System

Abstract

The system provided by the embodiment of the invention aims to monitor the safety of the user in real time and quickly detect and notify abnormal or dangerous conditions. A system according to an embodiment includes a recording unit, a transmission unit, an analysis unit, and a notification unit. The recording unit records an image of a user wearing the wearable camera. The transmission unit transmits the video recorded by the recording unit to the cloud server. The analysis unit analyzes the image transmitted by the transmission unit and detects an abnormal behavior or a dangerous situation. The notification unit notifies a family member or a related person of the user of the abnormality or dangerous situation detected by the analysis unit.

Description

TECHNICAL FIELD

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

[0002] Patent Literature 1 discloses a personalized chatbot control method executed by at least one processor, the method including: a step of receiving a user utterance; a step of adding the user utterance to a prompt containing an instruction to a chatbot character; a step of encoding the prompt; and a step of inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

[0003] Patent Literature 1: Japanese Patent Application Publication No. 2022-180282 SUMMARY

[0004] In the prior art, there is a problem that real-time monitoring of user safety and rapid detection and notification of abnormal or dangerous conditions have not been sufficiently implemented.

[0005] The system according to the present technology is aimed at real-time monitoring of user safety and rapid detection and notification of abnormal or dangerous conditions.

[0006] The system according to the present technology includes a recording unit, a transmission unit, an analysis unit, and a notification unit. The recording unit records an image of a user wearing a wearable camera. The transmission unit transmits the image recorded by the recording unit to a cloud server. The analysis unit analyzes the image transmitted by the transmission unit to detect abnormal behavior or dangerous conditions. The notification unit notifies the user's family members or relevant personnel of the abnormal or dangerous conditions detected by the analysis unit.

[0007] The system according to the present technology is capable of real-time monitoring of user safety and rapid detection and notification of abnormal or dangerous conditions. BRIEF DESCRIPTION OF DRAWINGS

[0008] Figure 1 is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment.

[0009] Figure 2 is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment.

[0010] Figure 3 is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment.

[0011] Figure 4 is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment.

[0012] Figure 5is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment.

[0013] Figure 6 is a conceptual diagram showing an example of the main functions of a data processing device and a head-mounted terminal according to the third embodiment.

[0014] Figure 7 is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment.

[0015] Figure 8 is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment.

[0016] Figure 9 is an emotion map in which a plurality of emotions are mapped.

[0017] Figure 10 is an emotion map in which a plurality of emotions are mapped.

[0018] Explanation of Reference Numerals

[0019] 10, 210, 310, 410 data processing system

[0020] 12 data processing device

[0021] 14 intelligent device

[0022] 214 intelligent glasses

[0023] 314 head-mounted terminal

[0024] 414 robot DETAILED DESCRIPTION

[0025] Hereinafter, an example of an embodiment of a system to which the present technology is applied will be described with reference to the accompanying drawings.

[0026] First, words used in the following description will be explained.

[0027] In the following embodiments, the processor (hereinafter, referred to as "processor") denoted by the reference sign can be one arithmetic device or a combination of a plurality of arithmetic devices. In addition, the processor can be one arithmetic device or a combination of a plurality of arithmetic devices. An example of the arithmetic device includes a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit), and the like.

[0028] In the following embodiments, the RAM (Random Access Memory) denoted by the reference sign is a memory that temporarily stores information, and is used as a work memory by the processor.

[0029] In the following embodiments, the memory denoted by the reference sign is one or a plurality of nonvolatile storage devices for storing various programs and various parameters, and the like. An example of the nonvolatile storage device includes a flash memory (SSD (Solid State Drive)), a magnetic disk (for example, a hard disk), a magnetic tape, and the like.

[0030] In the following embodiments, the communication I / F (Interface) denoted by the reference sign is an interface including a communication processor and an antenna, and the like. The communication I / F is responsible for communication between a plurality of computers. An example of the communication standard suitable for the communication I / F includes a wireless communication standard such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0031] 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 can be only A, it can be only B, or it can be a combination of A and B. In addition, in the present specification, when three or more items are described using "and / or", the same idea as "A and / or B" is also applied.

[0032] First Embodiment

[0033] Figure 1 An example of the configuration of the data processing system 10 of the first embodiment is shown.

[0034] like Figure 1 As shown, the data processing system 10 includes a data processing device 12 and an intelligent device 14. An example of the data processing device 12 is a server.

[0035] 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, RAM 30, and memory 32. The processor 28, RAM 30, and memory 32 are connected to a bus 34. Furthermore, the database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. An example of the network 54 includes a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0036] The smart device 14 includes a computer 36, a receiver 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, RAM 48, and memory 50. The processor 46, RAM 48, and memory 50 are connected to a bus 52. Furthermore, the receiver 38, output device 40, and camera 42 are also connected to the bus 52.

[0037] The receiving device 38 includes a touchscreen 38A and a microphone 38B, etc., for receiving user input. The touchscreen 38A receives user input generated by contact with an indicator (e.g., a pen or finger). The microphone 38B receives user input generated by sound by detecting the user's voice. The control unit 46A sends data representing user input received via the touchscreen 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, a specific processing unit 290 (see reference...) Figure 2 Get the data that represents the user input.

[0038] The output device 40 includes a display 40A and a speaker 40B, which present data to the user by outputting data in a user-perceptible format (e.g., sound and / or text). The display 40A displays visual information such as text and images according to instructions from the processor 46. The speaker 40B outputs sound 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 imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0039] Communication I / F 44 is connected to network 54. Communication I / F 44 and 26 are responsible for sending and receiving various information between processor 46 and processor 28 via network 54.

[0040] Figure 2 An example of the main functions of the data processing device 12 and the smart device 14 is shown.

[0041] like Figure 2 As shown, in the data processing apparatus 12, specific processing is performed by the processor 28. A specific processing program 56 is stored in the memory 32. The specific processing program 56 is an example of a "program" as understood in this disclosure. The processor 28 reads the specific processing program 56 from the memory 32 and executes the read specific processing program 56 on the RAM 30. Specific processing is implemented by the processor 28 operating as a specific processing unit 290 based on the specific processing program 56 executed on the RAM 30.

[0042] The memory 32 stores a data generation model 58 and an emotion-specific model 59. The data generation model 58 and the emotion-specific model 59 are used by the specific processing unit 290. The specific processing unit 290 can use the emotion-specific model 59 to infer the user's emotions and perform specific processing based on the user's emotions. The emotion inference function using the emotion-specific model 59 (emotion-specific function) includes inferring and predicting the user's emotions, performing various inferences and predictions related to the user's emotions, but is not limited to this example. Furthermore, emotion inference and prediction also include, for example, emotion analysis (analysis).

[0043] In the smart device 14, specific processing is performed by the processor 46. A specific processing program 60 is stored in the memory 50. The specific processing program 60 is used in conjunction with the data processing system 10. The processor 46 reads the specific processing program 60 from the memory 50 and executes the read specific processing program 60 on the RAM 48. Specific processing is implemented by the processor 46 operating as a control unit 46A based on the specific processing program 60 executed on the RAM 48. Furthermore, the smart device 14 may also have the same data generation model and emotion-specific model as the data generation model 58 and the emotion-specific model 59, and use these models to perform the same processing as the specific processing unit 290.

[0044] 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 the processing results (prediction results, etc.) using the data generation model 58 by communicating with the server device that has the data generation model 58. Furthermore, the data processing device 12 may be a server device or a user-owned terminal device (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of the processing performed by the data processing system 10 of the first embodiment will be described.

[0045] Implementation Method 1

[0046] The wearable camera system described in this invention is designed for crime prevention and addressing issues such as elderly people getting lost. This wearable camera system, worn by the user, records images in real-time from the user's perspective during daily life and transmits them to a cloud server. The cloud server analyzes the transmitted images, detecting abnormal behavior or dangerous situations. For example, a child being approached by a suspicious person, or an elderly person leaving a specific area. Detected abnormalities or dangerous situations immediately notify the user's family or relevant personnel. This enables rapid response, achieving the effects of crime prevention and ensuring the safety of the elderly. For example, the user wearing the wearable camera records images in real-time from the user's perspective during daily life, such as when a child goes to school or when an elderly person is taking a walk. The cloud server then analyzes the transmitted images. Generative AI detects abnormal behavior or dangerous situations in the images. For example, a child being approached by a suspicious person, or an elderly person leaving a specific area. Generative AI analyzes the movements and location information of people in the images to detect abnormalities. Detected abnormalities or dangerous situations immediately notify the user's family or relevant personnel, for example, by sending a notification via a smartphone application. This allows family or relevant personnel to take swift action. For example, if a child is approached by a suspicious person, family members can immediately call the police. If an elderly person leaves a specific area, family members can immediately begin a search. This mechanism enables crime prevention and ensures the safety of the elderly. Because users wear wearable cameras, their behavior is constantly monitored, which may deter suspicious individuals from committing crimes. It also reduces the risk of the elderly getting lost. For instance, if an elderly person wearing a wearable camera leaves a specific area, family members can immediately locate them and respond quickly. Thus, the wearable camera system records, transmits, analyzes, and notifies users of their images, thereby achieving crime prevention and ensuring the safety of the elderly.

[0047] The wearable camera system described in this embodiment includes a recording unit, a transmitting unit, an analysis unit, and a notification unit. The recording unit records images of the user wearing the wearable camera. For example, the recording unit can record images in real time from the user's perspective. The recording unit can also be worn by children going to school or by the elderly taking walks. The transmitting unit transmits the images recorded by the recording unit to a cloud server. For example, the transmitting unit can transmit image data to the cloud server in real time. The transmitting unit can also infer the user's emotions and adjust the timing of image transmission based on the inferred user emotions. The analysis unit analyzes the images transmitted by the transmitting unit and detects abnormal behavior or dangerous situations. For example, the analysis unit can use generative AI to analyze the movement and location information of people in the images to detect anomalies. The analysis unit can also detect anomalies when children are approached by suspicious persons or when the elderly leave a specific area. The analysis unit can also infer the user's emotions and adjust the analysis priority based on the inferred user emotions. The notification unit notifies the user's family or relevant personnel of any anomalies or dangerous situations detected by the analysis unit. For example, the notification unit sends notifications via a smartphone application. The notification unit can also infer the user's emotions and adjust the urgency of the notification based on the inferred user emotions. Therefore, wearable camera systems can prevent crime and protect the safety of the elderly by recording, sending, analyzing, and notifying users of their images.

[0048] The recording unit is capable of recording images in real time from the user's perspective. "Real time" includes, for example, a delay of less than one second, but is not limited to this. The recording unit is capable of recording images in real time from the user's perspective. The recording unit is capable of recording images in real time from the user's perspective. The recording unit is capable of recording images in real time from the user's perspective. Therefore, by recording images in real time from the user's perspective, the situation can be grasped more accurately.

[0049] The analysis unit can analyze the movement and location information of people in images and detect anomalies. Movements include, but are not limited to, speed, direction, and pattern. Location information includes, but is not limited to, GPS and Wi-Fi location information. Thus, by analyzing the movement and location information of people in images, abnormal behavior or dangerous situations can be quickly detected.

[0050] The notification department can send notifications via a smartphone application. This smartphone application may include, but is not limited to, notification functions and alarm functions. Thus, by sending notifications via a smartphone application, family members or relevant personnel can respond quickly.

[0051] The recording device can be worn by children when they go to school or by the elderly when they go for a walk. Children include, but are not limited to, minors and certain age groups. The elderly include, but are not limited to, those 65 years and older or 75 years and older. The recording device can be worn by children when they go to school. The recording device can be worn by the elderly when they go for a walk. The recording device can be worn by children when they go to school. Therefore, wearing the device when children or the elderly are engaged in certain activities can improve safety.

[0052] The analysis unit can detect anomalies when a child is approached by a suspicious person or when an elderly person leaves a specific area. Suspicious persons include, but are not limited to, strangers or specific behavioral patterns. Specific areas include, but are not limited to, the area around the home or designated safe zones. The analysis unit can detect anomalies, for example, when a child is approached by a suspicious person. The analysis unit can detect anomalies, for example, when an elderly person leaves a specific area. The analysis unit can detect anomalies, for example, when a child is approached by a suspicious person. Therefore, by detecting specific abnormal behaviors, rapid response can be achieved.

[0053] The recording unit can analyze user behavior patterns during recording and automatically begin recording when specific behaviors occur. Behavioral patterns include, but are not limited to, daily routines and specific actions. For example, the recording unit can automatically begin recording when the user enters a specific area. The recording unit can automatically begin recording when the user makes a specific gesture. The recording unit can automatically begin recording when the user performs a specific behavior within a specific time period. Therefore, automatically starting recording when specific behaviors occur ensures that important scenes are recorded.

[0054] The recording unit can simultaneously record ambient sound and synchronize video and audio. Ambient sound includes, but is not limited to, microphone type and speech recognition technology. The recording unit can, for example, record video and audio simultaneously and synchronize them during subsequent playback. The recording unit can, for example, analyze ambient sound in real time, highlighting important sounds. The recording unit can, for example, automatically synchronize video and audio to ensure seamless playback. Therefore, by synchronizing video and audio, the situation can be grasped more accurately.

[0055] The recording unit can simultaneously record the user's location information during recording and associate the image with the location information. Location information includes, but is not limited to, GPS and Wi-Fi location information. For example, the recording unit can record GPS data along with the image and display the location information during subsequent playback. The recording unit can also record the user's movement path and associate it with the image during playback. Furthermore, the recording unit can highlight the recorded location information when the user enters a specific area. Thus, by associating the image with the location information, the location information can be displayed during subsequent playback.

[0056] The recording unit can simultaneously record the user's health status (such as heart rate, body temperature, etc.) and issue an alarm when an abnormality is detected. Health status includes, but is not limited to, heart rate, body temperature, and blood pressure. For example, the recording unit can issue an alarm when the user's heart rate abnormally increases. For example, the recording unit can issue an alarm when the user's body temperature abnormally decreases. For example, the recording unit can issue an alarm immediately when the user's health status changes drastically. Therefore, it is possible to monitor the user's health status and respond quickly when an abnormality is detected.

[0057] The sending unit can adjust the compression ratio of the image data during transmission to optimize the transmission speed. Compression ratio includes, but is not limited to, bit rate and compression algorithm. For example, the sending unit can adjust the compression ratio based on network conditions to ensure optimal transmission speed. The sending unit can also adjust the compression ratio based on the importance of the image to ensure the transmission of necessary information. Furthermore, the sending unit can adjust the compression ratio based on user sentiment to provide a suitable transmission speed. Therefore, by adjusting the compression ratio of the image data, optimal transmission speed can be ensured.

[0058] The sending unit can select the sending path based on the load status of the target cloud server during transmission. Load status includes, but is not limited to, CPU utilization and memory utilization. For example, when the cloud server is under high load, the sending unit can send the data to other servers. The sending unit can monitor the cloud server load status in real time and select the optimal sending path. The sending unit can utilize multiple cloud servers for load balancing. Therefore, by considering the cloud server load status, the optimal sending path can be selected.

[0059] The sending unit can select the sending method based on the user's network connection status during transmission. Network connection status includes, but is not limited to, factors such as Wi-Fi signal strength and mobile data speed. For example, the sending unit can prioritize Wi-Fi transmission when a Wi-Fi connection is available. For example, the sending unit can use mobile data transmission when the mobile data connection is stable. For example, the sending unit can automatically select the optimal sending method based on the network connection status. Therefore, by selecting the optimal sending method based on the network connection status, stable transmission can be achieved.

[0060] The transmitting unit can encrypt image data during transmission to ensure data security. Encryption algorithms include, but are not limited to, AES and RSA. The transmitting unit can also encrypt image data before transmission to protect data during transmission. Furthermore, the transmitting unit can use encryption algorithms to prevent data from being accessed by third parties during transmission. Finally, the transmitting unit can automatically encrypt and decrypt image data to ensure security. Therefore, by encrypting image data, data security during transmission can be ensured.

[0061] The analysis unit is capable of identifying objects in an image during analysis and issuing an alert when a specific object appears in the image. Object identification includes, but is not limited to, image recognition algorithms and specific object definitions. For example, the analysis unit can issue an alert when a suspicious object appears in the image. For example, the analysis unit can issue an alert when a specific person appears in the image. For example, the analysis unit can issue an alert when a dangerous object appears in the image. Thus, by performing object identification in the image, an alert can be issued quickly when a specific object appears.

[0062] The analysis unit is capable of analyzing sound within the video during analysis, detecting specific sound patterns. Sound patterns include, but are not limited to, screams and the sound of breaking glass. For example, the analysis unit can detect screams in the video and issue an alarm. For example, the analysis unit can detect abnormal sound patterns in the video and issue an alarm. For example, the analysis unit can detect specific sound patterns in the video and issue an alarm. Thus, by performing sound analysis within the video, specific sound patterns can be detected and responded to quickly.

[0063] The analysis unit can detect anomalies by considering environmental information in the image during analysis. Environmental information includes, but is not limited to, weather, time of day, and temperature. For example, the analysis unit can detect anomalies by considering weather information in the image. For example, the analysis unit can detect anomalies by considering time period information in the image. For example, the analysis unit can detect anomalies by comprehensively considering environmental information in the image. Therefore, by considering environmental information in the image, anomalies can be detected more accurately.

[0064] The parsing unit can learn anomaly patterns by comparing them with past parsing data during parsing, thereby improving detection accuracy. Anomaly patterns include, but are not limited to, comparisons with past data and machine learning algorithms. For example, the parsing unit can learn anomaly patterns based on past parsing data to improve detection accuracy. For example, the parsing unit can compare past parsing data with real-time data to detect anomalies. For example, the parsing unit can utilize past parsing data to achieve early anomaly detection. Thus, by comparing with past parsing data, anomaly patterns can be learned, improving detection accuracy.

[0065] The notification department can automatically summarize the notification content upon notification, sending only important information. The summary may include, but is not limited to, methods for extracting highly important information. The notification department may, for example, automatically summarize the notification content, sending only important information. The notification department may, for example, concisely organize the notification content, highlighting important information. The notification department may, for example, summarize the notification content so that users can quickly understand it. Thus, by summarizing the notification content, important information can be quickly conveyed.

[0066] The notification department can optimize the notification method based on the target device when issuing a notification. Target devices include, but are not limited to, smartphones, tablets, and PCs. For example, when sending a notification to a smartphone, the notification department uses push notifications. When sending a notification to a tablet, the notification department provides a notification method suitable for a larger screen. The notification department can also automatically select the best notification method based on the target device. Therefore, by providing the best notification method based on the target device, rapid response can be achieved.

[0067] The notification department can select the best notification method by considering the location information of the recipient's family members or related personnel when issuing a notification. Location information includes, but is not limited to, GPS and Wi-Fi location information. For example, if the family member or related personnel are nearby, the notification department may suggest direct contact. If the family member or related personnel are far away, the notification department may suggest notifying them by phone or text message. The notification department may also automatically select the best notification method based on the location information of the family member or related personnel. Therefore, by considering the location information of the recipient, the optimal notification method can be selected.

[0068] The notification system supports multiple languages ​​when sending notifications, enabling it to reach family members or relevant personnel who speak different languages. Multilingual support includes, but is not limited to, the types of languages ​​supported and translation algorithms. For example, the notification system can automatically translate the notification content and send it to family members or relevant personnel who speak different languages. The notification system can also display the notification content in multiple languages ​​for the user to choose from. Furthermore, the notification system supports multilingual notification content, allowing for rapid responses to family members or relevant personnel who speak different languages. Therefore, by supporting multilingual notification content, it can quickly respond to family members or relevant personnel who speak different languages.

[0069] The system involved in this embodiment is not limited to the above examples. For example, various modifications can be made as follows.

[0070] Wearable camera systems may also include a battery management unit. This unit monitors the camera's remaining battery level and switches to power-saving mode when necessary. For example, when the battery level is low, the recording frequency of the recording unit can be set lower, and the transmission interval of the transmitting unit can be extended. Furthermore, the battery management unit can optimize battery consumption based on the user's behavior patterns. For instance, when the user plans to be away for an extended period, it can automatically switch to power-saving settings. This extends battery life and maximizes system uptime.

[0071] The recording unit can adjust the frame rate of the video while recording in real time from the user's perspective. For example, when the user is in an environment with little movement, the frame rate can be set to a lower level to save data. Conversely, when the user is engaged in sports or other active activities, the frame rate can be set to a higher level to record smoother video. Furthermore, the recording unit can automatically adjust the frame rate based on the user's movements. Thus, optimal video recording can be achieved according to the specific situation.

[0072] When analyzing the movement and position information of people in images, the analysis unit can take weather information into account. For example, in rainy weather with poor visibility, specific filtering techniques can be used to improve analysis accuracy. Furthermore, when analyzing at night or in low-light conditions, infrared cameras or low-light cameras can be used to enhance analysis accuracy. Moreover, the analysis unit can automatically adjust the analysis algorithm based on weather or time of day. This enables the detection of anomalies under various environmental conditions.

[0073] When sending notifications via a smartphone app, the notification department can customize the notification content. For example, it can change the notification's sound or vibration mode based on user preferences. Furthermore, notifications can be sent in the form of images or videos, not just text. Moreover, the notification department can learn from users' past reactions to suggest the optimal notification method. This enables the most effective notifications to be delivered to users.

[0074] The recording device can be worn by children going to school or by the elderly taking a walk. Furthermore, it can automatically start recording based on specific events or activities. For example, it can automatically start recording when the user participates in sports activities, ensuring no important moments are missed. Additionally, it can automatically start recording when the user arrives at a specific attraction during a trip. Thus, it can record based on specific events or activities.

[0075] The analysis unit can detect anomalies when children are approached by suspicious individuals or when elderly people leave a specific area. Furthermore, it can also detect anomalies based on the user's health status. For example, it can detect anomalies when a user's heart rate rises sharply or their body temperature increases abnormally. Additionally, it can detect anomalies when a user falls or remains motionless for an extended period. Therefore, it is possible to detect anomalies based on the user's health status.

[0076] The recording unit can analyze user behavior patterns during recording and automatically start recording when specific behaviors occur. Furthermore, it can adjust the recording termination time based on user behavior patterns. For example, recording can automatically end when the user leaves a specific area. Additionally, recording can also end when the user makes a specific gesture. This avoids unnecessary image recording and saves data capacity.

[0077] The recording unit can simultaneously record ambient sounds and synchronize video and audio. Furthermore, it can automatically begin recording upon detecting specific sound patterns. For example, it can automatically start recording upon detecting unusual sounds such as screams or breaking glass. Additionally, it can start recording upon detecting dialogue containing specific keywords. This ensures that no important sound events are missed.

[0078] The following is a brief description of the processing flow of Implementation Method 1.

[0079] Step 1: The recording unit records images of the user wearing the wearable camera. For example, the recording unit can record images in real time from the user's perspective. The recording unit is worn, for example, by children going to school or the elderly taking a walk.

[0080] Step 2: The sending unit transmits the images recorded by the recording unit to the cloud server. For example, the sending unit can transmit image data to the cloud server in real time. The sending unit can also predict the user's emotions and adjust the timing of image transmission based on the predicted user emotions.

[0081] Step 3: The analysis unit analyzes the images sent by the sending unit to detect abnormal behavior or dangerous situations. For example, the analysis unit can use generative AI to analyze the movement and location information of people in the images to detect anomalies. The analysis unit can also detect anomalies when children are approached by suspicious persons or when elderly people leave a specific area. The analysis unit can also infer the user's emotions and adjust the analysis priority based on the inferred user emotions.

[0082] Step 4: The Notification Department will notify the user's family or relevant personnel of any abnormal or dangerous situations detected by the Analysis Department. For example, the Notification Department may send a notification via a smartphone application. The Notification Department can also infer the user's emotions and adjust the urgency of the notification based on the inferred emotions.

[0083] Implementation Method 2

[0084] The wearable camera system described in this invention is designed for crime prevention and addressing issues such as elderly people getting lost. This wearable camera system, worn by the user, records images in real-time from the user's perspective during daily life and transmits them to a cloud server. The cloud server analyzes the transmitted images, detecting abnormal behavior or dangerous situations. For example, a child being approached by a suspicious person, or an elderly person leaving a specific area. Detected abnormalities or dangerous situations immediately notify the user's family or relevant personnel. This enables rapid response, achieving the effects of crime prevention and ensuring the safety of the elderly. For example, the user wearing the wearable camera records images in real-time from the user's perspective during daily life, such as when a child goes to school or when an elderly person is taking a walk. The cloud server then analyzes the transmitted images. Generative AI detects abnormal behavior or dangerous situations in the images. For example, a child being approached by a suspicious person, or an elderly person leaving a specific area. Generative AI analyzes the movements and location information of people in the images to detect abnormalities. Detected abnormalities or dangerous situations immediately notify the user's family or relevant personnel, for example, by sending a notification via a smartphone application. This allows family or relevant personnel to take swift action. For example, if a child is approached by a suspicious person, family members can immediately call the police. If an elderly person leaves a specific area, family members can immediately begin a search. This mechanism enables crime prevention and ensures the safety of the elderly. Because users wear wearable cameras, their behavior is constantly monitored, which may deter suspicious individuals from committing crimes. It also reduces the risk of the elderly getting lost. For instance, if an elderly person wearing a wearable camera leaves a specific area, family members can immediately locate them and respond quickly. Thus, the wearable camera system records, transmits, analyzes, and notifies users of their images, thereby achieving crime prevention and ensuring the safety of the elderly.

[0085] The wearable camera system described in this embodiment includes a recording unit, a transmitting unit, an analysis unit, and a notification unit. The recording unit records images of the user wearing the wearable camera. For example, the recording unit can record images in real time from the user's perspective. The recording unit can also be worn by children going to school or by the elderly taking walks. The transmitting unit transmits the images recorded by the recording unit to a cloud server. For example, the transmitting unit can transmit image data to the cloud server in real time. The transmitting unit can also infer the user's emotions and adjust the timing of image transmission based on the inferred user emotions. The analysis unit analyzes the images transmitted by the transmitting unit and detects abnormal behavior or dangerous situations. For example, the analysis unit can use generative AI to analyze the movement and location information of people in the images to detect anomalies. The analysis unit can also detect anomalies when children are approached by suspicious persons or when the elderly leave a specific area. The analysis unit can also infer the user's emotions and adjust the analysis priority based on the inferred user emotions. The notification unit notifies the user's family or relevant personnel of any anomalies or dangerous situations detected by the analysis unit. For example, the notification unit sends notifications via a smartphone application. The notification unit can also infer the user's emotions and adjust the urgency of the notification based on the inferred user emotions. Therefore, wearable camera systems can prevent crime and protect the safety of the elderly by recording, sending, analyzing, and notifying users of their images.

[0086] The recording unit is capable of recording images in real time from the user's perspective. "Real time" includes, for example, a delay of less than one second, but is not limited to this. The recording unit is capable of recording images in real time from the user's perspective. The recording unit is capable of recording images in real time from the user's perspective. The recording unit is capable of recording images in real time from the user's perspective. Therefore, by recording images in real time from the user's perspective, the situation can be grasped more accurately.

[0087] The analysis unit can analyze the movement and location information of people in images and detect anomalies. Movements include, but are not limited to, speed, direction, and pattern. Location information includes, but is not limited to, GPS and Wi-Fi location information. Thus, by analyzing the movement and location information of people in images, abnormal behavior or dangerous situations can be quickly detected.

[0088] The notification department can send notifications via a smartphone application. This smartphone application may include, but is not limited to, notification functions and alarm functions. Thus, by sending notifications via a smartphone application, family members or relevant personnel can respond quickly.

[0089] The recording device can be worn by children when they go to school or by the elderly when they go for a walk. Children include, but are not limited to, minors and certain age groups. The elderly include, but are not limited to, those 65 years and older or 75 years and older. The recording device can be worn by children when they go to school. The recording device can be worn by the elderly when they go for a walk. The recording device can be worn by children when they go to school. Therefore, wearing the device when children or the elderly are engaged in certain activities can improve safety.

[0090] The analysis unit can detect anomalies when a child is approached by a suspicious person or when an elderly person leaves a specific area. Suspicious persons include, but are not limited to, strangers or specific behavioral patterns. Specific areas include, but are not limited to, the area around the home or designated safe zones. The analysis unit can detect anomalies, for example, when a child is approached by a suspicious person. The analysis unit can detect anomalies, for example, when an elderly person leaves a specific area. The analysis unit can detect anomalies, for example, when a child is approached by a suspicious person. Therefore, by detecting specific abnormal behaviors, rapid response can be achieved.

[0091] The recording unit can infer the user's emotions and adjust the recording frequency accordingly. Emotions include, but are not limited to, facial expression analysis and voice analysis. For example, when the user is tense, the recording unit increases the recording frequency to capture detailed footage. When the user is relaxed, the recording unit decreases the recording frequency, recording only necessary scenes. When the user is excited, the recording unit sets the recording frequency to a medium level to avoid missing important scenes. Therefore, by adjusting the recording frequency based on the user's emotions, no important scenes can be missed.

[0092] The recording unit can analyze user behavior patterns during recording and automatically begin recording when specific behaviors occur. Behavioral patterns include, but are not limited to, daily routines and specific actions. For example, the recording unit can automatically begin recording when the user enters a specific area. The recording unit can automatically begin recording when the user makes a specific gesture. The recording unit can automatically begin recording when the user performs a specific behavior within a specific time period. Therefore, automatically starting recording when specific behaviors occur ensures that important scenes are recorded.

[0093] The recording unit can simultaneously record ambient sound and synchronize video and audio. Ambient sound includes, but is not limited to, microphone type and speech recognition technology. The recording unit can, for example, record video and audio simultaneously and synchronize them during subsequent playback. The recording unit can, for example, analyze ambient sound in real time, highlighting important sounds. The recording unit can, for example, automatically synchronize video and audio to ensure seamless playback. Therefore, by synchronizing video and audio, the situation can be grasped more accurately.

[0094] The recording unit can infer the user's emotions and adjust the resolution of the recorded image accordingly. Resolution settings include, but are not limited to, HD and 4K. For example, when the user is tense, the recording unit records at high resolution to provide detailed images. When the user is relaxed, the recording unit records at low resolution to save data volume. When the user is excited, the recording unit records at medium resolution to provide balanced images. Thus, by adjusting the image resolution according to the user's emotions, appropriate image quality can be provided.

[0095] The recording unit can simultaneously record the user's location information during recording and associate the image with the location information. Location information includes, but is not limited to, GPS and Wi-Fi location information. For example, the recording unit can record GPS data along with the image and display the location information during subsequent playback. The recording unit can also record the user's movement path and associate it with the image during playback. Furthermore, the recording unit can highlight the recorded location information when the user enters a specific area. Thus, by associating the image with the location information, the location information can be displayed during subsequent playback.

[0096] The recording unit can simultaneously record the user's health status (such as heart rate, body temperature, etc.) and issue an alarm when an abnormality is detected. Health status includes, but is not limited to, heart rate, body temperature, and blood pressure. For example, the recording unit can issue an alarm when the user's heart rate abnormally increases. For example, the recording unit can issue an alarm when the user's body temperature abnormally decreases. For example, the recording unit can issue an alarm immediately when the user's health status changes drastically. Therefore, it is possible to monitor the user's health status and respond quickly when an abnormality is detected.

[0097] The sending unit can predict the user's emotions and adjust the timing of image transmission accordingly. Emotions include, but are not limited to, facial expression analysis and voice analysis. For example, the sending unit might send images immediately when the user is tense. Or, it might send images at regular intervals when the user is relaxed. Or, it might send images frequently when the user is excited. Thus, by adjusting the transmission timing based on the user's emotions, images can be sent at appropriate times.

[0098] The sending unit can adjust the compression ratio of the image data during transmission to optimize the transmission speed. Compression ratio includes, but is not limited to, bit rate and compression algorithm. For example, the sending unit can adjust the compression ratio based on network conditions to ensure optimal transmission speed. The sending unit can also adjust the compression ratio based on the importance of the image to ensure the transmission of necessary information. Furthermore, the sending unit can adjust the compression ratio based on user sentiment to provide a suitable transmission speed. Therefore, by adjusting the compression ratio of the image data, optimal transmission speed can be ensured.

[0099] The sending unit can select the sending path based on the load status of the target cloud server during transmission. Load status includes, but is not limited to, CPU utilization and memory utilization. For example, when the cloud server is under high load, the sending unit can send the data to other servers. The sending unit can monitor the cloud server load status in real time and select the optimal sending path. The sending unit can utilize multiple cloud servers for load balancing. Therefore, by considering the cloud server load status, the optimal sending path can be selected.

[0100] The sending unit can infer the user's emotions and determine the priority of the images to be sent based on these inferences. Emotions may include, but are not limited to, facial expression analysis and voice analysis. For example, the sending unit may prioritize sending important images when the user is tense. Ordinary images may be sent when the user is relaxed. Specific images may be prioritized when the user is excited. Thus, by determining image priority based on the user's emotions, important images can be sent first.

[0101] The sending unit can select the sending method based on the user's network connection status during transmission. Network connection status includes, but is not limited to, factors such as Wi-Fi signal strength and mobile data speed. For example, the sending unit can prioritize Wi-Fi transmission when a Wi-Fi connection is available. For example, the sending unit can use mobile data transmission when the mobile data connection is stable. For example, the sending unit can automatically select the optimal sending method based on the network connection status. Therefore, by selecting the optimal sending method based on the network connection status, stable transmission can be achieved.

[0102] The transmitting unit can encrypt image data during transmission to ensure data security. Encryption algorithms include, but are not limited to, AES and RSA. The transmitting unit can also encrypt image data before transmission to protect data during transmission. Furthermore, the transmitting unit can use encryption algorithms to prevent data from being accessed by third parties during transmission. Finally, the transmitting unit can automatically encrypt and decrypt image data to ensure security. Therefore, by encrypting image data, data security during transmission can be ensured.

[0103] The parsing unit can infer a user's emotions and adjust the parsing priority accordingly. Emotions include, but are not limited to, facial expression parsing and voice parsing. For example, when a user is tense, the parsing unit increases the parsing priority to quickly detect anomalies. When a user is relaxed, the parsing unit performs parsing at its normal priority. When a user is excited, the parsing unit prioritizes specific parsing actions. Thus, by adjusting the parsing priority based on the user's emotions, anomalies can be detected quickly.

[0104] The analysis unit is capable of identifying objects in an image during analysis and issuing an alert when a specific object appears in the image. Object identification includes, but is not limited to, image recognition algorithms and specific object definitions. For example, the analysis unit can issue an alert when a suspicious object appears in the image. For example, the analysis unit can issue an alert when a specific person appears in the image. For example, the analysis unit can issue an alert when a dangerous object appears in the image. Thus, by performing object identification in the image, an alert can be issued quickly when a specific object appears.

[0105] The analysis unit is capable of analyzing sound within the video during analysis, detecting specific sound patterns. Sound patterns include, but are not limited to, screams and the sound of breaking glass. For example, the analysis unit can detect screams in the video and issue an alarm. For example, the analysis unit can detect abnormal sound patterns in the video and issue an alarm. For example, the analysis unit can detect specific sound patterns in the video and issue an alarm. Thus, by performing sound analysis within the video, specific sound patterns can be detected and responded to quickly.

[0106] The analysis unit can infer a user's emotions and adjust the display of the analysis results accordingly. Emotions include, but are not limited to, facial expression analysis and voice analysis. For example, when a user is tense, the analysis unit provides a concise and highly visual display. When a user is relaxed, the analysis unit provides a display containing detailed information. When a user is anxious, the analysis unit provides a display highlighting key points. Therefore, by adjusting the display of analysis results based on the user's emotions, visibility can be improved.

[0107] The analysis unit can detect anomalies by considering environmental information in the image during analysis. Environmental information includes, but is not limited to, weather, time of day, and temperature. For example, the analysis unit can detect anomalies by considering weather information in the image. For example, the analysis unit can detect anomalies by considering time period information in the image. For example, the analysis unit can detect anomalies by comprehensively considering environmental information in the image. Therefore, by considering environmental information in the image, anomalies can be detected more accurately.

[0108] The parsing unit can learn anomaly patterns by comparing them with past parsing data during parsing, thereby improving detection accuracy. Anomaly patterns include, but are not limited to, comparisons with past data and machine learning algorithms. For example, the parsing unit can learn anomaly patterns based on past parsing data to improve detection accuracy. For example, the parsing unit can compare past parsing data with real-time data to detect anomalies. For example, the parsing unit can utilize past parsing data to achieve early anomaly detection. Thus, by comparing with past parsing data, anomaly patterns can be learned, improving detection accuracy.

[0109] The notification department can infer a user's emotions and adjust the urgency of notifications accordingly. Emotions include, but are not limited to, facial expression analysis and voice analysis. For example, the notification department might prioritize sending high-urgency notifications when the user is anxious, send regular notifications when the user is relaxed, and prioritize sending specific notifications when the user is excited. Thus, by adjusting the urgency of notifications based on the user's emotions, appropriate responses can be achieved.

[0110] The notification department can automatically summarize the notification content upon notification, sending only important information. The summary may include, but is not limited to, methods for extracting highly important information. The notification department may, for example, automatically summarize the notification content, sending only important information. The notification department may, for example, concisely organize the notification content, highlighting important information. The notification department may, for example, summarize the notification content so that users can quickly understand it. Thus, by summarizing the notification content, important information can be quickly conveyed.

[0111] The notification department can optimize the notification method based on the target device when issuing a notification. Target devices include, but are not limited to, smartphones, tablets, and PCs. For example, when sending a notification to a smartphone, the notification department uses push notifications. When sending a notification to a tablet, the notification department provides a notification method suitable for a larger screen. The notification department can also automatically select the best notification method based on the target device. Therefore, by providing the best notification method based on the target device, rapid response can be achieved.

[0112] The notification department can infer a user's emotions and adjust the display of notifications accordingly. Emotions include, but are not limited to, facial expression analysis and voice analysis. For example, when a user is tense, the notification department provides a concise and highly visual display. When a user is relaxed, the notification department provides a display containing detailed information. When a user is anxious, the notification department provides a display highlighting key points. Therefore, by adjusting the notification display based on the user's emotions, visibility can be improved.

[0113] The notification department can select the best notification method by considering the location information of the recipient's family members or related personnel when issuing a notification. Location information includes, but is not limited to, GPS and Wi-Fi location information. For example, if the family member or related personnel are nearby, the notification department may suggest direct contact. If the family member or related personnel are far away, the notification department may suggest notifying them by phone or text message. The notification department may also automatically select the best notification method based on the location information of the family member or related personnel. Therefore, by considering the location information of the recipient, the optimal notification method can be selected.

[0114] The notification system supports multiple languages ​​when sending notifications, enabling it to reach family members or relevant personnel who speak different languages. Multilingual support includes, but is not limited to, the types of languages ​​supported and translation algorithms. For example, the notification system can automatically translate the notification content and send it to family members or relevant personnel who speak different languages. The notification system can also display the notification content in multiple languages ​​for the user to choose from. Furthermore, the notification system supports multilingual notification content, allowing for rapid responses to family members or relevant personnel who speak different languages. Therefore, by supporting multilingual notification content, it can quickly respond to family members or relevant personnel who speak different languages.

[0115] ===Hardware Implementation 1-1===

[0116] Each of the aforementioned elements—recording unit, transmitting unit, parsing unit, and notification unit—is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For instance, the recording unit records images in real-time from the user's perspective using the camera 42 of the smart device 14. The transmitting unit transmits the image data to a cloud server via the communication I / F 44 of the smart device 14. The parsing unit parses the transmitted images using the specific processing unit 290 of the data processing device 12, detecting abnormal behavior or dangerous situations. The notification unit notifies the user's family or relevant personnel of the detected abnormality or dangerous situation using the specific processing unit 290 of the data processing device 12.

[0117] ===Hardware Implementation 1-2===

[0118] Each of the aforementioned elements—recording unit, transmitting unit, parsing unit, and notification unit—is implemented, for example, by at least one of the smart glasses 214 and the data processing device 12. For instance, the recording unit records images in real-time from the user's perspective using the camera 42 of the smart glasses 214. The transmitting unit transmits the image data to a cloud server via the communication I / F 44 of the smart glasses 214. The parsing unit analyzes the transmitted images using the specific processing unit 290 of the data processing device 12, detecting abnormal behavior or dangerous situations. The notification unit notifies the user's family or relevant personnel of the detected abnormalities or dangerous situations using the specific processing unit 290 of the data processing device 12.

[0119] ===Hardware Implementation 1-3===

[0120] Each of the aforementioned elements—recording unit, transmitting unit, parsing unit, and notification unit—is implemented, for example, by at least one of the head-mounted terminal 314 and the data processing device 12. For instance, the recording unit records images in real-time from the user's perspective using the camera 42 of the head-mounted terminal 314. The transmitting unit transmits the image data to a cloud server via the communication I / F 44 of the head-mounted terminal 314. The parsing unit analyzes the transmitted images using the specific processing unit 290 of the data processing device 12, detecting abnormal behavior or dangerous situations. The notification unit notifies the user's family or relevant personnel of the detected abnormality or dangerous situation using the specific processing unit 290 of the data processing device 12.

[0121] ===Hardware Implementation 1-4===

[0122] Each of the aforementioned elements—recording unit, transmitting unit, parsing unit, and notification unit—is implemented, for example, by at least one of the robot 414 and the data processing device 12. For instance, the recording unit records images in real-time from the user's perspective using the robot 414's camera 42. The transmitting unit transmits the image data to a cloud server via the robot 414's communication I / F 44. The parsing unit parses the transmitted images using the data processing device 12's specific processing unit 290, detecting abnormal behavior or dangerous situations. The notification unit notifies the user's family or relevant personnel of the detected abnormalities or dangerous situations using the data processing device 12's specific processing unit 290.

[0123] The system involved in this embodiment is not limited to the above examples. For example, various modifications can be made as follows.

[0124] Wearable camera systems may also include a battery management unit. This unit monitors the camera's remaining battery level and switches to power-saving mode when necessary. For example, when the battery level is low, the recording frequency of the recording unit can be set lower, and the transmission interval of the transmitting unit can be extended. Furthermore, the battery management unit can optimize battery consumption based on the user's behavior patterns. For instance, when the user plans to be away for an extended period, it can automatically switch to power-saving settings. This extends battery life and maximizes system uptime.

[0125] The recording unit can adjust the frame rate of the video while recording in real time from the user's perspective. For example, when the user is in an environment with little movement, the frame rate can be set to a lower level to save data. Conversely, when the user is engaged in sports or other active activities, the frame rate can be set to a higher level to record smoother video. Furthermore, the recording unit can automatically adjust the frame rate based on the user's movements. Thus, optimal video recording can be achieved according to the specific situation.

[0126] When analyzing the movement and position information of people in images, the analysis unit can take weather information into account. For example, in rainy weather with poor visibility, specific filtering techniques can be used to improve analysis accuracy. Furthermore, when analyzing at night or in low-light conditions, infrared cameras or low-light cameras can be used to enhance analysis accuracy. Moreover, the analysis unit can automatically adjust the analysis algorithm based on weather or time of day. This enables the detection of anomalies under various environmental conditions.

[0127] When sending notifications via a smartphone app, the notification department can customize the notification content. For example, it can change the notification's sound or vibration mode based on user preferences. Furthermore, notifications can be sent in the form of images or videos, not just text. Moreover, the notification department can learn from users' past reactions to suggest the optimal notification method. This enables the most effective notifications to be delivered to users.

[0128] The recording device can be worn by children going to school or by the elderly taking a walk. Furthermore, it can automatically start recording based on specific events or activities. For example, it can automatically start recording when the user participates in sports activities, ensuring no important moments are missed. Additionally, it can automatically start recording when the user arrives at a specific attraction during a trip. Thus, it can record based on specific events or activities.

[0129] The analysis unit can detect anomalies when children are approached by suspicious individuals or when elderly people leave a specific area. Furthermore, it can also detect anomalies based on the user's health status. For example, it can detect anomalies when a user's heart rate rises sharply or their body temperature increases abnormally. Additionally, it can detect anomalies when a user falls or remains motionless for an extended period. Therefore, it is possible to detect anomalies based on the user's health status.

[0130] The recording unit can predict a user's emotions and adjust the recording frequency accordingly. Furthermore, it can adjust the resolution of the recorded images based on the user's mood. For example, when a user is tense, high resolution can be used to provide detailed images. Conversely, when a user is relaxed, low resolution can be used to save data. Thus, optimal image recording can be achieved based on the user's emotions.

[0131] The recording unit can analyze user behavior patterns during recording and automatically start recording when specific behaviors occur. Furthermore, it can adjust the recording termination time based on user behavior patterns. For example, recording can automatically end when the user leaves a specific area. Additionally, recording can also end when the user makes a specific gesture. This avoids unnecessary image recording and saves data capacity.

[0132] The recording unit can simultaneously record ambient sounds and synchronize video and audio. Furthermore, it can automatically begin recording upon detecting specific sound patterns. For example, it can automatically start recording upon detecting unusual sounds such as screams or breaking glass. Additionally, it can start recording upon detecting dialogue containing specific keywords. This ensures that no important sound events are missed.

[0133] The recording unit can anticipate the user's emotions and adjust the resolution of the recorded images accordingly. Furthermore, it can adjust the color tone of the recorded images based on the user's emotions. For example, when the user is tense, the color tone can be made vibrant to provide detailed images. Conversely, when the user is relaxed, the images can be recorded with a softer color tone. Thus, optimal image recording can be achieved based on the user's emotions.

[0134] The following is a brief description of the processing flow of Implementation Method 2.

[0135] Step 1: The recording unit records images of the user wearing the wearable camera. For example, the recording unit can record images in real time from the user's perspective. The recording unit is worn, for example, by children going to school or the elderly taking a walk.

[0136] Step 2: The sending unit transmits the images recorded by the recording unit to the cloud server. For example, the sending unit can transmit image data to the cloud server in real time. The sending unit can also predict the user's emotions and adjust the timing of image transmission based on the predicted user emotions.

[0137] Step 3: The analysis unit analyzes the images sent by the sending unit to detect abnormal behavior or dangerous situations. For example, the analysis unit can use generative AI to analyze the movement and location information of people in the images to detect anomalies. The analysis unit can also detect anomalies when children are approached by suspicious persons or when elderly people leave a specific area. The analysis unit can also infer the user's emotions and adjust the analysis priority based on the inferred user emotions.

[0138] Step 4: The Notification Department will notify the user's family or relevant personnel of any abnormal or dangerous situations detected by the Analysis Department. For example, the Notification Department may send a notification via a smartphone application. The Notification Department can also infer the user's emotions and adjust the urgency of the notification based on the inferred emotions.

[0139] The specific processing unit 290 sends 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 voice representing the user's input to the result of the specific processing. The control unit 46A sends the voice data representing the user's 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 voice data.

[0140] Data generation model 58 is what is known as generative AI (Artificial Intelligence). An example of data generation model 58 includes ChatGPT (registered trademark) (Internet search).<URL:https: / / openai.com / blog / chatgpt> Generative AI, such as data generation model 58, is obtained by performing deep learning on a neural network. The data generation model 58 is input with a prompt containing instructions, and with inference data such as speech data representing speech, text data representing text, and image data representing images (e.g., data of still images or data of moving images). The data generation model 58 infers the input inference data according to the instructions shown in the prompt, and outputs the inference result in one or more data forms such as speech data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the above-described specific processing while using the data generation model 58. The data generation model 58 can also be a fine-tuned model to output inference results from prompts without instructions; in this case, the data generation model 58 can output inference results from prompts without instructions. The data processing device 12, etc., includes multiple data generation models 58, including AI other than generative AI. AI other than generative AI includes, but is not limited to, 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. Furthermore, AI can also act as an AI agent. Additionally, when the processing described above is performed by AI, this processing may be partially or entirely performed by AI, but is not limited to this example. Moreover, processing performed by AI, including generative AI, can be replaced by rule-based processing, and vice versa.

[0141] Furthermore, the processing performed by the aforementioned data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it can also be executed jointly by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Additionally, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or external devices, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or external devices.

[0142] The correspondence between each part and the device or control unit is not limited to the examples mentioned above, and various changes can be made.

[0143] Second Implementation Method

[0144] Figure 3 An example of the configuration of the data processing system 210 according to the second embodiment is shown.

[0145] like Figure 3 As shown, the data processing system 210 includes a data processing device 12 and smart glasses 214. One example of the data processing device 12 is a server.

[0146] 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, RAM 30, and memory 32. The processor 28, RAM 30, and memory 32 are connected to a bus 34. Furthermore, the database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. An example of the network 54 includes a WAN and / or LAN, etc.

[0147] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, RAM 48, and memory 50. The processor 46, RAM 48, and memory 50 are connected to a bus 52. Furthermore, the microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0148] Microphone 238 receives user commands by receiving the user's voice. Microphone 238 captures the user's voice, converts the captured sound into speech data, and outputs it to processor 46. Speaker 240 outputs sound according to commands from processor 46.

[0149] The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, used to photograph the user's surroundings (e.g., the shooting range defined by an angle of view equivalent to the field of vision of an average healthy person).

[0150] Communication I / F 44 is connected to network 54. Communication I / F 44 and 26 are responsible for the transmission and reception of various information between processor 46 and processor 28 via network 54. The transmission and reception of various information between processor 46 and processor 28 using communication I / F 44 and 26 is performed in a secure state.

[0151] Figure 4 An example of the main functions of the data processing device 12 and the smart glasses 214 is shown. Figure 4 As shown, in the data processing device 12, specific processing is performed by the processor 28. The memory 32 stores a specific processing program 56.

[0152] The processor 28 reads a specific processing program 56 from the memory 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is implemented by the processor 28 operating as a specific processing unit 290 based on the specific processing program 56 executed on the RAM 30.

[0153] The memory 32 stores a data generation model 58 and an emotion-specific model 59. The data generation model 58 and the emotion-specific model 59 are used by the specific processing unit 290. The specific processing unit 290 can use the emotion-specific model 59 to infer the user's emotions and perform specific processing based on the user's emotions. The emotion inference function using the emotion-specific model 59 (emotion-specific function) includes inferring and predicting the user's emotions, performing various inferences and predictions related to the user's emotions, but is not limited to this example. Furthermore, emotion inference and prediction also include, for example, emotion analysis (analysis).

[0154] In the smart glasses 214, specific processing is performed by the processor 46. A specific processing program 60 is stored in the memory 50. The processor 46 reads the specific processing program 60 from the memory 50 and executes the read specific processing program 60 on the RAM 48. Specific processing is implemented by the processor 46 operating as a control unit 46A based on the specific processing program 60 executed on the RAM 48. Furthermore, the smart glasses 214 may also have the same data generation model and emotion-specific model as the data generation model 58 and the emotion-specific model 59, and use these models to perform the same processing as the specific processing unit 290.

[0155] 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 the processing results (prediction results, etc.) using the data generation model 58 by communicating with the server device that has the data generation model 58. In addition, the data processing device 12 may be a server device or a user-owned terminal device (e.g., a mobile phone, robot, home appliance, etc.).

[0156] The specific processing unit 290 sends 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 voice input representing the user's input to the specific processing result. The control unit 46A sends the voice data representing the user's input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0157] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 includes generative AIs such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt containing instructions is input to the data generation model 58, along with inference data such as speech data representing speech, text data representing text, and image data representing images (e.g., data from still images or moving images). The data generation model 58 infers from the input inference data according to the instructions shown in the prompt, and outputs the inference result in one or more data forms such as speech data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the aforementioned specific processing while using the data generation model 58. The data generation model 58 can also be a fine-tuned model to output inference results from prompts that do not contain instructions; in this case, the data generation model 58 can output inference results from prompts that do not contain instructions. The data processing device 12, etc., includes various data generation models 58, including AIs other than generative AI. AI other than generative AI includes, but is not limited to, 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. Furthermore, AI can also act as an AI agent. Additionally, when the processing described above is performed by AI, this processing may be partially or entirely performed by AI, but is not limited to this example. Moreover, processing performed by AI, including generative AI, can be replaced by rule-based processing, and vice versa.

[0158] The data processing system 210 of the second embodiment performs the same processing as the data processing system 10 of the first embodiment. The processing performed by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it can also be executed jointly by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or external devices, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or external devices.

[0159] The correspondence between each part and the device or control unit is not limited to the examples mentioned above, and various changes can be made.

[0160] Third Implementation Method

[0161] Figure 5An example of the configuration of the data processing system 310 according to the third embodiment is shown.

[0162] like Figure 5 As shown, the data processing system 310 includes a data processing device 12 and a head-mounted terminal 314. An example of the data processing device 12 is a server.

[0163] 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, RAM 30, and memory 32. The processor 28, RAM 30, and memory 32 are connected to a bus 34. Furthermore, the database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. An example of the network 54 includes a WAN and / or LAN, etc.

[0164] The head-mounted terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and memory 50. The processor 46, RAM 48, and memory 50 are connected to a bus 52. Furthermore, the microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0165] Microphone 238 receives user commands by receiving the user's voice. Microphone 238 captures the user's voice, converts the captured sound into speech data, and outputs it to processor 46. Speaker 240 outputs sound according to commands from processor 46.

[0166] The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, used to photograph the user's surroundings (e.g., the shooting range defined by an angle of view equivalent to the field of vision of an average healthy person).

[0167] Communication I / F 44 is connected to network 54. Communication I / F 44 and 26 are responsible for the transmission and reception of various information between processor 46 and processor 28 via network 54. The transmission and reception of various information between processor 46 and processor 28 using communication I / F 44 and 26 is performed in a secure state.

[0168] Figure 6 An example of the main functions of the data processing device 12 and the head-mounted terminal 314 is shown. Figure 6 As shown, in the data processing device 12, specific processing is performed by the processor 28. The memory 32 stores a specific processing program 56.

[0169] The processor 28 reads a specific processing program 56 from the memory 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is implemented by the processor 28 operating as a specific processing unit 290 based on the specific processing program 56 executed on the RAM 30.

[0170] The memory 32 stores a data generation model 58 and an emotion-specific model 59. The data generation model 58 and the emotion-specific model 59 are used by the specific processing unit 290. The specific processing unit 290 can use the emotion-specific model 59 to infer the user's emotions and perform specific processing based on the user's emotions. The emotion inference function using the emotion-specific model 59 (emotion-specific function) includes inferring and predicting the user's emotions, performing various inferences and predictions related to the user's emotions, but is not limited to this example. Furthermore, emotion inference and prediction also include, for example, emotion analysis (analysis).

[0171] In the head-mounted terminal 314, specific processing is performed by the processor 46. A specific program 60 is stored in the memory 50. The processor 46 reads the specific program 60 from the memory 50 and executes the read specific program 60 on the RAM 48. Specific processing is implemented by the processor 46 operating as a control unit 46A based on the specific program 60 executed on the RAM 48. Furthermore, the head-mounted terminal 314 may also have the same data generation model and emotion-specific model as the data generation model 58 and the emotion-specific model 59, and use these models to perform the same processing as the specific processing unit 290.

[0172] 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 the processing results (prediction results, etc.) using the data generation model 58 by communicating with the server device that has the data generation model 58. In addition, the data processing device 12 may be a server device or a user-owned terminal device (e.g., a mobile phone, robot, home appliance, etc.).

[0173] The specific processing unit 290 sends the result of the specific processing to the head-mounted terminal 314. In the head-mounted 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 voice representing the user's input to the specific processing result. The control unit 46A sends the voice data representing the user's input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0174] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 includes generative AIs such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt containing instructions is input to the data generation model 58, along with inference data such as speech data representing speech, text data representing text, and image data representing images (e.g., data from still images or moving images). The data generation model 58 infers from the input inference data according to the instructions shown in the prompt, and outputs the inference result in one or more data forms such as speech data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the aforementioned specific processing while using the data generation model 58. The data generation model 58 can also be a fine-tuned model to output inference results from prompts that do not contain instructions; in this case, the data generation model 58 can output inference results from prompts that do not contain instructions. The data processing device 12, etc., includes various data generation models 58, including AIs other than generative AI. AI other than generative AI includes, but is not limited to, 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. Furthermore, AI can also act as an AI agent. Additionally, when the processing described above is performed by AI, this processing may be partially or entirely performed by AI, but is not limited to this example. Moreover, processing performed by AI, including generative AI, can be replaced by rule-based processing, and vice versa.

[0175] The data processing system 310 of the third embodiment performs the same processing as the data processing system 10 of the first embodiment. The processing performed by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the head-mounted terminal 314, but it can also be executed jointly by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the head-mounted terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the head-mounted terminal 314 or external devices, and the head-mounted terminal 314 acquires or collects information required for processing from the data processing device 12 or external devices.

[0176] The correspondence between each part and the device or control unit is not limited to the examples mentioned above, and various changes can be made.

[0177] Fourth Implementation Method

[0178] Figure 7 An example of the configuration of the data processing system 410 according to the fourth embodiment is shown.

[0179] like Figure 7 As shown, 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.

[0180] 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, RAM 30, and memory 32. The processor 28, RAM 30, and memory 32 are connected to a bus 34. Furthermore, the database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. An example of the network 54 includes a WAN and / or LAN, etc.

[0181] Robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control object 443. The computer 36 includes a processor 46, RAM 48, and memory 50. The processor 46, RAM 48, and memory 50 are connected to a bus 52. Furthermore, the microphone 238, speaker 240, camera 42, and control object 443 are also connected to the bus 52.

[0182] Microphone 238 receives user commands by receiving the user's voice. Microphone 238 captures the user's voice, converts the captured sound into speech data, and outputs it to processor 46. Speaker 240 outputs sound according to commands from processor 46.

[0183] The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, used to photograph the user's surroundings (e.g., the shooting range defined by an angle of view equivalent to the field of vision of an average healthy person).

[0184] Communication I / F 44 is connected to network 54. Communication I / F 44 and 26 are responsible for the transmission and reception of various information between processor 46 and processor 28 via network 54. The transmission and reception of various information between processor 46 and processor 28 using communication I / F 44 and 26 is performed in a secure state.

[0185] The controlled object 443 includes a display device, LEDs for the eyes, and motors for driving the arms, hands, and feet. The posture and movements of the robot 414 are controlled by controlling the motors for the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. In addition, facial expressions of the robot 414 can also be expressed by controlling the illumination state of the LEDs for the robot 414's eyes.

[0186] Figure 8 An example of the main functions of the data processing device 12 and the robot 414 is shown. For example... Figure 8 As shown, in the data processing device 12, specific processing is performed by the processor 28. The memory 32 stores a specific processing program 56.

[0187] The processor 28 reads a specific processing program 56 from the memory 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is implemented by the processor 28 operating as a specific processing unit 290 based on the specific processing program 56 executed on the RAM 30.

[0188] The memory 32 stores a data generation model 58 and an emotion-specific model 59. The data generation model 58 and the emotion-specific model 59 are used by the specific processing unit 290. The specific processing unit 290 can use the emotion-specific model 59 to infer the user's emotions and perform specific processing based on the user's emotions. The emotion inference function using the emotion-specific model 59 (emotion-specific function) includes inferring and predicting the user's emotions, performing various inferences and predictions related to the user's emotions, but is not limited to this example. Furthermore, emotion inference and prediction also include, for example, emotion analysis (analysis).

[0189] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in memory 50. Processor 46 reads the specific program 60 from memory 50 and executes the read specific program 60 on RAM 48. Specific processing is achieved by processor 46 acting as control unit 46A based on the specific program 60 executed on RAM 48. Furthermore, robot 414 may also have the same data generation model and emotion-specific model as data generation model 58 and emotion-specific model 59, and use these models to perform the same processing as specific processing unit 290.

[0190] 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 the processing results (prediction results, etc.) using the data generation model 58 by communicating with the server device that has the data generation model 58. In addition, the data processing device 12 may be a server device or a user-owned terminal device (e.g., a mobile phone, robot, home appliance, etc.).

[0191] The specific processing unit 290 sends 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 voice representing the user's input regarding the result of the specific processing. The control unit 46A sends the voice data representing the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0192] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 includes generative AIs such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt containing instructions is input to the data generation model 58, along with inference data such as speech data representing speech, text data representing text, and image data representing images (e.g., data from still images or moving images). The data generation model 58 infers from the input inference data according to the instructions shown in the prompt, and outputs the inference result in one or more data forms such as speech data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the aforementioned specific processing while using the data generation model 58. The data generation model 58 can also be a fine-tuned model to output inference results from prompts that do not contain instructions; in this case, the data generation model 58 can output inference results from prompts that do not contain instructions. The data processing device 12, etc., includes various data generation models 58, including AIs other than generative AI. AI other than generative AI includes, but is not limited to, 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. Furthermore, AI can also act as an AI agent. Additionally, when the processing described above is performed by AI, this processing may be partially or entirely performed by AI, but is not limited to this example. Moreover, processing performed by AI, including generative AI, can be replaced by rule-based processing, and vice versa.

[0193] The data processing system 410 of the fourth embodiment performs the same processing as the data processing system 10 of the first embodiment. The processing performed by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it can also be executed jointly by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or external devices, and the robot 414 acquires or collects information required for processing from the data processing device 12 or external devices.

[0194] The correspondence between each part and the device or control unit is not limited to the examples mentioned above, and various changes can be made.

[0195] Furthermore, the emotion-specific model 59, serving as an emotion engine, can determine the user's emotion based on a specific mapping. Specifically, the emotion-specific model 59 can determine the user's emotion based on an emotion graph that serves as a specific mapping (see...). Figure 9 The robot's emotions can be determined by the emotion-specific model 59. In addition, the emotion-specific model 59 can also determine the robot's emotions in the same way, and the specific processing unit 290 can also perform specific processing using the robot's emotions.

[0196] Figure 9 This is a diagram representing an emotion map 400 that maps various emotions. In the emotion map 400, emotions are arranged radially from the center in concentric circles. The closer to the center of the concentric circles, the more primitive the emotion is. Further out on the concentric circles, emotions are arranged representing states or actions arising from mood. Emotion is a concept that includes both feelings and mental states. To the left of the concentric circles, emotions generated by reactions occurring in the brain are arranged roughly. To the right of the concentric circles, emotions guided by situational judgments are arranged roughly. Above and below the concentric circles, emotions generated by reactions occurring in the brain and guided by situational judgments are arranged roughly. Furthermore, the emotion of "pleasure" is arranged above the concentric circles, and the emotion of "unpleasantness" is arranged below. Thus, in the emotion map 400, various emotions are mapped according to the structure of emotion generation, while easily generated emotions are mapped nearby.

[0197] These emotions are distributed at the 3 o'clock position on the Emotion Chart 400, and usually fluctuate between peace and unease. In the right half of the Emotion Chart 400, because situational awareness is more dominant than internal feelings, it gives a sense of calm.

[0198] The inner side of the emotion diagram 400 represents the mind, and the outer side of the emotion diagram 400 represents actions. Therefore, the further you go to the outer side of the emotion diagram 400, the more the emotion can be seen (manifested in actions).

[0199] Here, human emotions are based on a balance of various factors such as posture and blood sugar levels. When these balances deviate from the ideal, it indicates unhappiness; when they approach the ideal, it indicates pleasure. In robots, cars, and motorcycles, emotions can also be created based on a balance of factors such as posture and remaining battery power. When these balances deviate from the ideal, it indicates unhappiness; when they approach the ideal, it indicates pleasure. Emotion maps can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Speech Emotion Recognition and Brain Physiological Signal Analysis Systems for Emotion, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). In the left half of the emotion map, emotions belonging to the "Reaction" domain, where sensation is dominant, are arranged. Furthermore, in the right half of the emotion map, emotions belonging to the "Situation" domain, where situational cognition is dominant, are arranged.

[0200] The emotion map defines two types of emotions that promote learning. One is a negative emotion located near the middle of "repentance" or "reflection" on the situation side. That is, when the robot experiences negative emotions such as "I never want to feel this way again" or "I never want to be scolded again." The other is a positive emotion located near "desire" on the response side. That is, when the robot experiences positive feelings such as "wanting more" or "wanting to know more."

[0201] The emotion-specific model 59 feeds user input into a pre-learned neural network to obtain emotion values ​​representing each emotion shown in the emotion graph 400, and determines the user's emotion. This neural network is pre-learned based on multiple learning data sets that combine user input with emotion values ​​representing each emotion shown in the emotion graph 400. Furthermore, this neural network is learned to... Figure 10 As shown in sentiment graph 900, sentiment values ​​in nearby configurations are similar to each other. Figure 10 Examples show that multiple emotions such as "peace of mind", "stability", and "reassurance" have similar emotional values.

[0202] In the above embodiments, a specific processing is described by a single computer 22, but the technology disclosed herein is not limited to this, and distributed processing by multiple computers, including computer 22, is also possible.

[0203] In the above embodiments, an example of storing a specific processing program 56 in memory 32 is illustrated, but the technology disclosed herein is not limited thereto. For example, the specific processing program 56 may also be stored in a portable computer-readable non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed into the computer 22 of the data processing device 12. The processor 28 performs specific processing according to the specific processing program 56.

[0204] Alternatively, the specific processing program 56 can be stored in a storage device such as a server connected to the data processing device 12 via a network 54, and the specific processing program 56 can be downloaded and installed into the computer 22 upon request from the data processing device 12.

[0205] Furthermore, it is not necessary to store the entire specific process 56 in a storage device such as a server connected to the data processing device 12 via the network 54, nor is it necessary to store the entire specific process 56 in the memory 32; a portion of the specific process 56 may also be stored.

[0206] As a hardware resource for performing specific processing, various processors can be used. For example, a CPU is a general-purpose processor that functions as a hardware resource for performing specific processing by executing software, i.e., programs. Additionally, a dedicated circuit can be listed as a processor; it is a processor with a circuit structure specifically designed for performing specific processing, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit). Every processor has built-in or connected memory, and every processor executes specific processing by using memory.

[0207] The hardware resources for performing a specific process can consist of one of these various processors, or a combination of two or more processors of the same or different types (e.g., a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Furthermore, the hardware resources for performing a specific process can also be a single processor.

[0208] As an example of a single processor, the first type consists of a combination of one or more CPUs and software, which functions as a hardware resource to perform specific processing. The second type uses a processor, such as a System-on-a-chip (SoC), which implements the entire system functionality, including multiple hardware resources for performing specific processing, using a single IC chip. In this case, the specific processing is implemented using one or more of the aforementioned processors as hardware resources.

[0209] Furthermore, as the hardware architecture of these various processors, more specifically, circuits combining semiconductor elements and other circuit components can be used. Moreover, the specific process described above is merely an example. Therefore, it goes without saying that, without departing from the main point, unnecessary steps can be removed, new steps can be added, or the processing order can be changed.

[0210] Furthermore, although the above examples have been described in terms of first to fourth embodiments, some or all of these embodiments can be combined. Additionally, the smart device 14, smart glasses 214, head-mounted terminal 314, and robot 414 are just examples and can be combined separately, or other devices may be used. Furthermore, although the above examples have been described in terms of morphological example 1 and morphological example 2, these can also be combined.

[0211] The foregoing descriptions and illustrations are detailed explanations of the parts covered by this disclosure and are merely one example of this disclosure. For instance, the descriptions of the above-described structure, function, role, and effect are just one example of the structure, function, role, and effect of the parts covered by this disclosure. Therefore, it goes without saying that, without departing from the spirit of this disclosure, unnecessary parts can be deleted, new elements can be added, or replacements can be made to the foregoing descriptions and illustrations. Furthermore, to avoid confusion and facilitate understanding of the parts covered by this disclosure, explanations of technical common sense that does not require special explanation for implementing this disclosure have been omitted from the foregoing descriptions and illustrations.

[0212] All documents, patent applications and technical standards described in this specification are incorporated herein by reference as if they were specifically and individually described as incorporated by reference.

Claims

1. A system, characterized by The system comprises: a recording unit configured to record images of a user wearing a wearable camera; a sending unit configured to send the images recorded by the recording unit to a cloud server; an analyzing unit configured to analyze the images sent by the sending unit to detect abnormal behavior or dangerous situations; a notifying unit configured to notify the abnormal or dangerous situations detected by the analyzing unit to the user's family members or relevant personnel.

2. The system of claim 1, wherein the recording unit is capable of recording images in real time from the user's perspective.

3. The system of claim 1, wherein the analyzing unit is capable of analyzing the actions and location information of the people in the images to detect abnormalities.

4. The system of claim 1, wherein the notifying unit sends notifications through a smartphone application.

5. The system of claim 1, wherein the recording unit is worn by children when they go to school or by the elderly when they go for a walk.

6. The system of claim 1, wherein the analyzing unit detects abnormalities when children are approached by suspicious persons or when the elderly leave a certain area.

7. The system of claim 1, wherein the recording unit is capable of inferring the user's emotions and adjusting the recording frequency of the images based on the inferred emotions.

8. The system of claim 1, wherein the recording unit analyzes the user's behavior patterns while recording and automatically starts recording when a certain behavior occurs.

9. The system of claim 1, wherein the recording unit records the surrounding sound at the same time while recording and enables synchronization of the images and sound.

10. The system of claim 1, wherein the recording unit is capable of inferring the user's emotions and adjusting the resolution of the recorded images based on the inferred emotions.

Images