system

Abstract

The system according to this embodiment aims to reduce security risks by identifying the communication destination only when communication is to be performed. [Solution] The system according to the embodiment comprises a discrimination unit, an establishment unit, and a termination unit. The discrimination unit discriminates the communication destination only when communication is to be performed. The establishment unit establishes a session with the communication destination discriminated by the discrimination unit. The termination unit terminates the session when communication is completed.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a security risk due to being constantly connected to the network, and there is room for improvement.

[0005] The system according to the embodiment aims to identify the communication destination only when communication is performed and reduce security risks.

Means for Solving the Problems

[0006] The system according to the embodiment includes a discrimination unit, an establishment unit, and an end unit. The discrimination unit discriminates the communication destination only when communication is performed. The establishment unit establishes a session with the communication destination discriminated by the discrimination unit. The end unit ends the session when the communication is completed.

Effects of the Invention

[0007] The system according to this embodiment can reduce security risks by identifying the communication destination only when communication is to be performed. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The security system according to an embodiment of the present invention is a mechanism for mitigating security risks associated with constant network connectivity. This security system provides a mechanism in which AI identifies the communication destination only when communication is required, establishes a session with the communication destination, and terminates the session once communication is complete. This reduces the risk of attacks and information leaks. For example, when using applications such as banking transactions or electronic money, the AI ​​identifies the communication destination and performs the necessary communication. At this time, the AI ​​evaluates the reliability of the communication destination and establishes secure communication. Next, once communication is complete, the established session is terminated. This allows the system to remain disconnected from the network when no communication is taking place. For example, it can be used as an alternative to digital communication for highly confidential communications or communications from the police or fire department. This mechanism reduces security risks associated with constant network connectivity and enables secure communication. For example, when using applications such as banking transactions or electronic money, the AI ​​identifies the communication destination and establishes secure communication, thereby reducing the risk of attacks and information leaks. Furthermore, by using it as an alternative to digital communication for highly confidential communications or communications from the police or fire department, secure communication can be achieved. This allows security systems to mitigate the security risks associated with constant network connectivity and ensure secure communication.

[0029] The security system according to this embodiment comprises a discrimination unit, an establishment unit, and a termination unit. The discrimination unit discriminates the communication destination only when communication is to be performed. The discrimination unit can discriminate the communication destination based on, for example, the IP address or domain name of the communication destination. The discrimination unit can also discriminate the communication destination based on a user ID or security certificate. For example, the discrimination unit analyzes the IP address of the communication destination and selects a highly reliable communication destination. Furthermore, the discrimination unit can analyze the domain name of the communication destination and select a highly reliable communication destination. The establishment unit establishes a session with the communication destination discriminated by the discrimination unit. The establishment unit can establish a session using, for example, the TCP / IP protocol. Furthermore, the establishment unit can establish a session using the HTTP protocol. For example, the establishment unit establishes a session with the communication destination using the TCP / IP protocol. Furthermore, the establishment unit can establish a session with the communication destination using the HTTP protocol. The termination unit terminates the session when communication is complete. The termination unit can terminate the session based on, for example, a timeout or user operation. Furthermore, the termination unit can terminate the session when an error occurs. For example, the termination unit terminates the session when a timeout occurs. Furthermore, the termination unit can also terminate the session based on user actions. This allows the security system according to the embodiment to reduce security risks by identifying the communication destination only when communication is to be performed, establishing a session, and terminating the session once communication is complete.

[0030] The discrimination unit identifies the communication destination only when communication is initiated. For example, the discrimination unit can identify the communication destination based on the destination's IP address or domain name. Specifically, when communication begins, the discrimination unit first obtains the destination's IP address and verifies whether that IP address is trustworthy. Whitelists and blacklists can be used to verify trustworthiness, thereby eliminating unauthorized communication destinations. The discrimination unit also parses the destination's domain name through a DNS server to verify its legitimacy. Furthermore, the discrimination unit can identify the communication destination based on user IDs and security certificates. For example, a user ID can be used to select communication destinations that only specific users can access. Security certificates can also be used to prove the legitimacy of the communication destination, increasing trustworthiness. This allows the discrimination unit to enhance the trustworthiness of communication destinations and prevent unauthorized communication. Additionally, because the discrimination unit identifies communication destinations in real time, communication delays can be minimized. This allows users to communicate comfortably.

[0031] The establishment unit establishes a session with the communication destination identified by the discrimination unit. The establishment unit can establish a session using, for example, the TCP / IP protocol. Specifically, it establishes a session with the communication destination using the TCP / IP three-party handshake. This handshake process ensures the reliability and stability of the communication. The establishment unit can also establish a session using the HTTP protocol. For example, it can establish communication between a web browser and a web server using the HTTP protocol. Furthermore, the establishment unit can use the SSL / TLS protocol to ensure secure communication. This encrypts the communication data, preventing eavesdropping and tampering by third parties. The establishment unit performs necessary authentication procedures at the start of communication to verify that the communication destination is legitimate. This allows the establishment unit to establish a secure and reliable communication session. Furthermore, the establishment unit monitors the quality of the communication and maintains communication stability by reconnecting or retrying as needed. This allows the establishment unit to provide users with a high-quality communication environment.

[0032] The termination unit terminates the session once communication is complete. The termination unit can terminate a session based on factors such as timeouts or user actions. Specifically, it detects timeouts if no communication occurs for a certain period and automatically terminates the session. This prevents unnecessary sessions from remaining and allows for efficient use of system resources. The termination unit can also terminate a session if the user explicitly terminates the communication. For example, it appropriately terminates the session when the user closes a web browser or terminates a communication application. Furthermore, the termination unit can terminate a session in the event of an error. For example, if a network error or protocol error occurs during communication, the termination unit detects this and safely terminates the session. This maintains system stability even in the event of errors. The termination unit performs necessary cleanup processes upon session termination and releases resources associated with the session. This allows for efficient management of system resources and preparation for the next communication. Additionally, the termination unit can log session terminations for later troubleshooting and auditing. This allows the termination unit to properly manage communication terminations and improve the overall reliability and efficiency of the system.

[0033] The evaluation unit can evaluate the reliability of the communication destination. For example, the evaluation unit can evaluate the reliability of the communication destination based on past communication history. The evaluation unit can also evaluate the reliability of the communication destination based on security certificates. For example, the evaluation unit can analyze past communication history and select a highly reliable communication destination. Furthermore, the evaluation unit can analyze security certificates and select a highly reliable communication destination. In this way, the evaluation unit can establish more secure communication by evaluating the reliability of the communication destination. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit can input past communication history into AI and have the AI ​​perform the reliability evaluation.

[0034] The app usage unit can provide functions specifically tailored for use in banking transactions and electronic money applications. For example, the app usage unit can protect communications using encryption technology. Furthermore, the app usage unit can enhance security using two-factor authentication. For instance, the app usage unit protects communication content using encryption technology. Additionally, the app usage unit can perform user authentication using two-factor authentication. This allows the app usage unit to improve security during banking transactions and electronic money applications by specializing in these uses. Some or all of the above-described processes in the app usage unit may be performed using AI, or not. For example, the app usage unit can have AI select encryption technologies.

[0035] The secure communications unit can provide functions specialized for highly secure communications. For example, the secure communications unit can protect communications using end-to-end encryption. Furthermore, the secure communications unit can protect the privacy of communications using anonymization techniques. For example, the secure communications unit protects the content of communications using end-to-end encryption. Furthermore, the secure communications unit can protect the privacy of communications using anonymization techniques. This allows the secure communications unit to enhance the protection of highly confidential information by specializing in highly secure communications. Some or all of the above-described processes in the secure communications unit may be performed using AI, for example, or without AI. For example, the secure communications unit can have AI select encryption technologies.

[0036] The Emergency Communications Unit can provide functions specifically tailored to police and fire communications. For example, the Emergency Communications Unit can conduct emergency communications using high-priority communication channels. Furthermore, the Emergency Communications Unit can conduct rapid communications using emergency protocols. By specializing in police and fire communications, the Emergency Communications Unit can improve the reliability and speed of emergency communications. Some or all of the above-described processes in the Emergency Communications Unit may be performed using AI, or not. For example, the Emergency Communications Unit can have AI select communication channels.

[0037] The discrimination unit can analyze the past communication history of the communication destination and select the optimal discrimination method. For example, the discrimination unit can prioritize selecting communication destinations that have a high reliability in the past. The discrimination unit can also select communication destinations that are highly reliable during a specific time period based on past communication history. Furthermore, the discrimination unit can analyze past communication history and apply the optimal discrimination algorithm. In this way, the discrimination unit can select the optimal communication destination by analyzing past communication history. Some or all of the above processing in the discrimination unit may be performed using AI, for example, or without AI. For example, the discrimination unit can input past communication history into AI and have the AI ​​select the optimal discrimination method.

[0038] The discrimination unit can perform discrimination while considering the geographical location information of the communication destination. For example, if the communication destination is nearby, the discrimination unit will prioritize communication speed in its discrimination. Also, if the communication destination is far away, the discrimination unit can prioritize reliability in its discrimination. Furthermore, the discrimination unit can select the optimal communication destination based on the geographical location information of the communication destination. In this way, the discrimination unit can select the optimal communication destination by considering the geographical location information of the communication destination. Some or all of the above processing in the discrimination unit may be performed using AI, for example, or without using AI. For example, the discrimination unit can input the geographical location information of the communication destination into AI and have the AI ​​perform the selection of the optimal communication destination.

[0039] The discrimination unit can update the reliability score of the communication destination in real time and reflect it in the discrimination. For example, the discrimination unit monitors the reliability score of the communication destination in real time and reflects it in the discrimination. The discrimination unit can also prioritize other communication destinations if the reliability score of a communication destination decreases. Furthermore, the discrimination unit can select the optimal communication destination based on the reliability score of the communication destination. In this way, the discrimination unit can select a more reliable communication destination by updating the reliability score of the communication destination in real time. Some or all of the above processing in the discrimination unit may be performed using AI, for example, or without using AI. For example, the discrimination unit can input the reliability score of the communication destination into the AI ​​and have the AI ​​perform real-time updates.

[0040] The discrimination unit can change its discrimination algorithm based on the industry and category of the communication destination. For example, in the case of banking transactions, the discrimination unit applies a discrimination algorithm that prioritizes reliability. The discrimination unit can also apply a discrimination algorithm that prioritizes speed when using an electronic money app. Furthermore, in the case of emergency communications, the discrimination unit can apply a discrimination algorithm that prioritizes rapid response. In this way, the discrimination unit can select a more appropriate communication destination by changing the discrimination algorithm based on the industry and category of the communication destination. Some or all of the above processing in the discrimination unit may be performed using AI, for example, or without AI. For example, the discrimination unit can input the industry and category of the communication destination into the AI ​​and have the AI ​​select the optimal discrimination algorithm.

[0041] The session establishment unit can optimize the session establishment procedure based on the reliability assessment of the communication destination. For example, the establishment unit can establish a session with a simplified procedure for highly reliable communication destinations. It can also establish a session with a strict procedure for less reliable communication destinations. Furthermore, the establishment unit can select the optimal session establishment procedure based on the reliability assessment of the communication destination. This allows the establishment unit to establish more secure communication by optimizing the session establishment procedure based on the reliability assessment of the communication destination. Some or all of the above processing in the establishment unit may be performed using AI, for example, or without AI. For example, the establishment unit can input the reliability assessment of the communication destination into AI and have AI select the optimal session establishment procedure.

[0042] The session establishment unit can monitor the network status of the communication destination in real time and select the optimal session establishment method. For example, if the communication destination network is congested, the session establishment unit will select another communication destination. The session establishment unit can also apply the normal session establishment method if the communication destination network is stable. Furthermore, the session establishment unit can select the optimal session establishment method based on the network status of the communication destination. In this way, the session establishment unit can select the optimal session establishment method by monitoring the network status of the communication destination in real time. Some or all of the above processing in the session establishment unit may be performed using AI, for example, or without AI. For example, the session establishment unit can input the network status of the communication destination into AI and have AI select the optimal session establishment method.

[0043] The session establishment unit can adjust the session establishment method considering the geographical location information of the communication destination. For example, if the communication destination is nearby, the establishment unit will prioritize communication speed when establishing a session. If the communication destination is far away, the establishment unit can also prioritize reliability when establishing a session. Furthermore, the establishment unit can select the optimal session establishment method based on the geographical location information of the communication destination. Thus, the establishment unit can select the optimal session establishment method by adjusting the session establishment method considering the geographical location information of the communication destination. Some or all of the above processing in the establishment unit may be performed using AI, for example, or without AI. For example, the establishment unit can input the geographical location information of the communication destination into AI and have the AI ​​select the optimal session establishment method.

[0044] The session establishment unit can change the session establishment protocol based on the industry and category of the communication partner. For example, in the case of banking transactions, the establishment unit will apply a protocol that prioritizes security. It can also apply a protocol that prioritizes speed when using electronic money applications. Furthermore, in the case of emergency communications, the establishment unit can apply a protocol that prioritizes rapid response. This allows the establishment unit to establish more appropriate communications by changing the session establishment protocol based on the industry and category of the communication partner. Some or all of the above processing in the establishment unit may be performed using AI, for example, or without AI. For example, the establishment unit can input the industry and category of the communication partner into the AI ​​and have the AI ​​select the optimal protocol.

[0045] The termination unit can optimize the session termination procedure based on the importance of the communication content. For example, if the communication content is of high importance, the termination unit will terminate the session using a strict procedure. If the communication content is of low importance, the termination unit can terminate the session using a simplified procedure. Furthermore, the termination unit can select the optimal session termination procedure based on the importance of the communication content. In this way, the termination unit can establish more secure communication by optimizing the session termination procedure based on the importance of the communication content. Some or all of the above processing in the termination unit may be performed using AI, for example, or without AI. For example, the termination unit can input the importance of the communication content into the AI ​​and have the AI ​​select the optimal session termination procedure.

[0046] The termination unit can monitor the network status of the communication destination in real time and select the optimal session termination method. For example, if the communication destination's network is congested, the termination unit will quickly terminate the session. The termination unit can also apply the normal session termination method if the communication destination's network is stable. Furthermore, the termination unit can select the optimal session termination method based on the communication destination's network status. In this way, the termination unit can select the optimal session termination method by monitoring the communication destination's network status in real time. Some or all of the above processing in the termination unit may be performed using AI, for example, or without AI. For example, the termination unit can input the communication destination's network status into AI and have the AI ​​select the optimal session termination method.

[0047] The termination unit can adjust the session termination method considering the geographical location information of the communication destination. For example, if the communication destination is nearby, the termination unit will terminate the session quickly. If the communication destination is far away, the termination unit can also terminate the session prioritizing reliability. Furthermore, the termination unit can select the optimal session termination method based on the geographical location information of the communication destination. Thus, the termination unit can select the optimal session termination method by adjusting the session termination method considering the geographical location information of the communication destination. Some or all of the above processing in the termination unit may be performed using AI, for example, or without AI. For example, the termination unit can input the geographical location information of the communication destination into AI and have the AI ​​select the optimal session termination method.

[0048] The termination unit can change the session termination protocol based on the industry and category of the communication partner. For example, in the case of banking transactions, the termination unit may apply a protocol that prioritizes security. It may also apply a protocol that prioritizes speed when using electronic money applications. Furthermore, in the case of emergency communications, the termination unit may apply a protocol that prioritizes rapid response. This allows the termination unit to terminate communications more appropriately by changing the session termination protocol based on the industry and category of the communication partner. Some or all of the above processing in the termination unit may be performed using AI, for example, or without AI. For example, the termination unit can input the industry and category of the communication partner into the AI ​​and have the AI ​​select the optimal protocol.

[0049] The evaluation unit can analyze past reliability data of communication destinations and optimize the evaluation algorithm. For example, the evaluation unit optimizes the reliability evaluation algorithm based on past reliability data. The evaluation unit can also evaluate communication destinations that are highly reliable during a specific time period based on past reliability data. Furthermore, the evaluation unit can analyze past reliability data and apply the optimal reliability evaluation algorithm. Thus, the evaluation unit can apply the optimal reliability evaluation algorithm by analyzing past reliability data. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input past reliability data into AI and have the AI ​​select the optimal evaluation algorithm.

[0050] The evaluation unit can perform reliability evaluations while considering the geographical location information of the communication destination. For example, the evaluation unit can perform a reliability evaluation quickly when the communication destination is nearby. Furthermore, the evaluation unit can perform a reliability evaluation more rigorously when the communication destination is far away. In addition, the evaluation unit can perform an optimal reliability evaluation based on the geographical location information of the communication destination. Thus, the evaluation unit can perform an optimal reliability evaluation by considering the geographical location information of the communication destination. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the geographical location information of the communication destination into the AI ​​and have the AI ​​perform an optimal reliability evaluation.

[0051] The app usage unit can analyze the app usage history and select the optimal usage method. For example, the app usage unit can suggest the optimal app usage method based on past usage history. The app usage unit can also select the optimal app usage method for a specific time period based on past usage history. Furthermore, the app usage unit can analyze past usage history and apply the optimal app usage method. In this way, the app usage unit can suggest the optimal app usage method by analyzing the app usage history. Some or all of the above processes in the app usage unit may be performed using AI, for example, or without AI. For example, the app usage unit can input past usage history into AI and have the AI ​​select the optimal usage method.

[0052] The app usage unit can monitor app usage in real time and suggest the optimal usage method. For example, the app usage unit can monitor app usage in real time and suggest the optimal usage method. Furthermore, if app usage changes, the app usage unit can also suggest the optimal usage method again. In addition, the app usage unit can select the optimal usage method based on app usage. Thus, by monitoring app usage in real time, the app usage unit can suggest the optimal usage method. Some or all of the above processing in the app usage unit may be performed using AI, for example, or without AI. For example, the app usage unit can input app usage data into AI and have the AI ​​select the optimal usage method.

[0053] The secure communication unit can analyze the history of secure communications and select the optimal communication method. For example, the secure communication unit can propose the optimal communication method based on past secure communication history. The secure communication unit can also select the optimal communication method for a specific time period from past secure communication history. Furthermore, the secure communication unit can analyze past secure communication history and apply the optimal communication method. In this way, the secure communication unit can propose the optimal communication method by analyzing the history of secure communications. Some or all of the above processing in the secure communication unit may be performed using AI, for example, or without AI. For example, the secure communication unit can input past secure communication history into AI and have the AI ​​select the optimal communication method.

[0054] The secure communication unit can monitor the status of secure communications in real time and propose the optimal communication method. For example, the secure communication unit can monitor the status of secure communications in real time and propose the optimal communication method. Furthermore, if the status of secure communications changes, the secure communication unit can re-propose the optimal communication method. In addition, the secure communication unit can select the optimal communication method based on the status of secure communications. Thus, the secure communication unit can propose the optimal communication method by monitoring the status of secure communications in real time. Some or all of the above processing in the secure communication unit may be performed using AI, for example, or without AI. For example, the secure communication unit can input the status of secure communications into AI and have the AI ​​select the optimal communication method.

[0055] The emergency communications unit can analyze the history of emergency communications and select the optimal communication method. For example, the emergency communications unit can propose the optimal communication method based on past emergency communications history. Furthermore, the emergency communications unit can select the optimal communication method for a specific time period based on past emergency communications history. In addition, the emergency communications unit can analyze past emergency communications history and apply the optimal communication method. Thus, the emergency communications unit can propose the optimal communication method by analyzing the history of emergency communications. Some or all of the above processes in the emergency communications unit may be performed using AI, for example, or without AI. For example, the emergency communications unit can input past emergency communications history into AI and have the AI ​​select the optimal communication method.

[0056] The Emergency Communications Department can monitor the status of emergency communications in real time and propose the optimal communication method. For example, the Emergency Communications Department can monitor the status of emergency communications in real time and propose the optimal communication method. Furthermore, if the status of emergency communications changes, the Emergency Communications Department can re-propose the optimal communication method. In addition, the Emergency Communications Department can select the optimal communication method based on the status of emergency communications. Thus, by monitoring the status of emergency communications in real time, the Emergency Communications Department can propose the optimal communication method. Some or all of the above processes in the Emergency Communications Department may be performed using AI, for example, or without AI. For example, the Emergency Communications Department can input the status of emergency communications into AI and have the AI ​​select the optimal communication method.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The security system may also include a learning unit that learns user behavior patterns. For example, the learning unit can learn a user's tendency to use a specific app at a specific time of day and use that information to improve the accuracy of destination identification. It can also learn a user's tendency to perform specific communications at specific locations and use that information to evaluate the reliability of destinations. Furthermore, the learning unit can analyze a user's past communication history and predict the optimal destination. In this way, the learning unit can select more appropriate destinations by learning user behavior patterns. Some or all of the above-described processes in the learning unit may be performed using AI, or not. For example, the learning unit can input user behavior data into an AI and have the AI ​​learn behavior patterns.

[0059] The security system may further include a third-party evaluation unit for assessing the reliability of communication destinations. This third-party evaluation unit may, for example, evaluate communication destinations based on reliability data provided by external security organizations. It may also collect feedback from other users and use that information to evaluate the reliability of communication destinations. Furthermore, it may collect publicly available information from the internet and use that information to evaluate the reliability of communication destinations. This allows the third-party evaluation unit to perform a more objective reliability assessment of communication destinations by utilizing external information. Some or all of the above-described processes in the third-party evaluation unit may be performed using AI, for example, or without AI. For example, the third-party evaluation unit may input external reliability data into an AI and have the AI ​​perform the reliability assessment.

[0060] The security system can further perform reliability assessments by considering the geographical location information of the communication destination. The evaluation unit can, for example, perform a rapid reliability assessment when the communication destination is nearby. The evaluation unit can also perform a rigorous reliability assessment when the communication destination is far away. Furthermore, the evaluation unit can perform an optimal reliability assessment based on the geographical location information of the communication destination. In this way, the evaluation unit can perform an optimal reliability assessment by considering the geographical location information of the communication destination. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the geographical location information of the communication destination into the AI ​​and have the AI ​​perform an optimal reliability assessment.

[0061] The security system can further update the reliability score of the communication destination in real time and reflect it in its determination. The determination unit, for example, monitors the reliability score of the communication destination in real time and reflects it in its determination. The determination unit can also prioritize other communication destinations if the reliability score of a communication destination decreases. Furthermore, the determination unit can select the optimal communication destination based on the reliability score of the communication destination. As a result, the determination unit can select a more reliable communication destination by updating the reliability score of the communication destination in real time. Some or all of the above processing in the determination unit may be performed using AI, for example, or without using AI. For example, the determination unit can input the reliability score of the communication destination into the AI ​​and have the AI ​​perform real-time updates.

[0062] The security system can further monitor the network status of the communication destination in real time and select the optimal session establishment method. For example, if the communication destination network is congested, the establishment unit will select another communication destination. The establishment unit can also apply the normal session establishment method if the communication destination network is stable. Furthermore, the establishment unit can select the optimal session establishment method based on the network status of the communication destination. In this way, the establishment unit can select the optimal session establishment method by monitoring the network status of the communication destination in real time. Some or all of the above processing in the establishment unit may be performed using AI, for example, or without AI. For example, the establishment unit can input the network status of the communication destination into the AI ​​and have the AI ​​select the optimal session establishment method.

[0063] The security system can further modify the session termination protocol based on the industry and category of the communication partner. For example, in the case of banking transactions, the termination unit may apply a protocol that prioritizes security. The termination unit may also apply a protocol that prioritizes speed when using an electronic money application. Furthermore, in the case of emergency communications, the termination unit may apply a protocol that prioritizes a quick response. In this way, the termination unit can terminate communications more appropriately by changing the session termination protocol based on the industry and category of the communication partner. Some or all of the above processing in the termination unit may be performed using AI, for example, or not. For example, the termination unit can input the industry and category of the communication partner into the AI ​​and have the AI ​​select the optimal protocol.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The discrimination unit identifies the communication destination only when communication is to be performed. For example, it can identify the communication destination based on the IP address or domain name of the communication destination. It can also identify the communication destination based on the user ID or security certificate. Specifically, it analyzes the IP address or domain name of the communication destination and selects a highly reliable communication destination. Step 2: The establishment unit establishes a session with the communication destination determined by the discrimination unit. For example, a session can be established using the TCP / IP protocol or the HTTP protocol. Specifically, a session with the communication destination is established using the TCP / IP protocol or the HTTP protocol. Step 3: The termination section ends the session once communication is complete. For example, the session can be terminated based on a timeout or user action. It can also be terminated in the event of an error. Specifically, the session will be terminated if a timeout occurs or based on user action.

[0066] (Example of form 2) The security system according to an embodiment of the present invention is a mechanism for mitigating security risks associated with constant network connectivity. This security system provides a mechanism in which AI identifies the communication destination only when communication is required, establishes a session with the communication destination, and terminates the session once communication is complete. This reduces the risk of attacks and information leaks. For example, when using applications such as banking transactions or electronic money, the AI ​​identifies the communication destination and performs the necessary communication. At this time, the AI ​​evaluates the reliability of the communication destination and establishes secure communication. Next, once communication is complete, the established session is terminated. This allows the system to remain disconnected from the network when no communication is taking place. For example, it can be used as an alternative to digital communication for highly confidential communications or communications from the police or fire department. This mechanism reduces security risks associated with constant network connectivity and enables secure communication. For example, when using applications such as banking transactions or electronic money, the AI ​​identifies the communication destination and establishes secure communication, thereby reducing the risk of attacks and information leaks. Furthermore, by using it as an alternative to digital communication for highly confidential communications or communications from the police or fire department, secure communication can be achieved. This allows security systems to mitigate the security risks associated with constant network connectivity and ensure secure communication.

[0067] The security system according to this embodiment comprises a discrimination unit, an establishment unit, and a termination unit. The discrimination unit discriminates the communication destination only when communication is to be performed. The discrimination unit can discriminate the communication destination based on, for example, the IP address or domain name of the communication destination. The discrimination unit can also discriminate the communication destination based on a user ID or security certificate. For example, the discrimination unit analyzes the IP address of the communication destination and selects a highly reliable communication destination. Furthermore, the discrimination unit can analyze the domain name of the communication destination and select a highly reliable communication destination. The establishment unit establishes a session with the communication destination discriminated by the discrimination unit. The establishment unit can establish a session using, for example, the TCP / IP protocol. Furthermore, the establishment unit can establish a session using the HTTP protocol. For example, the establishment unit establishes a session with the communication destination using the TCP / IP protocol. Furthermore, the establishment unit can establish a session with the communication destination using the HTTP protocol. The termination unit terminates the session when communication is complete. The termination unit can terminate the session based on, for example, a timeout or user operation. Furthermore, the termination unit can terminate the session when an error occurs. For example, the termination unit terminates the session when a timeout occurs. Furthermore, the termination unit can also terminate the session based on user actions. This allows the security system according to the embodiment to reduce security risks by identifying the communication destination only when communication is to be performed, establishing a session, and terminating the session once communication is complete.

[0068] The discrimination unit identifies the communication destination only when communication is initiated. For example, the discrimination unit can identify the communication destination based on the destination's IP address or domain name. Specifically, when communication begins, the discrimination unit first obtains the destination's IP address and verifies whether that IP address is trustworthy. Whitelists and blacklists can be used to verify trustworthiness, thereby eliminating unauthorized communication destinations. The discrimination unit also parses the destination's domain name through a DNS server to verify its legitimacy. Furthermore, the discrimination unit can identify the communication destination based on user IDs and security certificates. For example, a user ID can be used to select communication destinations that only specific users can access. Security certificates can also be used to prove the legitimacy of the communication destination, increasing trustworthiness. This allows the discrimination unit to enhance the trustworthiness of communication destinations and prevent unauthorized communication. Additionally, because the discrimination unit identifies communication destinations in real time, communication delays can be minimized. This allows users to communicate comfortably.

[0069] The establishment unit establishes a session with the communication destination identified by the discrimination unit. The establishment unit can establish a session using, for example, the TCP / IP protocol. Specifically, it establishes a session with the communication destination using the TCP / IP three-party handshake. This handshake process ensures the reliability and stability of the communication. The establishment unit can also establish a session using the HTTP protocol. For example, it can establish communication between a web browser and a web server using the HTTP protocol. Furthermore, the establishment unit can use the SSL / TLS protocol to ensure secure communication. This encrypts the communication data, preventing eavesdropping and tampering by third parties. The establishment unit performs necessary authentication procedures at the start of communication to verify that the communication destination is legitimate. This allows the establishment unit to establish a secure and reliable communication session. Furthermore, the establishment unit monitors the quality of the communication and maintains communication stability by reconnecting or retrying as needed. This allows the establishment unit to provide users with a high-quality communication environment.

[0070] The termination unit terminates the session once communication is complete. The termination unit can terminate a session based on factors such as timeouts or user actions. Specifically, it detects timeouts if no communication occurs for a certain period and automatically terminates the session. This prevents unnecessary sessions from remaining and allows for efficient use of system resources. The termination unit can also terminate a session if the user explicitly terminates the communication. For example, it appropriately terminates the session when the user closes a web browser or terminates a communication application. Furthermore, the termination unit can terminate a session in the event of an error. For example, if a network error or protocol error occurs during communication, the termination unit detects this and safely terminates the session. This maintains system stability even in the event of errors. The termination unit performs necessary cleanup processes upon session termination and releases resources associated with the session. This allows for efficient management of system resources and preparation for the next communication. Additionally, the termination unit can log session terminations for later troubleshooting and auditing. This allows the termination unit to properly manage communication terminations and improve the overall reliability and efficiency of the system.

[0071] The evaluation unit can evaluate the reliability of the communication destination. For example, the evaluation unit can evaluate the reliability of the communication destination based on past communication history. The evaluation unit can also evaluate the reliability of the communication destination based on security certificates. For example, the evaluation unit can analyze past communication history and select a highly reliable communication destination. Furthermore, the evaluation unit can analyze security certificates and select a highly reliable communication destination. In this way, the evaluation unit can establish more secure communication by evaluating the reliability of the communication destination. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit can input past communication history into AI and have the AI ​​perform the reliability evaluation.

[0072] The app usage unit can provide functions specifically tailored for use in banking transactions and electronic money applications. For example, the app usage unit can protect communications using encryption technology. Furthermore, the app usage unit can enhance security using two-factor authentication. For instance, the app usage unit protects communication content using encryption technology. Additionally, the app usage unit can perform user authentication using two-factor authentication. This allows the app usage unit to improve security during banking transactions and electronic money applications by specializing in these uses. Some or all of the above-described processes in the app usage unit may be performed using AI, or not. For example, the app usage unit can have AI select encryption technologies.

[0073] The secure communications unit can provide functions specialized for highly secure communications. For example, the secure communications unit can protect communications using end-to-end encryption. Furthermore, the secure communications unit can protect the privacy of communications using anonymization techniques. For example, the secure communications unit protects the content of communications using end-to-end encryption. Furthermore, the secure communications unit can protect the privacy of communications using anonymization techniques. This allows the secure communications unit to enhance the protection of highly confidential information by specializing in highly secure communications. Some or all of the above-described processes in the secure communications unit may be performed using AI, for example, or without AI. For example, the secure communications unit can have AI select encryption technologies.

[0074] The Emergency Communications Unit can provide functions specifically tailored to police and fire communications. For example, the Emergency Communications Unit can conduct emergency communications using high-priority communication channels. Furthermore, the Emergency Communications Unit can conduct rapid communications using emergency protocols. By specializing in police and fire communications, the Emergency Communications Unit can improve the reliability and speed of emergency communications. Some or all of the above-described processes in the Emergency Communications Unit may be performed using AI, or not. For example, the Emergency Communications Unit can have AI select communication channels.

[0075] The discrimination unit can estimate the user's emotions and adjust the accuracy of destination identification based on the estimated emotions. For example, if the user is nervous, the discrimination unit can increase its identification accuracy and prioritize reliable destinations. If the user is relaxed, the discrimination unit can set the identification accuracy to normal and prioritize communication speed. Furthermore, if the user is in a hurry, the discrimination unit can optimize its identification accuracy to quickly identify destinations. In this way, the discrimination unit can select a more appropriate destination by adjusting the accuracy of destination identification based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the discrimination unit may be performed using AI, for example, or without AI. For example, the discrimination unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0076] The discrimination unit can analyze the past communication history of the communication destination and select the optimal discrimination method. For example, the discrimination unit can prioritize selecting communication destinations that have a high reliability in the past. The discrimination unit can also select communication destinations that are highly reliable during a specific time period based on past communication history. Furthermore, the discrimination unit can analyze past communication history and apply the optimal discrimination algorithm. In this way, the discrimination unit can select the optimal communication destination by analyzing past communication history. Some or all of the above processing in the discrimination unit may be performed using AI, for example, or without AI. For example, the discrimination unit can input past communication history into AI and have the AI ​​select the optimal discrimination method.

[0077] The discrimination unit can perform discrimination while considering the geographical location information of the communication destination. For example, if the communication destination is nearby, the discrimination unit will prioritize communication speed in its discrimination. Also, if the communication destination is far away, the discrimination unit can prioritize reliability in its discrimination. Furthermore, the discrimination unit can select the optimal communication destination based on the geographical location information of the communication destination. In this way, the discrimination unit can select the optimal communication destination by considering the geographical location information of the communication destination. Some or all of the above processing in the discrimination unit may be performed using AI, for example, or without using AI. For example, the discrimination unit can input the geographical location information of the communication destination into AI and have the AI ​​perform the selection of the optimal communication destination.

[0078] The discrimination unit can estimate the user's emotions and determine the priority of communication destinations based on the estimated user emotions. For example, if the user is stressed, the discrimination unit will prioritize reliable communication destinations. If the user is relaxed, the discrimination unit can also prioritize communication destinations with faster communication speeds. Furthermore, if the user is in a hurry, the discrimination unit can prioritize communication destinations that can respond quickly. In this way, the discrimination unit can select a more appropriate communication destination by determining the priority of communication destinations based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the discrimination unit may be performed using AI, for example, or without AI. For example, the discrimination unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0079] The discrimination unit can update the reliability score of the communication destination in real time and reflect it in the discrimination. For example, the discrimination unit monitors the reliability score of the communication destination in real time and reflects it in the discrimination. The discrimination unit can also prioritize other communication destinations if the reliability score of a communication destination decreases. Furthermore, the discrimination unit can select the optimal communication destination based on the reliability score of the communication destination. In this way, the discrimination unit can select a more reliable communication destination by updating the reliability score of the communication destination in real time. Some or all of the above processing in the discrimination unit may be performed using AI, for example, or without using AI. For example, the discrimination unit can input the reliability score of the communication destination into the AI ​​and have the AI ​​perform real-time updates.

[0080] The discrimination unit can change its discrimination algorithm based on the industry and category of the communication destination. For example, in the case of banking transactions, the discrimination unit applies a discrimination algorithm that prioritizes reliability. The discrimination unit can also apply a discrimination algorithm that prioritizes speed when using an electronic money app. Furthermore, in the case of emergency communications, the discrimination unit can apply a discrimination algorithm that prioritizes rapid response. In this way, the discrimination unit can select a more appropriate communication destination by changing the discrimination algorithm based on the industry and category of the communication destination. Some or all of the above processing in the discrimination unit may be performed using AI, for example, or without AI. For example, the discrimination unit can input the industry and category of the communication destination into the AI ​​and have the AI ​​select the optimal discrimination algorithm.

[0081] The session establishment unit can estimate the user's emotions and adjust the timing of session establishment based on the estimated emotions. For example, if the user is nervous, the session establishment unit can establish the session quickly. If the user is relaxed, the session establishment unit can also establish the session at a normal time. Furthermore, if the user is in a hurry, the session establishment unit can establish the session in the shortest possible time. In this way, the session establishment unit can establish the session at a more appropriate time by adjusting the timing of session establishment based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the session establishment unit may be performed using AI, for example, or not using AI. For example, the session establishment unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0082] The session establishment unit can optimize the session establishment procedure based on the reliability assessment of the communication destination. For example, the establishment unit can establish a session with a simplified procedure for highly reliable communication destinations. It can also establish a session with a strict procedure for less reliable communication destinations. Furthermore, the establishment unit can select the optimal session establishment procedure based on the reliability assessment of the communication destination. This allows the establishment unit to establish more secure communication by optimizing the session establishment procedure based on the reliability assessment of the communication destination. Some or all of the above processing in the establishment unit may be performed using AI, for example, or without AI. For example, the establishment unit can input the reliability assessment of the communication destination into AI and have AI select the optimal session establishment procedure.

[0083] The session establishment unit can monitor the network status of the communication destination in real time and select the optimal session establishment method. For example, if the communication destination network is congested, the session establishment unit will select another communication destination. The session establishment unit can also apply the normal session establishment method if the communication destination network is stable. Furthermore, the session establishment unit can select the optimal session establishment method based on the network status of the communication destination. In this way, the session establishment unit can select the optimal session establishment method by monitoring the network status of the communication destination in real time. Some or all of the above processing in the session establishment unit may be performed using AI, for example, or without AI. For example, the session establishment unit can input the network status of the communication destination into AI and have AI select the optimal session establishment method.

[0084] The session establishment unit can estimate the user's emotions and determine the priority of session establishment based on the estimated user emotions. For example, if the user is nervous, the session establishment unit will prioritize reliable communication destinations for session establishment. If the user is relaxed, the session establishment unit can also prioritize communication destinations with faster communication speeds for session establishment. Furthermore, if the user is in a hurry, the session establishment unit can prioritize communication destinations that can respond quickly for session establishment. In this way, the session establishment unit can establish more appropriate communication destinations and sessions by determining the priority of session establishment based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the session establishment unit may be performed using AI, or not. For example, the session establishment unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0085] The session establishment unit can adjust the session establishment method considering the geographical location information of the communication destination. For example, if the communication destination is nearby, the establishment unit will prioritize communication speed when establishing a session. If the communication destination is far away, the establishment unit can also prioritize reliability when establishing a session. Furthermore, the establishment unit can select the optimal session establishment method based on the geographical location information of the communication destination. Thus, the establishment unit can select the optimal session establishment method by adjusting the session establishment method considering the geographical location information of the communication destination. Some or all of the above processing in the establishment unit may be performed using AI, for example, or without AI. For example, the establishment unit can input the geographical location information of the communication destination into AI and have the AI ​​select the optimal session establishment method.

[0086] The session establishment unit can change the session establishment protocol based on the industry and category of the communication partner. For example, in the case of banking transactions, the establishment unit will apply a protocol that prioritizes security. It can also apply a protocol that prioritizes speed when using electronic money applications. Furthermore, in the case of emergency communications, the establishment unit can apply a protocol that prioritizes rapid response. This allows the establishment unit to establish more appropriate communications by changing the session establishment protocol based on the industry and category of the communication partner. Some or all of the above processing in the establishment unit may be performed using AI, for example, or without AI. For example, the establishment unit can input the industry and category of the communication partner into the AI ​​and have the AI ​​select the optimal protocol.

[0087] The termination unit can estimate the user's emotions and adjust the timing of session termination based on the estimated emotions. For example, if the user is tense, the termination unit can terminate the session quickly. Alternatively, if the user is relaxed, the termination unit can terminate the session at a normal time. Furthermore, if the user is in a hurry, the termination unit can terminate the session in the shortest possible time. In this way, the termination unit can terminate the session at a more appropriate time by adjusting the timing of session termination based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the termination unit may be performed using AI or not using AI. For example, the termination unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0088] The termination unit can optimize the session termination procedure based on the importance of the communication content. For example, if the communication content is of high importance, the termination unit will terminate the session using a strict procedure. If the communication content is of low importance, the termination unit can terminate the session using a simplified procedure. Furthermore, the termination unit can select the optimal session termination procedure based on the importance of the communication content. In this way, the termination unit can establish more secure communication by optimizing the session termination procedure based on the importance of the communication content. Some or all of the above processing in the termination unit may be performed using AI, for example, or without AI. For example, the termination unit can input the importance of the communication content into the AI ​​and have the AI ​​select the optimal session termination procedure.

[0089] The termination unit can monitor the network status of the communication destination in real time and select the optimal session termination method. For example, if the communication destination's network is congested, the termination unit will quickly terminate the session. The termination unit can also apply the normal session termination method if the communication destination's network is stable. Furthermore, the termination unit can select the optimal session termination method based on the communication destination's network status. In this way, the termination unit can select the optimal session termination method by monitoring the communication destination's network status in real time. Some or all of the above processing in the termination unit may be performed using AI, for example, or without AI. For example, the termination unit can input the communication destination's network status into AI and have the AI ​​select the optimal session termination method.

[0090] The termination unit can estimate the user's emotions and determine the priority for ending the session based on the estimated emotions. For example, if the user is tense, the termination unit will prioritize high-priority communication content and end the session. If the user is relaxed, the termination unit can also end the session with normal priority. Furthermore, if the user is in a hurry, the termination unit can quickly end the session. In this way, by determining the priority for ending the session based on the user's emotions, the termination unit can end the session with a more appropriate communication destination. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the termination unit may be performed using AI or not using AI. For example, the termination unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0091] The termination unit can adjust the session termination method considering the geographical location information of the communication destination. For example, if the communication destination is nearby, the termination unit will terminate the session quickly. If the communication destination is far away, the termination unit can also terminate the session prioritizing reliability. Furthermore, the termination unit can select the optimal session termination method based on the geographical location information of the communication destination. Thus, the termination unit can select the optimal session termination method by adjusting the session termination method considering the geographical location information of the communication destination. Some or all of the above processing in the termination unit may be performed using AI, for example, or without AI. For example, the termination unit can input the geographical location information of the communication destination into AI and have the AI ​​select the optimal session termination method.

[0092] The termination unit can change the session termination protocol based on the industry and category of the communication partner. For example, in the case of banking transactions, the termination unit may apply a protocol that prioritizes security. It may also apply a protocol that prioritizes speed when using electronic money applications. Furthermore, in the case of emergency communications, the termination unit may apply a protocol that prioritizes rapid response. This allows the termination unit to terminate communications more appropriately by changing the session termination protocol based on the industry and category of the communication partner. Some or all of the above processing in the termination unit may be performed using AI, for example, or without AI. For example, the termination unit can input the industry and category of the communication partner into the AI ​​and have the AI ​​select the optimal protocol.

[0093] The evaluation unit can estimate the user's emotions and adjust the reliability evaluation criteria for the communication destination based on the estimated user emotions. For example, if the user is nervous, the evaluation unit can set stricter reliability evaluation criteria. Alternatively, if the user is relaxed, the evaluation unit can apply normal reliability evaluation criteria. Furthermore, if the user is in a hurry, the evaluation unit can apply criteria for rapid reliability evaluation. This allows the evaluation unit to evaluate more appropriate communication destinations by adjusting the reliability evaluation criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, or not. For example, the evaluation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0094] The evaluation unit can analyze past reliability data of communication destinations and optimize the evaluation algorithm. For example, the evaluation unit optimizes the reliability evaluation algorithm based on past reliability data. The evaluation unit can also evaluate communication destinations that are highly reliable during a specific time period based on past reliability data. Furthermore, the evaluation unit can analyze past reliability data and apply the optimal reliability evaluation algorithm. Thus, the evaluation unit can apply the optimal reliability evaluation algorithm by analyzing past reliability data. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input past reliability data into AI and have the AI ​​select the optimal evaluation algorithm.

[0095] The evaluation unit can estimate the user's emotions and determine the priority of reliability evaluations based on the estimated user emotions. For example, if the user is stressed, the evaluation unit will prioritize evaluating reliable communication destinations. If the user is relaxed, the evaluation unit can also perform reliability evaluations with normal priorities. Furthermore, if the user is in a hurry, the evaluation unit can perform reliability evaluations quickly. In this way, the evaluation unit can evaluate more appropriate communication destinations by determining the priority of reliability evaluations based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, or not using AI. For example, the evaluation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0096] The evaluation unit can perform reliability evaluations while considering the geographical location information of the communication destination. For example, the evaluation unit can perform a reliability evaluation quickly when the communication destination is nearby. Furthermore, the evaluation unit can perform a reliability evaluation more rigorously when the communication destination is far away. In addition, the evaluation unit can perform an optimal reliability evaluation based on the geographical location information of the communication destination. Thus, the evaluation unit can perform an optimal reliability evaluation by considering the geographical location information of the communication destination. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the geographical location information of the communication destination into the AI ​​and have the AI ​​perform an optimal reliability evaluation.

[0097] The app user unit can estimate the user's emotions and adjust the timing of app usage based on the estimated emotions. For example, if the user is feeling anxious, the app user unit can launch the app quickly. If the user is relaxed, the app user unit can launch the app at a normal time. Furthermore, if the user is in a hurry, the app user unit can launch the app in the shortest possible time. In this way, the app user unit can use the app at a more appropriate time by adjusting the timing of app usage based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the app user unit may be performed using AI, or not using AI. For example, the app user unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0098] The app usage unit can analyze the app usage history and select the optimal usage method. For example, the app usage unit can suggest the optimal app usage method based on past usage history. The app usage unit can also select the optimal app usage method for a specific time period based on past usage history. Furthermore, the app usage unit can analyze past usage history and apply the optimal app usage method. In this way, the app usage unit can suggest the optimal app usage method by analyzing the app usage history. Some or all of the above processes in the app usage unit may be performed using AI, for example, or without AI. For example, the app usage unit can input past usage history into AI and have the AI ​​select the optimal usage method.

[0099] The app user unit can estimate the user's emotions and determine the priority of app usage based on the estimated emotions. For example, if the user is stressed, the app user unit will prioritize important apps. If the user is relaxed, the app user unit can also prioritize apps with normal priority. Furthermore, if the user is in a hurry, the app user unit can prioritize apps that can be used quickly. In this way, the app user unit can prioritize more appropriate apps by determining the priority of app usage based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the app user unit may be performed using AI or not using AI. For example, the app user unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0100] The app usage unit can monitor app usage in real time and suggest the optimal usage method. For example, the app usage unit can monitor app usage in real time and suggest the optimal usage method. Furthermore, if app usage changes, the app usage unit can also suggest the optimal usage method again. In addition, the app usage unit can select the optimal usage method based on app usage. Thus, by monitoring app usage in real time, the app usage unit can suggest the optimal usage method. Some or all of the above processing in the app usage unit may be performed using AI, for example, or without AI. For example, the app usage unit can input app usage data into AI and have the AI ​​select the optimal usage method.

[0101] The secure communication unit can estimate the user's emotions and adjust the secure communication method based on the estimated emotions. For example, if the user is tense, the secure communication unit can apply a security-focused secure communication method. It can also apply a normal secure communication method if the user is relaxed. Furthermore, if the user is in a hurry, the secure communication unit can apply a method for rapid secure communication. This allows the secure communication unit to perform secure communication in a more appropriate manner by adjusting the method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the secure communication unit may be performed using AI, or not. For example, the secure communication unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0102] The secure communication unit can analyze the history of secure communications and select the optimal communication method. For example, the secure communication unit can propose the optimal communication method based on past secure communication history. The secure communication unit can also select the optimal communication method for a specific time period from past secure communication history. Furthermore, the secure communication unit can analyze past secure communication history and apply the optimal communication method. In this way, the secure communication unit can propose the optimal communication method by analyzing the history of secure communications. Some or all of the above processing in the secure communication unit may be performed using AI, for example, or without AI. For example, the secure communication unit can input past secure communication history into AI and have the AI ​​select the optimal communication method.

[0103] The secure communication unit can estimate the user's emotions and determine the priority of secure communications based on the estimated emotions. For example, if the user is stressed, the secure communication unit will prioritize important secure communications. If the user is relaxed, the secure communication unit can also prioritize secure communications at the normal priority level. Furthermore, if the user is in a hurry, the secure communication unit can also quickly send secure communications. In this way, the secure communication unit can prioritize more appropriate communications by determining the priority of secure communications based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the secure communication unit may be performed using AI, for example, or not using AI. For example, the secure communication unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0104] The secure communication unit can monitor the status of secure communications in real time and propose the optimal communication method. For example, the secure communication unit can monitor the status of secure communications in real time and propose the optimal communication method. Furthermore, if the status of secure communications changes, the secure communication unit can re-propose the optimal communication method. In addition, the secure communication unit can select the optimal communication method based on the status of secure communications. Thus, the secure communication unit can propose the optimal communication method by monitoring the status of secure communications in real time. Some or all of the above processing in the secure communication unit may be performed using AI, for example, or without AI. For example, the secure communication unit can input the status of secure communications into AI and have the AI ​​select the optimal communication method.

[0105] The emergency communications unit can estimate the user's emotions and adjust the method of emergency communications based on the estimated emotions. For example, if the user is tense, the emergency communications unit can apply a method for rapid emergency communications. If the user is relaxed, the emergency communications unit can also apply a normal emergency communications method. Furthermore, if the user is in a hurry, the emergency communications unit can apply a method for emergency communications in the shortest possible time. In this way, the emergency communications unit can deliver emergency communications in a more appropriate manner by adjusting the method of emergency communications based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the emergency communications unit may be performed using AI, or not using AI. For example, the emergency communications unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0106] The emergency communications unit can analyze the history of emergency communications and select the optimal communication method. For example, the emergency communications unit can propose the optimal communication method based on past emergency communications history. Furthermore, the emergency communications unit can select the optimal communication method for a specific time period based on past emergency communications history. In addition, the emergency communications unit can analyze past emergency communications history and apply the optimal communication method. Thus, the emergency communications unit can propose the optimal communication method by analyzing the history of emergency communications. Some or all of the above processes in the emergency communications unit may be performed using AI, for example, or without AI. For example, the emergency communications unit can input past emergency communications history into AI and have the AI ​​select the optimal communication method.

[0107] The emergency communications unit can estimate the user's emotions and determine the priority of emergency communications based on the estimated emotions. For example, if the user is stressed, the emergency communications unit will prioritize important emergency communications. If the user is relaxed, the emergency communications unit can also prioritize emergency communications at the normal priority level. Furthermore, if the user is in a hurry, the emergency communications unit can also quickly deliver emergency communications. In this way, the emergency communications unit can prioritize more appropriate communications by determining the priority of emergency communications based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the emergency communications unit may be performed using AI or not using AI. For example, the emergency communications unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0108] The Emergency Communications Department can monitor the status of emergency communications in real time and propose the optimal communication method. For example, the Emergency Communications Department can monitor the status of emergency communications in real time and propose the optimal communication method. Furthermore, if the status of emergency communications changes, the Emergency Communications Department can re-propose the optimal communication method. In addition, the Emergency Communications Department can select the optimal communication method based on the status of emergency communications. Thus, by monitoring the status of emergency communications in real time, the Emergency Communications Department can propose the optimal communication method. Some or all of the above processes in the Emergency Communications Department may be performed using AI, for example, or without AI. For example, the Emergency Communications Department can input the status of emergency communications into AI and have the AI ​​select the optimal communication method.

[0109] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0110] The security system may also include a learning unit that learns user behavior patterns. For example, the learning unit can learn a user's tendency to use a specific app at a specific time of day and use that information to improve the accuracy of destination identification. It can also learn a user's tendency to perform specific communications at specific locations and use that information to evaluate the reliability of destinations. Furthermore, the learning unit can analyze a user's past communication history and predict the optimal destination. In this way, the learning unit can select more appropriate destinations by learning user behavior patterns. Some or all of the above-described processes in the learning unit may be performed using AI, or not. For example, the learning unit can input user behavior data into an AI and have the AI ​​learn behavior patterns.

[0111] The security system may further include a third-party evaluation unit for assessing the reliability of communication destinations. This third-party evaluation unit may, for example, evaluate communication destinations based on reliability data provided by external security organizations. It may also collect feedback from other users and use that information to evaluate the reliability of communication destinations. Furthermore, it may collect publicly available information from the internet and use that information to evaluate the reliability of communication destinations. This allows the third-party evaluation unit to perform a more objective reliability assessment of communication destinations by utilizing external information. Some or all of the above-described processes in the third-party evaluation unit may be performed using AI, for example, or without AI. For example, the third-party evaluation unit may input external reliability data into an AI and have the AI ​​perform the reliability assessment.

[0112] The security system can further estimate the user's emotions and adjust the reliability evaluation of the communication destination based on the estimated user emotions. For example, if the user is feeling anxious, the evaluation unit can set a strict reliability evaluation. The evaluation unit can also apply a normal reliability evaluation if the user is feeling at ease. Furthermore, if the user is in a hurry, the evaluation unit can apply criteria for a rapid reliability evaluation. In this way, the evaluation unit can evaluate a more appropriate communication destination by adjusting the reliability evaluation based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI or not using AI. For example, the evaluation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0113] The security system can further perform reliability assessments by considering the geographical location information of the communication destination. The evaluation unit can, for example, perform a rapid reliability assessment when the communication destination is nearby. The evaluation unit can also perform a rigorous reliability assessment when the communication destination is far away. Furthermore, the evaluation unit can perform an optimal reliability assessment based on the geographical location information of the communication destination. In this way, the evaluation unit can perform an optimal reliability assessment by considering the geographical location information of the communication destination. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the geographical location information of the communication destination into the AI ​​and have the AI ​​perform an optimal reliability assessment.

[0114] The security system can further estimate the user's emotions and determine the priority of communication destinations based on the estimated emotions. For example, if the user is stressed, the discrimination unit will prioritize reliable communication destinations. If the user is relaxed, the discrimination unit may also prioritize communication destinations with faster communication speeds. Furthermore, if the user is in a hurry, the discrimination unit may also prioritize communication destinations that can respond quickly. In this way, the discrimination unit can select a more appropriate communication destination by determining the priority of communication destinations based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the discrimination unit may be performed using AI or not using AI. For example, the discrimination unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0115] The security system can further update the reliability score of the communication destination in real time and reflect it in its determination. The determination unit, for example, monitors the reliability score of the communication destination in real time and reflects it in its determination. The determination unit can also prioritize other communication destinations if the reliability score of a communication destination decreases. Furthermore, the determination unit can select the optimal communication destination based on the reliability score of the communication destination. As a result, the determination unit can select a more reliable communication destination by updating the reliability score of the communication destination in real time. Some or all of the above processing in the determination unit may be performed using AI, for example, or without using AI. For example, the determination unit can input the reliability score of the communication destination into the AI ​​and have the AI ​​perform real-time updates.

[0116] The security system can further estimate the user's emotions and adjust the timing of session establishment based on the estimated emotions. For example, if the user is tense, the establishment unit can quickly establish a session. Alternatively, if the user is relaxed, the establishment unit can establish a session at a normal time. Furthermore, if the user is in a hurry, the establishment unit can establish a session in the shortest possible time. This allows the establishment unit to establish a session at a more appropriate time by adjusting the timing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the establishment unit may be performed using AI or not. For example, the establishment unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0117] The security system can further monitor the network status of the communication destination in real time and select the optimal session establishment method. For example, if the communication destination network is congested, the establishment unit will select another communication destination. The establishment unit can also apply the normal session establishment method if the communication destination network is stable. Furthermore, the establishment unit can select the optimal session establishment method based on the network status of the communication destination. In this way, the establishment unit can select the optimal session establishment method by monitoring the network status of the communication destination in real time. Some or all of the above processing in the establishment unit may be performed using AI, for example, or without AI. For example, the establishment unit can input the network status of the communication destination into the AI ​​and have the AI ​​select the optimal session establishment method.

[0118] The security system can further estimate the user's emotions and adjust the timing of session termination based on the estimated emotions. For example, if the user is tense, the termination unit can terminate the session quickly. Alternatively, if the user is relaxed, the termination unit can terminate the session at a normal time. Furthermore, if the user is in a hurry, the termination unit can terminate the session in the shortest possible time. This allows the termination unit to terminate the session at a more appropriate time by adjusting the timing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the termination unit may be performed using AI or not. For example, the termination unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0119] The security system can further modify the session termination protocol based on the industry and category of the communication partner. For example, in the case of banking transactions, the termination unit may apply a protocol that prioritizes security. The termination unit may also apply a protocol that prioritizes speed when using an electronic money application. Furthermore, in the case of emergency communications, the termination unit may apply a protocol that prioritizes a quick response. In this way, the termination unit can terminate communications more appropriately by changing the session termination protocol based on the industry and category of the communication partner. Some or all of the above processing in the termination unit may be performed using AI, for example, or not. For example, the termination unit can input the industry and category of the communication partner into the AI ​​and have the AI ​​select the optimal protocol.

[0120] The following briefly describes the processing flow for example form 2.

[0121] Step 1: The discrimination unit identifies the communication destination only when communication is to be performed. For example, it can identify the communication destination based on the IP address or domain name of the communication destination. It can also identify the communication destination based on the user ID or security certificate. Specifically, it analyzes the IP address or domain name of the communication destination and selects a highly reliable communication destination. Step 2: The establishment unit establishes a session with the communication destination determined by the discrimination unit. For example, a session can be established using the TCP / IP protocol or the HTTP protocol. Specifically, a session with the communication destination is established using the TCP / IP protocol or the HTTP protocol. Step 3: The termination section ends the session once communication is complete. For example, the session can be terminated based on a timeout or user action. It can also be terminated in the event of an error. Specifically, the session will be terminated if a timeout occurs or based on user action.

[0122] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0124] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0125] Each of the multiple elements described above, including the discrimination unit, establishment unit, termination unit, evaluation unit, application usage unit, secure communication unit, and emergency communication unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the discrimination unit is implemented by the processor 46 of the smart device 14 and analyzes the IP address and domain name of the communication destination. The establishment unit is implemented by the identification processing unit 290 of the data processing unit 12 and establishes a session using the TCP / IP protocol. The termination unit is implemented by the control unit 46A of the smart device 14 and terminates the session based on a timeout or user operation. The evaluation unit is implemented by the identification processing unit 290 of the data processing unit 12 and evaluates reliability by analyzing past communication history. The application usage unit is implemented by the control unit 46A of the smart device 14 and enhances security using encryption technology and two-factor authentication. The secure communication unit is implemented by the identification processing unit 290 of the data processing unit 12 and protects communication using end-to-end encryption and anonymization technology. The emergency communication unit is implemented by the control unit 46A of the smart device 14 and performs emergency communications using a high-priority communication channel. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0126] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0127] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0129] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

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

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

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

[0133] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

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

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

[0136] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0138] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0140] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0141] Each of the multiple elements described above, including the discrimination unit, establishment unit, termination unit, evaluation unit, application usage unit, secure communication unit, and emergency communication unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the discrimination unit is implemented by the processor 46 of the smart glasses 214 and analyzes the IP address and domain name of the communication destination. The establishment unit is implemented by the identification processing unit 290 of the data processing unit 12 and establishes a session using the TCP / IP protocol. The termination unit is implemented by the control unit 46A of the smart glasses 214 and terminates the session based on a timeout or user operation. The evaluation unit is implemented by the identification processing unit 290 of the data processing unit 12 and evaluates reliability by analyzing past communication history. The application usage unit is implemented by the control unit 46A of the smart glasses 214 and enhances security using encryption technology and two-factor authentication. The secure communication unit is implemented by the identification processing unit 290 of the data processing unit 12 and protects communication using end-to-end encryption and anonymization technology. The emergency communication unit is implemented by the control unit 46A of the smart glasses 214 and performs emergency communications using a high-priority communication channel. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0142] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0143] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0145] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

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

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

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

[0149] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0150] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0151] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0152] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0153] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0154] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0155] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0156] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0157] Each of the multiple elements described above, including the discrimination unit, establishment unit, termination unit, evaluation unit, application usage unit, secure communication unit, and emergency communication unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the discrimination unit is implemented by the processor 46 of the headset terminal 314 and analyzes the IP address and domain name of the communication destination. The establishment unit is implemented by the identification processing unit 290 of the data processing unit 12 and establishes a session using the TCP / IP protocol. The termination unit is implemented by the control unit 46A of the headset terminal 314 and terminates the session based on a timeout or user operation. The evaluation unit is implemented by the identification processing unit 290 of the data processing unit 12 and evaluates reliability by analyzing past communication history. The application usage unit is implemented by the control unit 46A of the headset terminal 314 and enhances security using encryption technology and two-factor authentication. The secure communication unit is implemented by the identification processing unit 290 of the data processing unit 12 and protects communication using end-to-end encryption and anonymization technology. The emergency communication unit is implemented by the control unit 46A of the headset-type terminal 314, and performs emergency communications using a high-priority communication channel. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0158] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0159] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0160] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0161] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0162] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0163] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0164] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0165] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0166] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0167] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0168] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0169] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0170] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0171] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0172] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0173] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0174] Each of the multiple elements described above, including the discrimination unit, establishment unit, termination unit, evaluation unit, application usage unit, secure communication unit, and emergency communication unit, is implemented by at least one of the robot 414 and the data processing unit 12. For example, the discrimination unit is implemented by the processor 46 of the robot 414 and analyzes the IP address and domain name of the communication destination. The establishment unit is implemented by the identification processing unit 290 of the data processing unit 12 and establishes a session using the TCP / IP protocol. The termination unit is implemented by the control unit 46A of the robot 414 and terminates the session based on a timeout or user operation. The evaluation unit is implemented by the identification processing unit 290 of the data processing unit 12 and evaluates reliability by analyzing past communication history. The application usage unit is implemented by the control unit 46A of the robot 414 and enhances security using encryption technology and two-factor authentication. The secure communication unit is implemented by the identification processing unit 290 of the data processing unit 12 and protects communication using end-to-end encryption and anonymization technology. The emergency communication unit is implemented by the control unit 46A of the robot 414 and performs emergency communications using a high-priority communication channel. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0175] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0176] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0177] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0178] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0179] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0180] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0181] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0182] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0183] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0184] 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.

[0185] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0186] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0187] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0188] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0189] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0190] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0191] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0192] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0193] (Note 1) A discrimination unit that identifies the communication destination only when communication is to be performed, A session establishment unit establishes a session with the communication destination determined by the aforementioned determination unit, It includes an termination section that terminates the session once communication is complete. A system characterized by the following features. (Note 2) It includes an evaluation unit that assesses the reliability of the communication destination. The system described in Appendix 1, characterized by the features described herein. (Note 3) It features an app usage section specifically designed for banking transactions and the use of electronic money apps. The system described in Appendix 1, characterized by the features described herein. (Note 4) Equipped with a secure communications unit specializing in highly confidential communications. The system described in Appendix 1, characterized by the features described herein. (Note 5) It has an emergency communications unit specializing in police and fire department communications. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned discrimination unit is The system estimates the user's emotions and adjusts the accuracy of destination identification based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned discrimination unit is Analyze the past communication history of the communication destination and select the optimal identification method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned discrimination unit is The determination is made by considering the geographical location information of the communication destination. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned discrimination unit is It estimates the user's emotions and determines the priority of communication destinations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned discrimination unit is The reliability score of the communication destination is updated in real time and reflected in the determination. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned discrimination unit is The discrimination algorithm is changed based on the industry and category of the communication destination. The system described in Appendix 1, characterized by the features described herein. (Note 12) The establishment unit is, The system estimates the user's emotions and adjusts the timing of session establishment based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The establishment unit is, Optimize the session establishment procedure based on the reliability assessment of the communication destination. The system described in Appendix 1, characterized by the features described herein. (Note 14) The establishment unit is, The system monitors the network status of the communication destination in real time and selects the optimal method for establishing a session. The system described in Appendix 1, characterized by the features described herein. (Note 15) The establishment unit is, The system estimates the user's emotions and determines the priority for session establishment based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The establishment unit is, The method of establishing a session is adjusted to take into account the geographical location information of the communication partner. The system described in Appendix 1, characterized by the features described herein. (Note 17) The establishment unit is, The session establishment protocol is modified based on the industry and category of the communication partner. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned termination section is, It estimates the user's emotions and adjusts the session end time based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned termination section is, The procedure for ending a session is optimized based on the importance of the communication content. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned termination section is, The system monitors the network status of the communication destination in real time and selects the optimal session termination method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned termination section is, The system estimates the user's emotions and determines the priority for ending sessions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned termination section is, The method of ending the session will be adjusted considering the geographical location information of the communication partner. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned termination section is, The session termination protocol is changed based on the industry and category of the communication partner. The system described in Appendix 1, characterized by the features described herein. (Note 24) The evaluation unit described above, The system estimates the user's emotions and adjusts the reliability evaluation criteria for the communication destination based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 25) The evaluation unit described above, Analyze past reliability data of the communication partner and optimize the evaluation algorithm. The system described in Appendix 2, characterized by the features described herein. (Note 26) The evaluation unit described above, The system estimates user sentiment and determines the priority of reliability evaluations based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 27) The evaluation unit described above, Reliability evaluation is performed taking into account the geographical location information of the communication destination. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned application usage unit is: The system estimates the user's emotions and adjusts the timing of app usage based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 29) The aforementioned application usage unit is: Analyze app usage history to select the optimal usage method. The system described in Appendix 3, characterized by the features described herein. (Note 30) The aforementioned application usage unit is: The system estimates user emotions and prioritizes app usage based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 31) The aforementioned application usage unit is: It monitors app usage in real time and suggests the optimal way to use it. The system described in Appendix 3, characterized by the features described herein. (Note 32) The aforementioned confidential communications unit, It estimates the user's emotions and adjusts the method of secure communication based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 33) The aforementioned confidential communications unit, Analyze the history of secure communications and select the optimal communication method. The system described in Appendix 4, characterized by the features described herein. (Note 34) The aforementioned confidential communications unit, It estimates the user's emotions and determines the priority of secure communications based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 35) The aforementioned confidential communications unit, It monitors the status of secure communications in real time and proposes the optimal communication method. The system described in Appendix 4, characterized by the features described herein. (Note 36) The aforementioned emergency communications unit, The system estimates the user's emotions and adjusts the emergency communication method based on the estimated emotions. The system described in Appendix 5, characterized by the features described herein. (Note 37) The aforementioned emergency communications unit, Analyze the history of emergency communications and select the optimal communication method. The system described in Appendix 5, characterized by the features described herein. (Note 38) The aforementioned emergency communications unit, The system estimates the user's emotions and prioritizes emergency communications based on those emotions. The system described in Appendix 5, characterized by the features described herein. (Note 39) The aforementioned emergency communications unit, It monitors the status of emergency communications in real time and proposes the optimal communication method. The system described in Appendix 5, characterized by the features described herein. [Explanation of Symbols]

[0194] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A discrimination unit that identifies the communication destination only when communication is to be performed, A session establishment unit establishes a session with the communication destination determined by the aforementioned determination unit, It includes an termination section that terminates the session once communication is complete. A system characterized by the following features.

2. It includes an evaluation unit that assesses the reliability of the communication destination. The system according to feature 1.

3. It features an app usage section specifically designed for banking transactions and the use of electronic money apps. The system according to feature 1.

4. Equipped with a secure communications unit specializing in highly confidential communications. The system according to feature 1.

5. It has an emergency communications unit specializing in police and fire department communications. The system according to feature 1.

6. The aforementioned discrimination unit is The system estimates the user's emotions and adjusts the accuracy of destination identification based on the estimated emotions. The system according to feature 1.

7. The aforementioned discrimination unit is Analyze the past communication history of the communication destination and select the optimal identification method. The system according to feature 1.

8. The aforementioned discrimination unit is The determination is made by considering the geographical location information of the communication destination. The system according to feature 1.

9. The aforementioned discrimination unit is It estimates the user's emotions and determines the priority of communication destinations based on the estimated user emotions. The system according to feature 1.

10. The aforementioned discrimination unit is The reliability score of the communication destination is updated in real time and reflected in the determination. The system according to feature 1.

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