system

Abstract

The system according to this embodiment aims to identify suspicious individuals using behavioral patterns and facial recognition, and to provide support for preventing children from getting lost or encountering danger. [Solution] The system according to the embodiment comprises a discrimination unit, a calling unit, a guidance unit, and a notification unit. The discrimination unit identifies suspicious persons using behavioral patterns and facial recognition. The calling unit calls out to the suspicious person identified by the discrimination unit using the voice of a family member. The guidance unit provides route guidance in a conversational format if the person gets lost. The notification unit detects dangers such as traffic lights, cars, and bicycles and provides voice notifications.

Description

Technical Field

[0004] ,

[0006] , , ,

[0005] , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the discrimination of suspicious persons using behavior patterns and face recognition, and the support for lost children and danger avoidance are not sufficiently performed, and there is room for improvement.

[0005] The system according to the embodiment aims to discriminate suspicious persons using behavior patterns and face recognition, and provide support for lost children and danger avoidance.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a discrimination unit, a calling unit, a guidance unit, and a notification unit. The discrimination unit identifies suspicious persons using behavioral patterns and facial recognition. The calling unit calls out to the suspicious person identified by the discrimination unit using the voice of a family member. The guidance unit provides route guidance in a conversational format if the person gets lost. The notification unit detects dangers such as traffic lights, cars, and bicycles and provides voice notifications. [Effects of the Invention]

[0007] The system according to this embodiment can identify suspicious individuals using behavioral patterns and facial recognition, and provide support for preventing children from getting lost or encountering danger. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server. ​​​​​​The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The monitoring system according to an embodiment of the present invention is a new monitoring tool that keeps a close eye on loved ones, like a wearable dashcam, and provides assistance through the voices of family members. This monitoring system has the function of identifying suspicious individuals using behavioral patterns and facial recognition and calling out to them in the voices of family members. For example, if a child is approached by a stranger, the AI ​​analyzes the stranger's behavioral patterns and face, and if it is determined to be a suspicious person, it will call out to the child in the voice of a family member, "What's wrong?" Next, to help in the event of a lost child, it has the function of providing route guidance in a conversational format. For example, if a child asks, "Can you tell me the way home?", the AI ​​will guide them on the route from their current location to their destination. Furthermore, to help avoid danger, it has the function of detecting dangers such as traffic lights, cars, and bicycles and providing voice notifications. For example, if the traffic light turns red or a bicycle approaches from behind, the AI ​​will voice notifications such as "The traffic light is red" or "A bicycle is coming from behind." This tool can also be used as a support tool for visually impaired people. For example, it can detect dangers such as traffic lights, cars, and bicycles while a visually impaired person is walking and provide voice notifications, allowing them to walk safely. Thus, this invention is a next-generation monitoring system equipped with AI, providing peace of mind and safety through the voices of family members. This allows the monitoring system to closely watch over loved ones and provide assistance through the voices of family members.

[0029] The monitoring system according to this embodiment comprises a discrimination unit, a calling unit, a guidance unit, and a notification unit. The discrimination unit identifies suspicious persons using behavioral patterns and facial recognition. The discrimination unit, for example, analyzes behavioral patterns using AI and identifies suspicious persons using facial recognition technology. For example, the discrimination unit analyzes walking patterns and gestures and detects abnormalities by comparing them with normal behavior. The discrimination unit can also extract facial feature points using deep learning and compare them with a database of known suspicious persons. Furthermore, the discrimination unit can comprehensively identify suspicious persons by combining behavioral patterns and facial recognition results. The calling unit calls out to the suspicious person identified by the discrimination unit using the voice of a family member. The calling unit, for example, saves the voice of a family member as recorded data and plays it back when a suspicious person is detected. The calling unit can also generate the voice of a family member using speech synthesis technology and call out in real time. Furthermore, the calling unit can also call out to the family member using the voice of a family member, such as "What's wrong?" or "Are you okay?". The guidance unit provides route guidance in a conversational format if the person gets lost. The guidance unit, for example, uses AI to determine the current location and calculate the route to the destination. For example, the guidance unit obtains the current location using GPS data and guides the user along the optimal route by referring to a map database. The guidance unit can also interact with the user using natural language processing technology and provide route guidance. Furthermore, the guidance unit can generate appropriate answers to user questions and provide voice guidance. The notification unit detects dangers such as traffic lights, cars, and bicycles and provides voice notifications. For example, the notification unit uses AI to recognize the color of a traffic light and announces "The traffic light is red" if it is red. The notification unit can also detect approaching cars and bicycles with sensors and announce "A bicycle is coming from behind" by voice. Furthermore, the notification unit can monitor the user's surroundings in real time and immediately notify the user when danger occurs. As a result, the monitoring system according to this embodiment can keep a close eye on a loved one and provide assistance with the voices of family members.

[0030] The discrimination unit identifies suspicious individuals using behavioral patterns and facial recognition. For example, the discrimination unit analyzes behavioral patterns using AI and identifies suspicious individuals using facial recognition technology. Specifically, the discrimination unit analyzes walking patterns and gestures, and detects abnormalities by comparing them to normal behavior. For example, in walking pattern analysis, it monitors walking speed, stride length, and changes in direction in real time to detect patterns that differ from normal behavior. In gesture analysis, it monitors hand movements and body posture to detect abnormal movements. This data is analyzed based on a model learned by AI, and if abnormal behavior is detected, the person is identified as a suspicious individual. The discrimination unit can also extract facial feature points using deep learning and compare them with a database of known suspicious individuals. Facial recognition technology uses a highly accurate feature point extraction algorithm to capture facial contours and features such as eyes, nose, and mouth, and compares them with known suspicious individuals in the database. Furthermore, the discrimination unit can comprehensively identify suspicious individuals by combining behavioral patterns and facial recognition results. For example, if the behavioral pattern is abnormal and the facial recognition result matches, the person is identified as a suspicious individual with a high probability. In this way, the discrimination unit can identify suspicious individuals through a multifaceted approach, thereby improving the overall security of the system.

[0031] The calling unit calls out to the suspicious person identified by the identification unit using a family member's voice. For example, the calling unit can save a family member's voice as recorded data and play it back when a suspicious person is detected. Specifically, family members' voices are recorded in advance and registered in the system. This allows the system to play back the recorded family member's voice and call out to the suspicious person when one is detected. The calling unit can also generate a family member's voice using speech synthesis technology and call out in real time. Speech synthesis technology can learn the characteristics of the family member's voice and generate a natural-sounding voice. This makes it possible to call out using a family member's voice even if there is no recorded data. Furthermore, the calling unit can also use a family member's voice to ask questions such as "What's wrong?" or "Are you okay?". This puts psychological pressure on the suspicious person and allows the system to check on their situation. Through the playback and generation of voices, the calling unit can deal with suspicious people quickly and effectively.

[0032] The guidance unit provides route guidance in a conversational format if the user gets lost. For example, the guidance unit uses AI to determine the user's current location and calculate the route to their destination. Specifically, the guidance unit obtains the user's current location using GPS data and guides them along the optimal route by referring to a map database. The GPS data is updated in real time, allowing the system to accurately determine the user's current location. The map database contains information such as road information, landmarks, and public facilities, and the system uses this information to calculate the optimal route. The guidance unit can also interact with the user using natural language processing technology to provide route guidance. Natural language processing technology can understand the user's questions and instructions and generate appropriate answers. For example, if the user asks, "Where is the nearest station?", the guidance unit will calculate the route from the current location to the nearest station and provide voice guidance. Furthermore, the guidance unit can generate appropriate answers to the user's questions and provide voice guidance. As a result, the guidance unit can provide quick and accurate route guidance even if the user gets lost, ensuring the user's safety.

[0033] The notification unit detects hazards such as traffic lights, cars, and bicycles and provides voice alerts. For example, the notification unit uses AI to recognize the color of traffic lights and announces "The traffic light is red" if it is red. Specifically, it monitors the color of traffic lights in real time using cameras and sensors, and the AI ​​analyzes that information. When a red light is detected, it alerts the user by announcing "The traffic light is red." The notification unit can also detect approaching cars and bicycles with sensors and announce "A bicycle is coming from behind" by voice. The sensors use ultrasound and infrared to detect movement in the surroundings, and the AI ​​analyzes the data to determine the approach of cars and bicycles. Furthermore, the notification unit can monitor the user's surroundings in real time and provide immediate alerts when danger occurs. For example, if a car is rapidly approaching while a pedestrian is crossing a crosswalk, it will warn "A car is approaching" by voice. In this way, the notification unit can ensure the user's safety and minimize the risk of accidents.

[0034] The notification unit can also be used as a support tool for visually impaired people. For example, the notification unit can recognize the color of traffic lights while a visually impaired person is walking and provide voice notifications. For instance, if the traffic light is red, the notification unit will say, "The traffic light is red." The notification unit can also detect approaching cars and bicycles using sensors and provide voice notifications such as, "A bicycle is coming from behind." Furthermore, the notification unit can monitor the surrounding environment in real time to ensure that visually impaired people can walk safely and provide immediate notifications when danger occurs. This can support safe walking for visually impaired people.

[0035] The discrimination unit can optimize its discrimination algorithm by referring to past suspicious person data during discrimination. For example, the discrimination unit can prioritize the detection of individuals with specific behavioral patterns based on past suspicious person data. For example, the discrimination unit can analyze past suspicious person data and consider the tendency for suspicious persons to appear at specific times and locations. The discrimination unit can also improve the accuracy of its facial recognition algorithm using past suspicious person data. For example, the discrimination unit can adjust the parameters of its facial recognition algorithm based on past suspicious person data. This improves the accuracy of the discrimination algorithm by utilizing past data. Some or all of the above processing in the discrimination unit may be performed using AI, for example, or without using AI.

[0036] The discrimination unit can monitor changes in the user's behavior patterns in real time during discrimination and detect anomalies. The discrimination unit monitors the user's behavior patterns using, for example, real-time monitoring technology. For example, if the discrimination unit behaves differently from normal, it detects this as an anomaly and issues a warning. The discrimination unit can also detect an anomaly if the user stays in a specific area for a long period of time. Furthermore, the discrimination unit can detect an anomaly if the user suddenly starts running and issue a warning. This allows for rapid detection of anomalies through real-time monitoring. Some or all of the above processing in the discrimination unit may be performed using, for example, AI, or without using AI.

[0037] The discrimination unit can identify suspicious individuals by considering the user's geographical location information during the discrimination process. For example, the discrimination unit can obtain the user's current location using GPS data and use that geographical location information to identify suspicious individuals. For example, if the user is in a specific area, the discrimination unit will prioritize referring to the suspicious person data for that area. The discrimination unit can also identify suspicious individuals based on the user's current location if the user is on the move. Furthermore, if the discrimination unit has been staying in a specific location for a long time, it can also refer to the suspicious person data for that location. By considering geographical location information, more accurate suspicious person identification becomes possible. Some or all of the above processing in the discrimination unit may be performed using AI, for example, or without using AI.

[0038] The discrimination unit can analyze the user's social media activity during discrimination to help identify suspicious individuals. For example, the discrimination unit can analyze the user's contact history with specific individuals from the user's social media activity. For example, the discrimination unit can analyze the user's posts and followers to identify contact history with specific individuals. The discrimination unit can also refer to information about participation in specific events from the user's social media activity. For example, the discrimination unit can identify suspicious individuals based on information about events the user has participated in. Furthermore, the discrimination unit can also analyze the user's visit history to specific locations from the user's social media activity. For example, the discrimination unit can identify suspicious individuals based on information about places the user has visited. This makes it possible to identify suspicious individuals from a more multifaceted perspective by analyzing social media activity. Some or all of the above processing in the discrimination unit may be performed using AI, for example, or without using AI.

[0039] The calling unit can select the optimal calling method by referring to past calling history when making a call. For example, the calling unit may prioritize calling methods that have been effective in the past. For example, the calling unit may analyze past calling history and select a calling method appropriate to a specific situation. The calling unit can also select the most effective calling method based on past calling history. For example, the calling unit may refer to past calling history from a database and select a calling method appropriate to a specific situation. In this way, the optimal calling method can be selected by referring to past calling history. Some or all of the above processing in the calling unit may be performed using AI, for example, or without using AI.

[0040] The calling unit can adjust the timing of its calls based on the user's current situation. For example, if the user is moving, the calling unit will make a call at an appropriate time. For example, if the user is in a specific location, the calling unit will make a call at a time appropriate to that location. The calling unit can also make a call at a time appropriate to the user's action if the user is performing a specific action. For example, if the user is walking, the calling unit will make a call in accordance with the user's walking rhythm. By adjusting the timing of the calls according to the user's situation, more appropriate calls can be made. Some or all of the above processing in the calling unit may be performed using AI, for example, or without using AI.

[0041] The calling unit can select the optimal calling method when calling, taking into account the user's geographical location information. For example, the calling unit can obtain the user's current location using GPS data and select a calling method based on that geographical location information. For example, if the calling unit is in a specific area, it can select a calling method appropriate for that area. The calling unit can also select the optimal calling method based on the user's current location if the user is on the move. Furthermore, if the calling unit is staying in a specific location for an extended period, it can select a calling method appropriate for that location. In this way, a more appropriate calling method can be selected by taking geographical location information into consideration. Some or all of the above processing in the calling unit may be performed using AI, for example, or without using AI.

[0042] The call-to-action unit can analyze the user's social media activity and customize the content of the call-to-action. For example, the call-to-action unit can make calls that reflect specific interests and concerns based on the user's social media activity. For example, the call-to-action unit can analyze the user's posts and followers to identify specific interests and concerns. The call-to-action unit can also make calls related to specific events based on the user's social media activity. For example, the call-to-action unit can make relevant calls based on information about events the user has attended. Furthermore, the call-to-action unit can make calls related to specific locations based on the user's social media activity. For example, the call-to-action unit can make relevant calls based on information about places the user has visited. This allows for more personalized calls by analyzing social media activity. Some or all of the above processing in the call-to-action unit may be performed using AI, for example, or not using AI.

[0043] The guidance unit can select the optimal guidance method by referring to past route guidance history during guidance. For example, the guidance unit may prioritize selecting route guidance methods that have been effective in the past. For example, the guidance unit may analyze past route guidance history and select a guidance method appropriate to a specific situation. The guidance unit can also select the most effective guidance method based on past route guidance history. For example, the guidance unit may refer to past route guidance history from a database and select a guidance method appropriate to a specific situation. In this way, the optimal guidance method can be selected by referring to past route guidance history. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without using AI.

[0044] The guidance unit can adjust the timing of route guidance based on the user's current situation. For example, if the user is moving, the guidance unit will provide route guidance at an appropriate time. For example, if the user is in a specific location, the guidance unit will provide route guidance at a time appropriate to that location. The guidance unit can also provide route guidance at a time appropriate to the user's action. For example, if the user is walking, the guidance unit will provide route guidance in accordance with the user's walking rhythm. By adjusting the timing of route guidance according to the user's situation, more appropriate guidance becomes possible. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without using AI.

[0045] The guidance unit can select the optimal route guidance method by considering the user's geographical location information during guidance. For example, the guidance unit can obtain the user's current location using GPS data and select a route guidance method based on that geographical location information. For example, if the user is in a specific area, the guidance unit can select a route guidance method appropriate for that area. The guidance unit can also select the optimal route guidance method based on the user's current location if the user is on the move. Furthermore, if the guidance unit is staying in a specific location for an extended period of time, it can select a route guidance method appropriate for that location. In this way, by considering geographical location information, a more appropriate route guidance method can be selected. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without using AI.

[0046] The navigation system can analyze the user's social media activity and customize the route guidance during the guidance process. For example, the navigation system can provide route guidance that reflects the user's specific interests and concerns based on their social media activity. For instance, it can analyze the user's posts and followers to identify specific interests and concerns. The navigation system can also provide route guidance related to specific events based on the user's social media activity. For example, it can provide relevant route guidance based on information about events the user has attended. Furthermore, the navigation system can provide route guidance related to specific locations based on the user's social media activity. For example, it can provide relevant route guidance based on information about places the user has visited. This enables more personalized route guidance by analyzing social media activity. Some or all of the above processing in the navigation system may be performed using AI, for example, or without AI.

[0047] The notification unit can select the most suitable notification method by referring to past notification history when issuing a notification. For example, the notification unit may prioritize notification methods that have been effective in the past. For example, the notification unit may analyze past notification history and select a notification method appropriate to a specific situation. The notification unit can also select the most effective notification method based on past notification history. For example, the notification unit may refer to past notification history from a database and select a notification method appropriate to a specific situation. In this way, the optimal notification method can be selected by referring to past notification history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without using AI.

[0048] The notification unit can adjust the timing of notifications based on the user's current situation. For example, if the user is on the move, the notification unit will provide a notification at an appropriate time. For example, if the user is in a specific location, the notification unit will provide a notification at a time appropriate to that location. Furthermore, if the user is performing a specific action, the notification unit can provide a notification at a time appropriate to that action. For example, if the user is walking, the notification unit will provide a notification in accordance with the rhythm of the user's walking. By adjusting the timing of notifications according to the user's situation, more appropriate notifications can be provided. Some or all of the above processing in the notification unit may be performed using AI, for example, or without the use of AI.

[0049] The notification unit can select the most appropriate notification method when sending notifications, taking into account the user's geographical location. For example, the notification unit can obtain the user's current location using GPS data and select a notification method based on that geographical location. For example, if the user is in a specific area, the notification unit can select a notification method appropriate for that area. The notification unit can also select the most appropriate notification method based on the user's current location if the user is on the move. Furthermore, if the notification unit is staying in a specific location for an extended period, it can select a notification method appropriate for that location. In this way, a more appropriate notification method can be selected by taking geographical location information into consideration. Some or all of the above processing in the notification unit may be performed using AI, for example, or without using AI.

[0050] The notification system can analyze a user's social media activity and customize the content of notifications when sending them. For example, the notification system can send notifications that reflect specific interests and concerns based on the user's social media activity. For example, the notification system can analyze a user's posts and followers to identify specific interests and concerns. The notification system can also send notifications related to specific events based on the user's social media activity. For example, the notification system can send relevant notifications based on information about events the user has attended. Furthermore, the notification system can send notifications related to specific locations based on the user's social media activity. For example, the notification system can send relevant notifications based on information about places the user has visited. This allows for more personalized notifications by analyzing social media activity. Some or all of the above processing in the notification system may be performed using AI, for example, or not.

[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 monitoring system can also be equipped with a health monitoring unit. The health monitoring unit can monitor the user's biometric information, such as heart rate and body temperature, in real time and issue a warning if an abnormality is detected. For example, if the heart rate rises sharply, the health monitoring unit will announce, "Your heart rate is high," via voice. It can also warn, "Your body temperature is high," if the body temperature is abnormally high. Furthermore, the health monitoring unit can record the user's biometric information and generate health reports periodically. This allows for constant monitoring of the user's health status and early detection of abnormalities.

[0053] The monitoring system can also be equipped with an environmental monitoring unit. The environmental monitoring unit can monitor environmental information around the user in real time and issue a warning if an abnormality is detected. For example, if the concentration of harmful substances in the air is high, the environmental monitoring unit will announce "The air is polluted" by voice. It can also warn "The temperature is high" or "The humidity is high" if the temperature or humidity is abnormally high. Furthermore, the environmental monitoring unit can record environmental information around the user and periodically generate environmental reports. This allows the user to constantly understand the environmental conditions around them and detect abnormalities early.

[0054] The monitoring system can also be equipped with a learning support unit. This unit can monitor the user's learning progress in real time and provide appropriate learning advice. For example, if the user is doing homework, the unit can provide voice advice such as, "Let's move on to the next problem." It can also suggest, "Let's take a short break," if the user is having trouble concentrating. Furthermore, the unit can record the user's learning history and generate regular learning reports. This allows for constant monitoring of the user's learning progress and effective learning support.

[0055] The monitoring system can also be equipped with an entertainment provision unit. This unit can provide entertainment content tailored to the user's preferences. For example, if the user wants to listen to music, the unit can announce, "We'll play some recommended music." If the user wants to watch a movie, it can suggest, "We'll play some recommended movies." Furthermore, the unit can record the user's entertainment history and periodically suggest recommended content. This allows for entertainment tailored to the user's preferences, enabling them to relax and enjoy their time.

[0056] The monitoring system can also be equipped with a communication support unit. This unit can monitor the user's communication status in real time and provide appropriate communication advice. For example, if the user is talking to a friend, the unit can provide voice advice such as, "This is the topic you should talk about next." It can also suggest, "Let's try talking like this," if the user is having trouble communicating. Furthermore, the unit can record the user's communication history and generate regular communication reports. This allows for constant monitoring of the user's communication status and effective communication support.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The discrimination unit identifies suspicious individuals using behavioral patterns and facial recognition. The discrimination unit analyzes behavioral patterns using AI and identifies suspicious individuals using facial recognition technology. For example, it analyzes walking patterns and gestures and detects anomalies by comparing them with normal behavior. It can also extract facial feature points using deep learning and compare them with a database of known suspicious individuals. Furthermore, it can comprehensively identify suspicious individuals by combining behavioral patterns and facial recognition results. Step 2: The calling unit calls out to the suspicious person identified by the discrimination unit using a family member's voice. The calling unit saves the family member's voice as recorded data and plays it back when a suspicious person is detected. It can also generate a family member's voice using speech synthesis technology and call out in real time. Furthermore, it can use a family member's voice to ask questions such as "What's wrong?" or "Are you okay?" Step 3: The guidance unit provides route guidance in a conversational format if the user gets lost. The guidance unit uses AI to determine the user's current location and calculate the route to their destination. For example, it can obtain the current location using GPS data and guide the user along the optimal route by referring to a map database. It can also engage in dialogue with the user using natural language processing technology and provide route guidance. Furthermore, it can generate appropriate answers to user questions and provide voice guidance. Step 4: The notification unit detects hazards such as traffic lights, cars, and bicycles and provides voice alerts. The notification unit uses AI to recognize the color of traffic lights and announces "The traffic light is red" when it is red. It can also detect approaching cars and bicycles with sensors and announce "A bicycle is coming from behind" with a voice alert. Furthermore, it can monitor the user's surroundings in real time and provide immediate alerts when a hazard occurs.

[0059] (Example of form 2) The monitoring system according to an embodiment of the present invention is a new monitoring tool that keeps a close eye on loved ones, like a wearable dashcam, and provides assistance through the voices of family members. This monitoring system has the function of identifying suspicious individuals using behavioral patterns and facial recognition and calling out to them in the voices of family members. For example, if a child is approached by a stranger, the AI ​​analyzes the stranger's behavioral patterns and face, and if it is determined to be a suspicious person, it will call out to the child in the voice of a family member, "What's wrong?" Next, to help in the event of a lost child, it has the function of providing route guidance in a conversational format. For example, if a child asks, "Can you tell me the way home?", the AI ​​will guide them on the route from their current location to their destination. Furthermore, to help avoid danger, it has the function of detecting dangers such as traffic lights, cars, and bicycles and providing voice notifications. For example, if the traffic light turns red or a bicycle approaches from behind, the AI ​​will voice notifications such as "The traffic light is red" or "A bicycle is coming from behind." This tool can also be used as a support tool for visually impaired people. For example, it can detect dangers such as traffic lights, cars, and bicycles while a visually impaired person is walking and provide voice notifications, allowing them to walk safely. Thus, this invention is a next-generation monitoring system equipped with AI, providing peace of mind and safety through the voices of family members. This allows the monitoring system to closely watch over loved ones and provide assistance through the voices of family members.

[0060] The monitoring system according to this embodiment comprises a discrimination unit, a calling unit, a guidance unit, and a notification unit. The discrimination unit identifies suspicious persons using behavioral patterns and facial recognition. The discrimination unit, for example, analyzes behavioral patterns using AI and identifies suspicious persons using facial recognition technology. For example, the discrimination unit analyzes walking patterns and gestures and detects abnormalities by comparing them with normal behavior. The discrimination unit can also extract facial feature points using deep learning and compare them with a database of known suspicious persons. Furthermore, the discrimination unit can comprehensively identify suspicious persons by combining behavioral patterns and facial recognition results. The calling unit calls out to the suspicious person identified by the discrimination unit using the voice of a family member. The calling unit, for example, saves the voice of a family member as recorded data and plays it back when a suspicious person is detected. The calling unit can also generate the voice of a family member using speech synthesis technology and call out in real time. Furthermore, the calling unit can also call out to the family member using the voice of a family member, such as "What's wrong?" or "Are you okay?". The guidance unit provides route guidance in a conversational format if the person gets lost. The guidance unit, for example, uses AI to determine the current location and calculate the route to the destination. For example, the guidance unit obtains the current location using GPS data and guides the user along the optimal route by referring to a map database. The guidance unit can also interact with the user using natural language processing technology and provide route guidance. Furthermore, the guidance unit can generate appropriate answers to user questions and provide voice guidance. The notification unit detects dangers such as traffic lights, cars, and bicycles and provides voice notifications. For example, the notification unit uses AI to recognize the color of a traffic light and announces "The traffic light is red" if it is red. The notification unit can also detect approaching cars and bicycles with sensors and announce "A bicycle is coming from behind" by voice. Furthermore, the notification unit can monitor the user's surroundings in real time and immediately notify the user when danger occurs. As a result, the monitoring system according to this embodiment can keep a close eye on a loved one and provide assistance with the voices of family members.

[0061] The discrimination unit identifies suspicious individuals using behavioral patterns and facial recognition. For example, the discrimination unit analyzes behavioral patterns using AI and identifies suspicious individuals using facial recognition technology. Specifically, the discrimination unit analyzes walking patterns and gestures, and detects abnormalities by comparing them to normal behavior. For example, in walking pattern analysis, it monitors walking speed, stride length, and changes in direction in real time to detect patterns that differ from normal behavior. In gesture analysis, it monitors hand movements and body posture to detect abnormal movements. This data is analyzed based on a model learned by AI, and if abnormal behavior is detected, the person is identified as a suspicious individual. The discrimination unit can also extract facial feature points using deep learning and compare them with a database of known suspicious individuals. Facial recognition technology uses a highly accurate feature point extraction algorithm to capture facial contours and features such as eyes, nose, and mouth, and compares them with known suspicious individuals in the database. Furthermore, the discrimination unit can comprehensively identify suspicious individuals by combining behavioral patterns and facial recognition results. For example, if the behavioral pattern is abnormal and the facial recognition result matches, the person is identified as a suspicious individual with a high probability. In this way, the discrimination unit can identify suspicious individuals through a multifaceted approach, thereby improving the overall security of the system.

[0062] The calling unit calls out to the suspicious person identified by the identification unit using a family member's voice. For example, the calling unit can save a family member's voice as recorded data and play it back when a suspicious person is detected. Specifically, family members' voices are recorded in advance and registered in the system. This allows the system to play back the recorded family member's voice and call out to the suspicious person when one is detected. The calling unit can also generate a family member's voice using speech synthesis technology and call out in real time. Speech synthesis technology can learn the characteristics of the family member's voice and generate a natural-sounding voice. This makes it possible to call out using a family member's voice even if there is no recorded data. Furthermore, the calling unit can also use a family member's voice to ask questions such as "What's wrong?" or "Are you okay?". This puts psychological pressure on the suspicious person and allows the system to check on their situation. Through the playback and generation of voices, the calling unit can deal with suspicious people quickly and effectively.

[0063] The guidance unit provides route guidance in a conversational format if the user gets lost. For example, the guidance unit uses AI to determine the user's current location and calculate the route to their destination. Specifically, the guidance unit obtains the user's current location using GPS data and guides them along the optimal route by referring to a map database. The GPS data is updated in real time, allowing the system to accurately determine the user's current location. The map database contains information such as road information, landmarks, and public facilities, and the system uses this information to calculate the optimal route. The guidance unit can also interact with the user using natural language processing technology to provide route guidance. Natural language processing technology can understand the user's questions and instructions and generate appropriate answers. For example, if the user asks, "Where is the nearest station?", the guidance unit will calculate the route from the current location to the nearest station and provide voice guidance. Furthermore, the guidance unit can generate appropriate answers to the user's questions and provide voice guidance. As a result, the guidance unit can provide quick and accurate route guidance even if the user gets lost, ensuring the user's safety.

[0064] The notification unit detects hazards such as traffic lights, cars, and bicycles and provides voice alerts. For example, the notification unit uses AI to recognize the color of traffic lights and announces "The traffic light is red" if it is red. Specifically, it monitors the color of traffic lights in real time using cameras and sensors, and the AI ​​analyzes that information. When a red light is detected, it alerts the user by announcing "The traffic light is red." The notification unit can also detect approaching cars and bicycles with sensors and announce "A bicycle is coming from behind" by voice. The sensors use ultrasound and infrared to detect movement in the surroundings, and the AI ​​analyzes the data to determine the approach of cars and bicycles. Furthermore, the notification unit can monitor the user's surroundings in real time and provide immediate alerts when danger occurs. For example, if a car is rapidly approaching while a pedestrian is crossing a crosswalk, it will warn "A car is approaching" by voice. In this way, the notification unit can ensure the user's safety and minimize the risk of accidents.

[0065] The notification unit can also be used as a support tool for visually impaired people. For example, the notification unit can recognize the color of traffic lights while a visually impaired person is walking and provide voice notifications. For instance, if the traffic light is red, the notification unit will say, "The traffic light is red." The notification unit can also detect approaching cars and bicycles using sensors and provide voice notifications such as, "A bicycle is coming from behind." Furthermore, the notification unit can monitor the surrounding environment in real time to ensure that visually impaired people can walk safely and provide immediate notifications when danger occurs. This can support safe walking for visually impaired people.

[0066] The discrimination unit can estimate the user's emotions and adjust the accuracy of suspicious person detection based on the estimated user emotions. For example, the discrimination unit can estimate the user's emotions using facial recognition technology. For example, the discrimination unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The discrimination unit can also estimate the user's emotions using voice analysis technology. For example, the discrimination unit can analyze the tone and speed of the user's voice and estimate the emotions. Furthermore, the discrimination unit can adjust the accuracy of suspicious person detection based on the estimated emotions. For example, if the user is feeling fear, the discrimination accuracy can be increased to strengthen the detection of suspicious people. Conversely, if the user is relaxed, the discrimination accuracy can be returned to normal to reduce false positives. In this way, by adjusting the accuracy of suspicious person detection according to the user's emotions, more appropriate warnings can be issued. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0067] The discrimination unit can optimize its discrimination algorithm by referring to past suspicious person data during discrimination. For example, the discrimination unit can prioritize the detection of individuals with specific behavioral patterns based on past suspicious person data. For example, the discrimination unit can analyze past suspicious person data and consider the tendency for suspicious persons to appear at specific times and locations. The discrimination unit can also improve the accuracy of its facial recognition algorithm using past suspicious person data. For example, the discrimination unit can adjust the parameters of its facial recognition algorithm based on past suspicious person data. This improves the accuracy of the discrimination algorithm by utilizing past data. Some or all of the above processing in the discrimination unit may be performed using AI, for example, or without using AI.

[0068] The discrimination unit can monitor changes in the user's behavior patterns in real time during discrimination and detect anomalies. The discrimination unit monitors the user's behavior patterns using, for example, real-time monitoring technology. For example, if the discrimination unit behaves differently from normal, it detects this as an anomaly and issues a warning. The discrimination unit can also detect an anomaly if the user stays in a specific area for a long period of time. Furthermore, the discrimination unit can detect an anomaly if the user suddenly starts running and issue a warning. This allows for rapid detection of anomalies through real-time monitoring. Some or all of the above processing in the discrimination unit may be performed using, for example, AI, or without using AI.

[0069] The discrimination unit can estimate the user's emotions and adjust the order in which the suspicious person identification results are displayed based on the estimated user emotions. For example, the discrimination unit can estimate the user's emotions using facial recognition technology. For example, the discrimination unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The discrimination unit can also estimate the user's emotions using voice analysis technology. For example, the discrimination unit can analyze the tone and speed of the user's voice and estimate the emotions. Furthermore, the discrimination unit can adjust the order in which the suspicious person identification results are displayed based on the estimated emotions. For example, if the user is feeling frightened, the most dangerous suspicious person will be displayed preferentially. Conversely, if the user is relaxed, the suspicious persons can be displayed in the normal order. This allows for the provision of more appropriate information by adjusting the display order of the identification results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0070] The discrimination unit can identify suspicious individuals by considering the user's geographical location information during the discrimination process. For example, the discrimination unit can obtain the user's current location using GPS data and use that geographical location information to identify suspicious individuals. For example, if the user is in a specific area, the discrimination unit will prioritize referring to the suspicious person data for that area. The discrimination unit can also identify suspicious individuals based on the user's current location if the user is on the move. Furthermore, if the discrimination unit has been staying in a specific location for a long time, it can also refer to the suspicious person data for that location. By considering geographical location information, more accurate suspicious person identification becomes possible. Some or all of the above processing in the discrimination unit may be performed using AI, for example, or without using AI.

[0071] The discrimination unit can analyze the user's social media activity during discrimination to help identify suspicious individuals. For example, the discrimination unit can analyze the user's contact history with specific individuals from the user's social media activity. For example, the discrimination unit can analyze the user's posts and followers to identify contact history with specific individuals. The discrimination unit can also refer to information about participation in specific events from the user's social media activity. For example, the discrimination unit can identify suspicious individuals based on information about events the user has participated in. Furthermore, the discrimination unit can also analyze the user's visit history to specific locations from the user's social media activity. For example, the discrimination unit can identify suspicious individuals based on information about places the user has visited. This makes it possible to identify suspicious individuals from a more multifaceted perspective by analyzing social media activity. Some or all of the above processing in the discrimination unit may be performed using AI, for example, or without using AI.

[0072] The calling unit can estimate the user's emotions and adjust the way it calls based on those estimated emotions. For example, the calling unit can estimate the user's emotions using facial recognition technology. For instance, it might capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Alternatively, the calling unit can estimate the user's emotions using voice analysis technology. For example, it might analyze the tone and speed of the user's voice to estimate the emotion. Furthermore, the calling unit can adjust the way it calls based on the estimated emotion. For example, if the user is feeling frightened, it might call in a calm voice. Conversely, if the user is relaxed, it might call in a cheerful voice. This allows for more effective calling by adjusting the way the call is delivered according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0073] The calling unit can select the optimal calling method by referring to past calling history when making a call. For example, the calling unit may prioritize calling methods that have been effective in the past. For example, the calling unit may analyze past calling history and select a calling method appropriate to a specific situation. The calling unit can also select the most effective calling method based on past calling history. For example, the calling unit may refer to past calling history from a database and select a calling method appropriate to a specific situation. In this way, the optimal calling method can be selected by referring to past calling history. Some or all of the above processing in the calling unit may be performed using AI, for example, or without using AI.

[0074] The calling unit can adjust the timing of its calls based on the user's current situation. For example, if the user is moving, the calling unit will make a call at an appropriate time. For example, if the user is in a specific location, the calling unit will make a call at a time appropriate to that location. The calling unit can also make a call at a time appropriate to the user's action if the user is performing a specific action. For example, if the user is walking, the calling unit will make a call in accordance with the user's walking rhythm. By adjusting the timing of the calls according to the user's situation, more appropriate calls can be made. Some or all of the above processing in the calling unit may be performed using AI, for example, or without using AI.

[0075] The calling unit can estimate the user's emotions and determine the priority of calls based on the estimated emotions. For example, the calling unit can estimate the user's emotions using facial recognition technology. For instance, it can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Alternatively, the calling unit can estimate the user's emotions using voice analysis technology. For example, it can analyze the tone and speed of the user's voice to estimate their emotions. Furthermore, the calling unit can determine the priority of calls based on the estimated emotions. For example, if the user is feeling fear, it will prioritize the most important calls. Conversely, if the user is relaxed, it can make calls with the normal priority. This allows for prioritizing more important calls based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0076] The calling unit can select the optimal calling method when calling, taking into account the user's geographical location information. For example, the calling unit can obtain the user's current location using GPS data and select a calling method based on that geographical location information. For example, if the calling unit is in a specific area, it can select a calling method appropriate for that area. The calling unit can also select the optimal calling method based on the user's current location if the user is on the move. Furthermore, if the calling unit is staying in a specific location for an extended period, it can select a calling method appropriate for that location. In this way, a more appropriate calling method can be selected by taking geographical location information into consideration. Some or all of the above processing in the calling unit may be performed using AI, for example, or without using AI.

[0077] The call-to-action unit can analyze the user's social media activity and customize the content of the call-to-action. For example, the call-to-action unit can make calls that reflect specific interests and concerns based on the user's social media activity. For example, the call-to-action unit can analyze the user's posts and followers to identify specific interests and concerns. The call-to-action unit can also make calls related to specific events based on the user's social media activity. For example, the call-to-action unit can make relevant calls based on information about events the user has attended. Furthermore, the call-to-action unit can make calls related to specific locations based on the user's social media activity. For example, the call-to-action unit can make relevant calls based on information about places the user has visited. This allows for more personalized calls by analyzing social media activity. Some or all of the above processing in the call-to-action unit may be performed using AI, for example, or not using AI.

[0078] The guidance unit can estimate the user's emotions and adjust the way route guidance is presented based on the estimated emotions. For example, the guidance unit can estimate the user's emotions using facial recognition technology. For example, the guidance unit can capture the user's facial expression with a camera and estimate the emotions using an emotion estimation algorithm. The guidance unit can also estimate the user's emotions using voice analysis technology. For example, the guidance unit can analyze the tone and speed of the user's voice and estimate the emotions. Furthermore, the guidance unit can adjust the way route guidance is presented based on the estimated emotions. For example, if the user is feeling frightened, route guidance can be provided in a calm voice. Conversely, if the user is relaxed, route guidance can be provided in a cheerful voice. By adjusting the way route guidance is presented according to the user's emotions, more effective guidance becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The guidance unit can select the optimal guidance method by referring to past route guidance history during guidance. For example, the guidance unit may prioritize selecting route guidance methods that have been effective in the past. For example, the guidance unit may analyze past route guidance history and select a guidance method appropriate to a specific situation. The guidance unit can also select the most effective guidance method based on past route guidance history. For example, the guidance unit may refer to past route guidance history from a database and select a guidance method appropriate to a specific situation. In this way, the optimal guidance method can be selected by referring to past route guidance history. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without using AI.

[0080] The guidance unit can adjust the timing of route guidance based on the user's current situation. For example, if the user is moving, the guidance unit will provide route guidance at an appropriate time. For example, if the user is in a specific location, the guidance unit will provide route guidance at a time appropriate to that location. The guidance unit can also provide route guidance at a time appropriate to the user's action. For example, if the user is walking, the guidance unit will provide route guidance in accordance with the user's walking rhythm. By adjusting the timing of route guidance according to the user's situation, more appropriate guidance becomes possible. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without using AI.

[0081] The navigation system can estimate the user's emotions and determine the priority of route guidance based on those emotions. For example, the navigation system can estimate the user's emotions using facial recognition technology. For instance, it might capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Alternatively, the navigation system can estimate the user's emotions using voice analysis technology. For example, it might analyze the tone and speed of the user's voice to estimate their emotions. Furthermore, the navigation system can determine the priority of route guidance based on the estimated emotions. For example, if the user is feeling fear, it will prioritize the most important route guidance. Conversely, if the user is relaxed, it can provide route guidance with the normal priority. This allows for prioritizing more important guidance based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The guidance unit can select the optimal route guidance method by considering the user's geographical location information during guidance. For example, the guidance unit can obtain the user's current location using GPS data and select a route guidance method based on that geographical location information. For example, if the user is in a specific area, the guidance unit can select a route guidance method appropriate for that area. The guidance unit can also select the optimal route guidance method based on the user's current location if the user is on the move. Furthermore, if the guidance unit is staying in a specific location for an extended period of time, it can select a route guidance method appropriate for that location. In this way, by considering geographical location information, a more appropriate route guidance method can be selected. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without using AI.

[0083] The navigation system can analyze the user's social media activity and customize the route guidance during the guidance process. For example, the navigation system can provide route guidance that reflects the user's specific interests and concerns based on their social media activity. For instance, it can analyze the user's posts and followers to identify specific interests and concerns. The navigation system can also provide route guidance related to specific events based on the user's social media activity. For example, it can provide relevant route guidance based on information about events the user has attended. Furthermore, the navigation system can provide route guidance related to specific locations based on the user's social media activity. For example, it can provide relevant route guidance based on information about places the user has visited. This enables more personalized route guidance by analyzing social media activity. Some or all of the above processing in the navigation system may be performed using AI, for example, or without AI.

[0084] The notification unit can estimate the user's emotions and adjust the way notifications are delivered based on those estimated emotions. For example, the notification unit can estimate the user's emotions using facial recognition technology. For instance, it might capture the user's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. The notification unit can also estimate the user's emotions using voice analysis technology. For example, it might analyze the tone and speed of the user's voice to estimate their emotions. Furthermore, the notification unit can adjust the way notifications are delivered based on the estimated emotions. For example, if the user is feeling frightened, the notification can be delivered in a calm voice. Conversely, if the user is relaxed, the notification can be delivered in a cheerful voice. This allows for more effective notifications by adjusting the delivery method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The notification unit can select the most suitable notification method by referring to past notification history when issuing a notification. For example, the notification unit may prioritize notification methods that have been effective in the past. For example, the notification unit may analyze past notification history and select a notification method appropriate to a specific situation. The notification unit can also select the most effective notification method based on past notification history. For example, the notification unit may refer to past notification history from a database and select a notification method appropriate to a specific situation. In this way, the optimal notification method can be selected by referring to past notification history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without using AI.

[0086] The notification unit can adjust the timing of notifications based on the user's current situation. For example, if the user is on the move, the notification unit will provide a notification at an appropriate time. For example, if the user is in a specific location, the notification unit will provide a notification at a time appropriate to that location. Furthermore, if the user is performing a specific action, the notification unit can provide a notification at a time appropriate to that action. For example, if the user is walking, the notification unit will provide a notification in accordance with the rhythm of the user's walking. By adjusting the timing of notifications according to the user's situation, more appropriate notifications can be provided. Some or all of the above processing in the notification unit may be performed using AI, for example, or without the use of AI.

[0087] The notification unit can estimate the user's emotions and determine the priority of notifications based on those emotions. For example, the notification unit can estimate the user's emotions using facial recognition technology. For instance, it can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The notification unit can also estimate the user's emotions using voice analysis technology. For example, it can analyze the tone and speed of the user's voice to estimate their emotions. Furthermore, the notification unit can determine the priority of notifications based on the estimated emotions. For example, if the user is feeling fear, it will prioritize the most important notifications. Conversely, if the user is relaxed, it can deliver notifications with the normal priority. This allows for prioritizing more important notifications according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The notification unit can select the most appropriate notification method when sending notifications, taking into account the user's geographical location. For example, the notification unit can obtain the user's current location using GPS data and select a notification method based on that geographical location. For example, if the user is in a specific area, the notification unit can select a notification method appropriate for that area. The notification unit can also select the most appropriate notification method based on the user's current location if the user is on the move. Furthermore, if the notification unit is staying in a specific location for an extended period, it can select a notification method appropriate for that location. In this way, a more appropriate notification method can be selected by taking geographical location information into consideration. Some or all of the above processing in the notification unit may be performed using AI, for example, or without using AI.

[0089] The notification system can analyze a user's social media activity and customize the content of notifications when sending them. For example, the notification system can send notifications that reflect specific interests and concerns based on the user's social media activity. For example, the notification system can analyze a user's posts and followers to identify specific interests and concerns. The notification system can also send notifications related to specific events based on the user's social media activity. For example, the notification system can send relevant notifications based on information about events the user has attended. Furthermore, the notification system can send notifications related to specific locations based on the user's social media activity. For example, the notification system can send relevant notifications based on information about places the user has visited. This allows for more personalized notifications by analyzing social media activity. Some or all of the above processing in the notification system may be performed using AI, for example, or not.

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

[0091] The monitoring system can also be equipped with a health monitoring unit. The health monitoring unit can monitor the user's biometric information, such as heart rate and body temperature, in real time and issue a warning if an abnormality is detected. For example, if the heart rate rises sharply, the health monitoring unit will announce, "Your heart rate is high," via voice. It can also warn, "Your body temperature is high," if the body temperature is abnormally high. Furthermore, the health monitoring unit can record the user's biometric information and generate health reports periodically. This allows for constant monitoring of the user's health status and early detection of abnormalities.

[0092] The monitoring system can also be equipped with an environmental monitoring unit. The environmental monitoring unit can monitor environmental information around the user in real time and issue a warning if an abnormality is detected. For example, if the concentration of harmful substances in the air is high, the environmental monitoring unit will announce "The air is polluted" by voice. It can also warn "The temperature is high" or "The humidity is high" if the temperature or humidity is abnormally high. Furthermore, the environmental monitoring unit can record environmental information around the user and periodically generate environmental reports. This allows the user to constantly understand the environmental conditions around them and detect abnormalities early.

[0093] The monitoring system can also be equipped with a learning support unit. This unit can monitor the user's learning progress in real time and provide appropriate learning advice. For example, if the user is doing homework, the unit can provide voice advice such as, "Let's move on to the next problem." It can also suggest, "Let's take a short break," if the user is having trouble concentrating. Furthermore, the unit can record the user's learning history and generate regular learning reports. This allows for constant monitoring of the user's learning progress and effective learning support.

[0094] The monitoring system can also be equipped with an entertainment provision unit. This unit can provide entertainment content tailored to the user's preferences. For example, if the user wants to listen to music, the unit can announce, "We'll play some recommended music." If the user wants to watch a movie, it can suggest, "We'll play some recommended movies." Furthermore, the unit can record the user's entertainment history and periodically suggest recommended content. This allows for entertainment tailored to the user's preferences, enabling them to relax and enjoy their time.

[0095] The monitoring system can also be equipped with a communication support unit. This unit can monitor the user's communication status in real time and provide appropriate communication advice. For example, if the user is talking to a friend, the unit can provide voice advice such as, "This is the topic you should talk about next." It can also suggest, "Let's try talking like this," if the user is having trouble communicating. Furthermore, the unit can record the user's communication history and generate regular communication reports. This allows for constant monitoring of the user's communication status and effective communication support.

[0096] The monitoring system can estimate the user's emotions and adjust the warnings from the health monitoring unit based on those estimates. For example, if the user is feeling stressed, the health monitoring unit will advise them to "relax." If the user is relaxed, it can also encourage them by saying, "That's the spirit." Furthermore, the health monitoring unit can customize the content of health reports according to the user's emotions. This enables health monitoring tailored to the user's emotions, leading to more effective health management.

[0097] The monitoring system can estimate the user's emotions and adjust the warning content of the environmental monitoring unit based on those emotions. For example, if the user is feeling anxious, the environmental monitoring unit will reassure them with a voice message saying, "The air is clean." If the user is relaxed, it can also report, "The environment is comfortable." Furthermore, the environmental monitoring unit can customize the content of the environmental report according to the user's emotions. This enables environmental monitoring that is tailored to the user's emotions, resulting in a more comfortable environment management system.

[0098] The monitoring system can estimate the user's emotions and adjust the advice provided by the learning support department based on those estimates. For example, if the user is tired, the learning support department might suggest, "Let's take a short break," via voice. If the user is focused, it can also encourage them by saying, "That's the spirit." Furthermore, the learning support department can customize the content of learning reports according to the user's emotions. This enables learning support tailored to the user's feelings, leading to more effective learning.

[0099] The monitoring system can estimate the user's emotions and adjust the content suggestions from the entertainment department based on those estimates. For example, if the user is sad, the entertainment department might suggest, "I'll play some relaxing music." If the user is enjoying themselves, it might suggest, "I'll play a movie that you'll enjoy even more." Furthermore, the entertainment department can customize the content of entertainment reports according to the user's emotions. This enables the provision of entertainment tailored to the user's emotions, resulting in a more satisfying entertainment experience.

[0100] The monitoring system can estimate the user's emotions and adjust the advice provided by the communication support department based on those estimates. For example, if the user is feeling nervous, the communication support department will advise them verbally, "Let's relax and talk." If the user is relaxed, it can also encourage them by saying, "Keep up the good work." Furthermore, the communication support department can customize the content of the communication report according to the user's emotions. This enables communication support tailored to the user's emotions, leading to more effective communication.

[0101] The following briefly describes the processing flow for example form 2.

[0102] Step 1: The discrimination unit identifies suspicious individuals using behavioral patterns and facial recognition. The discrimination unit analyzes behavioral patterns using AI and identifies suspicious individuals using facial recognition technology. For example, it analyzes walking patterns and gestures and detects anomalies by comparing them with normal behavior. It can also extract facial feature points using deep learning and compare them with a database of known suspicious individuals. Furthermore, it can comprehensively identify suspicious individuals by combining behavioral patterns and facial recognition results. Step 2: The calling unit calls out to the suspicious person identified by the discrimination unit using a family member's voice. The calling unit saves the family member's voice as recorded data and plays it back when a suspicious person is detected. It can also generate a family member's voice using speech synthesis technology and call out in real time. Furthermore, it can use a family member's voice to ask questions such as "What's wrong?" or "Are you okay?" Step 3: The guidance unit provides route guidance in a conversational format if the user gets lost. The guidance unit uses AI to determine the user's current location and calculate the route to their destination. For example, it can obtain the current location using GPS data and guide the user along the optimal route by referring to a map database. It can also engage in dialogue with the user using natural language processing technology and provide route guidance. Furthermore, it can generate appropriate answers to user questions and provide voice guidance. Step 4: The notification unit detects hazards such as traffic lights, cars, and bicycles and provides voice alerts. The notification unit uses AI to recognize the color of traffic lights and announces "The traffic light is red" when it is red. It can also detect approaching cars and bicycles with sensors and announce "A bicycle is coming from behind" with a voice alert. Furthermore, it can monitor the user's surroundings in real time and provide immediate alerts when a hazard occurs.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating 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.

[0104] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0105] 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.

[0106] Each of the multiple elements described above, including the discrimination unit, calling unit, guidance unit, and notification unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the discrimination unit uses the camera 42 and microphone 38B of the smart device 14 to detect behavioral patterns and faces, and the control unit 46A analyzes them. The calling unit uses the speaker 40B of the smart device 14 to play the voices of family members. The guidance unit uses the GPS function of the smart device 14 to determine the current location, and the control unit 46A provides route guidance. The notification unit uses the camera 42 and sensors of the smart device 14 to detect hazards such as traffic signals, cars, and bicycles, and provides voice notifications using the speaker 40B. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

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

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.).

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating 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.

[0120] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0122] Each of the multiple elements described above, including the discrimination unit, calling unit, guidance unit, and notification unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the discrimination unit uses the camera 42 and microphone 238 of the smart glasses 214 to detect behavioral patterns and faces, and the control unit 46A analyzes them. The calling unit uses the speaker 240 of the smart glasses 214 to play the voices of family members. The guidance unit uses the GPS function of the smart glasses 214 to determine the current location, and the control unit 46A provides route guidance. The notification unit uses the camera 42 and sensors of the smart glasses 214 to detect hazards such as traffic signals, cars, and bicycles, and notifies the user by voice using the speaker 240. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

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

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.).

[0135] 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.

[0136] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

[0138] Each of the multiple elements described above, including the discrimination unit, calling unit, guidance unit, and notification unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the discrimination unit uses the camera 42 and microphone 238 of the headset terminal 314 to detect behavioral patterns and faces, and the control unit 46A analyzes them. The calling unit uses the speaker 240 of the headset terminal 314 to play the voices of family members. The guidance unit uses the GPS function of the headset terminal 314 to determine the current location, and the control unit 46A provides route guidance. The notification unit uses the camera 42 and sensors of the headset terminal 314 to detect hazards such as traffic signals, cars, and bicycles, and notifies the user by voice using the speaker 240. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

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

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.).

[0152] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0153] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0154] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

[0155] Each of the multiple elements described above, including the discrimination unit, calling unit, guidance unit, and notification unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the discrimination unit uses the camera 42 and microphone 238 of the robot 414 to detect behavioral patterns and faces, and the control unit 46A analyzes them. The calling unit uses the speaker 240 of the robot 414 to play the voices of family members. The guidance unit uses the GPS function of the robot 414 to determine the current location, and the control unit 46A provides route guidance. The notification unit uses the camera 42 and sensors of the robot 414 to detect hazards such as traffic signals, cars, and bicycles, and notifies the user by voice using the speaker 240. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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 multiple computers, including computer 22.

[0164] 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.

[0165] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] (Note 1) A discrimination unit that identifies suspicious individuals using behavioral patterns and facial recognition, The aforementioned discrimination unit has a calling unit that calls out to the suspicious person using the voice of a family member, A guidance unit that provides route directions in a conversational format if you get lost, It includes an alert unit that detects hazards such as traffic signals, cars, and bicycles and provides voice alerts. A system characterized by the following features. (Note 2) The aforementioned notification section is, It can also be used as a support tool for people with visual impairments. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned discrimination unit is The system estimates the user's emotions and adjusts the accuracy of identifying suspicious individuals based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned discrimination unit is During the identification process, the identification algorithm is optimized by referring to past suspicious person data. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned discrimination unit is During the identification process, changes in the user's behavior patterns are monitored in real time to detect anomalies. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned discrimination unit is The system estimates the user's emotions and adjusts the order in which it displays the suspicious person identification results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned discrimination unit is During the identification process, the user's geographical location information is taken into consideration when identifying suspicious individuals. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned discrimination unit is During the identification process, the user's social media activity is analyzed to help identify suspicious individuals. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned calling section is, It estimates the user's emotions and adjusts the way it addresses the user based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned calling section is, When making an appeal, the system will refer to past appeal history to select the most appropriate method of appeal. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned calling section is, When making a call, the timing of the call will be adjusted based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned calling section is, It estimates the user's emotions and determines the priority of calls to action based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned calling section is, When making a call, the system selects the most appropriate method of contact, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned calling section is, When making an appeal, analyze the user's social media activity and customize the content of the appeal. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned guide section is The system estimates the user's emotions and adjusts the way route guidance is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned guide section is When providing directions, the system will refer to past route guidance history to select the most suitable guidance method. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned guide section is When providing directions, the timing of route guidance is adjusted based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned guide section is The system estimates the user's emotions and determines route guidance priorities based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned guide section is When providing directions, the system selects the optimal route guidance method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned guide section is During navigation, the system analyzes the user's social media activity and customizes the route guidance accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned notification section is, The system estimates the user's emotions and adjusts the way notifications are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned notification section is, When sending notifications, the system will refer to past notification history to select the most appropriate notification method. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned notification section is, When sending notifications, we adjust the timing of the notifications based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned notification section is, It estimates the user's emotions and determines the priority of notifications based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned notification section is, When sending notifications, the system will select the most appropriate notification method, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned notification section is, When sending notifications, we analyze the user's social media activity and customize the content of the notifications. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0175] 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 discrimination unit that identifies suspicious individuals using behavioral patterns and facial recognition, The aforementioned discrimination unit has a calling unit that calls out to the suspicious person using the voice of a family member, A guidance unit that provides route directions in a conversational format if you get lost, It includes an alert unit that detects hazards such as traffic signals, cars, and bicycles and provides voice alerts. A system characterized by the following features.

2. The aforementioned notification section is, It can also be used as a support tool for people with visual impairments. The system according to feature 1.

3. The aforementioned discrimination unit is The system estimates the user's emotions and adjusts the accuracy of identifying suspicious individuals based on those estimated emotions. The system according to feature 1.

4. The aforementioned discrimination unit is During the identification process, the identification algorithm is optimized by referring to past suspicious person data. The system according to feature 1.

5. The aforementioned discrimination unit is During the identification process, changes in the user's behavior patterns are monitored in real time to detect anomalies. The system according to feature 1.

6. The aforementioned discrimination unit is The system estimates the user's emotions and adjusts the order in which it displays the suspicious person identification results based on the estimated user emotions. The system according to feature 1.

7. The aforementioned discrimination unit is During the identification process, the user's geographical location information is taken into consideration when identifying suspicious individuals. The system according to feature 1.

8. The aforementioned discrimination unit is During the identification process, the user's social media activity is analyzed to help identify suspicious individuals. The system according to feature 1.

9. The aforementioned calling section is, It estimates the user's emotions and adjusts the way it addresses the user based on those estimated emotions. The system according to feature 1.

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