system

Abstract

The system according to this embodiment aims to monitor user safety in real time and to quickly detect and notify of abnormal or dangerous situations. [Solution] The system according to the embodiment comprises a recording unit, a transmission unit, an analysis unit, and a notification unit. The recording unit records video of a user wearing a wearable camera. The transmission unit transmits the video recorded by the recording unit to a cloud server. The analysis unit analyzes the video transmitted by the transmission unit and detects abnormal behavior or dangerous situations. The notification unit notifies the user's family or related parties of the abnormal or dangerous situation detected by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that the safety of users is not sufficiently monitored in real time, and abnormal and dangerous situations are not detected and notified promptly.

[0005] The system according to the embodiment aims to monitor the safety of users in real time and quickly detect and notify abnormal and dangerous situations.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a recording unit, a transmission unit, an analysis unit, and a notification unit. The recording unit records video of a user wearing a wearable camera. The transmission unit transmits the video recorded by the recording unit to a cloud server. The analysis unit analyzes the video transmitted by the transmission unit and detects abnormal behavior or dangerous situations. The notification unit notifies the user's family or other relevant parties of the abnormal or dangerous situations detected by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can monitor user safety in real time and quickly detect and notify of abnormal or dangerous situations. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The wearable camera system according to an embodiment of the present invention is a system for crime deterrence and measures to prevent elderly people from wandering or going missing. This wearable camera system records video in real time from the user's point of view while the user wears the wearable camera and goes about their daily life, and transmits it to a cloud server. The cloud server analyzes the transmitted video and detects abnormal behavior or dangerous situations. For example, this could be when a child is approached by a suspicious person or when an elderly person wanders out of a designated area. Any detected abnormal or dangerous situations are immediately notified to the user's family or relevant parties. This enables a rapid response, thereby deterring crime and ensuring the safety of the elderly. For example, a user wearing a wearable camera goes about their daily life. This camera records video in real time from the user's point of view and transmits it to a cloud server. For example, it could be worn when a child goes to school or when an elderly person goes for a walk. Next, the cloud server analyzes the transmitted video. Using generated AI, it detects abnormal behavior or dangerous situations in the video. For example, this could be when a child is approached by a suspicious person or when an elderly person wanders out of a designated area. The generated AI analyzes the movement and location information of people in the video to detect anomalies. Any detected anomalies or dangerous situations are immediately notified to the user's family or relevant parties. For example, notifications are sent via a smartphone app. This allows family members and relevant parties to respond quickly. For instance, if a child is approached by a suspicious person, the family can immediately call the police. Also, if an elderly person wanders out of a designated area, the family can immediately begin a search. This system helps deter crime and ensure the safety of the elderly. Because the user's actions are constantly monitored when wearing a wearable camera, suspicious individuals will hesitate to commit crimes. It also reduces the risk of elderly people wandering off and going missing. For example, if an elderly person wearing a wearable camera wanders out of a designated area, the family can immediately locate their location and respond quickly. In this way, the wearable camera system can deter crime and ensure the safety of the elderly by recording, transmitting, analyzing, and notifying users of their video footage.

[0029] The wearable camera system according to this embodiment comprises a recording unit, a transmission unit, an analysis unit, and a notification unit. The recording unit records video of a user wearing the wearable camera. The recording unit records video in real time from the user's point of view, for example. The recording unit is worn, for example, when a child goes to school or when an elderly person goes for a walk. The transmission unit transmits the video recorded by the recording unit to a cloud server. The transmission unit transmits video data to the cloud server in real time, for example. The transmission unit can also estimate the user's emotions and adjust the timing of video transmission based on the estimated emotions of the user. The analysis unit analyzes the video transmitted by the transmission unit and detects abnormal behavior or dangerous situations. The analysis unit can also analyze the movement and location information of people in the video using, for example, a generative AI and detect anomalies. The analysis unit can detect anomalies, for example, when a child is approached by a suspicious person or when an elderly person leaves a specific area. The analysis unit can also estimate the user's emotions and adjust the priority of analysis based on the estimated emotions of the user. The notification unit notifies the user's family and relevant parties of any abnormalities or dangerous situations detected by the analysis unit. The notification unit sends notifications, for example, through a smartphone app. The notification unit can also estimate the user's emotions and adjust the urgency of the notification based on those emotions. This allows the wearable camera system to deter crime and ensure the safety of the elderly by recording, transmitting, analyzing, and notifying users of their video footage.

[0030] The recording unit can record video in real time from the user's perspective. Real time includes, for example, a delay of less than 1 second, but is not limited to such examples. The recording unit, for example, records video in real time from the user's perspective. The recording unit, for example, records video in real time from the user's perspective. The recording unit, for example, records video in real time from the user's perspective. This allows for a more accurate understanding of the situation by recording video in real time from the user's perspective.

[0031] The analysis unit can analyze the movement and location information of people in the video and detect anomalies. Movement includes, but is not limited to, speed, direction, and pattern. Location information includes, but is not limited to, GPS and Wi-Fi location information. The analysis unit can, for example, analyze the movement and location information of people in the video and detect anomalies. The analysis unit can, for example, analyze the movement and location information of people in the video and detect anomalies. The analysis unit can, for example, analyze the movement and location information of people in the video and detect anomalies. This allows for the rapid detection of abnormal behavior and dangerous situations by analyzing the movement and location information of people in the video.

[0032] The notification unit can send notifications via a smartphone app. The smartphone app may include, but is not limited to, notification and alert functions. The notification unit can, for example, send notifications via a smartphone app. The notification unit can, for example, send notifications via a smartphone app. The notification unit can, for example, send notifications via a smartphone app. This allows family members and relevant parties to respond quickly by sending notifications via a smartphone app.

[0033] The recording unit can be worn when children go to school or when elderly people go for a walk. Children include, for example, minors, certain age groups, etc., but are not limited to such examples. Elderly people include, for example, those 65 years of age or older, 75 years of age or older, etc., but are not limited to such examples. The recording unit can be worn, for example, when children go to school. The recording unit can be worn, for example, when elderly people go for a walk. The recording unit can be worn, for example, when children go to school. This improves safety when children or elderly people wear it while performing certain activities.

[0034] The analysis unit can detect anomalies when a child is approached by a suspicious person or when an elderly person leaves a designated area. Suspicious persons include, but are not limited to, strangers or individuals exhibiting specific behavioral patterns. Designated areas include, but are not limited to, the area around a house or a designated safe zone. 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 designated area. The analysis unit can detect anomalies, for example, when a child is approached by a suspicious person. This allows for a rapid response by detecting specific abnormal behaviors.

[0035] The recording unit can analyze user behavior patterns during recording and automatically start recording when specific actions occur. Behavior patterns include, but are not limited to, daily routines and specific actions. For example, the recording unit can automatically start recording when a user enters a specific area. For example, the recording unit can automatically start recording when a user performs a specific gesture. For example, the recording unit can automatically start recording when a user performs a specific action during a specific time period. This ensures that important moments are reliably recorded by automatically starting recording when specific actions occur.

[0036] The recording unit can simultaneously record ambient sound during recording, enabling synchronization of video and audio. Ambient sound includes, but is not limited to, microphone type and speech recognition technology. The recording unit can, for example, simultaneously record video and audio and synchronize them during later playback. The recording unit can, for example, analyze ambient sound in real time and highlight important sounds during recording. The recording unit can, for example, automatically synchronize video and audio to ensure a seamless playback. By synchronizing video and audio, a more accurate understanding of the situation becomes possible.

[0037] The recording unit can simultaneously record the user's location information during recording and link the video 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 video and display the location information during later playback. For example, the recording unit can record the user's movement route and play it back in conjunction with the video. For example, the recording unit can highlight and record location information when the user enters a specific area. This allows the location information to be displayed during later playback by linking the video with the location information.

[0038] The recording unit simultaneously records the user's health status (such as heart rate and body temperature) during recording and can issue an alert if an abnormality is detected. Health status includes, but is not limited to, heart rate, body temperature, and blood pressure. For example, the recording unit will issue an alert if the user's heart rate is abnormally high. For example, the recording unit will issue an alert if the user's body temperature is abnormally low. For example, the recording unit will immediately issue an alert if the user's health status changes suddenly. This allows for monitoring of the user's health status and prompt response if an abnormality is detected.

[0039] The transmitting unit can adjust the compression ratio of video data during transmission to optimize the transmission speed. The compression ratio includes, but is not limited to, the bitrate and compression algorithm. For example, the transmitting unit can adjust the compression ratio according to network conditions to ensure an optimal transmission speed. For example, the transmitting unit can adjust the compression ratio according to the importance of the video to ensure the transmission of necessary information. For example, the transmitting unit can adjust the compression ratio according to the user's emotions to provide an appropriate transmission speed. In this way, an optimal transmission speed can be ensured by adjusting the compression ratio of video data.

[0040] The transmitting unit can select a transmission route considering the load status of the destination cloud server during transmission. Load status includes, but is not limited to, CPU usage and memory usage. For example, if the cloud server is under heavy load, the transmitting unit may send to a different server. Alternatively, the transmitting unit may monitor the cloud server load status in real time and select the optimal transmission route. Another option is to use multiple cloud servers to distribute the load during transmission. This allows the transmitting unit to select the optimal transmission route by considering the cloud server load status.

[0041] The transmitting unit can select a transmission method at the time of transmission, taking into account the user's network connection status. Network connection status includes, but is not limited to, Wi-Fi signal strength and mobile data speed. For example, if a Wi-Fi connection is available, the transmitting unit will prioritize Wi-Fi transmission. For example, if a mobile data connection is stable, the transmitting unit will use mobile data for transmission. For example, the transmitting unit will automatically select the optimal transmission method according to the network connection status. This enables stable transmission by selecting the optimal transmission method according to the network connection status.

[0042] The transmitting unit can encrypt video data during transmission, ensuring data security in transit. Encryption includes, but is not limited to, encryption algorithms such as AES and RSA. For example, the transmitting unit can encrypt video data before transmission to protect data in transit. The transmitting unit can, for example, use encryption algorithms to protect data in transit from third parties. The transmitting unit can, for example, automatically encrypt and decrypt video data to ensure security. Thus, by encrypting video data, data security in transit can be ensured.

[0043] The analysis unit performs object recognition in the video during analysis and can issue an alert when a specific object appears in the video. Object recognition includes, but is not limited to, image recognition algorithms and definitions of specific objects. For example, the analysis unit can issue an alert when a suspicious object appears in the video. For example, the analysis unit can issue an alert when a specific person appears in the video. For example, the analysis unit can issue an alert when a dangerous object appears in the video. In this way, by performing object recognition in the video, it is possible to quickly issue an alert when a specific object appears.

[0044] The analysis unit can perform audio analysis within the video during the analysis process and detect specific audio patterns. These audio patterns include, but are not limited to, examples such as screams and the sound of breaking glass. For example, the analysis unit can detect screams in the video and issue an alert. For example, the analysis unit can detect abnormal audio patterns in the video and issue an alert. For example, the analysis unit can detect specific audio patterns in the video and issue an alert. This allows for the detection of specific audio patterns by performing audio analysis within the video, enabling a rapid response.

[0045] The analysis unit can detect anomalies by considering environmental information within the video 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 within the video. For example, the analysis unit can detect anomalies by considering time of day information within the video. For example, the analysis unit can detect anomalies by comprehensively considering environmental information within the video. This allows for more accurate anomaly detection by considering environmental information within the video.

[0046] The analysis unit can improve detection accuracy by learning anomaly patterns by comparing them with past analysis data during analysis. Anomaly patterns include, but are not limited to, comparisons with past data and machine learning algorithms. For example, the analysis unit can improve detection accuracy by learning anomaly patterns based on past analysis data. For example, the analysis unit can detect anomalies by comparing past analysis data with real-time data. For example, the analysis unit can perform early detection of anomalies by utilizing past analysis data. This allows the unit to learn anomaly patterns by comparing them with past analysis data, thereby improving detection accuracy.

[0047] The notification unit can automatically summarize notification content and send only the important information when a notification is sent. Summarization includes, but is not limited to, methods for extracting highly important information. For example, the notification unit can automatically summarize notification content and send only the important information. For example, the notification unit can concisely summarize notification content and highlight important information. For example, the notification unit can summarize notification content to enable users to quickly understand it. This allows for the rapid delivery of important information by summarizing notification content.

[0048] The notification unit can optimize the notification method depending on the recipient device when a notification is sent. Recipient devices include, but are not limited to, smartphones, tablets, and PCs. For example, when sending a notification to a smartphone, the notification unit uses push notifications. For example, when sending a notification to a tablet, the notification unit provides a notification method optimized for large screens. The notification unit automatically selects the optimal notification method depending on the recipient device. This enables a quick response by providing the most suitable notification method for each recipient device.

[0049] The notification unit can select the optimal notification method when sending a notification, taking into account the location information of the family or related parties to be notified. Location information includes, but is not limited to, GPS and Wi-Fi location information. For example, if the family or related parties are nearby, the notification unit may suggest a method of direct contact. If the family or related parties are far away, the notification unit may suggest methods of notification via phone or message. The notification unit can automatically select the optimal notification method based on the location information of the family or related parties. This allows for the selection of the most suitable notification method by considering the location information of the recipient.

[0050] The notification unit can provide multilingual notifications, accommodating family members and stakeholders who speak different languages. Multilingual support includes, but is not limited to, the types of languages ​​supported and translation algorithms. For example, the notification unit can automatically translate the notification content and send it to family members and stakeholders who speak different languages. For example, the notification unit can display the notification content in multiple languages, allowing the user to select the language. For example, the notification unit can provide multilingual notifications, enabling quick responses to family members and stakeholders who speak different languages. This allows for quick responses to family members and stakeholders who speak different languages ​​by providing multilingual notifications.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] The wearable camera system may also include a battery management unit. This unit monitors the camera's battery level and can switch to a power-saving mode as needed. For example, if the battery level is low, it can lower the recording frequency of the recording unit and extend the transmission interval of the transmitting unit. The battery management unit can also optimize battery consumption based on the user's behavior patterns. For instance, if the user plans to be out for an extended period, it can automatically switch to settings that reduce battery consumption. This extends battery life and maximizes system operating time.

[0053] The recording unit can adjust the frame rate of the video when recording video in real time from the user's perspective. For example, if the user is in an environment with little movement, the frame rate can be set low to save data capacity. Conversely, if the user is engaged in sports or active activities, the frame rate can be set high to record smooth video. Furthermore, the recording unit can automatically adjust the frame rate according to the user's movements. This enables optimal video recording tailored to the situation.

[0054] The analysis unit can take weather information into account when analyzing the movement and position of people in video footage. For example, since visibility is poor in rainy weather, specific filtering techniques can be used to improve the accuracy of the analysis. In addition, infrared cameras or low-light cameras can be used to improve the accuracy of analysis in nighttime or dark conditions. Furthermore, the analysis unit can automatically adjust the analysis algorithm according to the weather and time of day. This enables anomaly detection under various environmental conditions.

[0055] The notification unit can customize notification content when sending notifications through a smartphone app. For example, it can change the notification sound and vibration pattern according to the user's preferences. It can also send notifications in formats including images and videos, not just text. Furthermore, the notification unit can learn from the user's past responses and suggest the most effective notification method. This enables the most effective notifications for the user.

[0056] The recording unit can be worn by children going to school or by elderly people taking walks, but it can also automatically start recording in response to specific events or activities. For example, it can automatically start recording when a user participates in a sporting event, ensuring that important moments are not missed. It can also automatically start recording when a user arrives at a specific tourist destination while traveling. This enables recording tailored to specific events and activities.

[0057] The analysis unit can detect anomalies when a child is approached by a suspicious person or when an elderly person leaves a designated area, but it can also detect anomalies while considering the user's health condition. For example, it can detect anomalies if the user's heart rate suddenly increases or their body temperature becomes abnormally high. It can also detect anomalies if the user falls or remains motionless for a long period of time. This enables anomaly detection based on the user's health condition.

[0058] The recording unit can analyze user behavior patterns during recording and automatically start recording when a specific action occurs. Furthermore, it can adjust the recording termination timing based on user behavior patterns. For example, recording can automatically end when the user leaves a specific area. It can also end recording when the user performs a specific gesture. This avoids unnecessary video recording and saves data capacity.

[0059] The recording unit can simultaneously record ambient sounds during recording, enabling synchronization of video and audio. Furthermore, it can automatically start recording when it detects specific sound patterns. For example, it can automatically start recording when it detects unusual sounds such as screams or the sound of breaking glass. It can also start recording when it detects conversations containing specific keywords. This ensures that important audio events are not missed.

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

[0061] Step 1: The recording unit records video of the user wearing the wearable camera. The recording unit records video in real time from the user's point of view, for example. The recording unit is worn, for example, when a child goes to school or when an elderly person goes for a walk. Step 2: The transmitting unit sends the video recorded by the recording unit to the cloud server. The transmitting unit can, for example, send the video data to the cloud server in real time. The transmitting unit can also, for example, estimate the user's emotions and adjust the timing of video transmission based on the estimated user emotions. Step 3: The analysis unit analyzes the video transmitted by the transmission unit to detect abnormal behavior or dangerous situations. The analysis unit, for example, uses generated AI to analyze the movement and location information of people in the video and detect anomalies. For example, the analysis unit detects anomalies when a child is approached by a suspicious person or when an elderly person leaves a specific area. The analysis unit can also, for example, estimate the user's emotions and adjust the analysis priority based on the estimated user emotions. Step 4: The notification unit notifies the user's family and relevant parties of any abnormalities or dangerous situations detected by the analysis unit. The notification unit sends notifications, for example, through a smartphone app. The notification unit can also estimate the user's emotions and adjust the urgency of the notification based on the estimated emotions.

[0062] (Example of form 2) The wearable camera system according to an embodiment of the present invention is a system for crime deterrence and measures to prevent elderly people from wandering or going missing. This wearable camera system records video in real time from the user's point of view while the user wears the wearable camera and goes about their daily life, and transmits it to a cloud server. The cloud server analyzes the transmitted video and detects abnormal behavior or dangerous situations. For example, this could be when a child is approached by a suspicious person or when an elderly person wanders out of a designated area. Any detected abnormal or dangerous situations are immediately notified to the user's family or relevant parties. This enables a rapid response, thereby deterring crime and ensuring the safety of the elderly. For example, a user wearing a wearable camera goes about their daily life. This camera records video in real time from the user's point of view and transmits it to a cloud server. For example, it could be worn when a child goes to school or when an elderly person goes for a walk. Next, the cloud server analyzes the transmitted video. Using generated AI, it detects abnormal behavior or dangerous situations in the video. For example, this could be when a child is approached by a suspicious person or when an elderly person wanders out of a designated area. The generated AI analyzes the movement and location information of people in the video to detect anomalies. Any detected anomalies or dangerous situations are immediately notified to the user's family or relevant parties. For example, notifications are sent via a smartphone app. This allows family members and relevant parties to respond quickly. For instance, if a child is approached by a suspicious person, the family can immediately call the police. Also, if an elderly person wanders out of a designated area, the family can immediately begin a search. This system helps deter crime and ensure the safety of the elderly. Because the user's actions are constantly monitored when wearing a wearable camera, suspicious individuals will hesitate to commit crimes. It also reduces the risk of elderly people wandering off and going missing. For example, if an elderly person wearing a wearable camera wanders out of a designated area, the family can immediately locate their location and respond quickly. In this way, the wearable camera system can deter crime and ensure the safety of the elderly by recording, transmitting, analyzing, and notifying users of their video footage.

[0063] The wearable camera system according to this embodiment comprises a recording unit, a transmission unit, an analysis unit, and a notification unit. The recording unit records video of a user wearing the wearable camera. The recording unit records video in real time from the user's point of view, for example. The recording unit is worn, for example, when a child goes to school or when an elderly person goes for a walk. The transmission unit transmits the video recorded by the recording unit to a cloud server. The transmission unit transmits video data to the cloud server in real time, for example. The transmission unit can also estimate the user's emotions and adjust the timing of video transmission based on the estimated emotions of the user. The analysis unit analyzes the video transmitted by the transmission unit and detects abnormal behavior or dangerous situations. The analysis unit can also analyze the movement and location information of people in the video using, for example, a generative AI and detect anomalies. The analysis unit can detect anomalies, for example, when a child is approached by a suspicious person or when an elderly person leaves a specific area. The analysis unit can also estimate the user's emotions and adjust the priority of analysis based on the estimated emotions of the user. The notification unit notifies the user's family and relevant parties of any abnormalities or dangerous situations detected by the analysis unit. The notification unit sends notifications, for example, through a smartphone app. The notification unit can also estimate the user's emotions and adjust the urgency of the notification based on those emotions. This allows the wearable camera system to deter crime and ensure the safety of the elderly by recording, transmitting, analyzing, and notifying users of their video footage.

[0064] The recording unit can record video in real time from the user's perspective. Real time includes, for example, a delay of less than 1 second, but is not limited to such examples. The recording unit, for example, records video in real time from the user's perspective. The recording unit, for example, records video in real time from the user's perspective. The recording unit, for example, records video in real time from the user's perspective. This allows for a more accurate understanding of the situation by recording video in real time from the user's perspective.

[0065] The analysis unit can analyze the movement and location information of people in the video and detect anomalies. Movement includes, but is not limited to, speed, direction, and pattern. Location information includes, but is not limited to, GPS and Wi-Fi location information. The analysis unit can, for example, analyze the movement and location information of people in the video and detect anomalies. The analysis unit can, for example, analyze the movement and location information of people in the video and detect anomalies. The analysis unit can, for example, analyze the movement and location information of people in the video and detect anomalies. This allows for the rapid detection of abnormal behavior and dangerous situations by analyzing the movement and location information of people in the video.

[0066] The notification unit can send notifications via a smartphone app. The smartphone app may include, but is not limited to, notification and alert functions. The notification unit can, for example, send notifications via a smartphone app. The notification unit can, for example, send notifications via a smartphone app. The notification unit can, for example, send notifications via a smartphone app. This allows family members and relevant parties to respond quickly by sending notifications via a smartphone app.

[0067] The recording unit can be worn when children go to school or when elderly people go for a walk. Children include, for example, minors, certain age groups, etc., but are not limited to such examples. Elderly people include, for example, those 65 years of age or older, 75 years of age or older, etc., but are not limited to such examples. The recording unit can be worn, for example, when children go to school. The recording unit can be worn, for example, when elderly people go for a walk. The recording unit can be worn, for example, when children go to school. This improves safety when children or elderly people wear it while performing certain activities.

[0068] The analysis unit can detect anomalies when a child is approached by a suspicious person or when an elderly person leaves a designated area. Suspicious persons include, but are not limited to, strangers or individuals exhibiting specific behavioral patterns. Designated areas include, but are not limited to, the area around a house or a designated safe zone. 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 designated area. The analysis unit can detect anomalies, for example, when a child is approached by a suspicious person. This allows for a rapid response by detecting specific abnormal behaviors.

[0069] The recording unit can estimate the user's emotions and adjust the recording frequency of the video based on the estimated emotions. Emotions include, but are not limited to, facial expression analysis and voice analysis. For example, if the user is tense, the recording unit will increase the recording frequency to record detailed video. For example, if the user is relaxed, the recording unit will decrease the recording frequency to record only the necessary scenes. For example, if the user is excited, the recording unit will set the recording frequency to a moderate level to ensure that important scenes are not missed. In this way, by adjusting the recording frequency according to the user's emotions, important scenes can be recorded without being missed.

[0070] The recording unit can analyze user behavior patterns during recording and automatically start recording when specific actions occur. Behavior patterns include, but are not limited to, daily routines and specific actions. For example, the recording unit can automatically start recording when a user enters a specific area. For example, the recording unit can automatically start recording when a user performs a specific gesture. For example, the recording unit can automatically start recording when a user performs a specific action during a specific time period. This ensures that important moments are reliably recorded by automatically starting recording when specific actions occur.

[0071] The recording unit can simultaneously record ambient sound during recording, enabling synchronization of video and audio. Ambient sound includes, but is not limited to, microphone type and speech recognition technology. The recording unit can, for example, simultaneously record video and audio and synchronize them during later playback. The recording unit can, for example, analyze ambient sound in real time and highlight important sounds during recording. The recording unit can, for example, automatically synchronize video and audio to ensure a seamless playback. By synchronizing video and audio, a more accurate understanding of the situation becomes possible.

[0072] The recording unit can estimate the user's emotions and adjust the resolution of the recorded video based on the estimated emotions. Resolution settings include, but are not limited to, HD and 4K. For example, if the user is tense, the recording unit will record at high resolution to provide detailed video. For example, if the user is relaxed, the recording unit will record at low resolution to save data capacity. For example, if the user is excited, the recording unit will record at medium resolution to provide balanced video. In this way, appropriate video quality can be provided by adjusting the video resolution according to the user's emotions.

[0073] The recording unit can simultaneously record the user's location information during recording and link the video 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 video and display the location information during later playback. For example, the recording unit can record the user's movement route and play it back in conjunction with the video. For example, the recording unit can highlight and record location information when the user enters a specific area. This allows the location information to be displayed during later playback by linking the video with the location information.

[0074] The recording unit simultaneously records the user's health status (such as heart rate and body temperature) during recording and can issue an alert if an abnormality is detected. Health status includes, but is not limited to, heart rate, body temperature, and blood pressure. For example, the recording unit will issue an alert if the user's heart rate is abnormally high. For example, the recording unit will issue an alert if the user's body temperature is abnormally low. For example, the recording unit will immediately issue an alert if the user's health status changes suddenly. This allows for monitoring of the user's health status and prompt response if an abnormality is detected.

[0075] The transmitter can estimate the user's emotions and adjust the timing of video transmission based on the estimated emotions. Emotions include, but are not limited to, facial expression analysis and voice analysis. For example, if the user is tense, the transmitter will transmit video immediately. For example, if the user is relaxed, the transmitter will transmit video at regular intervals. For example, if the user is excited, the transmitter will transmit video frequently. In this way, by adjusting the transmission timing according to the user's emotions, video can be transmitted at the appropriate time.

[0076] The transmitting unit can adjust the compression ratio of video data during transmission to optimize the transmission speed. The compression ratio includes, but is not limited to, the bitrate and compression algorithm. For example, the transmitting unit can adjust the compression ratio according to network conditions to ensure an optimal transmission speed. For example, the transmitting unit can adjust the compression ratio according to the importance of the video to ensure the transmission of necessary information. For example, the transmitting unit can adjust the compression ratio according to the user's emotions to provide an appropriate transmission speed. In this way, an optimal transmission speed can be ensured by adjusting the compression ratio of video data.

[0077] The transmitting unit can select a transmission route considering the load status of the destination cloud server during transmission. Load status includes, but is not limited to, CPU usage and memory usage. For example, if the cloud server is under heavy load, the transmitting unit may send to a different server. Alternatively, the transmitting unit may monitor the cloud server load status in real time and select the optimal transmission route. Another option is to use multiple cloud servers to distribute the load during transmission. This allows the transmitting unit to select the optimal transmission route by considering the cloud server load status.

[0078] The transmitter can estimate the user's emotions and determine the priority of the videos to transmit based on the estimated emotions. Emotions include, but are not limited to, facial expression analysis and voice analysis. For example, if the user is tense, the transmitter will prioritize transmitting important videos. For example, if the user is relaxed, the transmitter will transmit normal videos. For example, if the user is excited, the transmitter will prioritize transmitting specific videos. In this way, important videos can be transmitted preferentially by determining the priority of videos according to the user's emotions.

[0079] The transmitting unit can select a transmission method at the time of transmission, taking into account the user's network connection status. Network connection status includes, but is not limited to, Wi-Fi signal strength and mobile data speed. For example, if a Wi-Fi connection is available, the transmitting unit will prioritize Wi-Fi transmission. For example, if a mobile data connection is stable, the transmitting unit will use mobile data for transmission. For example, the transmitting unit will automatically select the optimal transmission method according to the network connection status. This enables stable transmission by selecting the optimal transmission method according to the network connection status.

[0080] The transmitting unit can encrypt video data during transmission, ensuring data security in transit. Encryption includes, but is not limited to, encryption algorithms such as AES and RSA. For example, the transmitting unit can encrypt video data before transmission to protect data in transit. The transmitting unit can, for example, use encryption algorithms to protect data in transit from third parties. The transmitting unit can, for example, automatically encrypt and decrypt video data to ensure security. Thus, by encrypting video data, data security in transit can be ensured.

[0081] The analysis unit can estimate the user's emotions and adjust the analysis priority based on the estimated emotions. Emotions include, but are not limited to, facial expression analysis and voice analysis. For example, if the user is tense, the analysis unit will increase the analysis priority to quickly detect anomalies. For example, if the user is relaxed, the analysis unit will perform analysis at the normal priority. For example, if the user is excited, the analysis unit will prioritize certain analyses. In this way, by adjusting the analysis priority according to the user's emotions, anomalies can be detected quickly.

[0082] The analysis unit performs object recognition in the video during analysis and can issue an alert when a specific object appears in the video. Object recognition includes, but is not limited to, image recognition algorithms and definitions of specific objects. For example, the analysis unit can issue an alert when a suspicious object appears in the video. For example, the analysis unit can issue an alert when a specific person appears in the video. For example, the analysis unit can issue an alert when a dangerous object appears in the video. In this way, by performing object recognition in the video, it is possible to quickly issue an alert when a specific object appears.

[0083] The analysis unit can perform audio analysis within the video during the analysis process and detect specific audio patterns. These audio patterns include, but are not limited to, examples such as screams and the sound of breaking glass. For example, the analysis unit can detect screams in the video and issue an alert. For example, the analysis unit can detect abnormal audio patterns in the video and issue an alert. For example, the analysis unit can detect specific audio patterns in the video and issue an alert. This allows for the detection of specific audio patterns by performing audio analysis within the video, enabling a rapid response.

[0084] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. Emotions include, but are not limited to, facial expression analysis and voice analysis. For example, if the user is tense, the analysis unit provides a simple and highly visible display method. For example, if the user is relaxed, the analysis unit provides a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit provides a display method that gets straight to the point. By adjusting the display method of the analysis results according to the user's emotions, visibility is improved.

[0085] The analysis unit can detect anomalies by considering environmental information within the video 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 within the video. For example, the analysis unit can detect anomalies by considering time of day information within the video. For example, the analysis unit can detect anomalies by comprehensively considering environmental information within the video. This allows for more accurate anomaly detection by considering environmental information within the video.

[0086] The analysis unit can improve detection accuracy by learning anomaly patterns by comparing them with past analysis data during analysis. Anomaly patterns include, but are not limited to, comparisons with past data and machine learning algorithms. For example, the analysis unit can improve detection accuracy by learning anomaly patterns based on past analysis data. For example, the analysis unit can detect anomalies by comparing past analysis data with real-time data. For example, the analysis unit can perform early detection of anomalies by utilizing past analysis data. This allows the unit to learn anomaly patterns by comparing them with past analysis data, thereby improving detection accuracy.

[0087] The notification unit can estimate the user's emotions and adjust the urgency of notifications based on those emotions. Emotions include, but are not limited to, facial expression analysis and voice analysis. For example, if the user is stressed, the notification unit will prioritize sending high-urgency notifications. For example, if the user is relaxed, the notification unit will send normal notifications. For example, if the user is excited, the notification unit will prioritize sending specific notifications. This allows for appropriate responses by adjusting the urgency of notifications according to the user's emotions.

[0088] The notification unit can automatically summarize notification content and send only the important information when a notification is sent. Summarization includes, but is not limited to, methods for extracting highly important information. For example, the notification unit can automatically summarize notification content and send only the important information. For example, the notification unit can concisely summarize notification content and highlight important information. For example, the notification unit can summarize notification content to enable users to quickly understand it. This allows for the rapid delivery of important information by summarizing notification content.

[0089] The notification unit can optimize the notification method depending on the recipient device when a notification is sent. Recipient devices include, but are not limited to, smartphones, tablets, and PCs. For example, when sending a notification to a smartphone, the notification unit uses push notifications. For example, when sending a notification to a tablet, the notification unit provides a notification method optimized for large screens. The notification unit automatically selects the optimal notification method depending on the recipient device. This enables a quick response by providing the most suitable notification method for each recipient device.

[0090] The notification unit can estimate the user's emotions and adjust the way notifications are displayed based on those emotions. Emotions include, but are not limited to, facial expression analysis and voice analysis. For example, if the user is tense, the notification unit provides a simple and easily visible display. If the user is relaxed, the notification unit provides a display that includes detailed information. If the user is in a hurry, the notification unit provides a concise display. This improves visibility by adjusting the notification display according to the user's emotions.

[0091] The notification unit can select the optimal notification method when sending a notification, taking into account the location information of the family or related parties to be notified. Location information includes, but is not limited to, GPS and Wi-Fi location information. For example, if the family or related parties are nearby, the notification unit may suggest a method of direct contact. If the family or related parties are far away, the notification unit may suggest methods of notification via phone or message. The notification unit can automatically select the optimal notification method based on the location information of the family or related parties. This allows for the selection of the most suitable notification method by considering the location information of the recipient.

[0092] The notification unit can provide multilingual notifications, accommodating family members and stakeholders who speak different languages. Multilingual support includes, but is not limited to, the types of languages ​​supported and translation algorithms. For example, the notification unit can automatically translate the notification content and send it to family members and stakeholders who speak different languages. For example, the notification unit can display the notification content in multiple languages, allowing the user to select the language. For example, the notification unit can provide multilingual notifications, enabling quick responses to family members and stakeholders who speak different languages. This allows for quick responses to family members and stakeholders who speak different languages ​​by providing multilingual notifications. === Hard Collateral 1-1 === Each of the multiple elements described above, including the recording unit, transmission unit, analysis unit, and notification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the recording unit records video in real time from the user's point of view using the camera 42 of the smart device 14. The transmission unit transmits the video data to a cloud server via the communication I / F 44 of the smart device 14. The analysis unit analyzes the transmitted video using the identification processing unit 290 of the data processing unit 12 to detect abnormal behavior or dangerous situations. The notification unit notifies the user's family or relevant parties of the abnormal or dangerous situations detected by the identification processing unit 290 of the data processing unit 12. === Hard Collateral 1-2 === Each of the multiple elements described above, including the recording unit, transmission unit, analysis unit, and notification unit, is implemented in, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the recording unit records video in real time from the user's point of view using the camera 42 of the smart glasses 214. The transmission unit transmits the video data to a cloud server via the communication I / F 44 of the smart glasses 214. The analysis unit analyzes the transmitted video using the identification processing unit 290 of the data processing unit 12 to detect abnormal behavior or dangerous situations. The notification unit notifies the user's family or relevant parties of the abnormal or dangerous situation detected by the identification processing unit 290 of the data processing unit 12. === Hard Collateral 1-3 === Each of the multiple elements described above, including the recording unit, transmission unit, analysis unit, and notification unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the recording unit records video in real time from the user's point of view using the camera 42 of the headset terminal 314. The transmission unit transmits the video data to a cloud server via the communication I / F 44 of the headset terminal 314. The analysis unit analyzes the transmitted video using the identification processing unit 290 of the data processing unit 12 to detect abnormal behavior or dangerous situations. The notification unit notifies the user's family or relevant parties of the abnormal or dangerous situations detected by the identification processing unit 290 of the data processing unit 12. === Hard Collateral 1-4 === Each of the multiple elements described above, including the recording unit, transmission unit, analysis unit, and notification unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the recording unit records video in real time from the user's point of view using the camera 42 of the robot 414. The transmission unit transmits the video data to a cloud server via the communication I / F 44 of the robot 414. The analysis unit analyzes the video transmitted by the identification processing unit 290 of the data processing unit 12 to detect abnormal behavior or dangerous situations. The notification unit notifies the user's family or relevant parties of the abnormal or dangerous situations detected by the identification processing unit 290 of the data processing unit 12.

[0093] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0094] The wearable camera system may also include a battery management unit. This unit monitors the camera's battery level and can switch to a power-saving mode as needed. For example, if the battery level is low, it can lower the recording frequency of the recording unit and extend the transmission interval of the transmitting unit. The battery management unit can also optimize battery consumption based on the user's behavior patterns. For instance, if the user plans to be out for an extended period, it can automatically switch to settings that reduce battery consumption. This extends battery life and maximizes system operating time.

[0095] The recording unit can adjust the frame rate of the video when recording video in real time from the user's perspective. For example, if the user is in an environment with little movement, the frame rate can be set low to save data capacity. Conversely, if the user is engaged in sports or active activities, the frame rate can be set high to record smooth video. Furthermore, the recording unit can automatically adjust the frame rate according to the user's movements. This enables optimal video recording tailored to the situation.

[0096] The analysis unit can take weather information into account when analyzing the movement and position of people in video footage. For example, since visibility is poor in rainy weather, specific filtering techniques can be used to improve the accuracy of the analysis. In addition, infrared cameras or low-light cameras can be used to improve the accuracy of analysis in nighttime or dark conditions. Furthermore, the analysis unit can automatically adjust the analysis algorithm according to the weather and time of day. This enables anomaly detection under various environmental conditions.

[0097] The notification unit can customize notification content when sending notifications through a smartphone app. For example, it can change the notification sound and vibration pattern according to the user's preferences. It can also send notifications in formats including images and videos, not just text. Furthermore, the notification unit can learn from the user's past responses and suggest the most effective notification method. This enables the most effective notifications for the user.

[0098] The recording unit can be worn by children going to school or by elderly people taking walks, but it can also automatically start recording in response to specific events or activities. For example, it can automatically start recording when a user participates in a sporting event, ensuring that important moments are not missed. It can also automatically start recording when a user arrives at a specific tourist destination while traveling. This enables recording tailored to specific events and activities.

[0099] The analysis unit can detect anomalies when a child is approached by a suspicious person or when an elderly person leaves a designated area, but it can also detect anomalies while considering the user's health condition. For example, it can detect anomalies if the user's heart rate suddenly increases or their body temperature becomes abnormally high. It can also detect anomalies if the user falls or remains motionless for a long period of time. This enables anomaly detection based on the user's health condition.

[0100] The recording unit can estimate the user's emotions and adjust the recording frequency based on those emotions. Furthermore, it can adjust the video resolution based on the user's emotions. For example, if the user is tense, high-resolution recording can be used to provide detailed footage. Conversely, if the user is relaxed, low-resolution recording can be used to conserve data. This enables optimal video recording tailored to the user's emotions.

[0101] The recording unit can analyze user behavior patterns during recording and automatically start recording when a specific action occurs. Furthermore, it can adjust the recording termination timing based on user behavior patterns. For example, recording can automatically end when the user leaves a specific area. It can also end recording when the user performs a specific gesture. This avoids unnecessary video recording and saves data capacity.

[0102] The recording unit can simultaneously record ambient sounds during recording, enabling synchronization of video and audio. Furthermore, it can automatically start recording when it detects specific sound patterns. For example, it can automatically start recording when it detects unusual sounds such as screams or the sound of breaking glass. It can also start recording when it detects conversations containing specific keywords. This ensures that important audio events are not missed.

[0103] The recording unit can estimate the user's emotions and adjust the resolution of the recorded video based on those emotions. Furthermore, it can also adjust the color tone of the recorded video based on the user's emotions. For example, if the user is tense, the color tone can be made more vibrant to provide a more detailed image. Conversely, if the user is relaxed, the color tone can be made more subdued for the recorded video. This enables optimal video recording tailored to the user's emotions.

[0104] The following briefly describes the processing flow for example form 2.

[0105] Step 1: The recording unit records video of the user wearing the wearable camera. The recording unit records video in real time from the user's point of view, for example. The recording unit is worn, for example, when a child goes to school or when an elderly person goes for a walk. Step 2: The transmitting unit sends the video recorded by the recording unit to the cloud server. The transmitting unit can, for example, send the video data to the cloud server in real time. The transmitting unit can also, for example, estimate the user's emotions and adjust the timing of video transmission based on the estimated user emotions. Step 3: The analysis unit analyzes the video transmitted by the transmission unit to detect abnormal behavior or dangerous situations. The analysis unit, for example, uses generated AI to analyze the movement and location information of people in the video and detect anomalies. For example, the analysis unit detects anomalies when a child is approached by a suspicious person or when an elderly person leaves a specific area. The analysis unit can also, for example, estimate the user's emotions and adjust the analysis priority based on the estimated user emotions. Step 4: The notification unit notifies the user's family and relevant parties of any abnormalities or dangerous situations detected by the analysis unit. The notification unit sends notifications, for example, through a smartphone app. The notification unit can also estimate the user's emotions and adjust the urgency of the notification based on the estimated emotions.

[0106] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0107] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0108] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0109] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0110] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0111] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0112] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0113] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0114] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0116] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0117] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0118] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0119] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0120] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0121] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0122] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0124] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0125] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0126] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0127] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0128] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0129] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0130] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0132] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0133] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0134] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0135] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0136] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0137] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0138] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0140] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0141] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0142] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0143] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0144] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0145] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0146] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0148] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0149] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0150] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0151] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0159] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0160] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0161] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0162] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0163] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0164] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0165] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0166] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0167] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0168] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0169] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0170] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0171] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0172] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0173] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0174] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0175] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0176] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0177] [Explanation of Symbols]

[0178] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A recording unit that records video of a user wearing a wearable camera, A transmission unit that transmits the video recorded by the recording unit to a cloud server, An analysis unit analyzes the video transmitted by the aforementioned transmission unit and detects abnormal behavior or dangerous situations. The system includes a notification unit that notifies the user's family or related parties of any abnormalities or dangerous situations detected by the analysis unit. A system characterized by the following features.

2. The aforementioned recording unit is Record video in real time from the user's perspective. The system according to feature 1.

3. The aforementioned analysis unit, The system analyzes the movement and location information of people in the video to detect anomalies. The system according to feature 1.

4. The aforementioned notification unit, Send notifications via a smartphone app. The system according to feature 1.

5. The aforementioned recording unit is Children wear them when going to school or elderly people wear them when going for a walk. The system according to feature 1.

6. The aforementioned analysis unit, The system detects anomalies when a child is approached by a suspicious person or when an elderly person leaves a designated area. The system according to feature 1.

7. The aforementioned recording unit is The system estimates the user's emotions and adjusts the video recording frequency based on those emotions. The system according to feature 1.

8. The aforementioned recording unit is During recording, the system analyzes user behavior patterns and automatically starts recording when specific actions occur. The system according to feature 1.

9. The aforementioned recording unit is During recording, ambient sound is also recorded simultaneously, and the video and audio are synchronized. The system according to feature 1.

10. The aforementioned recording unit is It estimates the user's emotions and adjusts the resolution of the recorded video based on the estimated emotions. The system according to feature 1.

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