptly even when detecting suspicious activities.

[0005] The system according to an embodiment aims to detect suspicious activities and respond promptly.

Means for Solving the Problems

[0006] The system according to an embodiment includes a detection unit, an alarm unit, a photographing unit, and a reporting unit. The detection unit detects suspicious activities. The alarm unit sounds an alarm against the suspicious activities detected by the detection unit. The photographing unit photographs the situation on the spot with a camera when the alarm is sounded by the alarm unit. The reporting unit reports to the police based on the video photographed by the photographing unit. [Effects of the Invention]

[0007] The system according to this embodiment can detect suspicious activity and respond quickly. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 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 5​​​The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

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

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

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

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

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

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

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

[0028] (Example of form 1) The home robot pet according to an embodiment of the present invention is a highly functional security device equipped with an AI agent that sounds an alarm when it detects suspicious activity, takes pictures of the situation with a camera, and notifies the police. This home robot pet consists of the following steps to protect residents. First, the home robot pet detects suspicious activity. Next, it sounds an alarm in response to the detected suspicious activity. Furthermore, it takes pictures of the situation with a camera and notifies the police. This mechanism can protect residents. For example, the home robot pet's AI agent monitors the surrounding environment and detects suspicious movements or sounds. For example, it can detect the sound of a window being opened late at night or suspicious movements near the entrance. This allows for the early detection of suspicious activity. Next, the home robot pet sounds an alarm in response to the detected suspicious activity. The AI ​​agent immediately sounds an alarm upon detecting suspicious activity to warn residents of danger. For example, it can sound a loud siren to deter intruders and alert residents. Furthermore, the home robot pet can film the situation with its camera and notify the police. The AI ​​agent detects suspicious activity and immediately activates the camera to film the situation. The filmed footage is immediately sent to the police, allowing them to respond quickly. This system allows residents to enjoy both the comfort of a pet and security at the same time. The home robot pet looks like an ordinary pet, yet it can protect residents as a highly functional security device. For example, it has a design that easily blends into daily life, while being able to respond immediately if something unusual occurs. In addition, by using generative AI, it can detect anomalies and sound an alarm to prevent the damage from escalating. Furthermore, it can store information and immediately report it. This enhances the residents' sense of security. In this way, the home robot pet equipped with an AI agent aims to provide residents with a safe and comfortable living environment and enhance their sense of security. For example, it can realize a world where people can enjoy the joy of owning a pet and effective security measures at the same time.This allows home robotic pets to ensure the safety of residents and provide a sense of security.

[0029] The home robot pet according to this embodiment comprises a detection unit, an alarm unit, a camera unit, and a notification unit. The detection unit detects suspicious activity. The detection unit monitors the surrounding environment using, for example, an AI agent and detects suspicious movements or sounds. For example, the detection unit can detect the sound of a window being opened late at night or suspicious movements near the entrance. The detection unit can also use the AI ​​agent to detect abnormal activity in specific areas or time periods. For example, the detection unit can detect abnormal movement in a specific area and sound an alarm. The alarm unit sounds an alarm in response to the suspicious activity detected by the detection unit. The alarm unit can, for example, sound a loud siren to deter intruders and alert residents. The alarm unit can also use the AI ​​agent to adjust the volume and type of the alarm. For example, the alarm unit can adjust the volume of the alarm according to the resident's emotions and provide an appropriate alarm. The camera unit takes pictures of the situation when the alarm unit sounds an alarm. The camera unit, for example, uses an AI agent to activate the camera and film the situation. The camera unit can also automatically activate the camera when an anomaly is detected and film detailed footage of the anomaly. The camera unit can also use the AI ​​agent to adjust the angle and range of the footage. For example, the camera unit can adjust the angle of the footage according to the resident's emotions to provide appropriate footage. The reporting unit reports to the police based on the footage filmed by the camera unit. The reporting unit can also use an AI agent to analyze the footage and report to the police. For example, the reporting unit can transmit the footage to the police in real time when an anomaly is detected. The reporting unit can also use an AI agent to adjust the content and method of the report. For example, the reporting unit can adjust the content of the report according to the resident's emotions to make an appropriate report. As a result, the home robot pet according to this embodiment can protect the resident by detecting suspicious activity, sounding an alarm, filming the situation, and reporting to the police.

[0030] The detection unit detects suspicious activity. For example, the detection unit uses an AI agent to monitor the surrounding environment and detect suspicious movements and sounds. Specifically, the AI ​​agent uses a combination of speech recognition technology and image recognition technology. Speech recognition technology detects unusual sounds such as the sound of a window being opened, glass breaking, or doors opening and closing. This allows for the detection of sounds such as a window being opened late at night or suspicious movements near the entrance. In addition, image recognition technology is used to analyze suspicious movements from camera footage in real time. For example, it can detect unusual movements in a specific area and sound an alarm. The AI ​​agent learns from past data and identifies anomalies by detecting movements that differ from normal activity patterns. Furthermore, the AI ​​agent can also detect unusual activity during specific time periods. For example, it can detect unusual movements during times when activity is normally low, such as late at night or early in the morning, and sound an alarm. As a result, the detection unit can detect even subtle anomalies that residents may not notice, ensuring the safety of residents.

[0031] The alarm unit sounds an alarm in response to suspicious activity detected by the detection unit. The alarm unit can, for example, deter intruders and alert residents by sounding a loud siren. Specifically, the alarm unit uses an AI agent to adjust the volume and type of alarm. The AI ​​agent adjusts the alarm volume according to the resident's emotions and situation, providing an appropriate alarm. For example, if the resident is sleeping at night, the alarm volume is set low, and if the resident is awake, the volume is set high. The type of alarm can also be adjusted. For example, it selects an appropriate alarm depending on the situation, such as a siren, bell, or voice message. Furthermore, the alarm unit can customize the content of the alarm depending on the location and time of the alarm. For example, it can sound the front door alarm in response to suspicious activity near the front door, and the window alarm in response to unusual noises near a window. This allows the alarm unit to provide residents with a quick and appropriate alarm, deterring intruders.

[0032] The camera unit captures the situation with cameras when an alarm is sounded by the alarm unit. The camera unit uses, for example, an AI agent to activate the cameras and capture the situation. Specifically, it automatically activates the cameras when an anomaly is detected and captures detailed footage of the anomaly. The AI ​​agent adjusts the timing of camera activation, the angle of shooting, and the range. For example, it points the camera towards the area where the anomaly was detected and shoots at the optimal angle. The AI ​​agent can also track moving objects during shooting and record detailed footage. Furthermore, the camera unit analyzes the captured footage in real time to understand the details of the anomaly. For example, it can identify the face, clothing, and movements of a suspicious person from the captured footage and save it as evidence to provide to the police. This allows the camera unit to quickly and accurately record the situation when an anomaly occurs, which can be used to assist in subsequent responses.

[0033] The reporting unit reports to the police based on the video footage captured by the filming unit. The reporting unit analyzes the captured video using, for example, an AI agent, and then reports to the police. Specifically, when an anomaly is detected, the captured video is sent to the police in real time. The AI ​​agent analyzes the content of the video and determines whether a report is necessary. For example, it analyzes the face and movements of a suspicious person, unusual sounds, etc., and if a report is necessary, it sends a report to the police. It can also adjust the content and method of the report. For example, it adjusts the content of the report according to the emotions of the residents to make an appropriate report. Furthermore, the reporting unit automatically generates the necessary information for the report and provides it to the police. For example, it generates a report that includes the location and time the anomaly occurred, details of the captured video, etc., and sends it to the police. This allows the reporting unit to report to the police quickly and accurately, ensuring the safety of residents.

[0034] The detection unit can monitor its surroundings and detect suspicious movements and sounds. For example, the detection unit can use an AI agent to monitor its surroundings and detect suspicious movements and sounds. For instance, the detection unit can detect the sound of a window being opened late at night or suspicious movements near the entrance. The detection unit can also use an AI agent to detect abnormal activity in specific areas or time periods. For example, the detection unit can detect abnormal activity in a specific area and sound an alarm. This allows for the early detection of suspicious activity by monitoring the surroundings and detecting suspicious movements and sounds.

[0035] The alarm unit can emit a loud siren. For example, by emitting a loud siren, the alarm unit can deter intruders and alert residents. Furthermore, the alarm unit can use an AI agent to adjust the volume and type of alarm. For instance, the alarm unit can adjust the alarm volume according to the resident's emotions, providing an appropriate alarm. This allows for the use of a loud siren to deter intruders and alert residents.

[0036] The camera unit can activate the camera and record the situation on the scene. For example, the camera unit can use an AI agent to activate the camera and record the situation. For example, the camera unit can automatically activate the camera when an anomaly is detected and record detailed footage of the anomaly. The camera unit can also use an AI agent to adjust the shooting angle and range. For example, the camera unit can adjust the shooting angle according to the emotions of the residents to provide appropriate footage. In this way, evidence can be preserved by activating the camera and recording the situation on the scene.

[0037] The reporting unit can report to the police based on the recorded video footage. For example, the reporting unit can use an AI agent to analyze the recorded video and report it to the police. For example, if an anomaly is detected, the reporting unit can transmit the recorded video to the police in real time. Furthermore, the reporting unit can use an AI agent to adjust the content and method of the report. For example, the reporting unit can adjust the content of the report according to the resident's emotions to make an appropriate report. This allows for a swift response by reporting to the police based on the recorded video footage.

[0038] The detection unit can learn the residents' lifestyle patterns and more accurately detect abnormal behavior. For example, the detection unit can learn the pattern of a resident returning home at a fixed time each day and detect any abnormalities that occur during that time. For example, the detection unit can learn the pattern of a resident performing specific actions on specific days of the week and detect any abnormalities that occur on those days. For example, the detection unit can learn the resident's normal range of activity and detect abnormal movements outside that range. In this way, by learning the residents' lifestyle patterns, abnormal behavior can be detected more accurately.

[0039] The detection unit can detect changes in ambient temperature and identify abnormal temperature changes as suspicious activity. For example, if the room temperature rises rapidly, the detection unit can detect the possibility of a fire. For example, if the room temperature drops rapidly, the detection unit can detect the possibility that a window or door has been opened. For example, if the temperature in a specific area changes abnormally, the detection unit can detect suspicious activity in that area. In this way, by detecting changes in ambient temperature, abnormal temperature changes can be identified as suspicious activity.

[0040] The detection unit can learn the behavior of residents' pets and detect abnormal behavior in pets. For example, the detection unit can detect abnormal behavior if a pet frequently goes to places it doesn't normally go. For example, the detection unit can detect abnormal behavior if a pet deviates from its normal behavioral patterns. For example, the detection unit can detect abnormal behavior if a pet behaves abnormally during a specific time period. In this way, by learning the behavior of residents' pets, it can detect abnormal behavior in pets.

[0041] The detection unit can work in conjunction with the resident's smart home devices to detect abnormal device operation. For example, the detection unit can detect an abnormality if a smart lock is operated outside of its normal operating hours. For example, the detection unit can detect an abnormality if a smart light is turned on and off irregularly. For example, the detection unit can detect an operation if a smart thermostat is changed to an abnormal temperature setting. In this way, by working in conjunction with the resident's smart home devices, abnormal device operation can be detected.

[0042] The alarm unit can learn the resident's past alarm history and select the optimal alarm method. For example, the alarm unit can prioritize alarm methods that were effective in the past. For example, the alarm unit can select the alarm method that the resident is most likely to respond to based on past alarm history. For example, the alarm unit can analyze past alarm history and select the optimal alarm timing. In this way, the optimal alarm method can be selected by learning the resident's past alarm history.

[0043] The alarm unit can issue an alarm using light and vibration in addition to an alarm sound. For example, the alarm unit can issue a visual alarm by flashing the room lights simultaneously with the alarm sound. For example, the alarm unit can issue a tactile alarm by activating a vibration device simultaneously with the alarm sound. For example, the alarm unit can issue an alarm by sending a notification to a smartphone simultaneously with the alarm sound. In this way, by using light and vibration in addition to the alarm sound, an alarm can be issued visually and tactilely.

[0044] The alarm unit can send notifications to residents' smartphones. For example, if suspicious activity is detected, the alarm unit will send a notification to the resident's smartphone at the same time as the alarm. For example, when an alarm sounds, the alarm unit can send detailed information about the alarm to the smartphone. For example, when an alarm is deactivated, the alarm unit can send information that the alarm has been deactivated to the smartphone. This allows residents to immediately understand any abnormalities by sending notifications to their smartphones.

[0045] The alarm unit can notify nearby residents of an alarm. For example, if suspicious activity is detected, the alarm unit will send a notification to nearby residents at the same time as the alarm. For example, when an alarm is sounded, the alarm unit can send detailed information about the alarm to nearby residents. For example, when an alarm is deactivated, the alarm unit can send information that the alarm has been deactivated to nearby residents. This allows for the cooperation of the surrounding community by notifying nearby residents of an alarm.

[0046] The imaging unit can activate multiple cameras simultaneously when an anomaly is detected, allowing it to capture images from different angles. For example, when an anomaly is detected, the imaging unit can activate multiple cameras simultaneously and capture images from different angles. For example, when an anomaly is detected, the imaging unit can automatically adjust the camera positions to capture images from the optimal angle. For example, when an anomaly is detected, the imaging unit can use the camera's zoom function to capture detailed images. This allows for the simultaneous activation of multiple cameras when an anomaly is detected, enabling the capture of detailed images from different angles.

[0047] The imaging unit can use an infrared camera during nighttime imaging. For example, if an anomaly is detected at night, the imaging unit will use the infrared camera to capture the image. For example, if an anomaly is detected at night, the imaging unit can use both the infrared camera and the conventional camera simultaneously for imaging. For example, if an anomaly is detected at night, the imaging unit can adjust the sensitivity of the infrared camera to capture the optimal image. This allows for the capture of clear images even at night.

[0048] The camera unit can simultaneously record audio when an anomaly is detected. For example, if an anomaly is detected, the camera unit can activate the microphone at the same time as the camera and record audio. For example, if an anomaly is detected, the camera unit can automatically adjust the audio recording level to record optimal audio. For example, if an anomaly is detected, the camera unit can record audio and video in sync. This allows for the recording of more detailed information by also recording audio when an anomaly is detected.

[0049] The camera unit can automatically zoom in when an anomaly is detected. For example, if an anomaly is detected, the camera will automatically zoom in to capture detailed footage. For example, if an anomaly is detected, the camera unit can automatically adjust the zoom range to capture optimal footage. For example, if an anomaly is detected, the camera unit can repeatedly zoom in and zoom out to capture detailed footage. This allows for the capture of detailed footage by automatically zooming in when an anomaly is detected.

[0050] The reporting unit can send notifications to the emergency contacts of residents in addition to reporting to the police. For example, if an anomaly is detected, the reporting unit will send a notification to the emergency contacts of residents at the same time as reporting to the police. The reporting unit can, for example, send the details of the report to the emergency contacts of residents to share the situation. When sending the details of the report to emergency contacts, the reporting unit can also include the current situation of the residents. This allows for a quicker response by sending notifications to the emergency contacts of residents at the same time as reporting to the police.

[0051] The reporting unit can transmit video footage captured at the time of a report to the police in real time. For example, if an anomaly is detected, the reporting unit will transmit the captured video footage to the police in real time. For example, the reporting unit can transmit real-time video footage along with the report content to the police, allowing them to immediately understand the situation. For example, when transmitting real-time video footage to the police, the reporting unit can automatically adjust the video quality to ensure optimal transmission. This enables a rapid response by transmitting video footage captured at the time of a report to the police in real time.

[0052] The reporting unit can include the resident's location information in the report. For example, if an anomaly is detected, the reporting unit will include the resident's current location information in the report and send it to the police. By including the resident's location information in the report, the reporting unit can enable the police to respond quickly. When sending a report that includes location information, the reporting unit can automatically adjust the accuracy of the location information to send it in an optimal state. This allows the police to respond quickly by including the resident's location information in the report.

[0053] The notification unit can send a notification to the resident's smartphone when an alarm is triggered. For example, if an anomaly is detected, the notification unit will send a notification to the resident's smartphone at the same time as the alarm is triggered. The notification unit can also send the details of the alarm to the resident's smartphone to share the situation. For example, when sending the details of the alarm, the notification unit can send a real-time notification to the resident's smartphone. This allows residents to immediately understand the anomaly by sending a notification to their smartphone when an alarm is triggered.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] Home robotic pets can monitor the health of residents and sound an alarm if an abnormality is detected. For example, they can sound an alarm to alert residents if they detect abnormalities in heart rate or body temperature. They can also collect and regularly analyze health data to aid in health management. Furthermore, if an abnormality is detected, they can send a notification to emergency contacts. This allows for constant monitoring of residents' health and prompt response if an abnormality occurs.

[0056] Home robotic pets can manage residents' schedules and remind them of important appointments. For example, they can notify residents in advance of important events such as meetings or doctor's appointments. They can also set alarms at appropriate times based on the schedule. Furthermore, they can send real-time notifications if there are any changes to the schedule. This helps residents manage their schedules and ensures they don't forget important appointments.

[0057] Home robotic pets can learn residents' dietary preferences and suggest appropriate recipes. For example, if a resident likes a particular ingredient, the robot can suggest recipes using that ingredient. It can also suggest nutritionally balanced recipes based on the resident's health condition. Furthermore, it can suggest easy-to-prepare recipes that fit the resident's schedule. This allows for the provision of recipes tailored to residents' dietary preferences, supporting a healthy eating lifestyle.

[0058] Home robotic pets can support residents' exercise habits and suggest appropriate exercise plans. For example, if a resident is not getting enough exercise, it can suggest simple exercises. It can also suggest appropriate exercise intensity and frequency based on the resident's health condition. Furthermore, it can suggest the timing of exercise according to the resident's schedule. In this way, it can support residents' exercise habits and promote a healthy lifestyle.

[0059] Home robotic pets can monitor the health of residents' pets and send notifications if abnormalities are detected. For example, if abnormalities in the pet's body temperature or heart rate are detected, a notification can be sent to the resident to alert them. They can also learn the pet's behavior patterns and send notifications if abnormal behavior is detected. Furthermore, by accumulating and regularly analyzing data on the pet's health, it can be used to help with health management. This allows for constant monitoring of the resident's pet's health and prompt response if any abnormalities are detected.

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

[0061] Step 1: The detection unit detects suspicious activity. The detection unit uses an AI agent to monitor the surrounding environment and detect suspicious movements and sounds. For example, it can detect the sound of a window being opened late at night or suspicious movements near the entrance. It can also detect unusual activity in specific areas or time periods. Step 2: The alarm unit sounds an alarm in response to suspicious activity detected by the detection unit. The alarm unit can deter intruders and alert residents by sounding a loud siren. The volume and type of the alarm can also be adjusted using an AI agent. Step 3: The camera unit captures images of the situation when the alarm unit sounds an alarm. The camera unit uses an AI agent to activate the camera and capture images of the situation. When an anomaly is detected, the camera is automatically activated to capture detailed footage of the anomaly. The camera angle and range can also be adjusted. Step 4: The reporting unit reports to the police based on the footage captured by the filming unit. The reporting unit uses an AI agent to analyze the footage and report to the police. If an anomaly is detected, the footage is sent to the police in real time. The content and method of reporting can also be adjusted.

[0062] (Example of form 2) The home robot pet according to an embodiment of the present invention is a highly functional security device equipped with an AI agent that sounds an alarm when it detects suspicious activity, takes pictures of the situation with a camera, and notifies the police. This home robot pet consists of the following steps to protect residents. First, the home robot pet detects suspicious activity. Next, it sounds an alarm in response to the detected suspicious activity. Furthermore, it takes pictures of the situation with a camera and notifies the police. This mechanism can protect residents. For example, the home robot pet's AI agent monitors the surrounding environment and detects suspicious movements or sounds. For example, it can detect the sound of a window being opened late at night or suspicious movements near the entrance. This allows for the early detection of suspicious activity. Next, the home robot pet sounds an alarm in response to the detected suspicious activity. The AI ​​agent immediately sounds an alarm upon detecting suspicious activity to warn residents of danger. For example, it can sound a loud siren to deter intruders and alert residents. Furthermore, the home robot pet can film the situation with its camera and notify the police. The AI ​​agent detects suspicious activity and immediately activates the camera to film the situation. The filmed footage is immediately sent to the police, allowing them to respond quickly. This system allows residents to enjoy both the comfort of a pet and security at the same time. The home robot pet looks like an ordinary pet, yet it can protect residents as a highly functional security device. For example, it has a design that easily blends into daily life, while being able to respond immediately if something unusual occurs. In addition, by using generative AI, it can detect anomalies and sound an alarm to prevent the damage from escalating. Furthermore, it can store information and immediately report it. This enhances the residents' sense of security. In this way, the home robot pet equipped with an AI agent aims to provide residents with a safe and comfortable living environment and enhance their sense of security. For example, it can realize a world where people can enjoy the joy of owning a pet and effective security measures at the same time.This allows home robotic pets to ensure the safety of residents and provide a sense of security.

[0063] The home robot pet according to this embodiment comprises a detection unit, an alarm unit, a camera unit, and a notification unit. The detection unit detects suspicious activity. The detection unit monitors the surrounding environment using, for example, an AI agent and detects suspicious movements or sounds. For example, the detection unit can detect the sound of a window being opened late at night or suspicious movements near the entrance. The detection unit can also use the AI ​​agent to detect abnormal activity in specific areas or time periods. For example, the detection unit can detect abnormal movement in a specific area and sound an alarm. The alarm unit sounds an alarm in response to the suspicious activity detected by the detection unit. The alarm unit can, for example, sound a loud siren to deter intruders and alert residents. The alarm unit can also use the AI ​​agent to adjust the volume and type of the alarm. For example, the alarm unit can adjust the volume of the alarm according to the resident's emotions and provide an appropriate alarm. The camera unit takes pictures of the situation when the alarm unit sounds an alarm. The camera unit, for example, uses an AI agent to activate the camera and film the situation. The camera unit can also automatically activate the camera when an anomaly is detected and film detailed footage of the anomaly. The camera unit can also use the AI ​​agent to adjust the angle and range of the footage. For example, the camera unit can adjust the angle of the footage according to the resident's emotions to provide appropriate footage. The reporting unit reports to the police based on the footage filmed by the camera unit. The reporting unit can also use an AI agent to analyze the footage and report to the police. For example, the reporting unit can transmit the footage to the police in real time when an anomaly is detected. The reporting unit can also use an AI agent to adjust the content and method of the report. For example, the reporting unit can adjust the content of the report according to the resident's emotions to make an appropriate report. As a result, the home robot pet according to this embodiment can protect the resident by detecting suspicious activity, sounding an alarm, filming the situation, and reporting to the police.

[0064] The detection unit detects suspicious activity. For example, the detection unit uses an AI agent to monitor the surrounding environment and detect suspicious movements and sounds. Specifically, the AI ​​agent uses a combination of speech recognition technology and image recognition technology. Speech recognition technology detects unusual sounds such as the sound of a window being opened, glass breaking, or doors opening and closing. This allows for the detection of sounds such as a window being opened late at night or suspicious movements near the entrance. In addition, image recognition technology is used to analyze suspicious movements from camera footage in real time. For example, it can detect unusual movements in a specific area and sound an alarm. The AI ​​agent learns from past data and identifies anomalies by detecting movements that differ from normal activity patterns. Furthermore, the AI ​​agent can also detect unusual activity during specific time periods. For example, it can detect unusual movements during times when activity is normally low, such as late at night or early in the morning, and sound an alarm. As a result, the detection unit can detect even subtle anomalies that residents may not notice, ensuring the safety of residents.

[0065] The alarm unit sounds an alarm in response to suspicious activity detected by the detection unit. The alarm unit can, for example, deter intruders and alert residents by sounding a loud siren. Specifically, the alarm unit uses an AI agent to adjust the volume and type of alarm. The AI ​​agent adjusts the alarm volume according to the resident's emotions and situation, providing an appropriate alarm. For example, if the resident is sleeping at night, the alarm volume is set low, and if the resident is awake, the volume is set high. The type of alarm can also be adjusted. For example, it selects an appropriate alarm depending on the situation, such as a siren, bell, or voice message. Furthermore, the alarm unit can customize the content of the alarm depending on the location and time of the alarm. For example, it can sound the front door alarm in response to suspicious activity near the front door, and the window alarm in response to unusual noises near a window. This allows the alarm unit to provide residents with a quick and appropriate alarm, deterring intruders.

[0066] The camera unit captures the situation with cameras when an alarm is sounded by the alarm unit. The camera unit uses, for example, an AI agent to activate the cameras and capture the situation. Specifically, it automatically activates the cameras when an anomaly is detected and captures detailed footage of the anomaly. The AI ​​agent adjusts the timing of camera activation, the angle of shooting, and the range. For example, it points the camera towards the area where the anomaly was detected and shoots at the optimal angle. The AI ​​agent can also track moving objects during shooting and record detailed footage. Furthermore, the camera unit analyzes the captured footage in real time to understand the details of the anomaly. For example, it can identify the face, clothing, and movements of a suspicious person from the captured footage and save it as evidence to provide to the police. This allows the camera unit to quickly and accurately record the situation when an anomaly occurs, which can be used to assist in subsequent responses.

[0067] The reporting unit reports to the police based on the video footage captured by the filming unit. The reporting unit analyzes the captured video using, for example, an AI agent, and then reports to the police. Specifically, when an anomaly is detected, the captured video is sent to the police in real time. The AI ​​agent analyzes the content of the video and determines whether a report is necessary. For example, it analyzes the face and movements of a suspicious person, unusual sounds, etc., and if a report is necessary, it sends a report to the police. It can also adjust the content and method of the report. For example, it adjusts the content of the report according to the emotions of the residents to make an appropriate report. Furthermore, the reporting unit automatically generates the necessary information for the report and provides it to the police. For example, it generates a report that includes the location and time the anomaly occurred, details of the captured video, etc., and sends it to the police. This allows the reporting unit to report to the police quickly and accurately, ensuring the safety of residents.

[0068] The detection unit can monitor its surroundings and detect suspicious movements and sounds. For example, the detection unit can use an AI agent to monitor its surroundings and detect suspicious movements and sounds. For instance, the detection unit can detect the sound of a window being opened late at night or suspicious movements near the entrance. The detection unit can also use an AI agent to detect abnormal activity in specific areas or time periods. For example, the detection unit can detect abnormal activity in a specific area and sound an alarm. This allows for the early detection of suspicious activity by monitoring the surroundings and detecting suspicious movements and sounds.

[0069] The alarm unit can emit a loud siren. For example, by emitting a loud siren, the alarm unit can deter intruders and alert residents. Furthermore, the alarm unit can use an AI agent to adjust the volume and type of alarm. For instance, the alarm unit can adjust the alarm volume according to the resident's emotions, providing an appropriate alarm. This allows for the use of a loud siren to deter intruders and alert residents.

[0070] The camera unit can activate the camera and record the situation on the scene. For example, the camera unit can use an AI agent to activate the camera and record the situation. For example, the camera unit can automatically activate the camera when an anomaly is detected and record detailed footage of the anomaly. The camera unit can also use an AI agent to adjust the shooting angle and range. For example, the camera unit can adjust the shooting angle according to the emotions of the residents to provide appropriate footage. In this way, evidence can be preserved by activating the camera and recording the situation on the scene.

[0071] The reporting unit can report to the police based on the recorded video footage. For example, the reporting unit can use an AI agent to analyze the recorded video and report it to the police. For example, if an anomaly is detected, the reporting unit can transmit the recorded video to the police in real time. Furthermore, the reporting unit can use an AI agent to adjust the content and method of the report. For example, the reporting unit can adjust the content of the report according to the resident's emotions to make an appropriate report. This allows for a swift response by reporting to the police based on the recorded video footage.

[0072] The detection unit can estimate the resident's emotions and adjust the detection sensitivity of suspicious activity based on the estimated emotions. For example, if the resident is feeling anxious, the detection unit can increase the detection sensitivity to detect even subtle movements and sounds. For example, if the resident is relaxed, the detection unit can lower the detection sensitivity to reduce false positives. For example, if the resident is out, the detection unit can set the detection sensitivity higher than usual to detect anomalies earlier. By adjusting the detection sensitivity according to the resident's emotions, more appropriate detection becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0073] The detection unit can learn the residents' lifestyle patterns and more accurately detect abnormal behavior. For example, the detection unit can learn the pattern of a resident returning home at a fixed time each day and detect any abnormalities that occur during that time. For example, the detection unit can learn the pattern of a resident performing specific actions on specific days of the week and detect any abnormalities that occur on those days. For example, the detection unit can learn the resident's normal range of activity and detect abnormal movements outside that range. In this way, by learning the residents' lifestyle patterns, abnormal behavior can be detected more accurately.

[0074] The detection unit can detect changes in ambient temperature and identify abnormal temperature changes as suspicious activity. For example, if the room temperature rises rapidly, the detection unit can detect the possibility of a fire. For example, if the room temperature drops rapidly, the detection unit can detect the possibility that a window or door has been opened. For example, if the temperature in a specific area changes abnormally, the detection unit can detect suspicious activity in that area. In this way, by detecting changes in ambient temperature, abnormal temperature changes can be identified as suspicious activity.

[0075] The detection unit can estimate the resident's emotions and adjust the timing of detecting suspicious activity based on the estimated emotions. For example, if the resident is feeling anxious, the detection unit can speed up the detection timing to respond quickly. For example, if the resident is relaxed, the detection unit can delay the detection timing to reduce false positives. For example, if the resident is out, the detection unit can set the detection timing earlier than usual to detect anomalies early. By adjusting the detection timing according to the resident's emotions, suspicious activity can be detected at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0076] The detection unit can learn the behavior of residents' pets and detect abnormal behavior in pets. For example, the detection unit can detect abnormal behavior if a pet frequently goes to places it doesn't normally go. For example, the detection unit can detect abnormal behavior if a pet deviates from its normal behavioral patterns. For example, the detection unit can detect abnormal behavior if a pet behaves abnormally during a specific time period. In this way, by learning the behavior of residents' pets, it can detect abnormal behavior in pets.

[0077] The detection unit can work in conjunction with the resident's smart home devices to detect abnormal device operation. For example, the detection unit can detect an abnormality if a smart lock is operated outside of its normal operating hours. For example, the detection unit can detect an abnormality if a smart light is turned on and off irregularly. For example, the detection unit can detect an operation if a smart thermostat is changed to an abnormal temperature setting. In this way, by working in conjunction with the resident's smart home devices, abnormal device operation can be detected.

[0078] The alarm unit can estimate the resident's emotions and adjust the volume and type of alarm based on the estimated emotions. For example, if the resident is feeling anxious, the alarm unit can set the volume high to draw attention. For example, if the resident is relaxed, the alarm unit can set the volume low to avoid causing excessive stress. For example, if the resident is out, the alarm unit can set the volume to maximum to alert those nearby of an anomaly. By adjusting the volume and type of alarm according to the resident's emotions, more effective alarms can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The alarm unit can learn the resident's past alarm history and select the optimal alarm method. For example, the alarm unit can prioritize alarm methods that were effective in the past. For example, the alarm unit can select the alarm method that the resident is most likely to respond to based on past alarm history. For example, the alarm unit can analyze past alarm history and select the optimal alarm timing. In this way, the optimal alarm method can be selected by learning the resident's past alarm history.

[0080] The alarm unit can issue an alarm using light and vibration in addition to an alarm sound. For example, the alarm unit can issue a visual alarm by flashing the room lights simultaneously with the alarm sound. For example, the alarm unit can issue a tactile alarm by activating a vibration device simultaneously with the alarm sound. For example, the alarm unit can issue an alarm by sending a notification to a smartphone simultaneously with the alarm sound. In this way, by using light and vibration in addition to the alarm sound, an alarm can be issued visually and tactilely.

[0081] The alarm unit can estimate the resident's emotions and adjust the alarm duration based on the estimated emotions. For example, if the resident is feeling anxious, the alarm unit can set a longer duration to draw attention. For example, if the resident is relaxed, the alarm unit can set a shorter duration to avoid causing excessive stress. For example, if the resident is out, the alarm unit can set the duration to the maximum to alert those nearby of an anomaly. By adjusting the alarm duration according to the resident's emotions, a more effective alarm can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The alarm unit can send notifications to residents' smartphones. For example, if suspicious activity is detected, the alarm unit will send a notification to the resident's smartphone at the same time as the alarm. For example, when an alarm sounds, the alarm unit can send detailed information about the alarm to the smartphone. For example, when an alarm is deactivated, the alarm unit can send information that the alarm has been deactivated to the smartphone. This allows residents to immediately understand any abnormalities by sending notifications to their smartphones.

[0083] The alarm unit can notify nearby residents of an alarm. For example, if suspicious activity is detected, the alarm unit will send a notification to nearby residents at the same time as the alarm. For example, when an alarm is sounded, the alarm unit can send detailed information about the alarm to nearby residents. For example, when an alarm is deactivated, the alarm unit can send information that the alarm has been deactivated to nearby residents. This allows for the cooperation of the surrounding community by notifying nearby residents of an alarm.

[0084] The camera unit can estimate the resident's emotions and adjust the camera angle and range based on the estimated emotions. For example, if the resident is feeling anxious, the camera unit can adjust the camera angle to capture a wide area. For example, if the resident is relaxed, the camera unit can adjust the camera angle to focus on a specific area. For example, if the resident is out, the camera unit can adjust the camera angle to capture the entire area. This allows for the capture of more appropriate footage by adjusting the camera angle and range according to the resident's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The imaging unit can activate multiple cameras simultaneously when an anomaly is detected, allowing it to capture images from different angles. For example, when an anomaly is detected, the imaging unit can activate multiple cameras simultaneously and capture images from different angles. For example, when an anomaly is detected, the imaging unit can automatically adjust the camera positions to capture images from the optimal angle. For example, when an anomaly is detected, the imaging unit can use the camera's zoom function to capture detailed images. This allows for the simultaneous activation of multiple cameras when an anomaly is detected, enabling the capture of detailed images from different angles.

[0086] The imaging unit can use an infrared camera during nighttime imaging. For example, if an anomaly is detected at night, the imaging unit will use the infrared camera to capture the image. For example, if an anomaly is detected at night, the imaging unit can use both the infrared camera and the conventional camera simultaneously for imaging. For example, if an anomaly is detected at night, the imaging unit can adjust the sensitivity of the infrared camera to capture the optimal image. This allows for the capture of clear images even at night.

[0087] The camera unit can estimate the resident's emotions and adjust the resolution of the footage based on the estimated emotions. For example, if the resident is feeling anxious, the camera unit can shoot in high resolution to provide detailed footage. For example, if the resident is relaxed, the camera unit can shoot in standard resolution to save data. For example, if the resident is out, the camera unit can shoot in high resolution to record any abnormalities in detail. This allows for the provision of more appropriate footage by adjusting the resolution of the footage according to the resident's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The camera unit can simultaneously record audio when an anomaly is detected. For example, if an anomaly is detected, the camera unit can activate the microphone at the same time as the camera and record audio. For example, if an anomaly is detected, the camera unit can automatically adjust the audio recording level to record optimal audio. For example, if an anomaly is detected, the camera unit can record audio and video in sync. This allows for the recording of more detailed information by also recording audio when an anomaly is detected.

[0089] The camera unit can automatically zoom in when an anomaly is detected. For example, if an anomaly is detected, the camera will automatically zoom in to capture detailed footage. For example, if an anomaly is detected, the camera unit can automatically adjust the zoom range to capture optimal footage. For example, if an anomaly is detected, the camera unit can repeatedly zoom in and zoom out to capture detailed footage. This allows for the capture of detailed footage by automatically zooming in when an anomaly is detected.

[0090] The reporting unit can estimate the resident's emotions and adjust the content and method of reporting based on the estimated emotions. For example, if the resident is feeling anxious, the reporting unit can send a detailed report to the police. For example, if the resident is relaxed, the reporting unit can send a concise report to the police. For example, if the resident is out, the reporting unit can send the report quickly. This allows for more appropriate reporting by adjusting the content and method of reporting according to the resident's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The reporting unit can send notifications to the emergency contacts of residents in addition to reporting to the police. For example, if an anomaly is detected, the reporting unit will send a notification to the emergency contacts of residents at the same time as reporting to the police. The reporting unit can, for example, send the details of the report to the emergency contacts of residents to share the situation. When sending the details of the report to emergency contacts, the reporting unit can also include the current situation of the residents. This allows for a quicker response by sending notifications to the emergency contacts of residents at the same time as reporting to the police.

[0092] The reporting unit can transmit video footage captured at the time of a report to the police in real time. For example, if an anomaly is detected, the reporting unit will transmit the captured video footage to the police in real time. For example, the reporting unit can transmit real-time video footage along with the report content to the police, allowing them to immediately understand the situation. For example, when transmitting real-time video footage to the police, the reporting unit can automatically adjust the video quality to ensure optimal transmission. This enables a rapid response by transmitting video footage captured at the time of a report to the police in real time.

[0093] The notification system can estimate the resident's emotions and adjust the timing of the notification based on the estimated emotions. For example, if the resident is feeling anxious, the notification system can speed up the notification to respond quickly. For example, if the resident is relaxed, the notification system can delay the notification to reduce false alarms. For example, if the resident is out, the notification system can set the notification earlier than usual to report any abnormalities early. By adjusting the timing of the notification according to the resident's emotions, it becomes possible to report at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The reporting unit can include the resident's location information in the report. For example, if an anomaly is detected, the reporting unit will include the resident's current location information in the report and send it to the police. By including the resident's location information in the report, the reporting unit can enable the police to respond quickly. When sending a report that includes location information, the reporting unit can automatically adjust the accuracy of the location information to send it in an optimal state. This allows the police to respond quickly by including the resident's location information in the report.

[0095] The notification unit can send a notification to the resident's smartphone when an alarm is triggered. For example, if an anomaly is detected, the notification unit will send a notification to the resident's smartphone at the same time as the alarm is triggered. The notification unit can also send the details of the alarm to the resident's smartphone to share the situation. For example, when sending the details of the alarm, the notification unit can send a real-time notification to the resident's smartphone. This allows residents to immediately understand the anomaly by sending a notification to their smartphone when an alarm is triggered.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] Home robotic pets can monitor the health of residents and sound an alarm if an abnormality is detected. For example, they can sound an alarm to alert residents if they detect abnormalities in heart rate or body temperature. They can also collect and regularly analyze health data to aid in health management. Furthermore, if an abnormality is detected, they can send a notification to emergency contacts. This allows for constant monitoring of residents' health and prompt response if an abnormality occurs.

[0098] Home robotic pets can estimate the emotions of their inhabitants and play music based on those emotions. For example, if an inhabitant is stressed, it can play relaxing music. If an inhabitant is happy, it can play cheerful music to lift their spirits. Furthermore, if an inhabitant is sad, it can play comforting music. This allows for the provision of music tailored to the inhabitant's emotions, creating a comfortable living environment.

[0099] Home robotic pets can manage residents' schedules and remind them of important appointments. For example, they can notify residents in advance of important events such as meetings or doctor's appointments. They can also set alarms at appropriate times based on the schedule. Furthermore, they can send real-time notifications if there are any changes to the schedule. This helps residents manage their schedules and ensures they don't forget important appointments.

[0100] Home robotic pets can estimate the resident's emotions and adjust the lighting brightness based on those emotions. For example, if the resident is relaxed, the lighting can be adjusted to a warm, soft light. If the resident is concentrating, the lighting can be brightened to provide a more conducive work environment. Furthermore, when the resident is going to sleep, the lighting can be gradually dimmed to create a comfortable sleeping environment. In this way, it can provide a lighting environment that responds to the resident's emotions and support a comfortable life.

[0101] Home robotic pets can learn residents' dietary preferences and suggest appropriate recipes. For example, if a resident likes a particular ingredient, the robot can suggest recipes using that ingredient. It can also suggest nutritionally balanced recipes based on the resident's health condition. Furthermore, it can suggest easy-to-prepare recipes that fit the resident's schedule. This allows for the provision of recipes tailored to residents' dietary preferences, supporting a healthy eating lifestyle.

[0102] A home robot pet can estimate the resident's emotions and adjust the room temperature based on those emotions. For example, if the resident feels cold, the room temperature can be raised to a comfortable level. If the resident feels hot, the room temperature can be lowered to provide a cooler environment. Furthermore, if the resident is relaxed, the room temperature can be maintained at an appropriate level to ensure a comfortable environment. In this way, the room temperature can be adjusted according to the resident's emotions, providing a comfortable living environment.

[0103] Home robotic pets can support residents' exercise habits and suggest appropriate exercise plans. For example, if a resident is not getting enough exercise, it can suggest simple exercises. It can also suggest appropriate exercise intensity and frequency based on the resident's health condition. Furthermore, it can suggest the timing of exercise according to the resident's schedule. In this way, it can support residents' exercise habits and promote a healthy lifestyle.

[0104] A home robot pet can estimate the resident's emotions and provide scents based on those estimates. For example, if the resident wants to relax, it can provide the scent of lavender. If the resident wants to concentrate, it can provide the scent of peppermint to enhance focus. Furthermore, when the resident is going to sleep, it can provide the scent of chamomile to support comfortable sleep. In this way, it can provide scents that match the resident's emotions and create a pleasant living environment.

[0105] Home robotic pets can monitor the health of residents' pets and send notifications if abnormalities are detected. For example, if abnormalities in the pet's body temperature or heart rate are detected, a notification can be sent to the resident to alert them. They can also learn the pet's behavior patterns and send notifications if abnormal behavior is detected. Furthermore, by accumulating and regularly analyzing data on the pet's health, it can be used to help with health management. This allows for constant monitoring of the resident's pet's health and prompt response if any abnormalities are detected.

[0106] Home robot pets can estimate the emotions of their inhabitants and communicate based on those estimated emotions. For example, if an inhabitant is feeling lonely, the robot can talk to them to soothe their mood. If the inhabitant is happy, the robot can play with them and share the joy. Furthermore, if the inhabitant is sad, the robot can offer comforting words to provide emotional support. In this way, the robot can communicate in a way that is tailored to the inhabitant's emotions and provide a comfortable living environment.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The detection unit detects suspicious activity. The detection unit uses an AI agent to monitor the surrounding environment and detect suspicious movements and sounds. For example, it can detect the sound of a window being opened late at night or suspicious movements near the entrance. It can also detect unusual activity in specific areas or time periods. Step 2: The alarm unit sounds an alarm in response to suspicious activity detected by the detection unit. The alarm unit can deter intruders and alert residents by sounding a loud siren. The volume and type of the alarm can also be adjusted using an AI agent. Step 3: The camera unit captures images of the situation when the alarm unit sounds an alarm. The camera unit uses an AI agent to activate the camera and capture images of the situation. When an anomaly is detected, the camera is automatically activated to capture detailed footage of the anomaly. The camera angle and range can also be adjusted. Step 4: The reporting unit reports to the police based on the footage captured by the filming unit. The reporting unit uses an AI agent to analyze the footage and report to the police. If an anomaly is detected, the footage is sent to the police in real time. The content and method of reporting can also be adjusted.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0111] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] Each of the multiple elements described above, including the detection unit, alarm unit, imaging unit, and notification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the detection unit uses the camera 42 and microphone 38B of the smart device 14 to detect suspicious movements or sounds, and the control unit 46A monitors the surrounding situation. The alarm unit uses the speaker 40B of the smart device 14 to sound a loud siren to intimidate suspicious individuals. The imaging unit uses the camera 42 of the smart device 14 to photograph the situation. The notification unit analyzes the captured video using the identification processing unit 290 of the data processing unit 12 and notifies the police. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the detection unit, alarm unit, imaging unit, and notification unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the detection unit uses the camera 42 and microphone 238 of the smart glasses 214 to detect suspicious movements or sounds, and the control unit 46A monitors the surrounding situation. The alarm unit uses the speaker 240 of the smart glasses 214 to sound a loud siren to intimidate suspicious individuals. The imaging unit uses the camera 42 of the smart glasses 214 to photograph the situation. The notification unit analyzes the captured video using the identification processing unit 290 of the data processing unit 12 and notifies the police. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0132] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

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

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

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

[0136] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

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

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

[0139] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the detection unit, alarm unit, imaging unit, and notification unit, is implemented in at least one of the following: the headset terminal 314 and the data processing unit 12. For example, the detection unit uses the camera 42 and microphone 238 of the headset terminal 314 to detect suspicious movements or sounds, and the control unit 46A monitors the surrounding situation. The alarm unit uses the speaker 240 of the headset terminal 314 to sound a loud siren to intimidate suspicious individuals. The imaging unit uses the camera 42 of the headset terminal 314 to photograph the situation. The notification unit analyzes the captured video using the identification processing unit 290 of the data processing unit 12 and notifies the police. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

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

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

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

[0152] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0153] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the detection unit, alarm unit, imaging unit, and notification unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the detection unit uses the camera 42 and microphone 238 of the robot 414 to detect suspicious movements or sounds, and the control unit 46A monitors the surrounding situation. The alarm unit uses the speaker 240 of the robot 414 to sound a loud siren to intimidate suspicious individuals. The imaging unit uses the camera 42 of the robot 414 to photograph the situation. The notification unit analyzes the captured video using the identification processing unit 290 of the data processing unit 12 and notifies the police. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0162] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0164] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0165] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0166] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0170] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0171] 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.

[0172] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0173] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0174] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0175] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0177] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0178] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0179] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0180] (Note 1) A detection unit that detects suspicious activity, An alarm unit sounds an alarm in response to suspicious activity detected by the aforementioned detection unit, When the alarm is sounded by the aforementioned alarm unit, the camera unit takes pictures of the situation at the scene with a camera, The system includes a reporting unit that reports to the police based on the video footage captured by the aforementioned filming unit. A system characterized by the following features. (Note 2) The detection unit is It monitors the surrounding environment and detects suspicious movements and sounds. The system described in Appendix 1, characterized by the features described herein. (Note 3) The alarm unit is, Sound a loud siren The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned imaging unit is Activate the camera and take a picture of the situation. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reporting unit, We will report the incident to the police based on the recorded footage. The system described in Appendix 1, characterized by the features described herein. (Note 6) The detection unit is The system estimates the residents' emotions and adjusts the sensitivity of suspicious activity detection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The detection unit is Learns the lifestyle patterns of residents and detects abnormal behavior more accurately. The system described in Appendix 1, characterized by the features described herein. (Note 8) The detection unit is It detects changes in ambient temperature and identifies abnormal temperature changes as suspicious activity. The system described in Appendix 1, characterized by the features described herein. (Note 9) The detection unit is The system estimates the residents' emotions and adjusts the timing of suspicious activity detection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The detection unit is Learns the behavior of residents' pets and detects abnormal pet behavior. The system described in Appendix 1, characterized by the features described herein. (Note 11) The detection unit is It works with residents' smart home devices to detect abnormal device operations. The system described in Appendix 1, characterized by the features described herein. (Note 12) The alarm unit is, The system estimates the residents' emotions and adjusts the volume and type of alarm based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The alarm unit is, The system learns the resident's past alarm history and selects the optimal alarm method. The system described in Appendix 1, characterized by the features described herein. (Note 14) The alarm unit is, In addition to sound, the system uses light and vibration to issue alarms. The system described in Appendix 1, characterized by the features described herein. (Note 15) The alarm unit is, The system estimates the residents' emotions and adjusts the alarm duration based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The alarm unit is, Send a notification to the resident's smartphone. The system described in Appendix 1, characterized by the features described herein. (Note 17) The alarm unit is, Notify nearby residents of the warning. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned imaging unit is The system estimates the residents' emotions and adjusts the shooting angle and range based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned imaging unit is When an anomaly is detected, multiple cameras are activated simultaneously to capture images from different angles. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned imaging unit is Use an infrared camera when shooting at night. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned imaging unit is The system estimates the residents' emotions and adjusts the resolution of the images based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned imaging unit is When an anomaly is detected, audio is also recorded simultaneously. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned imaging unit is Automatically zooms in when an anomaly is detected. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned reporting unit, The system estimates the residents' emotions and adjusts the content and method of reporting based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned reporting unit, In addition to notifying the police, a notification will also be sent to the emergency contacts of the residents. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned reporting unit, The video footage captured at the time of the report is transmitted to the police in real time. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned reporting unit, The system estimates the resident's emotions and adjusts the timing of notifications based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned reporting unit, Include the resident's location information in the report. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned reporting unit, A notification is sent to the resident's smartphone when an alarm is triggered. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A detection unit that detects suspicious activity, An alarm unit sounds an alarm in response to suspicious activity detected by the aforementioned detection unit, When the alarm is sounded by the aforementioned alarm unit, the camera unit takes pictures of the situation at the scene with a camera, The system includes a reporting unit that reports to the police based on the video footage captured by the aforementioned filming unit. A system characterized by the following features.

2. The detection unit is It monitors the surrounding environment and detects suspicious movements and sounds. The system according to feature 1.

3. The alarm unit is, Sound a loud siren The system according to feature 1.

4. The aforementioned imaging unit is Activate the camera and take a picture of the situation. The system according to feature 1.

5. The aforementioned reporting unit, We will report the incident to the police based on the recorded footage. The system according to feature 1.

6. The detection unit is The system estimates the residents' emotions and adjusts the sensitivity of suspicious activity detection based on the estimated emotions. The system according to feature 1.

7. The detection unit is Learns the lifestyle patterns of residents and detects abnormal behavior more accurately. The system according to feature 1.

8. The detection unit is It detects changes in ambient temperature and identifies abnormal temperature changes as suspicious activity. The system according to feature 1.

Images