system

Abstract

The system according to the embodiment aims to identify fraudulent calls in real time and take appropriate measures. [Solution] A system according to an embodiment includes an analysis unit, an identification unit, a warning unit, and a reporting unit. The analysis unit analyzes caller information of an incoming call. The identification unit identifies fraudulent calls based on the information analyzed by the analysis unit. The warning unit provides a real-time warning for fraudulent calls identified by the identification unit. The reporting unit makes an emergency call for fraudulent calls warned about by the warning unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that it is difficult to identify fraudulent calls in real time and take appropriate measures.

[0005] The system according to the embodiment aims to identify fraudulent calls in real time and take appropriate measures. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, an identification unit, a warning unit, and a reporting unit. The analysis unit analyzes caller information of an incoming call. The identification unit identifies fraudulent calls based on the information analyzed by the analysis unit. The warning unit provides a real-time warning for fraudulent calls identified by the identification unit. The reporting unit makes an emergency call for fraudulent calls warned about by the warning unit. [Effects of the Invention]

[0007] The system according to the embodiment can identify fraudulent calls in real time and take appropriate measures. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A fraudulent call prevention system according to an embodiment of the present invention analyzes caller information of incoming calls, identifies fraudulent calls, provides warnings in real time, and makes emergency calls. The fraudulent call prevention system analyzes the caller information of incoming calls and determines its trustworthiness. Next, an AI that has learned common patterns and characteristics of fraudulent calls identifies calls that may be fraudulent. If a fraudulent call is detected, real-time advice and warnings are provided via audio glasses. Furthermore, in the case of an emergency fraudulent call, an emergency call can be made with one tap. For example, the fraudulent call prevention system analyzes the caller information of incoming calls. In this process, the AI ​​collects information such as the caller's phone number, past call history, and voice analysis, and determines its trustworthiness. For example, if the caller has previously been reported as a fraudulent call, the AI ​​determines that the call is untrustworthy based on that information. Next, the AI ​​that has learned common patterns and characteristics of fraudulent calls identifies calls that may be fraudulent. For example, if specific phrases, speaking styles, and content emphasizing urgency are learned as characteristics of fraudulent calls, the AI ​​determines the likelihood of fraud based on those characteristics. If a fraudulent call is detected, real-time advice and warnings are provided via audio glasses. For example, a voice warning such as "This call may be fraudulent. Please be careful" is provided. This allows users to respond immediately to fraudulent calls. Furthermore, in the case of urgent fraudulent calls, an emergency call can be made with just one tap. For example, by pressing a button on the Audio Glasses, an emergency call can be sent to the police or family. This allows for a quick response and minimizes damage. As a result, the fraudulent call prevention system can prevent fraudulent call damage, especially among the elderly. This allows users to easily avoid the risk of fraudulent calls and use the telephone with peace of mind.

[0029] The fraudulent call prevention system according to the embodiment includes an analysis unit, an identification unit, a warning unit, and a reporting unit. The analysis unit analyzes caller information of an incoming call. For example, the analysis unit acquires the caller's phone number and evaluates its trustworthiness. The analysis unit can also analyze past call history to determine the caller's trustworthiness. The analysis unit can also perform voice analysis to analyze the characteristics of the caller's voice. For example, if the caller's phone number has been reported as a fraudulent call in the past, the analysis unit can evaluate the caller's trustworthiness low based on that information. The analysis unit can also analyze the caller's tone and speed of voice to determine the possibility of fraud. The identification unit identifies fraudulent calls based on the information analyzed by the analysis unit. For example, the identification unit learns common patterns and characteristics of fraudulent calls and identifies calls that may be fraudulent based on that information. For example, the identification unit determines the possibility of fraud based on specific phrases, speaking styles, content that emphasizes urgency, etc. The warning unit provides a real-time warning for fraudulent calls identified by the identification unit. The warning unit provides a voice warning via the audio glasses, such as, "This call may be fraudulent. Please be careful." This allows the user to respond immediately to the fraudulent call. The reporting unit makes an emergency call in response to the fraudulent call warned about by the warning unit. The reporting unit sends an emergency call to the police or family, for example, by pressing a button on the audio glasses. This allows for a quick response and minimizes damage. As a result, the fraudulent call prevention system according to the embodiment can prevent damage from fraudulent calls before it occurs.

[0030] The analysis unit can perform voice analysis of the caller's phone number, past call history, or voice. The analysis unit, for example, obtains the caller's phone number and evaluates its reliability. For example, if the caller's phone number has been reported as a scam call in the past, the reliability is evaluated as low based on that information. The analysis unit can also analyze past call history to determine the caller's reliability. For example, if the caller has been reported as a scam call in the past, the reliability is evaluated as low based on that call history. Furthermore, the analysis unit can perform voice analysis to analyze the characteristics of the caller's voice. For example, the analysis unit can analyze the tone and speed of the caller's voice to determine the possibility of fraud. This can increase the reliability of the caller information.

[0031] The identification unit can learn the general patterns and characteristics of fraudulent calls and identify whether or not a call is fraudulent. For example, the identification unit learns the general patterns and characteristics of fraudulent calls and identifies calls that may be fraudulent based on that. For example, the identification unit determines the possibility of fraud based on specific phrases, speaking styles, content that emphasizes urgency, etc. The identification unit can also collect and analyze data on past fraudulent calls in order to learn the characteristics of fraudulent calls. For example, the identification unit analyzes voice data from past fraudulent calls and extracts common characteristics. This can improve the accuracy of identifying fraudulent calls.

[0032] If a fraudulent call is detected, the warning unit can provide advice or a warning in real time via the audio glasses. For example, the warning unit can provide a warning via the audio glasses, such as "This call may be fraudulent. Please be careful." This allows the user to respond to the fraudulent call immediately. The warning unit can also provide specific advice depending on the content of the fraudulent call. For example, it can provide advice such as "Do not provide personal information over this call." Furthermore, the warning unit can estimate the user's emotions and adjust the way the warning is expressed based on the estimated emotions. For example, if the user is nervous, a concise and highly visible warning can be provided. This allows the user to respond appropriately to the fraudulent call. This allows the user to respond to the fraudulent call immediately.

[0033] In the case of an emergency scam call, the reporting unit can make an emergency call with one tap. For example, the reporting unit can send an emergency call to the police or family by pressing a button on the audio glasses. This enables a quick response and minimizes damage. The reporting unit can also estimate the user's emotions and adjust the emergency call method based on the estimated emotions. For example, if the user is nervous, it can provide a simple and quick emergency call method. Furthermore, the reporting unit can select the optimal call method by referring to the user's past call history. For example, it can select the optimal call method based on the emergency call methods used by the user in the past. This enables a quick response and minimizes damage.

[0034] The analysis unit can analyze the reliability of a caller's phone number based not only on past call history but also on social media activity history. For example, if the caller's phone number has been reported as a scam call in the past, the analysis unit can evaluate the reliability as low based on that information. The analysis unit can also evaluate the reliability as low if the caller does not have a social media account. Furthermore, the analysis unit can also evaluate the reliability as low if the caller's social media activity is unnatural. This allows for a more accurate evaluation of the reliability of caller information.

[0035] During voice analysis, the analysis unit can perform a detailed analysis of the caller's voice tone, speed, and emotional changes. For example, if the caller's voice tone suddenly changes, the analysis unit can determine that there is a possibility of fraud. The analysis unit can also determine that there is a possibility of fraud if the caller speaks at an abnormally fast speed. Furthermore, the analysis unit can also determine that there is a possibility of fraud if the caller's voice sounds tense or impatient. In this way, by analyzing the characteristics of the caller's voice in detail, the possibility of a fraudulent call can be increased.

[0036] If the caller's telephone number is international, the analysis unit can analyze it based on the fraud patterns of that country. For example, if the caller's telephone number is from a specific country, the analysis unit evaluates the caller's trustworthiness based on the fraud patterns of that country. In addition, if the caller's telephone number is international, the analysis unit can evaluate the caller's trustworthiness by referring to a fraud report database of that country. Furthermore, if the caller's telephone number is international, the analysis unit can analyze it based on information about the fraud methods of that country. This allows for improved accuracy in identifying fraudulent calls by taking international fraud patterns into account.

[0037] When identifying fraudulent calls, the identification unit can analyze not only specific phrases or speaking styles but also the entire context of the conversation. For example, if a caller uses a specific phrase, the identification unit analyzes the context before and after that phrase. In addition, if the caller's speaking style is unnatural, the identification unit can also analyze the entire context of that speaking style. Furthermore, if the caller emphasizes urgency, the identification unit can analyze the entire context. In this way, by analyzing the entire context of the conversation, the accuracy of identifying fraudulent calls can be improved.

[0038] When identifying fraudulent calls, the identification unit can improve the accuracy of the identification based on the caller's past behavioral patterns. For example, if the caller has been reported as a fraudulent caller in the past, the identification unit takes those behavioral patterns into consideration. The identification unit can also determine the possibility of fraud based on the caller's past call history. Furthermore, the identification unit can analyze the caller's past behavioral patterns and highly evaluate the possibility of fraud. In this way, by taking the caller's past behavioral patterns into consideration, the accuracy of identifying fraudulent calls can be improved.

[0039] When identifying a fraudulent call, the identification unit can analyze the characteristics of the caller's voice in detail to determine the possibility of fraud. For example, the identification unit can determine that there is a possibility of fraud if the caller's tone of voice is unnatural. The identification unit can also determine that there is a possibility of fraud if the caller's voice speed is abnormally fast. Furthermore, the identification unit can also determine that there is a possibility of fraud if the caller's voice sounds tense or impatient. In this way, by analyzing the characteristics of the caller's voice in detail, the accuracy of identifying fraudulent calls can be improved.

[0040] When a fraudulent call is detected, the warning unit can customize the content of the warning based on the user's past behavior history. For example, if the user has received a fraudulent call in the past, the warning unit adjusts the content of the warning based on that experience. The warning unit can also customize the content of the warning based on feedback provided by the user in the past. Furthermore, the warning unit can provide optimal content of the warning based on the user's past behavior history. In this way, the effectiveness of the warning can be improved by providing content of the warning based on the user's past behavior history.

[0041] The warning unit can adjust the warning method based on the user's current situation when issuing a warning. For example, the warning unit can provide a voice warning when the user is driving. The warning unit can also provide a visual warning when the user is walking. Furthermore, the warning unit can provide a detailed warning when the user is stationary. This can improve the effectiveness of the warning by providing a warning method that suits the user's current situation.

[0042] The warning unit can improve the accuracy of the warning by reflecting the user's past feedback when issuing a warning. For example, the warning unit can adjust the content of the warning based on feedback provided by the user in the past. The warning unit can also customize the content of the warning based on information about fraudulent calls reported by the user in the past. Furthermore, the warning unit can adjust the display method of the warning based on feedback provided by the user in the past. In this way, the accuracy of the warning can be improved by reflecting the user's past feedback.

[0043] When making an emergency call, the reporting unit can select an appropriate reporting method by referring to the user's past reporting history. For example, the reporting unit selects the optimal reporting method based on the emergency reporting methods used by the user in the past. The reporting unit can also select a rapid reporting method from the user's past reporting history. Furthermore, the reporting unit can analyze the user's past reporting history and select the most effective reporting method. This enables a rapid response by providing the optimal reporting method based on the user's past reporting history.

[0044] The notification unit can adjust the notification method based on the user's current situation when making an emergency call. For example, if the user is driving, the notification unit can make an audio emergency call. If the user is walking, the notification unit can also make a visual emergency call. Furthermore, if the user is stationary, the notification unit can also make a detailed emergency call. This allows for a quick response by providing a notification method that suits the user's current situation.

[0045] The reporting unit can improve the reporting method by reflecting user feedback when making an emergency call. For example, the reporting unit adjusts the reporting method based on feedback previously provided by the user. The reporting unit can also customize the reporting method based on information about emergency calls previously reported by the user. Furthermore, the reporting unit can adjust the display method of the call based on feedback previously provided by the user. In this way, the accuracy of the reporting method can be improved by reflecting user feedback.

[0046] When making an emergency call, the reporting unit can select an appropriate reporting method taking into account the user's geographical location information. For example, the reporting unit makes an emergency call to the local police based on the user's geographical location information. The reporting unit can also make a call to nearby emergency contacts based on the user's geographical location information. Furthermore, the reporting unit can select the optimal emergency reporting method based on the user's geographical location information. This allows for a rapid response by providing the optimal reporting method based on the user's geographical location information.

[0047] The notification unit can analyze the user's social media activity and issue a related notification when making an emergency call. For example, the notification unit can issue an emergency call regarding a location where the user has checked in on social media. The notification unit can also analyze the content of the user's social media posts and issue a related emergency call. Furthermore, the notification unit can also issue a related emergency call by referring to the activity of the user's friends on social media. This allows for a quick response by providing a call based on the user's social media activity.

[0048] The notification unit can improve the accuracy of an emergency call by checking the call against the user's contact list. For example, if the caller's phone number is in the user's contact list, the notification unit can set the priority of the emergency call to low. Also, if the caller's phone number is not in the user's contact list, the notification unit can set the priority of the emergency call to high. Furthermore, if the caller's phone number is similar to the phone number in the user's contact list, the notification unit can set the priority of the emergency call to medium. This allows for a quick response by providing a call based on the user's contact list.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The analysis unit can take the caller's geographic location information into account when evaluating the reliability of the caller's phone number. For example, if the caller's phone number is from a specific region, the analysis unit can evaluate the reliability by referring to a fraud report database for that region. The analysis unit can also evaluate the caller's reliability lower if the caller's geographic location information is unnatural. Furthermore, the analysis unit can determine that the caller's geographic location information changes frequently, indicating the possibility of fraud. This allows for a more accurate evaluation of the reliability of the caller's information.

[0051] When identifying fraudulent calls, the identification unit can analyze not only the characteristics of the caller's voice but also the background sound. For example, the identification unit determines that there is a possibility of fraud if the caller's voice tone is unnatural. The identification unit can also determine that there is a possibility of fraud if the caller's background sound is unnatural. Furthermore, the identification unit can also determine that there is a possibility of fraud if the caller's background sound matches a specific pattern. This allows for detailed analysis of the caller's voice characteristics and background sound, thereby improving the accuracy of identifying fraudulent calls.

[0052] When a fraudulent call is detected, the warning unit can customize the content of the warning based on the user's current activity. For example, if the user is driving, a voice warning can be provided. If the user is walking, a visual warning can be provided. Furthermore, if the user is stationary, a detailed warning can be provided. This can improve the effectiveness of the warning by providing a warning method that corresponds to the user's current activity.

[0053] When making an emergency call, the reporting unit can select an appropriate reporting method taking into account the user's geographical location information. For example, an emergency call can be made to the local police based on the user's geographical location information. Also, a call can be made to a nearby emergency contact based on the user's geographical location information. Furthermore, the optimal emergency reporting method can be selected based on the user's geographical location information. This allows for a quick response by providing the optimal reporting method based on the user's geographical location information.

[0054] When assessing the reliability of a caller's phone number, the analysis unit can also take into account the caller's social media activity history. For example, if a caller's phone number has been reported as a scam call in the past, the analysis unit can assess the caller's reliability as low based on that information. The analysis unit can also assess the caller's reliability as low if the caller does not have a social media account. Furthermore, if the caller's social media activity is unnatural, the analysis unit can assess the caller's reliability as low. This allows for a more accurate assessment of the reliability of the caller's information.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The analysis unit analyzes the caller information of the incoming call. The analysis unit obtains the caller's phone number and evaluates its trustworthiness. It can also analyze past call history to determine the caller's trustworthiness. It can also perform voice analysis to analyze the characteristics of the caller's voice. For example, if the caller's phone number has been reported as a scam call in the past, it can use that information to evaluate the caller's trustworthiness as low. It can also analyze the caller's tone and speed of voice to determine the possibility of fraud. Step 2: The identification unit identifies fraudulent calls based on the information analyzed by the analysis unit. The identification unit learns the general patterns and characteristics of fraudulent calls and identifies calls that may be fraudulent based on these. For example, it determines the possibility of fraud based on specific phrases, speaking styles, and content that emphasizes urgency. Step 3: The warning unit provides a real-time warning for the fraudulent call identified by the identification unit. The warning unit provides a voice warning via audio glasses, such as "This call may be fraudulent. Please be careful." This allows the user to respond immediately to the fraudulent call. Step 4: The reporting unit makes an emergency call to the fraudulent call that was flagged by the warning unit. By pressing a button on the audio glasses, the reporting unit sends an emergency call to the police or family. This allows for a quick response and minimizes damage.

[0057] (Example 2) A fraudulent call prevention system according to an embodiment of the present invention analyzes caller information of incoming calls, identifies fraudulent calls, provides warnings in real time, and makes emergency calls. The fraudulent call prevention system analyzes the caller information of incoming calls and determines its trustworthiness. Next, an AI that has learned common patterns and characteristics of fraudulent calls identifies calls that may be fraudulent. If a fraudulent call is detected, real-time advice and warnings are provided via audio glasses. Furthermore, in the case of an emergency fraudulent call, an emergency call can be made with one tap. For example, the fraudulent call prevention system analyzes the caller information of incoming calls. In this process, the AI ​​collects information such as the caller's phone number, past call history, and voice analysis, and determines its trustworthiness. For example, if the caller has previously been reported as a fraudulent call, the AI ​​determines that the call is untrustworthy based on that information. Next, the AI ​​that has learned common patterns and characteristics of fraudulent calls identifies calls that may be fraudulent. For example, if specific phrases, speaking styles, and content emphasizing urgency are learned as characteristics of fraudulent calls, the AI ​​determines the likelihood of fraud based on those characteristics. If a fraudulent call is detected, real-time advice and warnings are provided via audio glasses. For example, a voice warning such as "This call may be fraudulent. Please be careful" is provided. This allows users to respond immediately to fraudulent calls. Furthermore, in the case of urgent fraudulent calls, an emergency call can be made with just one tap. For example, by pressing a button on the Audio Glasses, an emergency call can be sent to the police or family. This allows for a quick response and minimizes damage. As a result, the fraudulent call prevention system can prevent fraudulent call damage, especially among the elderly. This allows users to easily avoid the risk of fraudulent calls and use the telephone with peace of mind.

[0058] The fraudulent call prevention system according to the embodiment includes an analysis unit, an identification unit, a warning unit, and a reporting unit. The analysis unit analyzes caller information of an incoming call. For example, the analysis unit acquires the caller's phone number and evaluates its trustworthiness. The analysis unit can also analyze past call history to determine the caller's trustworthiness. The analysis unit can also perform voice analysis to analyze the characteristics of the caller's voice. For example, if the caller's phone number has been reported as a fraudulent call in the past, the analysis unit can evaluate the caller's trustworthiness low based on that information. The analysis unit can also analyze the caller's tone and speed of voice to determine the possibility of fraud. The identification unit identifies fraudulent calls based on the information analyzed by the analysis unit. For example, the identification unit learns common patterns and characteristics of fraudulent calls and identifies calls that may be fraudulent based on that information. For example, the identification unit determines the possibility of fraud based on specific phrases, speaking styles, content that emphasizes urgency, etc. The warning unit provides a real-time warning for fraudulent calls identified by the identification unit. The warning unit provides a voice warning via the audio glasses, such as, "This call may be fraudulent. Please be careful." This allows the user to respond immediately to the fraudulent call. The reporting unit makes an emergency call in response to the fraudulent call warned about by the warning unit. The reporting unit sends an emergency call to the police or family, for example, by pressing a button on the audio glasses. This allows for a quick response and minimizes damage. As a result, the fraudulent call prevention system according to the embodiment can prevent damage from fraudulent calls before it occurs.

[0059] The analysis unit can perform voice analysis of the caller's phone number, past call history, or voice. The analysis unit, for example, obtains the caller's phone number and evaluates its reliability. For example, if the caller's phone number has been reported as a scam call in the past, the reliability is evaluated as low based on that information. The analysis unit can also analyze past call history to determine the caller's reliability. For example, if the caller has been reported as a scam call in the past, the reliability is evaluated as low based on that call history. Furthermore, the analysis unit can perform voice analysis to analyze the characteristics of the caller's voice. For example, the analysis unit can analyze the tone and speed of the caller's voice to determine the possibility of fraud. This can increase the reliability of the caller information.

[0060] The identification unit can learn the general patterns and characteristics of fraudulent calls and identify whether or not a call is fraudulent. For example, the identification unit learns the general patterns and characteristics of fraudulent calls and identifies calls that may be fraudulent based on that. For example, the identification unit determines the possibility of fraud based on specific phrases, speaking styles, content that emphasizes urgency, etc. The identification unit can also collect and analyze data on past fraudulent calls in order to learn the characteristics of fraudulent calls. For example, the identification unit analyzes voice data from past fraudulent calls and extracts common characteristics. This can improve the accuracy of identifying fraudulent calls.

[0061] If a fraudulent call is detected, the warning unit can provide advice or a warning in real time via the audio glasses. For example, the warning unit can provide a warning via the audio glasses, such as "This call may be fraudulent. Please be careful." This allows the user to respond to the fraudulent call immediately. The warning unit can also provide specific advice depending on the content of the fraudulent call. For example, it can provide advice such as "Do not provide personal information over this call." Furthermore, the warning unit can estimate the user's emotions and adjust the way the warning is expressed based on the estimated emotions. For example, if the user is nervous, a concise and highly visible warning can be provided. This allows the user to respond appropriately to the fraudulent call. This allows the user to respond to the fraudulent call immediately.

[0062] In the case of an emergency scam call, the reporting unit can make an emergency call with one tap. For example, the reporting unit can send an emergency call to the police or family by pressing a button on the audio glasses. This enables a quick response and minimizes damage. The reporting unit can also estimate the user's emotions and adjust the emergency call method based on the estimated emotions. For example, if the user is nervous, it can provide a simple and quick emergency call method. Furthermore, the reporting unit can select the optimal call method by referring to the user's past call history. For example, it can select the optimal call method based on the emergency call methods used by the user in the past. This enables a quick response and minimizes damage.

[0063] The analysis unit can estimate the user's emotions and adjust the analysis method of the caller information based on the estimated user emotions. For example, if the user is nervous, the analysis unit can quickly provide analysis results without providing detailed explanations. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results and carefully explain the reliability of the caller information. Furthermore, if the user is feeling anxious, the analysis unit can concisely summarize the analysis results and use expressions that provide a sense of security. This allows for providing an analysis method that corresponds to the user's emotions, thereby improving the reliability of the analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0064] The analysis unit can analyze the reliability of a caller's phone number based not only on past call history but also on social media activity history. For example, if the caller's phone number has been reported as a scam call in the past, the analysis unit can evaluate the reliability as low based on that information. The analysis unit can also evaluate the reliability as low if the caller does not have a social media account. Furthermore, the analysis unit can also evaluate the reliability as low if the caller's social media activity is unnatural. This allows for a more accurate evaluation of the reliability of caller information.

[0065] During voice analysis, the analysis unit can perform a detailed analysis of the caller's voice tone, speed, and emotional changes. For example, if the caller's voice tone suddenly changes, the analysis unit can determine that there is a possibility of fraud. The analysis unit can also determine that there is a possibility of fraud if the caller speaks at an abnormally fast speed. Furthermore, the analysis unit can also determine that there is a possibility of fraud if the caller's voice sounds tense or impatient. In this way, by analyzing the characteristics of the caller's voice in detail, the possibility of a fraudulent call can be increased.

[0066] If the caller's telephone number is international, the analysis unit can analyze it based on the fraud patterns of that country. For example, if the caller's telephone number is from a specific country, the analysis unit evaluates the caller's trustworthiness based on the fraud patterns of that country. In addition, if the caller's telephone number is international, the analysis unit can evaluate the caller's trustworthiness by referring to a fraud report database of that country. Furthermore, if the caller's telephone number is international, the analysis unit can analyze it based on information about the fraud methods of that country. This allows for improved accuracy in identifying fraudulent calls by taking international fraud patterns into account.

[0067] The identification unit can estimate the user's emotions and adjust the criteria for identifying fraudulent calls based on the estimated user emotions. For example, if the user is nervous, the identification unit can set strict criteria for identifying fraudulent calls. Furthermore, if the user is relaxed, the identification unit can also set lenient criteria for identifying fraudulent calls. Furthermore, if the user is feeling anxious, the identification unit can also set moderate criteria for identifying fraudulent calls. This provides identification criteria according to the user's emotions, thereby improving the accuracy of identifying fraudulent calls. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0068] When identifying fraudulent calls, the identification unit can analyze not only specific phrases or speaking styles but also the entire context of the conversation. For example, if a caller uses a specific phrase, the identification unit analyzes the context before and after that phrase. In addition, if the caller's speaking style is unnatural, the identification unit can also analyze the entire context of that speaking style. Furthermore, if the caller emphasizes urgency, the identification unit can analyze the entire context. In this way, by analyzing the entire context of the conversation, the accuracy of identifying fraudulent calls can be improved.

[0069] When identifying fraudulent calls, the identification unit can improve the accuracy of the identification based on the caller's past behavioral patterns. For example, if the caller has been reported as a fraudulent caller in the past, the identification unit takes those behavioral patterns into consideration. The identification unit can also determine the possibility of fraud based on the caller's past call history. Furthermore, the identification unit can analyze the caller's past behavioral patterns and highly evaluate the possibility of fraud. In this way, by taking the caller's past behavioral patterns into consideration, the accuracy of identifying fraudulent calls can be improved.

[0070] When identifying a fraudulent call, the identification unit can analyze the characteristics of the caller's voice in detail to determine the possibility of fraud. For example, the identification unit can determine that there is a possibility of fraud if the caller's tone of voice is unnatural. The identification unit can also determine that there is a possibility of fraud if the caller's voice speed is abnormally fast. Furthermore, the identification unit can also determine that there is a possibility of fraud if the caller's voice sounds tense or impatient. In this way, by analyzing the characteristics of the caller's voice in detail, the accuracy of identifying fraudulent calls can be improved.

[0071] The warning unit can estimate the user's emotions and adjust the way the warning is expressed based on the estimated user's emotions. For example, if the user is nervous, the warning unit can provide a concise and highly visible warning. Furthermore, if the user is relaxed, the warning unit can provide a warning with detailed information. Furthermore, if the user is feeling anxious, the warning unit can provide a warning that gives a sense of security. This can improve the effectiveness of the warning by providing a warning method that corresponds to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0072] When a fraudulent call is detected, the warning unit can customize the content of the warning based on the user's past behavior history. For example, if the user has received a fraudulent call in the past, the warning unit adjusts the content of the warning based on that experience. The warning unit can also customize the content of the warning based on feedback provided by the user in the past. Furthermore, the warning unit can provide optimal content of the warning based on the user's past behavior history. In this way, the effectiveness of the warning can be improved by providing content of the warning based on the user's past behavior history.

[0073] The warning unit can adjust the warning method based on the user's current situation when issuing a warning. For example, the warning unit can provide a voice warning when the user is driving. The warning unit can also provide a visual warning when the user is walking. Furthermore, the warning unit can provide a detailed warning when the user is stationary. This can improve the effectiveness of the warning by providing a warning method that suits the user's current situation.

[0074] The warning unit can improve the accuracy of the warning by reflecting the user's past feedback when issuing a warning. For example, the warning unit can adjust the content of the warning based on feedback provided by the user in the past. The warning unit can also customize the content of the warning based on information about fraudulent calls reported by the user in the past. Furthermore, the warning unit can adjust the display method of the warning based on feedback provided by the user in the past. In this way, the accuracy of the warning can be improved by reflecting the user's past feedback.

[0075] The notification unit can estimate the user's emotions and adjust the emergency call method based on the estimated user emotions. For example, if the user is nervous, the notification unit can provide a simple and quick emergency call method. Furthermore, if the user is relaxed, the notification unit can provide a detailed emergency call method. Furthermore, if the user is feeling anxious, the notification unit can provide an emergency call method that gives a sense of security. This allows for a quick response by providing an emergency call method that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0076] When making an emergency call, the reporting unit can select an appropriate reporting method by referring to the user's past reporting history. For example, the reporting unit selects the optimal reporting method based on the emergency reporting methods used by the user in the past. The reporting unit can also select a rapid reporting method from the user's past reporting history. Furthermore, the reporting unit can analyze the user's past reporting history and select the most effective reporting method. This enables a rapid response by providing the optimal reporting method based on the user's past reporting history.

[0077] The notification unit can adjust the notification method based on the user's current situation when making an emergency call. For example, if the user is driving, the notification unit can make an audio emergency call. If the user is walking, the notification unit can also make a visual emergency call. Furthermore, if the user is stationary, the notification unit can also make a detailed emergency call. This allows for a quick response by providing a notification method that suits the user's current situation.

[0078] The reporting unit can improve the reporting method by reflecting user feedback when making an emergency call. For example, the reporting unit adjusts the reporting method based on feedback previously provided by the user. The reporting unit can also customize the reporting method based on information about emergency calls previously reported by the user. Furthermore, the reporting unit can adjust the display method of the call based on feedback previously provided by the user. In this way, the accuracy of the reporting method can be improved by reflecting user feedback.

[0079] The notification unit can estimate the user's emotions and determine the priority of emergency calls based on the estimated user emotions. For example, if the user is nervous, the notification unit can prioritize the most important emergency call. Furthermore, if the user is relaxed, the notification unit can sequentially make detailed emergency calls. Furthermore, if the user is feeling anxious, the notification unit can prioritize emergency calls that will give the user a sense of security. This allows for a rapid response by providing a priority order for emergency calls according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0080] When making an emergency call, the reporting unit can select an appropriate reporting method taking into account the user's geographical location information. For example, the reporting unit makes an emergency call to the local police based on the user's geographical location information. The reporting unit can also make a call to nearby emergency contacts based on the user's geographical location information. Furthermore, the reporting unit can select the optimal emergency reporting method based on the user's geographical location information. This allows for a rapid response by providing the optimal reporting method based on the user's geographical location information.

[0081] The notification unit can analyze the user's social media activity and issue a related notification when making an emergency call. For example, the notification unit can issue an emergency call regarding a location where the user has checked in on social media. The notification unit can also analyze the content of the user's social media posts and issue a related emergency call. Furthermore, the notification unit can also issue a related emergency call by referring to the activity of the user's friends on social media. This allows for a quick response by providing a call based on the user's social media activity.

[0082] The notification unit can improve the accuracy of an emergency call by checking the call against the user's contact list. For example, if the caller's phone number is in the user's contact list, the notification unit can set the priority of the emergency call to low. Also, if the caller's phone number is not in the user's contact list, the notification unit can set the priority of the emergency call to high. Furthermore, if the caller's phone number is similar to the phone number in the user's contact list, the notification unit can set the priority of the emergency call to medium. This allows for a quick response by providing a call based on the user's contact list. === Hard Collateral 1-1 === Each of the multiple elements, including the analysis unit, identification unit, warning unit, and notification unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the smart device 14 and collects information such as the caller's phone number, past call history, and voice analysis, and evaluates its reliability. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and learns common patterns and characteristics of fraudulent calls to identify calls that may be fraudulent. The warning unit is realized, for example, by the control unit 46A of the smart device 14 and provides a real-time warning via audio glasses. The notification unit is realized, for example, by the control unit 46A of the smart device 14 and sends an emergency call by pressing a button on the audio glasses. === Hard Collateral 1-2 === Each of the multiple elements, including the analysis unit, identification unit, warning unit, and notification unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the smart glasses 214 and collects information such as the caller's phone number, past call history, and voice analysis, and evaluates its reliability. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and learns common patterns and characteristics of fraudulent calls to identify calls that may be fraudulent. The warning unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides a real-time warning via audio glasses. The notification unit is realized, for example, by the control unit 46A of the smart glasses 214 and sends an emergency call by pressing a button on the audio glasses. === Hard Collateral 1-3 === Each of the multiple elements, including the analysis unit, identification unit, warning unit, and notification unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the headset-type terminal 314 and collects information such as the caller's phone number, past call history, and voice analysis, and evaluates its reliability. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and learns general patterns and characteristics of fraudulent calls to identify calls that may be fraudulent. The warning unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and provides a real-time warning by voice via the audio glasses. The notification unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and sends an emergency call by pressing a button on the audio glasses. === Hard Collateral 1-4 === Each of the multiple elements, including the analysis unit, identification unit, warning unit, and notification unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the robot 414 and collects information such as the caller's phone number, past call history, and voice analysis, and evaluates their reliability. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and learns common patterns and characteristics of fraudulent calls to identify calls that may be fraudulent. The warning unit is realized, for example, by the control unit 46A of the robot 414 and provides a real-time warning via audio glasses. The notification unit is realized, for example, by the control unit 46A of the robot 414 and sends an emergency call by pressing a button on the audio glasses.

[0083] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0084] The analysis unit can take the caller's geographic location information into account when evaluating the reliability of the caller's phone number. For example, if the caller's phone number is from a specific region, the analysis unit can evaluate the reliability by referring to a fraud report database for that region. The analysis unit can also evaluate the caller's reliability lower if the caller's geographic location information is unnatural. Furthermore, the analysis unit can determine that the caller's geographic location information changes frequently, indicating the possibility of fraud. This allows for a more accurate evaluation of the reliability of the caller's information.

[0085] When identifying fraudulent calls, the identification unit can analyze not only the characteristics of the caller's voice but also the background sound. For example, the identification unit determines that there is a possibility of fraud if the caller's voice tone is unnatural. The identification unit can also determine that there is a possibility of fraud if the caller's background sound is unnatural. Furthermore, the identification unit can also determine that there is a possibility of fraud if the caller's background sound matches a specific pattern. This allows for detailed analysis of the caller's voice characteristics and background sound, thereby improving the accuracy of identifying fraudulent calls.

[0086] When a fraudulent call is detected, the warning unit can customize the content of the warning based on the user's current activity. For example, if the user is driving, a voice warning can be provided. If the user is walking, a visual warning can be provided. Furthermore, if the user is stationary, a detailed warning can be provided. This can improve the effectiveness of the warning by providing a warning method that corresponds to the user's current activity.

[0087] When making an emergency call, the reporting unit can select an appropriate reporting method taking into account the user's geographical location information. For example, an emergency call can be made to the local police based on the user's geographical location information. Also, a call can be made to a nearby emergency contact based on the user's geographical location information. Furthermore, the optimal emergency reporting method can be selected based on the user's geographical location information. This allows for a quick response by providing the optimal reporting method based on the user's geographical location information.

[0088] When assessing the reliability of a caller's phone number, the analysis unit can also take into account the caller's social media activity history. For example, if a caller's phone number has been reported as a scam call in the past, the analysis unit can assess the caller's reliability as low based on that information. The analysis unit can also assess the caller's reliability as low if the caller does not have a social media account. Furthermore, if the caller's social media activity is unnatural, the analysis unit can assess the caller's reliability as low. This allows for a more accurate assessment of the reliability of the caller's information.

[0089] The analysis unit can estimate the user's emotions and adjust the analysis method of the caller information based on the estimated user emotions. For example, if the user is nervous, the analysis results can be provided quickly without detailed explanations. Alternatively, if the user is relaxed, detailed analysis results can be provided and the reliability of the caller information can be carefully explained. Furthermore, if the user is feeling anxious, the analysis results can be summarized concisely and expressions that give a sense of security can be used. This makes it possible to improve the reliability of the analysis results by providing an analysis method that corresponds to the user's emotions.

[0090] The identification unit can estimate the user's emotions and adjust the criteria for identifying fraudulent calls based on the estimated user emotions. For example, if the user is nervous, the criteria for identifying fraudulent calls can be set to be strict. Alternatively, if the user is relaxed, the criteria for identifying fraudulent calls can be set to be lenient. Furthermore, if the user is feeling anxious, the criteria for identifying fraudulent calls can be set to be moderate. This allows for the accuracy of identifying fraudulent calls to be improved by providing identification criteria that correspond to the user's emotions.

[0091] The warning unit can estimate the user's emotions and adjust the way the warning is expressed based on the estimated user's emotions. For example, if the user is nervous, a concise and highly visible warning can be provided. If the user is relaxed, a warning including detailed information can be provided. Furthermore, if the user is feeling anxious, a warning that gives a sense of security can be provided. In this way, the effectiveness of the warning can be improved by providing a warning method that corresponds to the user's emotions.

[0092] The reporting unit can estimate the user's emotions and adjust the emergency call method based on the estimated user's emotions. For example, if the user is nervous, a simple and quick emergency call method can be provided. Alternatively, if the user is relaxed, a detailed emergency call method can be provided. Furthermore, if the user is feeling anxious, a method that gives a sense of security can be provided. This allows for a quick response by providing an emergency call method that corresponds to the user's emotions.

[0093] The reporting unit can estimate the user's emotions and determine the priority of emergency calls based on the estimated user's emotions. For example, if the user is nervous, the most important emergency call can be given priority. If the user is relaxed, more detailed emergency calls can be made sequentially. Furthermore, if the user is feeling anxious, emergency calls that provide a sense of security can be given priority. This allows for a quick response by providing the priority of emergency calls according to the user's emotions.

[0094] The processing flow of the second embodiment will be briefly explained below.

[0095] Step 1: The analysis unit analyzes the caller information of the incoming call. The analysis unit obtains the caller's phone number and evaluates its trustworthiness. It can also analyze past call history to determine the caller's trustworthiness. It can also perform voice analysis to analyze the characteristics of the caller's voice. For example, if the caller's phone number has been reported as a scam call in the past, it can use that information to evaluate the caller's trustworthiness as low. It can also analyze the caller's tone and speed of voice to determine the possibility of fraud. Step 2: The identification unit identifies fraudulent calls based on the information analyzed by the analysis unit. The identification unit learns the general patterns and characteristics of fraudulent calls and identifies calls that may be fraudulent based on these. For example, it determines the possibility of fraud based on specific phrases, speaking styles, and content that emphasizes urgency. Step 3: The warning unit provides a real-time warning for the fraudulent call identified by the identification unit. The warning unit provides a voice warning via audio glasses, such as "This call may be fraudulent. Please be careful." This allows the user to respond immediately to the fraudulent call. Step 4: The reporting unit makes an emergency call to the fraudulent call that was flagged by the warning unit. By pressing a button on the audio glasses, the reporting unit sends an emergency call to the police or family. This allows for a quick response and minimizes damage.

[0096] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0097] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0098] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0099] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0100] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0101] 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.

[0102] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0103] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0104] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0105] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0106] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0107] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0108] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0109] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0110] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0111] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0112] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0113] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0114] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0115] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0116] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0117] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0124] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0126] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0127] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0128] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0131] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0132] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0133] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0134] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0135] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0136] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0138] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0139] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0140] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0141] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0143] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0144] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0145] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0147] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0150] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0151] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0152] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0153] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0154] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0155] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0156] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0157] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0158] 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.

[0159] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0160] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0161] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0162] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0163] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0164] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0165] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0166] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0167] [Explanation of symbols]

[0168] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an analysis unit that analyzes caller information of an incoming call; an identification unit that identifies fraudulent calls based on the information analyzed by the analysis unit; a warning unit that provides real-time warnings about fraudulent calls identified by the identification unit; a reporting unit that makes an emergency call in response to the fraudulent call warned by the warning unit. A system characterized by:

2. The analysis unit Caller's phone number or past call history, voice analysis 2. The system of claim 1.

3. The identification unit Learns common patterns and characteristics of fraudulent calls and identifies whether or not they are fraudulent 2. The system of claim 1.

4. The warning unit If a fraudulent call is detected, real-time advice and warnings will be provided via Audio Glasses.

2. The system of claim 1.

5. The reporting unit In case of an emergency scam call, make an emergency call with one tap 2. The system of claim 1.

6. The analysis unit The method estimates the user's emotions and adjusts the analysis method of the caller information based on the estimated user emotions.

2. The system of claim 1.

7. The analysis unit Analyze the reliability of the caller's phone number based on past call history as well as social media activity history 2. The system of claim 1.

8. The analysis unit During voice analysis, the caller's tone, speed, and emotional changes are analyzed in detail.

2. The system of claim 1.

Images