System

Abstract

An object of the system according to the embodiment is to simply perform a survival check.SOLUTION: A system according to an embodiment includes a button, a communication unit, and a server analysis unit. The button generates a signal. The communication unit transmits the signal generated by the button. The server analysis unit analyzes the signal transmitted by the communication unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has limited means for easily confirming someone's existence, which has been inconvenient for elderly people and people with disabilities in particular.

[0005] The system according to the embodiment aims to easily confirm the existence of a person. [Means for solving the problem]

[0006] A system according to an embodiment includes a button, a communication unit, and a server analysis unit. The button generates a signal. The communication unit transmits the signal generated by the button. The server analysis unit analyzes the signal transmitted by the communication unit. [Effects of the Invention]

[0007] The system according to the embodiment can easily confirm the existence of a person. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention allows a user to send "I'm alive" data to a local government by simply pressing a single button on a mobile phone. When a user presses a specific button on a communication device, the system generates a signal indicating the user's survival. This signal is then transmitted to a local government server via the communication device's communications function. The local government server analyzes the received signal and confirms the user's survival. This allows elderly people and people living alone to easily report their survival and enables the local government to respond quickly. For example, when a user presses a specific button on a communication device, a message "I'm alive" is generated and transmitted via the communication device's communications function. The signal is encrypted to ensure security. For example, the signal is encrypted using the SSL / TLS protocol. The local government server analyzes the received signal and confirms the "I'm alive" message. A specific algorithm is used for this analysis. This allows the local government to periodically receive signals to confirm the user's survival and respond quickly in emergencies.

[0029] A survival confirmation system according to an embodiment includes a button for generating a signal, a communication unit, and a server analysis unit. The signal-generating button generates a signal indicating that a user is alive. For example, when a user presses the button, a message saying "I'm alive" is generated. The communication unit transmits the generated signal. For example, the communication unit can encrypt and transmit the signal. The encryption can be performed using the SSL / TLS protocol or the like. The server analysis unit analyzes the received signal and confirms that the user is alive. For example, the server analysis unit analyzes the received signal and confirms that the user is alive. As a result, the survival confirmation system according to an embodiment can transmit a survival confirmation signal to a local government and analyze it simply by the user pressing a button.

[0030] The communication unit includes an encryption unit that encrypts and transmits signals. The encryption unit encrypts and transmits signals. For example, the encryption unit encrypts signals using the SSL / TLS protocol. The encryption unit can also use encryption algorithms such as AES and RSA. This ensures the security of the signals. For example, the encryption unit encrypts signals to prevent unauthorized access by third parties. This allows the communication unit to transmit signals securely.

[0031] The communication unit has a function of periodically transmitting a signal. The communication unit periodically transmits the signal. For example, the communication unit can transmit the signal at intervals such as daily, weekly, or monthly. By transmitting the signal periodically, the user's existence is continuously confirmed. For example, the communication unit transmits a signal at a fixed time every day to confirm the user's existence. The communication unit can also adjust the transmission interval according to the user's settings. This allows the communication unit to periodically transmit the existence confirmation signal.

[0032] The server analysis unit includes an algorithm that analyzes the received signal and verifies that the user is alive. The server analysis unit analyzes the received signal and verifies that the user is alive. For example, the server analysis unit analyzes the received signal and verifies that the user is alive. A data analysis algorithm is used for the analysis. For example, the server analysis unit can analyze the signal using a machine learning algorithm. The server analysis unit can also analyze the signal using a rule-based algorithm. This enables the server analysis unit to verify that the user is alive.

[0033] The server analysis unit has the function of analyzing received signals and responding quickly in the event of an emergency. The server analysis unit analyzes received signals and responds quickly in the event of an emergency. For example, the server analysis unit analyzes received signals and issues a warning if an abnormality is detected. The warning may be a voice alert or a text message. For example, if an abnormality is detected, the server analysis unit sends a notification to a local government official. The server analysis unit can also automatically contact emergency services in the event of an emergency. This enables the server analysis unit to respond quickly in the event of an emergency.

[0034] A button that generates a signal authenticates a user's fingerprint when the button is pressed, thereby confirming the user's identity. A button that generates a signal authenticates a user's fingerprint when the button is pressed, thereby confirming the user's identity. For example, a fingerprint sensor is activated and scans the user's fingerprint at the same time as the button is pressed. A signal is generated only if fingerprint authentication is successful. If fingerprint authentication fails, a message is displayed prompting the user to press the button again. In this way, the button that generates a signal can confirm the user's identity when the button is pressed. For fingerprint authentication, for example, an optical fingerprint sensor or an ultrasonic fingerprint sensor is used. In this way, the button that generates a signal authenticates a user's fingerprint, thereby ensuring the security of the signal.

[0035] A signal-generating button monitors a user's health condition when the button is pressed, and automatically generates a signal if an abnormality is detected. A signal-generating button monitors a user's health condition when the button is pressed, and automatically generates a signal if an abnormality is detected. For example, a built-in sensor measures the user's heart rate at the same time as the button is pressed. If the heart rate indicates an abnormal value, a signal is automatically generated. If the health condition monitoring results are normal, a normal signal is generated. In this way, the signal-generating button can monitor the user's health condition and automatically generate a signal if an abnormality is detected. For example, a heart rate sensor or a blood pressure sensor is used to monitor the health condition. In this way, the signal-generating button can grasp the user's health condition in detail and take appropriate action if an abnormality is detected.

[0036] When the signal-generating button is pressed, it acquires the user's location information and includes it in the signal. When the signal-generating button is pressed, it acquires the user's location information and includes it in the signal. For example, when the button is pressed, the GPS function is activated and the user's current location is acquired. The acquired location information is included in the signal and transmitted. If the location information cannot be acquired, a message prompting the user to press the button again is displayed. In this way, the signal-generating button can include the user's location information in the signal. For example, GPS or Wi-Fi location information is used to acquire the location information. In this way, the signal-generating button can grasp the user's location information in detail and include it in the signal.

[0037] The signal-generating button recognizes the user's voice when pressed and transmits it as an audio signal. The signal-generating button recognizes the user's voice when pressed and transmits it as an audio signal. For example, a microphone is activated and records the user's voice at the same time as pressing the button. The recorded voice is converted into a signal and transmitted. If voice recognition fails, a message is displayed prompting the user to press the button again. In this way, the signal-generating button can recognize the user's voice and transmit it as an audio signal. For example, a voice recognition algorithm or a type of microphone is used for voice recognition. In this way, the signal-generating button can accurately recognize the user's voice and transmit it as a signal.

[0038] A signal-generating button records environmental sounds around the user when the button is pressed, and includes the sounds in the signal. A signal-generating button records environmental sounds around the user when the button is pressed, and includes the sounds in the signal. For example, a microphone is activated and records the ambient environmental sounds at the same time as the button is pressed. The recorded ambient sounds are converted into a signal and transmitted. If the ambient sounds cannot be recorded, a message prompting the user to press the button again is displayed. In this way, the signal-generating button can include the ambient sounds around the user in the signal. For example, a microphone or a recording device is used to record the ambient sounds. In this way, the signal-generating button can grasp the situation around the user in detail and include the information in the signal.

[0039] When the button that generates a signal is pressed, it refers to the user's past press history and displays a warning if an abnormality is detected. When the button that generates a signal is pressed, it refers to the user's past press history and displays a warning if an abnormality is detected. For example, the past press history is referenced at the same time as the button is pressed. If an abnormal press pattern is detected, a warning message is displayed. If no abnormality is detected, a normal signal is generated. This allows the button that generates a signal to refer to the user's past press history and display a warning if an abnormality is detected. The press history is recorded using, for example, the number of presses and the press duration. This allows the button that generates a signal to grasp the user's press pattern in detail and take appropriate action if an abnormality occurs.

[0040] When transmitting a signal, the communication unit monitors the stability of communication and selects the optimal communication path. When transmitting a signal, the communication unit monitors the stability of communication and selects the optimal communication path. For example, when transmitting a signal, the communication stability is monitored in real time. If communication is unstable, the optimal communication path is automatically selected. After the selection of the communication path is completed, the signal is transmitted. This allows the communication unit to ensure the stability of communication and select the optimal communication path. For example, packet loss and delay time are used to monitor the stability of communication. This allows the communication unit to transmit signals stably.

[0041] The communication unit adjusts the communication speed when transmitting a signal, and performs efficient data transmission. The communication unit adjusts the communication speed when transmitting a signal, and performs efficient data transmission. For example, the communication speed is adjusted in real time when transmitting a signal. If the communication speed is slow, data compression is performed to achieve efficient transmission. If the communication speed is fast, normal data transmission is performed. In this way, the communication unit adjusts the communication speed, and efficient data transmission is possible. To adjust the communication speed, for example, bandwidth adjustment or data compression is used. In this way, the communication unit can transmit signals efficiently.

[0042] The communication unit has a redundancy function for improving the reliability of communication when transmitting a signal. The communication unit has a redundancy function for improving the reliability of communication when transmitting a signal. For example, redundancy is achieved by using multiple communication paths when transmitting a signal. If a failure occurs in one of the communication paths, the signal is transmitted using another path. The redundancy function improves the reliability of communication. This allows the communication unit to improve the reliability of communication. To achieve the redundancy function, for example, dual link or failover is used. This allows the communication unit to transmit signals stably.

[0043] When transmitting a signal, the communication unit adjusts the transmission timing taking into account the remaining battery level of the user's device. When transmitting a signal, the communication unit adjusts the transmission timing taking into account the remaining battery level of the user's device. For example, when transmitting a signal, the remaining battery level of the device is checked in real time. If the remaining battery level is low, the transmission timing is adjusted to minimize battery consumption. If the remaining battery level is sufficient, the signal is transmitted at the normal transmission timing. This allows the communication unit to adjust the transmission timing taking into account the remaining battery level. To check the remaining battery level, for example, battery monitoring or low power mode is used. This allows the communication unit to transmit signals efficiently.

[0044] When transmitting a signal, the communication unit analyzes the user's network environment and selects the optimal communication protocol. When transmitting a signal, the communication unit analyzes the user's network environment and selects the optimal communication protocol. For example, when transmitting a signal, the network environment is analyzed in real time. The optimal communication protocol is automatically selected depending on the network environment. After the selection of the communication protocol is completed, the signal is transmitted. This allows the communication unit to select the optimal communication protocol depending on the network environment. For example, network topology, connection speed, etc. are used to analyze the network environment. This allows the communication unit to transmit the signal efficiently.

[0045] When transmitting a signal, the communication unit refers to the user's past communication history and selects the optimal transmission method. When transmitting a signal, the communication unit refers to the user's past communication history and selects the optimal transmission method. For example, when transmitting a signal, the past communication history is referred to. The optimal transmission method is automatically selected from the past communication history. After the selection of the transmission method is completed, the signal is transmitted. This allows the communication unit to select the optimal transmission method based on the past communication history. For example, the number of transmissions and the transmission time are used to record the communication history. This allows the communication unit to transmit signals efficiently.

[0046] When analyzing a signal, the server analysis unit refers to past analysis data to improve the analysis accuracy. When analyzing a signal, the server analysis unit refers to past analysis data to improve the analysis accuracy. For example, when analyzing a signal, the server analysis unit refers to past analysis data. The analysis algorithm is optimized based on the past analysis data. By improving the analysis accuracy, accurate analysis results are provided. This allows the server analysis unit to improve the analysis accuracy based on the past analysis data. For example, the analysis date and time and the analysis results are used to record the analysis data. This allows the server analysis unit to analyze signals efficiently.

[0047] When analyzing the signal, the server analysis unit applies an anomaly detection algorithm and issues a warning if an abnormality is found. When analyzing the signal, the server analysis unit applies an anomaly detection algorithm and issues a warning if an abnormality is found. For example, the server analysis unit applies an anomaly detection algorithm when analyzing the signal. If an abnormality is detected, a warning message is issued. If no abnormality is found, a normal analysis result is provided. This allows the server analysis unit to apply an anomaly detection algorithm and issue a warning if an abnormality is found. For example, machine learning-based anomaly detection or rule-based anomaly detection is used as the anomaly detection algorithm. This allows the server analysis unit to quickly detect abnormalities in the signal and take appropriate action.

[0048] When analyzing signals, the server analysis unit updates the analysis results in real time to provide the latest information. When analyzing signals, the server analysis unit updates the analysis results in real time to provide the latest information. For example, when analyzing signals, the analysis results are updated in real time. The user's survival is confirmed based on the latest analysis results. After the analysis result update is complete, the latest information is provided. This allows the server analysis unit to update the analysis results in real time to provide the latest information. For example, the data update frequency and update method are used for real-time updates. This allows the server analysis unit to always perform analysis based on the latest information.

[0049] When analyzing signals, the server analysis unit customizes the analysis results taking into account the user's location information. When analyzing signals, the server analysis unit customizes the analysis results taking into account the user's location information. For example, when analyzing signals, the server analysis unit references the user's location information. The analysis results are customized based on the location information and appropriate information is provided. If location information cannot be obtained, standard analysis results are provided. This allows the server analysis unit to customize the analysis results based on the user's location information. Location information can be obtained using, for example, GPS data or Wi-Fi location information. This allows the server analysis unit to provide appropriate information according to the user's location.

[0050] When analyzing signals, the server analysis unit refers to the user's health data to improve the accuracy of the analysis. When analyzing signals, the server analysis unit refers to the user's health data to improve the accuracy of the analysis. For example, when analyzing signals, the server analysis unit refers to the user's health data. The analysis algorithm is optimized based on the health data. If health data cannot be obtained, a standard analysis algorithm is applied. This allows the server analysis unit to improve the accuracy of the analysis based on the user's health data. Health data can be obtained using, for example, heart rate, blood pressure, body temperature, etc. This allows the server analysis unit to understand the user's health condition in detail and improve the accuracy of the analysis.

[0051] When analyzing signals, the server analysis unit links the analysis results with other systems to share information. When analyzing signals, the server analysis unit links the analysis results with other systems to share information. For example, when analyzing signals, the analysis results are linked with other systems. The analysis results are shared with linked systems to prompt them to take appropriate action. If linking fails, linking is attempted again. This allows the server analysis unit to link the analysis results with other systems and share information. Information can be shared using, for example, API linking or database sharing. This allows the server analysis unit to link with other systems to efficiently share information.

[0052] The encryption unit applies the latest encryption algorithm when encrypting a signal to enhance security. The encryption unit applies the latest encryption algorithm when encrypting a signal to enhance security. For example, the latest encryption algorithm is applied when encrypting a signal. The security of the signal is enhanced by the latest algorithm. After application of the algorithm is complete, the signal is transmitted. This allows the encryption unit to apply the latest encryption algorithm to enhance security. Examples of the latest encryption algorithms used include AES-256 and RSA-2048. This allows the encryption unit to transmit signals securely.

[0053] The encryption unit optimizes the encryption key management method when encrypting a signal. The encryption unit optimizes the encryption key management method when encrypting a signal. For example, the encryption key management method is optimized when encrypting a signal. The optimized management method improves the security of the encryption key. After encryption key management is completed, the signal is transmitted. This allows the encryption unit to optimize the encryption key management method. For example, a key generation method, a key storage method, etc. are used as the encryption key management method. This allows the encryption unit to transmit the signal securely.

[0054] When encrypting a signal, the encryption unit performs optimization to improve the efficiency of the encryption process. When encrypting a signal, the encryption unit performs optimization to improve the efficiency of the encryption process. For example, when encrypting a signal, the efficiency of the encryption process is optimized. Efficient encryption is achieved by the optimized process. After the optimization of the encryption process is completed, the signal is transmitted. This allows the encryption unit to improve the efficiency of the encryption process. To improve the efficiency of the encryption process, for example, optimization of computational resources, parallel processing, etc. are used. This allows the encryption unit to transmit the signal efficiently.

[0055] When encrypting a signal, the encryption unit refers to the security settings of the user's device to select an encryption method. When encrypting a signal, the encryption unit refers to the security settings of the user's device to select an encryption method. For example, when encrypting a signal, the encryption unit refers to the security settings of the device. The optimal encryption method is automatically selected based on the security settings. After the encryption method selection is complete, the signal is transmitted. This allows the encryption unit to select the optimal encryption method based on the security settings of the device. For example, firewall settings and access control are used to refer to the security settings. This allows the encryption unit to transmit the signal securely.

[0056] When encrypting a signal, the encryption unit refers to the user's past encryption history to select the optimal encryption method. When encrypting a signal, the encryption unit refers to the user's past encryption history to select the optimal encryption method. For example, when encrypting a signal, the encryption unit refers to the past encryption history. The optimal encryption method is automatically selected from the past encryption history. After the encryption method selection is complete, the signal is transmitted. This allows the encryption unit to select the optimal encryption method based on the past encryption history. For example, the encryption date and time and the encryption algorithm are used to record the encryption history. This allows the encryption unit to encrypt the signal efficiently.

[0057] When encrypting a signal, the encryption unit records a log of the encryption process so that it can be analyzed later. When encrypting a signal, the encryption unit records a log of the encryption process so that it can be analyzed later. For example, when encrypting a signal, a log of the encryption process is recorded. The encryption process is later analyzed based on the recorded log. After recording of the log is completed, the signal is transmitted. In this way, the encryption unit can record a log of the encryption process so that it can be analyzed later. For example, the location where the log is saved and the contents of the log are used to record the log of the encryption process. In this way, the encryption unit can grasp the signal encryption process in detail so that it can be analyzed later.

[0058] The periodic transmission function applies an algorithm for optimizing the transmission timing when transmitting a signal. The periodic transmission function applies an algorithm for optimizing the transmission timing when transmitting a signal. For example, an algorithm for optimizing the transmission timing is applied when transmitting a signal. Efficient signal transmission is achieved by the optimized algorithm. After application of the algorithm is complete, the signal is transmitted. In this way, the periodic transmission function optimizes the transmission timing, thereby enabling efficient signal transmission. For example, optimal transmission time or transmission frequency is used to optimize the transmission timing. In this way, the periodic transmission function can transmit signals efficiently.

[0059] The periodic transmission function compresses the transmission data when transmitting a signal, thereby achieving efficient data transmission. The periodic transmission function compresses the transmission data when transmitting a signal, thereby achieving efficient data transmission. For example, the transmission data is compressed when transmitting a signal. Efficient signal transmission is achieved using the compressed data. After data compression is complete, the signal is transmitted. In this way, the periodic transmission function compresses the transmission data, thereby enabling efficient data transmission. For example, a compression algorithm or a compression rate is used for data compression. In this way, the periodic transmission function can transmit signals efficiently.

[0060] The periodic transmission function has a retransmission function in the event of a transmission failure when transmitting a signal. The periodic transmission function has a retransmission function in the event of a transmission failure when transmitting a signal. For example, when transmitting a signal, it detects a transmission failure. If a transmission failure is detected, it automatically performs a retransmission. It attempts to retransmit at regular intervals until the retransmission is successful. As a result, the periodic transmission function improves the reliability of signal transmission by retransmitting when a transmission fails. To realize the retransmission function, for example, the number of retransmissions and the timing of retransmissions are used. As a result, the periodic transmission function can reliably transmit signals.

[0061] The periodic transmission function adjusts the transmission timing by referring to the user's schedule when transmitting a signal. The periodic transmission function adjusts the transmission timing by referring to the user's schedule when transmitting a signal. For example, the user's schedule is referenced when transmitting a signal. The optimum transmission timing is automatically adjusted based on the schedule. After the transmission timing adjustment is complete, the signal is transmitted. This allows the periodic transmission function to adjust the transmission timing based on the user's schedule. The schedule can be referenced, for example, by linking with a calendar app or by using a schedule input method. This allows the periodic transmission function to provide the optimum transmission timing according to the user's schedule.

[0062] The periodic transmission function adjusts the transmission interval taking into account the remaining battery level of the user's device when transmitting a signal. The periodic transmission function adjusts the transmission interval taking into account the remaining battery level of the user's device when transmitting a signal. For example, when transmitting a signal, the remaining battery level of the device is checked in real time. If the remaining battery level is low, the transmission interval is adjusted to minimize battery consumption. If the remaining battery level is sufficient, the signal is transmitted at a normal transmission interval. This allows the periodic transmission function to adjust the transmission interval based on the remaining battery level. To adjust the transmission interval, for example, a transmission interval setting method or an interval optimization method is used. This allows the periodic transmission function to transmit signals efficiently.

[0063] When transmitting a signal, the periodic transmission function refers to the user's past transmission history to select the optimal transmission method. When transmitting a signal, the periodic transmission function refers to the user's past transmission history to select the optimal transmission method. For example, when transmitting a signal, the past transmission history is referenced. The optimal transmission method is automatically selected from the past transmission history. After the transmission method selection is complete, the signal is transmitted. This allows the periodic transmission function to select the optimal transmission method based on the past transmission history. The transmission history is recorded using, for example, the number of transmissions and the transmission time. This allows the periodic transmission function to transmit signals efficiently.

[0064] When analyzing a signal, the survival confirmation algorithm refers to past survival confirmation data to improve the analysis accuracy. When analyzing a signal, the survival confirmation algorithm refers to past survival confirmation data to improve the analysis accuracy. For example, when analyzing a signal, past survival confirmation data is referenced. The analysis algorithm is optimized based on the past data. By improving the analysis accuracy, accurate survival confirmation is performed. This allows the survival confirmation algorithm to improve the analysis accuracy based on the past survival confirmation data. For example, heart rate, blood pressure, body temperature, etc. are used to record the survival confirmation data. This allows the survival confirmation algorithm to grasp the user's health condition in detail and improve the analysis accuracy.

[0065] The liveness confirmation algorithm applies an anomaly detection algorithm when analyzing a signal, and issues a warning if an abnormality is found. The liveness confirmation algorithm applies an anomaly detection algorithm when analyzing a signal, and issues a warning if an abnormality is found. For example, an anomaly detection algorithm is applied when analyzing a signal. If an abnormality is detected, a warning message is issued. If no abnormality is found, a normal analysis result is provided. This allows the liveness confirmation algorithm to apply the anomaly detection algorithm and issue a warning if an abnormality is found. For example, machine learning-based anomaly detection or rule-based anomaly detection is used as the anomaly detection algorithm. This allows the liveness confirmation algorithm to quickly detect signal abnormalities and take appropriate action.

[0066] When analyzing a signal, the survival confirmation algorithm updates the analysis results in real time to provide the latest information. When analyzing a signal, the survival confirmation algorithm updates the analysis results in real time to provide the latest information. For example, when analyzing a signal, the analysis results are updated in real time. The survival of the user is confirmed based on the latest analysis results. After the analysis result update is complete, the latest information is provided. In this way, the survival confirmation algorithm can update the analysis results in real time to provide the latest information. For example, the data update frequency and update method are used for the real-time update. In this way, the survival confirmation algorithm can always perform analysis based on the latest information.

[0067] When analyzing signals, the liveness confirmation algorithm customizes the analysis results by taking into account the user's location information. When analyzing signals, the liveness confirmation algorithm customizes the analysis results by taking into account the user's location information. For example, when analyzing signals, the algorithm refers to the user's location information. The algorithm customizes the analysis results and provides appropriate information based on the location information. If location information cannot be obtained, the algorithm provides standard analysis results. This allows the liveness confirmation algorithm to customize the analysis results based on the user's location information. Location information can be obtained using, for example, GPS data or Wi-Fi location information. This allows the liveness confirmation algorithm to provide appropriate information according to the user's location.

[0068] When analyzing a signal, the survival confirmation algorithm refers to the user's health data to improve the accuracy of the analysis. When analyzing a signal, the survival confirmation algorithm refers to the user's health data to improve the accuracy of the analysis. For example, when analyzing a signal, the user's health data is referenced. The analysis algorithm is optimized based on the health data. If health data cannot be obtained, a standard analysis algorithm is applied. This allows the survival confirmation algorithm to improve the accuracy of the analysis based on the user's health data. Health data can be obtained using, for example, heart rate, blood pressure, body temperature, etc. This allows the survival confirmation algorithm to grasp the user's health condition in detail and improve the accuracy of the analysis.

[0069] When analyzing a signal, the survival confirmation algorithm links the analysis results with other systems to share information. When analyzing a signal, the survival confirmation algorithm links the analysis results with other systems to share information. For example, when analyzing a signal, the analysis results are linked with other systems. The analysis results are shared with the linked systems to prompt them to take appropriate action. If the linking fails, the linking is attempted again. This allows the survival confirmation algorithm to link the analysis results with other systems and share information. Information can be shared using, for example, API linking or database sharing. This allows the survival confirmation algorithm to link with other systems and share information efficiently.

[0070] When analyzing signals, the emergency response function refers to past emergency response data to optimize the response method. When analyzing signals, the emergency response function refers to past emergency response data to optimize the response method. For example, when analyzing signals, past emergency response data is referenced. The response method is optimized based on the past data. A quick and appropriate response is taken using the optimized response method. This allows the emergency response function to optimize the response method based on past emergency response data. Emergency response data is recorded using, for example, past response history and response results. This allows the emergency response function to efficiently analyze signals and take an appropriate response.

[0071] The emergency response function applies an anomaly detection algorithm when analyzing signals, and responds quickly if an abnormality is detected. The emergency response function applies an anomaly detection algorithm when analyzing signals, and responds quickly if an abnormality is detected. For example, an anomaly detection algorithm is applied when analyzing signals. If an abnormality is detected, a response is initiated quickly. If no abnormality is detected, a normal response method is applied. This allows the emergency response function to apply an anomaly detection algorithm and respond quickly in the event of an abnormality. For example, machine learning-based anomaly detection or rule-based anomaly detection is used as the anomaly detection algorithm. This allows the emergency response function to quickly detect signal abnormalities and take appropriate action.

[0072] The emergency response function updates the response results in real time when analyzing signals, and provides the latest information. The emergency response function updates the response results in real time when analyzing signals, and provides the latest information. For example, the response results are updated in real time when analyzing signals. An appropriate response is taken based on the latest response results. After the response results have been updated, the latest information is provided. This allows the emergency response function to update the response results in real time and provide the latest information. For example, the data update frequency and update method are used for real-time updates. This allows the emergency response function to always take action based on the latest information.

[0073] When analyzing signals, the emergency response function customizes the response method taking into account the user's location information. When analyzing signals, the emergency response function customizes the response method taking into account the user's location information. For example, the user's location information is referenced when analyzing signals. The response method is customized and an appropriate response is taken based on the location information. If location information cannot be obtained, a standard response method is applied. This allows the emergency response function to customize the response method based on the user's location information. Location information can be obtained using, for example, GPS data or Wi-Fi location information. This allows the emergency response function to take an appropriate response according to the user's location.

[0074] When analyzing a signal, the emergency response function refers to the user's health data to optimize the response method. When analyzing a signal, the emergency response function refers to the user's health data to optimize the response method. For example, the user's health data is referenced when analyzing a signal. The response method is optimized based on the health data. If health data cannot be obtained, a standard response method is applied. This allows the emergency response function to optimize the response method based on the user's health data. Health data obtained includes, for example, heart rate, blood pressure, and body temperature. This allows the emergency response function to understand the user's health condition in detail and take appropriate action.

[0075] When analyzing signals, the emergency response function links the response results with other systems to share information. When analyzing signals, the emergency response function links the response results with other systems to share information. For example, when analyzing signals, the response results are linked with other systems. The response results are shared with linked systems to encourage appropriate responses. If linking fails, linking is attempted again. This allows the emergency response function to link the response results with other systems and share information. Information can be shared using, for example, API linking or database sharing. This allows the emergency response function to link with other systems to share information efficiently.

[0076] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0077] The survival confirmation system can further include a health monitoring unit that monitors the user's health condition. The health monitoring unit acquires data such as the user's heart rate, blood pressure, and body temperature, and evaluates the user's health condition based on this data. For example, if an abnormality is detected, such as an abnormally high or low heart rate or blood pressure exceeding the normal range, a warning signal can be generated and sent to a local government server. The health monitoring unit can also periodically record the user's health data to support long-term health management. This allows the user's health condition to be monitored in real time, and any abnormalities to be dealt with promptly.

[0078] The encryption unit can further select an encryption method by referring to the security settings of the user's device. For example, if the device's security settings are high, a stronger encryption algorithm can be used, and if the security settings are low, a standard encryption algorithm can be used. This allows the optimal encryption method to be selected according to the security level of the user's device, ensuring signal security. The encryption unit also optimizes the encryption key management method, allowing for efficient key generation, storage, and update. This improves the security of the encryption key and enables secure signal transmission.

[0079] The communication unit can further analyze the user's network environment and select the optimal communication protocol. For example, if the Wi-Fi connection is stable, signals can be transmitted using Wi-Fi, and if mobile data communication is stable, mobile data communication can be used. This allows the communication unit to select the optimal communication protocol according to the user's network environment and efficiently transmit signals. The communication unit can also monitor the stability of communication and automatically select the optimal communication path if communication is unstable. This improves communication reliability and ensures reliable signal transmission.

[0080] The server analysis unit can further customize the analysis results by taking into account the user's location information. For example, if the user is in a specific area, the analysis results can be provided taking into account the health risks and environmental factors specific to that area. This allows the server analysis unit to provide appropriate information based on the user's location information and perform more accurate survival confirmation. Furthermore, if location information cannot be obtained, standard analysis results can be provided. This allows the server analysis unit to customize the analysis results based on the user's location information and take appropriate action.

[0081] The server analysis unit can further apply anomaly detection algorithms and issue a warning if an abnormality is detected. For example, if an abnormality is detected in the received signal, a warning message can be sent to local government officials to prompt a prompt response. The anomaly detection algorithms used include machine learning-based anomaly detection and rule-based anomaly detection. This allows the server analysis unit to quickly detect abnormalities in the signal and take appropriate action. In addition, if an abnormality is detected, a warning message can be sent to the user to prompt them to take the necessary action. This allows the server analysis unit to take prompt and appropriate action in the event of an abnormality.

[0082] The processing flow of the first embodiment will be briefly explained below.

[0083] Step 1: Generate a signal The button generates a signal that indicates the user is alive. For example, when the user presses the button, the message "I'm alive" is generated. Step 2: The communication unit transmits the generated signal. For example, the communication unit can encrypt the signal before transmitting it. For encryption, the SSL / TLS protocol or the like is used. Step 3: The server analyzer analyzes the received signal and verifies that the user is alive. For example, the server analyzer analyzes the received signal and verifies that the user is alive.

[0084] (Example 2) A system according to an embodiment of the present invention allows a user to send "I'm alive" data to a local government by simply pressing a single button on a mobile phone. When a user presses a specific button on a communication device, the system generates a signal indicating the user's survival. This signal is then transmitted to a local government server via the communication device's communications function. The local government server analyzes the received signal and confirms the user's survival. This allows elderly people and people living alone to easily report their survival and enables the local government to respond quickly. For example, when a user presses a specific button on a communication device, a message "I'm alive" is generated and transmitted via the communication device's communications function. The signal is encrypted to ensure security. For example, the signal is encrypted using the SSL / TLS protocol. The local government server analyzes the received signal and confirms the "I'm alive" message. A specific algorithm is used for this analysis. This allows the local government to periodically receive signals to confirm the user's survival and respond quickly in emergencies.

[0085] A survival confirmation system according to an embodiment includes a button for generating a signal, a communication unit, and a server analysis unit. The signal-generating button generates a signal indicating that a user is alive. For example, when a user presses the button, a message saying "I'm alive" is generated. The communication unit transmits the generated signal. For example, the communication unit can encrypt and transmit the signal. The encryption can be performed using the SSL / TLS protocol or the like. The server analysis unit analyzes the received signal and confirms that the user is alive. For example, the server analysis unit analyzes the received signal and confirms that the user is alive. As a result, the survival confirmation system according to an embodiment can transmit a survival confirmation signal to a local government and analyze it simply by the user pressing a button.

[0086] The communication unit includes an encryption unit that encrypts and transmits signals. The encryption unit encrypts and transmits signals. For example, the encryption unit encrypts signals using the SSL / TLS protocol. The encryption unit can also use encryption algorithms such as AES and RSA. This ensures the security of the signals. For example, the encryption unit encrypts signals to prevent unauthorized access by third parties. This allows the communication unit to transmit signals securely.

[0087] The communication unit has a function of periodically transmitting a signal. The communication unit periodically transmits the signal. For example, the communication unit can transmit the signal at intervals such as daily, weekly, or monthly. By transmitting the signal periodically, the user's existence is continuously confirmed. For example, the communication unit transmits a signal at a fixed time every day to confirm the user's existence. The communication unit can also adjust the transmission interval according to the user's settings. This allows the communication unit to periodically transmit the existence confirmation signal.

[0088] The server analysis unit includes an algorithm that analyzes the received signal and verifies that the user is alive. The server analysis unit analyzes the received signal and verifies that the user is alive. For example, the server analysis unit analyzes the received signal and verifies that the user is alive. A data analysis algorithm is used for the analysis. For example, the server analysis unit can analyze the signal using a machine learning algorithm. The server analysis unit can also analyze the signal using a rule-based algorithm. This enables the server analysis unit to verify that the user is alive.

[0089] The server analysis unit has the function of analyzing received signals and responding quickly in the event of an emergency. The server analysis unit analyzes received signals and responds quickly in the event of an emergency. For example, the server analysis unit analyzes received signals and issues a warning if an abnormality is detected. The warning may be a voice alert or a text message. For example, if an abnormality is detected, the server analysis unit sends a notification to a local government official. The server analysis unit can also automatically contact emergency services in the event of an emergency. This enables the server analysis unit to respond quickly in the event of an emergency.

[0090] The button that generates a signal estimates the user's emotion and adjusts the button's press sensitivity based on the estimated user's emotion. The button that generates a signal estimates the user's emotion and adjusts the button's press sensitivity based on the estimated user's emotion. For example, if the user is nervous, the button's press sensitivity is reduced so that it can be pressed with light force. On the other hand, if the user is relaxed, the button's press sensitivity is set to normal so that it can be pressed with moderate force. On the other hand, if the user is tired, the button's press sensitivity is increased so that it requires a slightly stronger press. In this way, the button's press sensitivity can be adjusted according to the user's emotion. Emotion estimation is performed using technologies such as facial expression recognition and voice analysis. As a result, the button that generates a signal can provide press sensitivity that corresponds to the user's emotion.

[0091] A button that generates a signal authenticates a user's fingerprint when the button is pressed, thereby confirming the user's identity. A button that generates a signal authenticates a user's fingerprint when the button is pressed, thereby confirming the user's identity. For example, a fingerprint sensor is activated and scans the user's fingerprint at the same time as the button is pressed. A signal is generated only if fingerprint authentication is successful. If fingerprint authentication fails, a message is displayed prompting the user to press the button again. In this way, the button that generates a signal can confirm the user's identity when the button is pressed. For fingerprint authentication, for example, an optical fingerprint sensor or an ultrasonic fingerprint sensor is used. In this way, the button that generates a signal authenticates a user's fingerprint, thereby ensuring the security of the signal.

[0092] A signal-generating button monitors a user's health condition when the button is pressed, and automatically generates a signal if an abnormality is detected. A signal-generating button monitors a user's health condition when the button is pressed, and automatically generates a signal if an abnormality is detected. For example, a built-in sensor measures the user's heart rate at the same time as the button is pressed. If the heart rate indicates an abnormal value, a signal is automatically generated. If the health condition monitoring results are normal, a normal signal is generated. In this way, the signal-generating button can monitor the user's health condition and automatically generate a signal if an abnormality is detected. For example, a heart rate sensor or a blood pressure sensor is used to monitor the health condition. In this way, the signal-generating button can grasp the user's health condition in detail and take appropriate action if an abnormality is detected.

[0093] When the signal-generating button is pressed, it acquires the user's location information and includes it in the signal. When the signal-generating button is pressed, it acquires the user's location information and includes it in the signal. For example, when the button is pressed, the GPS function is activated and the user's current location is acquired. The acquired location information is included in the signal and transmitted. If the location information cannot be acquired, a message prompting the user to press the button again is displayed. In this way, the signal-generating button can include the user's location information in the signal. For example, GPS or Wi-Fi location information is used to acquire the location information. In this way, the signal-generating button can grasp the user's location information in detail and include it in the signal.

[0094] The signal-generating button estimates the user's emotion and changes the color and shape of the button based on the estimated user's emotion. The signal-generating button estimates the user's emotion and changes the color and shape of the button based on the estimated user's emotion. For example, if the user is nervous, the button color changes to blue to calm the user. If the user is relaxed, the button color changes to green to give a sense of security. If the user is excited, the button shape changes to attract the user's attention. In this way, the signal-generating button can change the color and shape of the button according to the user's emotion. Emotion estimation is performed using technologies such as facial expression recognition and voice analysis. In this way, the signal-generating button can provide visual feedback according to the user's emotion.

[0095] The signal-generating button recognizes the user's voice when pressed and transmits it as an audio signal. The signal-generating button recognizes the user's voice when pressed and transmits it as an audio signal. For example, a microphone is activated and records the user's voice at the same time as pressing the button. The recorded voice is converted into a signal and transmitted. If voice recognition fails, a message is displayed prompting the user to press the button again. In this way, the signal-generating button can recognize the user's voice and transmit it as an audio signal. For example, a voice recognition algorithm or a type of microphone is used for voice recognition. In this way, the signal-generating button can accurately recognize the user's voice and transmit it as a signal.

[0096] A signal-generating button records environmental sounds around the user when the button is pressed, and includes the sounds in the signal. A signal-generating button records environmental sounds around the user when the button is pressed, and includes the sounds in the signal. For example, a microphone is activated and records the ambient environmental sounds at the same time as the button is pressed. The recorded ambient sounds are converted into a signal and transmitted. If the ambient sounds cannot be recorded, a message prompting the user to press the button again is displayed. In this way, the signal-generating button can include the ambient sounds around the user in the signal. For example, a microphone or a recording device is used to record the ambient sounds. In this way, the signal-generating button can grasp the situation around the user in detail and include the information in the signal.

[0097] When the button that generates a signal is pressed, it refers to the user's past press history and displays a warning if an abnormality is detected. When the button that generates a signal is pressed, it refers to the user's past press history and displays a warning if an abnormality is detected. For example, the past press history is referenced at the same time as the button is pressed. If an abnormal press pattern is detected, a warning message is displayed. If no abnormality is detected, a normal signal is generated. This allows the button that generates a signal to refer to the user's past press history and display a warning if an abnormality is detected. The press history is recorded using, for example, the number of presses and the press duration. This allows the button that generates a signal to grasp the user's press pattern in detail and take appropriate action if an abnormality occurs.

[0098] The communication unit estimates the user's emotion and adjusts the priority of communication based on the estimated user's emotion. The communication unit estimates the user's emotion and adjusts the priority of communication based on the estimated user's emotion. For example, if the user is nervous, the communication priority is set high and a signal is sent quickly. If the user is relaxed, a signal is sent with normal priority. If the user is excited, the communication priority is adjusted and a signal is sent at an appropriate time. This allows the communication unit to adjust the priority of communication according to the user's emotion. Emotion estimation is performed using technologies such as facial expression recognition and voice analysis. This allows the communication unit to provide a priority of communication according to the user's emotion.

[0099] When transmitting a signal, the communication unit monitors the stability of communication and selects the optimal communication path. When transmitting a signal, the communication unit monitors the stability of communication and selects the optimal communication path. For example, when transmitting a signal, the communication stability is monitored in real time. If communication is unstable, the optimal communication path is automatically selected. After the selection of the communication path is completed, the signal is transmitted. This allows the communication unit to ensure the stability of communication and select the optimal communication path. For example, packet loss and delay time are used to monitor the stability of communication. This allows the communication unit to transmit signals stably.

[0100] The communication unit adjusts the communication speed when transmitting a signal, and performs efficient data transmission. The communication unit adjusts the communication speed when transmitting a signal, and performs efficient data transmission. For example, the communication speed is adjusted in real time when transmitting a signal. If the communication speed is slow, data compression is performed to achieve efficient transmission. If the communication speed is fast, normal data transmission is performed. In this way, the communication unit adjusts the communication speed, and efficient data transmission is possible. To adjust the communication speed, for example, bandwidth adjustment or data compression is used. In this way, the communication unit can transmit signals efficiently.

[0101] The communication unit has a redundancy function for improving the reliability of communication when transmitting a signal. The communication unit has a redundancy function for improving the reliability of communication when transmitting a signal. For example, redundancy is achieved by using multiple communication paths when transmitting a signal. If a failure occurs in one of the communication paths, the signal is transmitted using another path. The redundancy function improves the reliability of communication. This allows the communication unit to improve the reliability of communication. To achieve the redundancy function, for example, dual link or failover is used. This allows the communication unit to transmit signals stably.

[0102] The communication unit estimates the user's emotion and adjusts the frequency of communication based on the estimated user's emotion. The communication unit estimates the user's emotion and adjusts the frequency of communication based on the estimated user's emotion. For example, if the user is nervous, the communication frequency is set high and signals are sent frequently. On the other hand, if the user is relaxed, signals are sent at a normal frequency. On the other hand, if the user is excited, the communication frequency is adjusted and signals are sent at an appropriate timing. This allows the communication unit to adjust the frequency of communication according to the user's emotion. Emotion estimation is performed using technologies such as facial expression recognition and voice analysis. This allows the communication unit to provide a frequency of communication according to the user's emotion.

[0103] When transmitting a signal, the communication unit adjusts the transmission timing taking into account the remaining battery level of the user's device. When transmitting a signal, the communication unit adjusts the transmission timing taking into account the remaining battery level of the user's device. For example, when transmitting a signal, the remaining battery level of the device is checked in real time. If the remaining battery level is low, the transmission timing is adjusted to minimize battery consumption. If the remaining battery level is sufficient, the signal is transmitted at the normal transmission timing. This allows the communication unit to adjust the transmission timing taking into account the remaining battery level. To check the remaining battery level, for example, battery monitoring or low power mode is used. This allows the communication unit to transmit signals efficiently.

[0104] When transmitting a signal, the communication unit analyzes the user's network environment and selects the optimal communication protocol. When transmitting a signal, the communication unit analyzes the user's network environment and selects the optimal communication protocol. For example, when transmitting a signal, the network environment is analyzed in real time. The optimal communication protocol is automatically selected depending on the network environment. After the selection of the communication protocol is completed, the signal is transmitted. This allows the communication unit to select the optimal communication protocol depending on the network environment. For example, network topology, connection speed, etc. are used to analyze the network environment. This allows the communication unit to transmit the signal efficiently.

[0105] When transmitting a signal, the communication unit refers to the user's past communication history and selects the optimal transmission method. When transmitting a signal, the communication unit refers to the user's past communication history and selects the optimal transmission method. For example, when transmitting a signal, the past communication history is referred to. The optimal transmission method is automatically selected from the past communication history. After the selection of the transmission method is completed, the signal is transmitted. This allows the communication unit to select the optimal transmission method based on the past communication history. For example, the number of transmissions and the transmission time are used to record the communication history. This allows the communication unit to transmit signals efficiently.

[0106] The server analysis unit estimates the user's emotions and adjusts the display method of the analysis results based on the estimated user emotions. The server analysis unit estimates the user's emotions and adjusts the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method is provided. If the user is relaxed, a display method including detailed information is provided. If the user is in a hurry, a display method that focuses on the main points is provided. This allows the server analysis unit to adjust the display method of the analysis results according to the user's emotions. Emotions are estimated using technologies such as facial expression recognition and voice analysis. This allows the server analysis unit to provide a display method according to the user's emotions.

[0107] When analyzing a signal, the server analysis unit refers to past analysis data to improve the analysis accuracy. When analyzing a signal, the server analysis unit refers to past analysis data to improve the analysis accuracy. For example, when analyzing a signal, the server analysis unit refers to past analysis data. The analysis algorithm is optimized based on the past analysis data. By improving the analysis accuracy, accurate analysis results are provided. This allows the server analysis unit to improve the analysis accuracy based on the past analysis data. For example, the analysis date and time and the analysis results are used to record the analysis data. This allows the server analysis unit to analyze signals efficiently.

[0108] When analyzing the signal, the server analysis unit applies an anomaly detection algorithm and issues a warning if an abnormality is found. When analyzing the signal, the server analysis unit applies an anomaly detection algorithm and issues a warning if an abnormality is found. For example, the server analysis unit applies an anomaly detection algorithm when analyzing the signal. If an abnormality is detected, a warning message is issued. If no abnormality is found, a normal analysis result is provided. This allows the server analysis unit to apply an anomaly detection algorithm and issue a warning if an abnormality is found. For example, machine learning-based anomaly detection or rule-based anomaly detection is used as the anomaly detection algorithm. This allows the server analysis unit to quickly detect abnormalities in the signal and take appropriate action.

[0109] When analyzing signals, the server analysis unit updates the analysis results in real time to provide the latest information. When analyzing signals, the server analysis unit updates the analysis results in real time to provide the latest information. For example, when analyzing signals, the analysis results are updated in real time. The user's survival is confirmed based on the latest analysis results. After the analysis result update is complete, the latest information is provided. This allows the server analysis unit to update the analysis results in real time to provide the latest information. For example, the data update frequency and update method are used for real-time updates. This allows the server analysis unit to always perform analysis based on the latest information.

[0110] The server analysis unit estimates the user's emotions and determines the priority of the analysis results based on the estimated user emotions. The server analysis unit estimates the user's emotions and determines the priority of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis results are set to a high priority and displayed quickly. If the user is relaxed, the analysis results are displayed with normal priority. If the user is excited, the analysis results are adjusted in priority and displayed at an appropriate time. This allows the server analysis unit to determine the priority of the analysis results according to the user's emotions. Emotions are estimated using technologies such as facial expression recognition and voice analysis. This allows the server analysis unit to provide analysis results with a priority according to the user's emotions.

[0111] When analyzing signals, the server analysis unit customizes the analysis results taking into account the user's location information. When analyzing signals, the server analysis unit customizes the analysis results taking into account the user's location information. For example, when analyzing signals, the server analysis unit references the user's location information. The analysis results are customized based on the location information and appropriate information is provided. If location information cannot be obtained, standard analysis results are provided. This allows the server analysis unit to customize the analysis results based on the user's location information. Location information can be obtained using, for example, GPS data or Wi-Fi location information. This allows the server analysis unit to provide appropriate information according to the user's location.

[0112] When analyzing signals, the server analysis unit refers to the user's health data to improve the accuracy of the analysis. When analyzing signals, the server analysis unit refers to the user's health data to improve the accuracy of the analysis. For example, when analyzing signals, the server analysis unit refers to the user's health data. The analysis algorithm is optimized based on the health data. If health data cannot be obtained, a standard analysis algorithm is applied. This allows the server analysis unit to improve the accuracy of the analysis based on the user's health data. Health data can be obtained using, for example, heart rate, blood pressure, body temperature, etc. This allows the server analysis unit to understand the user's health condition in detail and improve the accuracy of the analysis.

[0113] When analyzing signals, the server analysis unit links the analysis results with other systems to share information. When analyzing signals, the server analysis unit links the analysis results with other systems to share information. For example, when analyzing signals, the analysis results are linked with other systems. The analysis results are shared with linked systems to prompt them to take appropriate action. If linking fails, linking is attempted again. This allows the server analysis unit to link the analysis results with other systems and share information. Information can be shared using, for example, API linking or database sharing. This allows the server analysis unit to link with other systems to efficiently share information.

[0114] The encryption unit estimates the user's emotion and adjusts the encryption strength based on the estimated user's emotion. The encryption unit estimates the user's emotion and adjusts the encryption strength based on the estimated user's emotion. For example, if the user is nervous, the encryption strength is set high to enhance security. If the user is relaxed, a signal is transmitted at normal encryption strength. If the user is excited, the encryption strength is adjusted to maintain an appropriate security level. This allows the encryption unit to adjust the encryption strength according to the user's emotion. Emotion estimation is performed using technologies such as facial expression recognition and voice analysis. This allows the encryption unit to provide encryption strength according to the user's emotion.

[0115] The encryption unit applies the latest encryption algorithm when encrypting a signal to enhance security. The encryption unit applies the latest encryption algorithm when encrypting a signal to enhance security. For example, the latest encryption algorithm is applied when encrypting a signal. The security of the signal is enhanced by the latest algorithm. After application of the algorithm is complete, the signal is transmitted. This allows the encryption unit to apply the latest encryption algorithm to enhance security. Examples of the latest encryption algorithms used include AES-256 and RSA-2048. This allows the encryption unit to transmit signals securely.

[0116] The encryption unit optimizes the encryption key management method when encrypting a signal. The encryption unit optimizes the encryption key management method when encrypting a signal. For example, the encryption key management method is optimized when encrypting a signal. The optimized management method improves the security of the encryption key. After encryption key management is completed, the signal is transmitted. This allows the encryption unit to optimize the encryption key management method. For example, a key generation method, a key storage method, etc. are used as the encryption key management method. This allows the encryption unit to transmit the signal securely.

[0117] When encrypting a signal, the encryption unit performs optimization to improve the efficiency of the encryption process. When encrypting a signal, the encryption unit performs optimization to improve the efficiency of the encryption process. For example, when encrypting a signal, the efficiency of the encryption process is optimized. Efficient encryption is achieved by the optimized process. After the optimization of the encryption process is completed, the signal is transmitted. This allows the encryption unit to improve the efficiency of the encryption process. To improve the efficiency of the encryption process, for example, optimization of computational resources, parallel processing, etc. are used. This allows the encryption unit to transmit the signal efficiently.

[0118] The encryption unit estimates the user's emotion and adjusts the timing of encryption based on the estimated user's emotion. The encryption unit estimates the user's emotion and adjusts the timing of encryption based on the estimated user's emotion. For example, if the user is nervous, the encryption timing is set earlier and a signal is sent quickly. On the other hand, if the user is relaxed, encryption is performed at a normal timing. On the other hand, if the user is excited, the encryption timing is adjusted and a signal is sent at an appropriate timing. This allows the encryption unit to adjust the timing of encryption according to the user's emotion. Emotion estimation is performed using technologies such as facial expression recognition and voice analysis. This allows the encryption unit to provide encryption timing according to the user's emotion.

[0119] When encrypting a signal, the encryption unit refers to the security settings of the user's device to select an encryption method. When encrypting a signal, the encryption unit refers to the security settings of the user's device to select an encryption method. For example, when encrypting a signal, the encryption unit refers to the security settings of the device. The optimal encryption method is automatically selected based on the security settings. After the encryption method selection is complete, the signal is transmitted. This allows the encryption unit to select the optimal encryption method based on the security settings of the device. For example, firewall settings and access control are used to refer to the security settings. This allows the encryption unit to transmit the signal securely.

[0120] When encrypting a signal, the encryption unit refers to the user's past encryption history to select the optimal encryption method. When encrypting a signal, the encryption unit refers to the user's past encryption history to select the optimal encryption method. For example, when encrypting a signal, the encryption unit refers to the past encryption history. The optimal encryption method is automatically selected from the past encryption history. After the encryption method selection is complete, the signal is transmitted. This allows the encryption unit to select the optimal encryption method based on the past encryption history. For example, the encryption date and time and the encryption algorithm are used to record the encryption history. This allows the encryption unit to encrypt the signal efficiently.

[0121] When encrypting a signal, the encryption unit records a log of the encryption process so that it can be analyzed later. When encrypting a signal, the encryption unit records a log of the encryption process so that it can be analyzed later. For example, when encrypting a signal, a log of the encryption process is recorded. The encryption process is later analyzed based on the recorded log. After recording of the log is completed, the signal is transmitted. In this way, the encryption unit can record a log of the encryption process so that it can be analyzed later. For example, the location where the log is saved and the contents of the log are used to record the log of the encryption process. In this way, the encryption unit can grasp the signal encryption process in detail so that it can be analyzed later.

[0122] The periodic transmission function estimates the user's emotions and adjusts the interval between periodic transmissions based on the estimated user emotions. The periodic transmission function estimates the user's emotions and adjusts the interval between periodic transmissions based on the estimated user emotions. For example, if the user is nervous, the periodic transmission interval is set short and signals are sent frequently. If the user is relaxed, signals are sent at normal intervals. If the user is excited, the periodic transmission interval is adjusted and signals are sent at appropriate times. This allows the periodic transmission function to adjust the interval between periodic transmissions according to the user's emotions. Emotions are estimated using technologies such as facial expression recognition and voice analysis. This allows the periodic transmission function to provide transmission intervals according to the user's emotions.

[0123] The periodic transmission function applies an algorithm for optimizing the transmission timing when transmitting a signal. The periodic transmission function applies an algorithm for optimizing the transmission timing when transmitting a signal. For example, an algorithm for optimizing the transmission timing is applied when transmitting a signal. Efficient signal transmission is achieved by the optimized algorithm. After application of the algorithm is complete, the signal is transmitted. In this way, the periodic transmission function optimizes the transmission timing, thereby enabling efficient signal transmission. For example, optimal transmission time or transmission frequency is used to optimize the transmission timing. In this way, the periodic transmission function can transmit signals efficiently.

[0124] The periodic transmission function compresses the transmission data when transmitting a signal, thereby achieving efficient data transmission. The periodic transmission function compresses the transmission data when transmitting a signal, thereby achieving efficient data transmission. For example, the transmission data is compressed when transmitting a signal. Efficient signal transmission is achieved using the compressed data. After data compression is complete, the signal is transmitted. In this way, the periodic transmission function compresses the transmission data, thereby enabling efficient data transmission. For example, a compression algorithm or a compression rate is used for data compression. In this way, the periodic transmission function can transmit signals efficiently.

[0125] The periodic transmission function has a retransmission function in the event of a transmission failure when transmitting a signal. The periodic transmission function has a retransmission function in the event of a transmission failure when transmitting a signal. For example, when transmitting a signal, it detects a transmission failure. If a transmission failure is detected, it automatically performs a retransmission. It attempts to retransmit at regular intervals until the retransmission is successful. As a result, the periodic transmission function improves the reliability of signal transmission by retransmitting when a transmission fails. To realize the retransmission function, for example, the number of retransmissions and the timing of retransmissions are used. As a result, the periodic transmission function can reliably transmit signals.

[0126] The periodic transmission function estimates the user's emotions and determines the priority of periodic transmission based on the estimated user emotions. The periodic transmission function estimates the user's emotions and determines the priority of periodic transmission based on the estimated user emotions. For example, if the user is nervous, the priority of periodic transmission is set high and a signal is sent quickly. If the user is relaxed, a signal is sent with normal priority. If the user is excited, the priority of periodic transmission is adjusted and a signal is sent at an appropriate time. This allows the periodic transmission function to determine the priority of periodic transmission according to the user's emotions. Emotions are estimated using technologies such as facial expression recognition and voice analysis. This allows the periodic transmission function to send signals with a priority according to the user's emotions.

[0127] The periodic transmission function adjusts the transmission timing by referring to the user's schedule when transmitting a signal. The periodic transmission function adjusts the transmission timing by referring to the user's schedule when transmitting a signal. For example, the user's schedule is referenced when transmitting a signal. The optimum transmission timing is automatically adjusted based on the schedule. After the transmission timing adjustment is complete, the signal is transmitted. This allows the periodic transmission function to adjust the transmission timing based on the user's schedule. The schedule can be referenced, for example, by linking with a calendar app or by using a schedule input method. This allows the periodic transmission function to provide the optimum transmission timing according to the user's schedule.

[0128] The periodic transmission function adjusts the transmission interval taking into account the remaining battery level of the user's device when transmitting a signal. The periodic transmission function adjusts the transmission interval taking into account the remaining battery level of the user's device when transmitting a signal. For example, when transmitting a signal, the remaining battery level of the device is checked in real time. If the remaining battery level is low, the transmission interval is adjusted to minimize battery consumption. If the remaining battery level is sufficient, the signal is transmitted at a normal transmission interval. This allows the periodic transmission function to adjust the transmission interval based on the remaining battery level. To adjust the transmission interval, for example, a transmission interval setting method or an interval optimization method is used. This allows the periodic transmission function to transmit signals efficiently.

[0129] When transmitting a signal, the periodic transmission function refers to the user's past transmission history to select the optimal transmission method. When transmitting a signal, the periodic transmission function refers to the user's past transmission history to select the optimal transmission method. For example, when transmitting a signal, the past transmission history is referenced. The optimal transmission method is automatically selected from the past transmission history. After the transmission method selection is complete, the signal is transmitted. This allows the periodic transmission function to select the optimal transmission method based on the past transmission history. The transmission history is recorded using, for example, the number of transmissions and the transmission time. This allows the periodic transmission function to transmit signals efficiently.

[0130] The existence confirmation algorithm estimates the user's emotions and adjusts the criteria for existence confirmation based on the estimated user emotions. The existence confirmation algorithm estimates the user's emotions and adjusts the criteria for existence confirmation based on the estimated user emotions. For example, if the user is nervous, the criteria for existence confirmation are set stricter and a more detailed confirmation is performed. If the user is relaxed, the existence confirmation is performed at normal standards. If the user is excited, the criteria for existence confirmation are adjusted and an appropriate confirmation is performed. This allows the existence confirmation algorithm to adjust the criteria for existence confirmation according to the user's emotions. Emotions are estimated using technologies such as facial expression recognition and voice analysis. This allows the existence confirmation algorithm to perform existence confirmation based on standards according to the user's emotions.

[0131] When analyzing a signal, the survival confirmation algorithm refers to past survival confirmation data to improve the analysis accuracy. When analyzing a signal, the survival confirmation algorithm refers to past survival confirmation data to improve the analysis accuracy. For example, when analyzing a signal, past survival confirmation data is referenced. The analysis algorithm is optimized based on the past data. By improving the analysis accuracy, accurate survival confirmation is performed. This allows the survival confirmation algorithm to improve the analysis accuracy based on the past survival confirmation data. For example, heart rate, blood pressure, body temperature, etc. are used to record the survival confirmation data. This allows the survival confirmation algorithm to grasp the user's health condition in detail and improve the analysis accuracy.

[0132] The liveness confirmation algorithm applies an anomaly detection algorithm when analyzing a signal, and issues a warning if an abnormality is found. The liveness confirmation algorithm applies an anomaly detection algorithm when analyzing a signal, and issues a warning if an abnormality is found. For example, an anomaly detection algorithm is applied when analyzing a signal. If an abnormality is detected, a warning message is issued. If no abnormality is found, a normal analysis result is provided. This allows the liveness confirmation algorithm to apply the anomaly detection algorithm and issue a warning if an abnormality is found. For example, machine learning-based anomaly detection or rule-based anomaly detection is used as the anomaly detection algorithm. This allows the liveness confirmation algorithm to quickly detect signal abnormalities and take appropriate action.

[0133] When analyzing a signal, the survival confirmation algorithm updates the analysis results in real time to provide the latest information. When analyzing a signal, the survival confirmation algorithm updates the analysis results in real time to provide the latest information. For example, when analyzing a signal, the analysis results are updated in real time. The survival of the user is confirmed based on the latest analysis results. After the analysis result update is complete, the latest information is provided. In this way, the survival confirmation algorithm can update the analysis results in real time to provide the latest information. For example, the data update frequency and update method are used for the real-time update. In this way, the survival confirmation algorithm can always perform analysis based on the latest information.

[0134] The existence confirmation algorithm estimates the user's emotions and determines the priority of existence confirmation based on the estimated user emotions. The existence confirmation algorithm estimates the user's emotions and determines the priority of existence confirmation based on the estimated user emotions. For example, if the user is nervous, the priority of existence confirmation is set high and confirmation is performed quickly. On the other hand, if the user is relaxed, existence confirmation is performed with normal priority. On the other hand, if the user is excited, the priority of existence confirmation is adjusted and confirmation is performed at an appropriate time. In this way, the existence confirmation algorithm can determine the priority of existence confirmation according to the user's emotions. Emotions are estimated using technologies such as facial expression recognition and voice analysis. In this way, the existence confirmation algorithm can perform existence confirmation with priority according to the user's emotions.

[0135] When analyzing signals, the liveness confirmation algorithm customizes the analysis results by taking into account the user's location information. When analyzing signals, the liveness confirmation algorithm customizes the analysis results by taking into account the user's location information. For example, when analyzing signals, the algorithm refers to the user's location information. The algorithm customizes the analysis results and provides appropriate information based on the location information. If location information cannot be obtained, the algorithm provides standard analysis results. This allows the liveness confirmation algorithm to customize the analysis results based on the user's location information. Location information can be obtained using, for example, GPS data or Wi-Fi location information. This allows the liveness confirmation algorithm to provide appropriate information according to the user's location.

[0136] When analyzing a signal, the survival confirmation algorithm refers to the user's health data to improve the accuracy of the analysis. When analyzing a signal, the survival confirmation algorithm refers to the user's health data to improve the accuracy of the analysis. For example, when analyzing a signal, the user's health data is referenced. The analysis algorithm is optimized based on the health data. If health data cannot be obtained, a standard analysis algorithm is applied. This allows the survival confirmation algorithm to improve the accuracy of the analysis based on the user's health data. Health data can be obtained using, for example, heart rate, blood pressure, body temperature, etc. This allows the survival confirmation algorithm to grasp the user's health condition in detail and improve the accuracy of the analysis.

[0137] When analyzing a signal, the survival confirmation algorithm links the analysis results with other systems to share information. When analyzing a signal, the survival confirmation algorithm links the analysis results with other systems to share information. For example, when analyzing a signal, the analysis results are linked with other systems. The analysis results are shared with the linked systems to prompt them to take appropriate action. If the linking fails, the linking is attempted again. This allows the survival confirmation algorithm to link the analysis results with other systems and share information. Information can be shared using, for example, API linking or database sharing. This allows the survival confirmation algorithm to link with other systems and share information efficiently.

[0138] The emergency response function estimates the user's emotions and adjusts the emergency response method based on the estimated user's emotions. The emergency response function estimates the user's emotions and adjusts the emergency response method based on the estimated user's emotions. For example, if the user is nervous, a quick and calm response is made. If the user is relaxed, a normal response method is applied. If the user is excited, an appropriate response method is selected and a quick response is made. This allows the emergency response function to adjust the emergency response method according to the user's emotions. Emotions are estimated using technologies such as facial expression recognition and voice analysis. This allows the emergency response function to provide a response method according to the user's emotions.

[0139] When analyzing signals, the emergency response function refers to past emergency response data to optimize the response method. When analyzing signals, the emergency response function refers to past emergency response data to optimize the response method. For example, when analyzing signals, past emergency response data is referenced. The response method is optimized based on the past data. A quick and appropriate response is taken using the optimized response method. This allows the emergency response function to optimize the response method based on past emergency response data. Emergency response data is recorded using, for example, past response history and response results. This allows the emergency response function to efficiently analyze signals and take an appropriate response.

[0140] The emergency response function applies an anomaly detection algorithm when analyzing signals, and responds quickly if an abnormality is detected. The emergency response function applies an anomaly detection algorithm when analyzing signals, and responds quickly if an abnormality is detected. For example, an anomaly detection algorithm is applied when analyzing signals. If an abnormality is detected, a response is initiated quickly. If no abnormality is detected, a normal response method is applied. This allows the emergency response function to apply an anomaly detection algorithm and respond quickly in the event of an abnormality. For example, machine learning-based anomaly detection or rule-based anomaly detection is used as the anomaly detection algorithm. This allows the emergency response function to quickly detect signal abnormalities and take appropriate action.

[0141] The emergency response function updates the response results in real time when analyzing signals, and provides the latest information. The emergency response function updates the response results in real time when analyzing signals, and provides the latest information. For example, the response results are updated in real time when analyzing signals. An appropriate response is taken based on the latest response results. After the response results have been updated, the latest information is provided. This allows the emergency response function to update the response results in real time and provide the latest information. For example, the data update frequency and update method are used for real-time updates. This allows the emergency response function to always take action based on the latest information.

[0142] The emergency response function estimates the user's emotions and determines the priority of emergency responses based on the estimated user emotions. The emergency response function estimates the user's emotions and determines the priority of emergency responses based on the estimated user emotions. For example, if the user is nervous, the priority of emergency responses is set high and a response is made quickly. If the user is relaxed, the response is made at a normal priority. If the user is excited, the priority of emergency responses is adjusted and a response is made at an appropriate time. This allows the emergency response function to determine the priority of emergency responses according to the user's emotions. Emotions are estimated using technologies such as facial expression recognition and voice analysis. This allows the emergency response function to make responses with a priority according to the user's emotions.

[0143] When analyzing signals, the emergency response function customizes the response method taking into account the user's location information. When analyzing signals, the emergency response function customizes the response method taking into account the user's location information. For example, the user's location information is referenced when analyzing signals. The response method is customized and an appropriate response is taken based on the location information. If location information cannot be obtained, a standard response method is applied. This allows the emergency response function to customize the response method based on the user's location information. Location information can be obtained using, for example, GPS data or Wi-Fi location information. This allows the emergency response function to take an appropriate response according to the user's location.

[0144] When analyzing a signal, the emergency response function refers to the user's health data to optimize the response method. When analyzing a signal, the emergency response function refers to the user's health data to optimize the response method. For example, the user's health data is referenced when analyzing a signal. The response method is optimized based on the health data. If health data cannot be obtained, a standard response method is applied. This allows the emergency response function to optimize the response method based on the user's health data. Health data obtained includes, for example, heart rate, blood pressure, and body temperature. This allows the emergency response function to understand the user's health condition in detail and take appropriate action.

[0145] When analyzing signals, the emergency response function links the response results with other systems to share information. When analyzing signals, the emergency response function links the response results with other systems to share information. For example, when analyzing signals, the response results are linked with other systems. The response results are shared with linked systems to encourage appropriate responses. If linking fails, linking is attempted again. This allows the emergency response function to link the response results with other systems and share information. Information can be shared using, for example, API linking or database sharing. This allows the emergency response function to link with other systems to share information efficiently. === Hard Collateral 1-1 === Each of the multiple elements, including the button for generating the signal, the communication unit, and the server analysis unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the button for generating the signal is realized by the reception device 38 of the smart device 14, and when the user presses the button, a message saying "I'm alive" is generated. The communication unit is realized, for example, by the communication I / F 44 of the smart device 14, and encrypts and transmits the generated signal. The server analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the received signal to confirm that the user is alive. === Hard Collateral 1-2 === Each of the multiple elements, including the button for generating the signal, the communication unit, and the server analysis unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the button for generating the signal is realized by the microphone 238 of the smart glasses 214, and when the user presses the button, a message saying "I'm alive" is generated. The communication unit is realized, for example, by the communication I / F 44 of the smart glasses 214, and encrypts and transmits the generated signal. The server analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the received signal to confirm that the user is alive. === Hard Collateral 1-3 === Each of the multiple elements including the button for generating the signal, the communication unit, and the server analysis unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the button for generating the signal is realized by the microphone 238 of the headset terminal 314, and when the user presses the button, a message saying "I'm alive" is generated. The communication unit is realized, for example, by the communication I / F 44 of the headset terminal 314, and encrypts and transmits the generated signal. The server analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the received signal to confirm that the user is alive. === Hard Collateral 1-4 === Each of the multiple elements including the button for generating the signal, the communication unit, and the server analysis unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the button for generating the signal is realized by the microphone 238 of the robot 414, and when the user presses the button, a message saying "I'm alive" is generated. The communication unit is realized, for example, by the communication I / F 44 of the robot 414, and encrypts and transmits the generated signal. The server analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the received signal to confirm that the user is alive.

[0146] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0147] The survival confirmation system can further include a health monitoring unit that monitors the user's health condition. The health monitoring unit acquires data such as the user's heart rate, blood pressure, and body temperature, and evaluates the user's health condition based on this data. For example, if an abnormality is detected, such as an abnormally high or low heart rate or blood pressure exceeding the normal range, a warning signal can be generated and sent to a local government server. The health monitoring unit can also periodically record the user's health data to support long-term health management. This allows the user's health condition to be monitored in real time, and any abnormalities to be dealt with promptly.

[0148] The encryption unit can further select an encryption method by referring to the security settings of the user's device. For example, if the device's security settings are high, a stronger encryption algorithm can be used, and if the security settings are low, a standard encryption algorithm can be used. This allows the optimal encryption method to be selected according to the security level of the user's device, ensuring signal security. The encryption unit also optimizes the encryption key management method, allowing for efficient key generation, storage, and update. This improves the security of the encryption key and enables secure signal transmission.

[0149] The communication unit can further analyze the user's network environment and select the optimal communication protocol. For example, if the Wi-Fi connection is stable, signals can be transmitted using Wi-Fi, and if mobile data communication is stable, mobile data communication can be used. This allows the communication unit to select the optimal communication protocol according to the user's network environment and efficiently transmit signals. The communication unit can also monitor the stability of communication and automatically select the optimal communication path if communication is unstable. This improves communication reliability and ensures reliable signal transmission.

[0150] The server analysis unit can further customize the analysis results by taking into account the user's location information. For example, if the user is in a specific area, the analysis results can be provided taking into account the health risks and environmental factors specific to that area. This allows the server analysis unit to provide appropriate information based on the user's location information and perform more accurate survival confirmation. Furthermore, if location information cannot be obtained, standard analysis results can be provided. This allows the server analysis unit to customize the analysis results based on the user's location information and take appropriate action.

[0151] The server analysis unit can further apply anomaly detection algorithms and issue a warning if an abnormality is detected. For example, if an abnormality is detected in the received signal, a warning message can be sent to local government officials to prompt a prompt response. The anomaly detection algorithms used include machine learning-based anomaly detection and rule-based anomaly detection. This allows the server analysis unit to quickly detect abnormalities in the signal and take appropriate action. In addition, if an abnormality is detected, a warning message can be sent to the user to prompt them to take the necessary action. This allows the server analysis unit to take prompt and appropriate action in the event of an abnormality.

[0152] The button that generates the signal can estimate the user's emotion and adjust the button's press sensitivity based on the estimated user's emotion. For example, if the user is nervous, the button's press sensitivity can be reduced so that it can be pressed with a lighter force. If the user is relaxed, the button's press sensitivity can be set to normal so that it can be pressed with a moderate force. If the user is tired, the button's press sensitivity can be increased so that it requires a slightly stronger press. In this way, the button's press sensitivity can be adjusted according to the user's emotion. Emotion estimation is performed using technologies such as facial expression recognition and voice analysis. As a result, the button that generates the signal can provide press sensitivity that corresponds to the user's emotion.

[0153] The communication unit can estimate the user's emotions and adjust the priority of communication based on the estimated user's emotions. For example, if the user is nervous, the communication priority is set high and a signal is sent quickly. If the user is relaxed, a signal is sent with normal priority. If the user is excited, the communication priority is adjusted and a signal is sent at an appropriate timing. This allows the communication unit to adjust the priority of communication according to the user's emotions. Emotion estimation is performed using techniques such as facial expression recognition and voice analysis. This allows the communication unit to provide a priority of communication according to the user's emotions.

[0154] The server analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method is provided. If the user is relaxed, a display method including detailed information is provided. If the user is in a hurry, a display method that focuses on the main points is provided. This allows the server analysis unit to adjust the display method of the analysis results according to the user's emotions. Emotions are estimated using technologies such as facial expression recognition and voice analysis. This allows the server analysis unit to provide a display method that suits the user's emotions.

[0155] The encryption unit can estimate the user's emotions and adjust the encryption strength based on the estimated user's emotions. For example, if the user is nervous, the encryption strength is set high to enhance security. If the user is relaxed, a signal is transmitted at normal encryption strength. If the user is excited, the encryption strength is adjusted to maintain an appropriate security level. This allows the encryption unit to adjust the encryption strength according to the user's emotions. Emotions are estimated using techniques such as facial expression recognition and voice analysis. This allows the encryption unit to provide encryption strength according to the user's emotions.

[0156] The emergency response function can estimate the user's emotions and adjust the emergency response method based on the estimated user's emotions. For example, if the user is nervous, a quick and calm response is made. If the user is relaxed, a normal response method is applied. If the user is excited, an appropriate response method is selected and a quick response is made. This allows the emergency response function to adjust the emergency response method according to the user's emotions. Emotions are estimated using technologies such as facial expression recognition and voice analysis. This allows the emergency response function to provide a response method according to the user's emotions.

[0157] The processing flow of the second embodiment will be briefly explained below.

[0158] Step 1: Generate a signal The button generates a signal that indicates the user is alive. For example, when the user presses the button, the message "I'm alive" is generated. Step 2: The communication unit transmits the generated signal. For example, the communication unit can encrypt the signal before transmitting it. For encryption, the SSL / TLS protocol or the like is used. Step 3: The server analyzer analyzes the received signal and verifies that the user is alive. For example, the server analyzer analyzes the received signal and verifies that the user is alive.

[0159] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0161] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0162] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0163] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0164] 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.

[0165] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0166] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0167] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0168] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0169] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0170] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0171] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0172] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0173] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0174] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0175] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0176] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0177] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0178] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0179] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0180] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0181] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0182] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0183] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0184] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0185] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0186] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0187] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0188] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0189] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0190] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0191] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0192] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0193] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0194] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0195] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0196] 7, a 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.

[0197] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0198] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0199] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0200] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0201] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0202] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0203] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0204] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0205] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0206] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0207] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0208] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0209] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0210] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0211] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0212] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0213] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0214] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0215] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0216] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0217] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0218] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0219] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0220] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0221] 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.

[0222] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0223] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0224] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0225] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0226] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0227] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0228] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0229] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0230] [Explanation of symbols]

[0231] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a button for generating a signal; a communication unit for transmitting a signal generated by the button; a server analysis unit that analyzes the signal transmitted by the communication unit; Equipped with A system characterized by:

2. The communication unit Equipped with an encryption unit that encrypts and transmits signals 2. The system of claim 1.

3. The communication unit Equipped with the function of periodically sending signals 2. The system of claim 1.

4. The server analysis unit It has an algorithm that analyzes the received signal and verifies that the user is alive.

2. The system of claim 1.

5. The server analysis unit Equipped with the ability to analyze received signals and respond quickly in emergencies 2. The system of claim 1.

6. The button for generating the signal is Estimate the user's emotions and adjust the button press sensitivity based on the estimated user emotions.

2. The system of claim 1.

7. The button for generating the signal is When the button is pressed, the user's fingerprint is authenticated to verify their identity.

2. The system of claim 1.

8. The button for generating the signal is When the button is pressed, the device monitors the user's health and automatically generates a signal if there is an abnormality.

2. The system of claim 1.

Images