System

Abstract

An object of a system according to an embodiment is to enable an elderly person to appropriately respond to a fraudulent call.SOLUTION: A system includes audio glasses, a generation AI, a caller information analysis unit, a fraud detection unit, a user setting learning unit, a real-time advice unit, and an emergency call unit. The audio glasses acquire the voice of the caller. The generation AI analyzes the voice of the caller acquired by the audio glasses. A caller information analysis part evaluates the identity and reliability of the caller on the basis of the information analyzed by the generation AI. The fraud detector determines the likelihood of fraud based on the information evaluated by the caller information analyzer. The user setting learning unit learns setting information of a user and past report data. The real-time advice unit provides advice in real time based on the information determined by the fraud detection unit. The emergency notification unit performs emergency notification when the fraud detection unit determines that there is a high possibility of fraud.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it is difficult for elderly people to respond appropriately to fraudulent phone calls, and there is room for improvement in security.

[0005] The system according to the embodiment aims to enable elderly people to respond appropriately to fraudulent phone calls. [Means for solving the problem]

[0006] The system according to the embodiment comprises audio glasses, a generation AI, a caller information analysis unit, a fraud detection unit, a user setting learning unit, a real-time advice unit, and an emergency call unit. The audio glasses acquire the caller's voice. The generation AI analyzes the caller's voice acquired by the audio glasses. The caller information analysis unit evaluates the caller's identity and trustworthiness based on the information analyzed by the generation AI. The fraud detection unit determines the possibility of fraud based on the information evaluated by the caller information analysis unit. The user setting learning unit learns the user's setting information and past report data. The real-time advice unit provides advice in real time based on the information determined by the fraud detection unit. The emergency call unit makes an emergency call if the fraud detection unit determines that there is a high possibility of fraud. [Effects of the Invention]

[0007] The system according to the embodiment can enable elderly people to respond appropriately to fraudulent phone calls. [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) The fraud prevention system according to an embodiment of the present invention is a fraud prevention application targeted at seniors. The system utilizes AudioGlasses to analyze caller information and detect fraud, learn user preferences and reports, provide real-time advice, and include emergency call functionality. This allows the fraud prevention system to provide a powerful tool for seniors to protect themselves from fraudulent calls.

[0029] The fraudulent call prevention system according to the embodiment includes audio glasses, a generation AI, a caller information analysis unit, a fraud detection unit, a user setting learning unit, a real-time advice unit, and an emergency call unit. The audio glasses acquire the caller's voice. For example, the audio glasses capture the caller's voice in real time and save it as digital data. The audio glasses can also connect to a smartphone via Bluetooth and transmit the voice data. The audio glasses also have a noise-canceling function, allowing for clear voice data to be acquired. The generation AI analyzes the caller's voice acquired by the audio glasses. For example, the generation AI converts the voice data into text using voice recognition technology. The generation AI can also analyze the caller's speech using natural language processing technology. The generation AI can also estimate the caller's emotional state from the voice data. The caller information analysis unit evaluates the caller's identity and trustworthiness based on the information analyzed by the generation AI. For example, the caller information analysis unit analyzes the caller's voiceprint using voiceprint analysis technology and compares it with past call history. The caller information analysis unit can also analyze the caller's tone and speed of voice to detect emotions such as nervousness or impatience. The caller information analysis unit can also use an emotion estimation function to analyze the caller's emotional state and evaluate the possibility of fraud. The fraud detection unit determines the possibility of fraud based on the information evaluated by the caller information analysis unit. For example, the fraud detection unit can analyze the caller's speech using fraud pattern recognition technology to determine whether there is a high possibility of fraud. The fraud detection unit can also evaluate the caller's credibility using risk assessment criteria. The fraud detection unit can also refer to a database of past fraud cases and compare them with the caller's speech. The user setting learning unit learns the user's setting information and past report data. For example, if a user sets a specific caller as trustworthy, the user setting learning unit learns that information and determines that the caller can be trusted for future calls. The user setting learning unit can also analyze the user's report data to learn fraud patterns and improve detection accuracy.The user setting learning unit can also analyze user setting information and customize the fraud detection algorithm to suit each individual user. The real-time advice unit provides real-time advice based on the information determined by the fraud detection unit. For example, the real-time advice unit can analyze the content of utterances made during a call and provide real-time advice based on specific keywords or phrases. The real-time advice unit can also analyze voice data during a call and provide advice based on the caller's tone and speed of voice. The real-time advice unit can also use an emotion estimation function to analyze the caller's emotional state during a call and provide appropriate advice to the user. The emergency call unit makes an emergency call when the fraud detection unit determines that there is a high possibility of fraud. For example, the emergency call unit analyzes the caller's voice data during an emergency call and saves it as evidence of fraud. The emergency call unit can also automatically obtain the user's location information during an emergency call and notify the police or family. The emergency call unit can also use the emotion estimation function to analyze the user's emotional state during an emergency call and provide an appropriate response. As a result, the fraud call prevention system according to the embodiment can provide a powerful tool for elderly people to protect themselves from fraudulent calls. For example, audio glasses can analyze caller information in real time and provide immediate advice in the event of a potential fraud, while automatically notifying the elderly in an emergency to ensure their safety.

[0030] The caller information analysis unit can analyze the caller's voiceprint and compare it with the call history to evaluate the caller's consistency. The caller information analysis unit evaluates the caller's consistency, for example, by analyzing the caller's voiceprint and comparing it with past call history. For example, if the voiceprint matches that of a caller previously determined to be trustworthy, the caller is determined to be trustworthy. The caller information analysis unit also uses voiceprint analysis to extract characteristics of the caller's voice and compares it with past call history. For example, it analyzes fluctuations in voice tone and pitch to evaluate whether there is consistency. The caller information analysis unit also stores the caller's voiceprint data in a database and compares it with that data in subsequent calls. For example, if the voiceprint matches that of a caller previously determined to be suspected of fraud, a warning is displayed. In this way, the caller's consistency can be evaluated, thereby increasing reliability.

[0031] The caller information analysis unit can also apply caller information analysis to video calls, evaluating the reliability of the caller from both video and audio. For example, the caller information analysis unit simultaneously analyzes the video and audio of the caller during a video call to evaluate reliability. For example, it analyzes facial expressions and tone of voice to determine whether there is consistency. The caller information analysis unit also uses video analysis technology to analyze the caller's facial expressions and movements and compares them with audio data. For example, if the facial expression appears tense, it determines that the caller is unreliable. The caller information analysis unit also analyzes the video data of the video call to confirm the caller's identity. For example, it analyzes video of the caller presenting their identification card and evaluates whether it matches the audio data. This makes it possible to evaluate the reliability of the caller even during video calls.

[0032] The caller information analysis unit can extend caller information analysis to emails and messaging apps to perform text-based fraud detection. The caller information analysis unit, for example, analyzes text data from emails and messaging apps to evaluate the credibility of the caller. For example, it detects specific keywords and phrases and determines the possibility of fraud. The caller information analysis unit also uses text analysis technology to analyze the content of emails and messages to verify the identity of the sender. For example, it compares the content with past interactions and evaluates whether it is consistent. The caller information analysis unit also analyzes text data from emails and messaging apps to display a warning if there is a high possibility of fraud. For example, if the text contains unnatural requests or content that emphasizes urgency, it determines that there is a high possibility of fraud. This makes it possible to detect fraud even in emails and messaging apps.

[0033] The user setting learning unit can analyze the user's setting information and customize the fraud detection algorithm to suit each individual user. The user setting learning unit, for example, analyzes the user's setting information and customizes the fraud detection algorithm to suit each individual user. For example, if a specific caller is set to be trustworthy, the algorithm is adjusted based on that information. The user setting learning unit also stores the user's setting information in a database and customizes the fraud detection algorithm based on the analysis results. For example, if a user sets a watchlist for specific keywords, the analysis focuses on those keywords. The user setting learning unit also analyzes the user's setting information and builds a system that customizes the fraud detection algorithm to suit each individual user. For example, the parameters of the algorithm are adjusted according to the user's settings. This allows the fraud detection algorithm to be customized based on the user's setting information.

[0034] The user setting learning unit can analyze user reported data and learn fraud patterns to improve detection accuracy. The user setting learning unit, for example, analyzes user reported data and learns fraud patterns. For example, it identifies fraud patterns based on data on fraud cases reported in the past. The user setting learning unit also stores the reported data in a database and improves the fraud detection algorithm based on the analysis results. For example, it learns patterns of specific phrases and expressions to improve detection accuracy. The user setting learning unit also analyzes user reported data and builds a system that learns fraud patterns. For example, it automatically updates the algorithm based on the reported data to improve detection accuracy. This makes it possible to learn fraud patterns based on user reported data and improve detection accuracy.

[0035] The real-time advice unit can analyze the content of utterances made during a call and provide advice in real time based on specific keywords and phrases. The real-time advice unit, for example, analyzes the content of utterances made during a call and provides advice in real time based on specific keywords and phrases. For example, if a phrase such as "immediate action is required" is included, advice warning of caution is provided. The real-time advice unit also builds a system that analyzes the content of utterances in real time and provides advice based on specific keywords and phrases. For example, if phrases such as "free" or "benefit" are included, a warning is issued that there is a high possibility of fraud. The real-time advice unit also analyzes the content of utterances made during a call and develops an algorithm that provides advice in real time based on specific keywords and phrases. For example, if phrases such as "confirm" or "important" are included, advice warning of caution is provided. This makes it possible to provide advice in real time based on the content of utterances made during a call.

[0036] The real-time advice unit can analyze voice data during a call and provide advice based on the tone and speed of the caller's voice. The real-time advice unit, for example, analyzes voice data during a call and provides advice based on the tone and speed of the caller's voice. For example, if the caller sounds nervous, advice to call attention is provided. The real-time advice unit also uses voice analysis technology to build a system that analyzes voice data during a call in real time and provides advice based on the tone and speed of the caller's voice. For example, if the caller sounds impatient, a warning is displayed. The real-time advice unit also analyzes voice data during a call and develops an algorithm that provides advice in real time based on the tone and speed of the caller's voice. For example, if the caller's voice is biased toward a high-pitched range, advice to call attention is provided. This makes it possible to provide advice in real time based on voice data during a call.

[0037] The emergency call unit can analyze the caller's voice data when an emergency call is made and store it as evidence of fraud. For example, the emergency call unit builds a system that analyzes the caller's voice data when an emergency call is made and stores it as evidence of fraud. For example, the content of the call is recorded and stored as evidence if fraud is suspected. The emergency call unit also analyzes the caller's voice data in real time and stores it as evidence of fraud. For example, if specific keywords or phrases are included, the voice data is automatically stored. The emergency call unit also develops an algorithm that analyzes the caller's voice data when an emergency call is made and stores it as evidence of fraud. For example, it automatically detects statements that are suspected of fraud and stores the voice data. This makes it possible to store evidence of fraud when an emergency call is made.

[0038] The emergency call unit can automatically acquire the user's location information when an emergency call is made and notify the police and family. The emergency call unit, for example, builds a system that automatically acquires the user's location information when an emergency call is made and notifies the police and family. For example, it uses GPS data to identify the user's location and makes an emergency call. The emergency call unit also acquires the user's location information in real time and notifies the police and family when an emergency call is made. For example, it sends the location information by SMS or email. The emergency call unit also develops an algorithm that automatically acquires the user's location information when an emergency call is made and notifies the police and family. For example, it stores the location information in the cloud and makes it accessible in an emergency. This allows the user's location information to be notified to the police and family when an emergency call is made.

[0039] The emergency call unit can link the emergency call function with a medical alert system to provide emergency responses based on the user's health status. The emergency call unit, for example, links the emergency call function with the medical alert system to build a system that provides emergency responses based on the user's health status. For example, it analyzes heart rate and blood pressure data and notifies a medical institution in the event of an emergency. The emergency call unit also links with the medical alert system to provide emergency responses based on the user's health status when an emergency call is made. For example, it provides appropriate medical responses based on the user's health data. The emergency call unit also links the emergency call function with the medical alert system to develop an algorithm that provides emergency responses based on the user's health status. For example, it automatically sends the user's health data to a medical institution when an emergency call is made. This makes it possible to provide emergency responses based on the user's health status when an emergency call is made.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The fraud prevention system can also analyze a user's call history and learn past call patterns. For example, if a user frequently speaks with a particular caller, it can determine that the caller is trustworthy. Also, if a user has previously reported a suspected fraudulent call, the system can learn that call pattern and display a warning if a similar pattern is detected in subsequent calls. Furthermore, if the system analyzes a user's call history and finds that calls with a high probability of fraud tend to occur during certain times of the day or on certain days of the week, it can provide advice to be vigilant during those times or on those days. This allows the possibility of fraud to be evaluated based on the user's call history.

[0042] The fraud prevention system can also analyze the content of a user's call and evaluate the likelihood of fraud based on specific keywords and phrases. For example, if a phrase such as "immediate action required" is included, it can determine that there is a high likelihood of fraud. It can also determine that there is a high likelihood of fraud if phrases such as "free" or "special offer" are included. Furthermore, if phrases such as "confirm" or "important" are included, it can provide advice to warn users. This allows the likelihood of fraud to be evaluated based on the content of the call.

[0043] The fraud prevention system can also analyze the voice data during a user's call and evaluate the possibility of fraud based on the caller's tone and speed of voice. For example, if the caller sounds nervous, it can determine that there is a high possibility of fraud. It can also determine that there is a high possibility of fraud if the caller sounds rushed. Furthermore, if the caller's voice is biased toward higher frequencies, it can also determine that there is a high possibility of fraud. This makes it possible to evaluate the possibility of fraud based on the voice data during a call.

[0044] The fraud prevention system can also analyze the voice data during a user's call and evaluate the possibility of fraud based on the caller's tone and speed of voice. For example, if the caller sounds nervous, it can determine that there is a high possibility of fraud. It can also determine that there is a high possibility of fraud if the caller sounds rushed. Furthermore, if the caller's voice is biased toward higher frequencies, it can also determine that there is a high possibility of fraud. This makes it possible to evaluate the possibility of fraud based on the voice data during a call.

[0045] The fraud prevention system can also analyze the voice data during a user's call and evaluate the possibility of fraud based on the caller's tone and speed of voice. For example, if the caller sounds nervous, it can determine that there is a high possibility of fraud. It can also determine that there is a high possibility of fraud if the caller sounds rushed. Furthermore, if the caller's voice is biased toward higher frequencies, it can also determine that there is a high possibility of fraud. This makes it possible to evaluate the possibility of fraud based on the voice data during a call.

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: The audio glasses capture the caller's voice. For example, audio glasses can capture the caller's voice in real time and save it as digital data. Audio glasses can also connect to a smartphone via Bluetooth to transmit voice data. Furthermore, audio glasses have a noise-canceling function, allowing for clear voice data to be captured. Step 2: The generation AI analyzes the caller's voice captured by the audio glasses. For example, the generation AI converts the voice data into text using voice recognition technology. The generation AI can also analyze the caller's speech using natural language processing technology. Furthermore, the generation AI can estimate the caller's emotional state from the voice data. Step 3: The caller information analysis unit evaluates the caller's identity and trustworthiness based on the information analyzed by the generation AI. For example, the caller information analysis unit uses voiceprint analysis technology to analyze the caller's voiceprint and compare it with past call history. The caller information analysis unit can also analyze the caller's tone and speed of voice to detect emotions such as nervousness or impatience. Furthermore, the caller information analysis unit uses emotion estimation functionality to analyze the caller's emotional state and evaluate the possibility of fraud. Step 4: The fraud detection unit determines the possibility of fraud based on the information evaluated by the caller information analysis unit. For example, the fraud detection unit may use fraud pattern recognition technology to analyze the caller's statements and determine whether there is a high possibility of fraud. The fraud detection unit may also evaluate the caller's credibility using risk assessment criteria. Furthermore, the fraud detection unit may refer to a database of past fraud cases and compare the caller's statements with those. Step 5: The user setting learning unit learns the user's setting information and past report data. For example, if a user sets a particular caller as trustworthy, the user setting learning unit learns that information and determines that the caller can be trusted for future calls. The user setting learning unit can also analyze the user's report data and learn fraud patterns to improve detection accuracy. Furthermore, the user setting learning unit can analyze the user's setting information and customize the fraud detection algorithm to suit each individual user. Step 6: The real-time advice unit provides advice in real time based on the information determined by the fraud detection unit. For example, the real-time advice unit analyzes the content of statements made during a call and provides advice in real time based on specific keywords or phrases. The real-time advice unit can also analyze audio data during a call and provide advice based on the caller's tone and speed of voice. Furthermore, the real-time advice unit can use an emotion estimation function to analyze the caller's emotional state during a call and provide appropriate advice to the user. Step 7: The emergency call unit makes an emergency call if the fraud detection unit determines that there is a high possibility of fraud. For example, the emergency call unit analyzes the caller's voice data when making an emergency call and saves it as evidence of fraud. The emergency call unit can also automatically obtain the user's location information when making an emergency call and notify the police or family. Furthermore, the emergency call unit can use an emotion estimation function to analyze the user's emotional state when making an emergency call and provide an appropriate response.

[0048] (Example 2) The fraud prevention system according to an embodiment of the present invention is a fraud prevention application targeted at seniors. The system utilizes AudioGlasses to analyze caller information and detect fraud, learn user preferences and reports, provide real-time advice, and include emergency call functionality. This allows the fraud prevention system to provide a powerful tool for seniors to protect themselves from fraudulent calls.

[0049] The fraudulent call prevention system according to the embodiment includes audio glasses, a generation AI, a caller information analysis unit, a fraud detection unit, a user setting learning unit, a real-time advice unit, and an emergency call unit. The audio glasses acquire the caller's voice. For example, the audio glasses capture the caller's voice in real time and save it as digital data. The audio glasses can also connect to a smartphone via Bluetooth and transmit the voice data. The audio glasses also have a noise-canceling function, allowing for clear voice data to be acquired. The generation AI analyzes the caller's voice acquired by the audio glasses. For example, the generation AI converts the voice data into text using voice recognition technology. The generation AI can also analyze the caller's speech using natural language processing technology. The generation AI can also estimate the caller's emotional state from the voice data. The caller information analysis unit evaluates the caller's identity and trustworthiness based on the information analyzed by the generation AI. For example, the caller information analysis unit analyzes the caller's voiceprint using voiceprint analysis technology and compares it with past call history. The caller information analysis unit can also analyze the caller's tone and speed of voice to detect emotions such as nervousness or impatience. The caller information analysis unit can also use an emotion estimation function to analyze the caller's emotional state and evaluate the possibility of fraud. The fraud detection unit determines the possibility of fraud based on the information evaluated by the caller information analysis unit. For example, the fraud detection unit can analyze the caller's speech using fraud pattern recognition technology to determine whether there is a high possibility of fraud. The fraud detection unit can also evaluate the caller's credibility using risk assessment criteria. The fraud detection unit can also refer to a database of past fraud cases and compare them with the caller's speech. The user setting learning unit learns the user's setting information and past report data. For example, if a user sets a specific caller as trustworthy, the user setting learning unit learns that information and determines that the caller can be trusted for future calls. The user setting learning unit can also analyze the user's report data to learn fraud patterns and improve detection accuracy.The user setting learning unit can also analyze user setting information and customize the fraud detection algorithm to suit each individual user. The real-time advice unit provides real-time advice based on the information determined by the fraud detection unit. For example, the real-time advice unit can analyze the content of utterances made during a call and provide real-time advice based on specific keywords or phrases. The real-time advice unit can also analyze voice data during a call and provide advice based on the caller's tone and speed of voice. The real-time advice unit can also use an emotion estimation function to analyze the caller's emotional state during a call and provide appropriate advice to the user. The emergency call unit makes an emergency call when the fraud detection unit determines that there is a high possibility of fraud. For example, the emergency call unit analyzes the caller's voice data during an emergency call and saves it as evidence of fraud. The emergency call unit can also automatically obtain the user's location information during an emergency call and notify the police or family. The emergency call unit can also use the emotion estimation function to analyze the user's emotional state during an emergency call and provide an appropriate response. As a result, the fraud call prevention system according to the embodiment can provide a powerful tool for elderly people to protect themselves from fraudulent calls. For example, audio glasses can analyze caller information in real time and provide immediate advice in the event of a potential fraud, while automatically notifying the elderly in an emergency to ensure their safety.

[0050] The caller information analysis unit can analyze the caller's voiceprint and compare it with the call history to evaluate the caller's consistency. The caller information analysis unit evaluates the caller's consistency, for example, by analyzing the caller's voiceprint and comparing it with past call history. For example, if the voiceprint matches that of a caller previously determined to be trustworthy, the caller is determined to be trustworthy. The caller information analysis unit also uses voiceprint analysis to extract characteristics of the caller's voice and compares it with past call history. For example, it analyzes fluctuations in voice tone and pitch to evaluate whether there is consistency. The caller information analysis unit also stores the caller's voiceprint data in a database and compares it with that data in subsequent calls. For example, if the voiceprint matches that of a caller previously determined to be suspected of fraud, a warning is displayed. In this way, the caller's consistency can be evaluated, thereby increasing reliability.

[0051] The caller information analysis unit can analyze the caller's tone and speed of voice to detect emotions such as nervousness or impatience and evaluate trustworthiness. The caller information analysis unit, for example, analyzes the caller's tone and speed of voice to detect emotions such as nervousness or impatience. For example, if there is a trembling voice or a sudden change in speed, it is determined that the caller is nervous. The caller information analysis unit also evaluates the caller's emotional state using voice tone analysis. For example, if the caller's voice tends to get higher, it is determined that the caller may be impatient. The caller information analysis unit also analyzes the caller's voice speed and compares it with the caller's normal speaking speed. For example, if the caller speaks at a faster speed than normal, it is determined that the caller is impatient. In this way, the caller's emotions can be analyzed to evaluate trustworthiness.

[0052] The caller information analysis unit can use the emotion estimation function to analyze the caller's emotional state and evaluate the possibility of fraud. For example, the caller information analysis unit uses the emotion estimation function to analyze the caller's tone and speed of voice and evaluate the emotional state. For example, if the caller expresses strong feelings of anger or impatience, it determines that there is a high possibility of fraud. The caller information analysis unit also analyzes the characteristics of the caller's voice and evaluates the emotional state using an emotion estimation algorithm. For example, if there is a trembling voice or an increase in the high-pitched range, it determines that the caller is nervous. The caller information analysis unit also uses the emotion estimation function to analyze the caller's emotional state in real time and evaluate the possibility of fraud. For example, if there are drastic emotional fluctuations, it determines that there is a high possibility of fraud. In this way, the possibility of fraud can be evaluated by analyzing the caller's emotional state.

[0053] The caller information analysis unit can also apply caller information analysis to video calls, evaluating the reliability of the caller from both video and audio. For example, the caller information analysis unit simultaneously analyzes the video and audio of the caller during a video call to evaluate reliability. For example, it analyzes facial expressions and tone of voice to determine whether there is consistency. The caller information analysis unit also uses video analysis technology to analyze the caller's facial expressions and movements and compares them with audio data. For example, if the facial expression appears tense, it determines that the caller is unreliable. The caller information analysis unit also analyzes the video data of the video call to confirm the caller's identity. For example, it analyzes video of the caller presenting their identification card and evaluates whether it matches the audio data. This makes it possible to evaluate the reliability of the caller even during video calls.

[0054] The caller information analysis unit can extend caller information analysis to emails and messaging apps to perform text-based fraud detection. The caller information analysis unit, for example, analyzes text data from emails and messaging apps to evaluate the credibility of the caller. For example, it detects specific keywords and phrases and determines the possibility of fraud. The caller information analysis unit also uses text analysis technology to analyze the content of emails and messages to verify the identity of the sender. For example, it compares the content with past interactions and evaluates whether it is consistent. The caller information analysis unit also analyzes text data from emails and messaging apps to display a warning if there is a high possibility of fraud. For example, if the text contains unnatural requests or content that emphasizes urgency, it determines that there is a high possibility of fraud. This makes it possible to detect fraud even in emails and messaging apps.

[0055] The caller information analysis unit can use the emotion estimation function to analyze the caller's emotional state in real time and provide visual feedback to the user. The caller information analysis unit can, for example, use the emotion estimation function to analyze the caller's emotional state in real time and provide visual feedback to the user. For example, if the caller is nervous, a warning icon is displayed on the screen. The caller information analysis unit can also analyze the caller's tone and speed of voice and evaluate the emotional state using an emotion estimation algorithm. For example, if the caller is impatient, a warning message is displayed on the screen. The caller information analysis unit can also use the emotion estimation function to analyze the caller's emotional state in real time and provide visual feedback to the user. For example, if the caller is angry, a warning message is displayed on the screen. This allows the caller's emotional state to be analyzed in real time and visual feedback to be provided to the user.

[0056] The user setting learning unit can analyze the user's setting information and customize the fraud detection algorithm to suit each individual user. The user setting learning unit, for example, analyzes the user's setting information and customizes the fraud detection algorithm to suit each individual user. For example, if a specific caller is set to be trustworthy, the algorithm is adjusted based on that information. The user setting learning unit also stores the user's setting information in a database and customizes the fraud detection algorithm based on the analysis results. For example, if a user sets a watchlist for specific keywords, the analysis focuses on those keywords. The user setting learning unit also analyzes the user's setting information and builds a system that customizes the fraud detection algorithm to suit each individual user. For example, the parameters of the algorithm are adjusted according to the user's settings. This allows the fraud detection algorithm to be customized based on the user's setting information.

[0057] The user setting learning unit can analyze user reported data and learn fraud patterns to improve detection accuracy. The user setting learning unit, for example, analyzes user reported data and learns fraud patterns. For example, it identifies fraud patterns based on data on fraud cases reported in the past. The user setting learning unit also stores the reported data in a database and improves the fraud detection algorithm based on the analysis results. For example, it learns patterns of specific phrases and expressions to improve detection accuracy. The user setting learning unit also analyzes user reported data and builds a system that learns fraud patterns. For example, it automatically updates the algorithm based on the reported data to improve detection accuracy. This makes it possible to learn fraud patterns based on user reported data and improve detection accuracy.

[0058] The user setting learning unit can analyze the emotional state of a user using the emotion estimation function and provide optimal advice to each individual user. The user setting learning unit, for example, uses the emotion estimation function to analyze the emotional state of a user and provide optimal advice to each individual user. For example, if a user is feeling anxious, it provides reassuring advice. The user setting learning unit also analyzes the emotional state of a user in real time and provides optimal advice using an emotion estimation algorithm. For example, if a user is nervous, it provides relaxing advice. The user setting learning unit also uses the emotion estimation function to analyze the emotional state of a user and build a system that provides optimal advice to each individual user. For example, it adjusts the content of the advice according to the user's emotional state. This makes it possible to provide optimal advice based on the user's emotional state.

[0059] The real-time advice unit can analyze the content of utterances made during a call and provide advice in real time based on specific keywords and phrases. The real-time advice unit, for example, analyzes the content of utterances made during a call and provides advice in real time based on specific keywords and phrases. For example, if a phrase such as "immediate action is required" is included, advice warning of caution is provided. The real-time advice unit also builds a system that analyzes the content of utterances in real time and provides advice based on specific keywords and phrases. For example, if phrases such as "free" or "benefit" are included, a warning is issued that there is a high possibility of fraud. The real-time advice unit also analyzes the content of utterances made during a call and develops an algorithm that provides advice in real time based on specific keywords and phrases. For example, if phrases such as "confirm" or "important" are included, advice warning of caution is provided. This makes it possible to provide advice in real time based on the content of utterances made during a call.

[0060] The real-time advice unit can analyze voice data during a call and provide advice based on the tone and speed of the caller's voice. The real-time advice unit, for example, analyzes voice data during a call and provides advice based on the tone and speed of the caller's voice. For example, if the caller sounds nervous, advice to call attention is provided. The real-time advice unit also uses voice analysis technology to build a system that analyzes voice data during a call in real time and provides advice based on the tone and speed of the caller's voice. For example, if the caller sounds impatient, a warning is displayed. The real-time advice unit also analyzes voice data during a call and develops an algorithm that provides advice in real time based on the tone and speed of the caller's voice. For example, if the caller's voice is biased toward a high-pitched range, advice to call attention is provided. This makes it possible to provide advice in real time based on voice data during a call.

[0061] The real-time advice unit can use the emotion estimation function to analyze the emotional state of a caller during a call and provide appropriate advice to the user. The real-time advice unit, for example, uses the emotion estimation function to analyze the emotional state of a caller during a call in real time and provide appropriate advice to the user. For example, if the caller is nervous, advice to call attention is provided. The real-time advice unit also analyzes the emotional state of the caller and uses an emotion estimation algorithm to provide appropriate advice to the user. For example, if the caller is impatient, a warning is displayed. The real-time advice unit also uses the emotion estimation function to analyze the emotional state of a caller during a call in real time and build a system to provide appropriate advice to the user. For example, if the caller is angry, advice to call attention is provided. This makes it possible to provide appropriate advice based on the emotional state of the caller during a call.

[0062] The emergency call unit can analyze the caller's voice data when an emergency call is made and store it as evidence of fraud. For example, the emergency call unit builds a system that analyzes the caller's voice data when an emergency call is made and stores it as evidence of fraud. For example, the content of the call is recorded and stored as evidence if fraud is suspected. The emergency call unit also analyzes the caller's voice data in real time and stores it as evidence of fraud. For example, if specific keywords or phrases are included, the voice data is automatically stored. The emergency call unit also develops an algorithm that analyzes the caller's voice data when an emergency call is made and stores it as evidence of fraud. For example, it automatically detects statements that are suspected of fraud and stores the voice data. This makes it possible to store evidence of fraud when an emergency call is made.

[0063] The emergency call unit can automatically acquire the user's location information when an emergency call is made and notify the police and family. The emergency call unit, for example, builds a system that automatically acquires the user's location information when an emergency call is made and notifies the police and family. For example, it uses GPS data to identify the user's location and makes an emergency call. The emergency call unit also acquires the user's location information in real time and notifies the police and family when an emergency call is made. For example, it sends the location information by SMS or email. The emergency call unit also develops an algorithm that automatically acquires the user's location information when an emergency call is made and notifies the police and family. For example, it stores the location information in the cloud and makes it accessible in an emergency. This allows the user's location information to be notified to the police and family when an emergency call is made.

[0064] The emergency call unit can use the emotion estimation function to analyze the emotional state of the user when making an emergency call and provide an appropriate response. The emergency call unit, for example, uses the emotion estimation function to analyze the emotional state of the user in real time when making an emergency call and provide an appropriate response. For example, if the user is feeling anxious, a response that reassures the user is provided. The emergency call unit also analyzes the emotional state of the user and uses an emotion estimation algorithm to provide an appropriate response when making an emergency call. For example, if the user is feeling nervous, a response that relaxes the user is provided. The emergency call unit also uses the emotion estimation function to build a system that analyzes the emotional state of the user when making an emergency call and provides an appropriate response. For example, the content of the response is adjusted depending on the emotional state of the user. This makes it possible to provide an appropriate response based on the emotional state of the user when making an emergency call.

[0065] The emergency call unit can link the emergency call function with a medical alert system to provide emergency responses based on the user's health status. The emergency call unit, for example, links the emergency call function with the medical alert system to build a system that provides emergency responses based on the user's health status. For example, it analyzes heart rate and blood pressure data and notifies a medical institution in the event of an emergency. The emergency call unit also links with the medical alert system to provide emergency responses based on the user's health status when an emergency call is made. For example, it provides appropriate medical responses based on the user's health data. The emergency call unit also links the emergency call function with the medical alert system to develop an algorithm that provides emergency responses based on the user's health status. For example, it automatically sends the user's health data to a medical institution when an emergency call is made. This makes it possible to provide emergency responses based on the user's health status when an emergency call is made.

[0066] The emergency call unit can use the emotion estimation function to analyze the user's emotional state when making an emergency call and provide visual feedback to family members and caregivers. The emergency call unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time when making an emergency call and provide visual feedback to family members and caregivers. For example, if the user is feeling anxious, a warning icon is displayed to the family members and caregivers. The emergency call unit also analyzes the user's emotional state and uses an emotion estimation algorithm to provide visual feedback to the family members and caregivers. For example, if the user is feeling nervous, a warning message is displayed to the family members and caregivers. The emergency call unit also uses the emotion estimation function to analyze the user's emotional state in real time when making an emergency call and builds a system that provides visual feedback to the family members and caregivers. For example, the content of the feedback can be adjusted depending on the user's emotional state. This makes it possible to provide visual feedback of the user's emotional state to the family members and caregivers when making an emergency call.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] The fraud prevention system can also analyze the tone and speed of the user's voice to estimate the user's emotional state. For example, if the user is nervous during a call, the system can detect that nervousness and provide advice to the user to relax. If the user is impatient, the system can detect that impatience and provide advice to calm down. Furthermore, if the user is angry, the system can detect that anger and provide advice to calm down. This makes it possible to provide appropriate advice based on the user's emotional state.

[0069] The fraud prevention system can also analyze a user's call history and learn past call patterns. For example, if a user frequently speaks with a particular caller, it can determine that the caller is trustworthy. Also, if a user has previously reported a suspected fraudulent call, the system can learn that call pattern and display a warning if a similar pattern is detected in subsequent calls. Furthermore, if the system analyzes a user's call history and finds that calls with a high probability of fraud tend to occur during certain times of the day or on certain days of the week, it can provide advice to be vigilant during those times or on those days. This allows the possibility of fraud to be evaluated based on the user's call history.

[0070] The fraud prevention system can also analyze the content of a user's call and evaluate the likelihood of fraud based on specific keywords and phrases. For example, if a phrase such as "immediate action required" is included, it can determine that there is a high likelihood of fraud. It can also determine that there is a high likelihood of fraud if phrases such as "free" or "special offer" are included. Furthermore, if phrases such as "confirm" or "important" are included, it can provide advice to warn users. This allows the likelihood of fraud to be evaluated based on the content of the call.

[0071] The fraud prevention system can further analyze the user's voice data during a call to estimate the user's emotional state. For example, if the user is nervous during a call, the system can detect that nervousness and provide advice to the user to relax. If the user is impatient, the system can detect that impatience and provide advice to encourage the user to stay calm. Furthermore, if the user is angry, the system can detect that anger and provide advice to calm down. This makes it possible to provide appropriate advice based on the user's emotional state.

[0072] The fraud prevention system can also analyze the voice data during a user's call and evaluate the possibility of fraud based on the caller's tone and speed of voice. For example, if the caller sounds nervous, it can determine that there is a high possibility of fraud. It can also determine that there is a high possibility of fraud if the caller sounds rushed. Furthermore, if the caller's voice is biased toward higher frequencies, it can also determine that there is a high possibility of fraud. This makes it possible to evaluate the possibility of fraud based on the voice data during a call.

[0073] The fraud prevention system can further analyze the user's voice data during a call to estimate the user's emotional state. For example, if the user is nervous during a call, the system can detect that nervousness and provide advice to the user to relax. If the user is impatient, the system can detect that impatience and provide advice to encourage the user to stay calm. Furthermore, if the user is angry, the system can detect that anger and provide advice to calm down. This makes it possible to provide appropriate advice based on the user's emotional state.

[0074] The fraud prevention system can also analyze the voice data during a user's call and evaluate the possibility of fraud based on the caller's tone and speed of voice. For example, if the caller sounds nervous, it can determine that there is a high possibility of fraud. It can also determine that there is a high possibility of fraud if the caller sounds rushed. Furthermore, if the caller's voice is biased toward higher frequencies, it can also determine that there is a high possibility of fraud. This makes it possible to evaluate the possibility of fraud based on the voice data during a call.

[0075] The fraud prevention system can further analyze the user's voice data during a call to estimate the user's emotional state. For example, if the user is nervous during a call, the system can detect that nervousness and provide advice to the user to relax. If the user is impatient, the system can detect that impatience and provide advice to encourage the user to stay calm. Furthermore, if the user is angry, the system can detect that anger and provide advice to calm down. This makes it possible to provide appropriate advice based on the user's emotional state.

[0076] The fraud prevention system can also analyze the voice data during a user's call and evaluate the possibility of fraud based on the caller's tone and speed of voice. For example, if the caller sounds nervous, it can determine that there is a high possibility of fraud. It can also determine that there is a high possibility of fraud if the caller sounds rushed. Furthermore, if the caller's voice is biased toward higher frequencies, it can also determine that there is a high possibility of fraud. This makes it possible to evaluate the possibility of fraud based on the voice data during a call.

[0077] The fraud prevention system can further analyze the user's voice data during a call to estimate the user's emotional state. For example, if the user is nervous during a call, the system can detect that nervousness and provide advice to the user to relax. If the user is impatient, the system can detect that impatience and provide advice to encourage the user to stay calm. Furthermore, if the user is angry, the system can detect that anger and provide advice to calm down. This makes it possible to provide appropriate advice based on the user's emotional state.

[0078] The processing flow of the second embodiment will be briefly explained below.

[0079] Step 1: The audio glasses capture the caller's voice. For example, audio glasses can capture the caller's voice in real time and save it as digital data. Audio glasses can also connect to a smartphone via Bluetooth to transmit voice data. Furthermore, audio glasses have a noise-canceling function, allowing for clear voice data to be captured. Step 2: The generation AI analyzes the caller's voice captured by the audio glasses. For example, the generation AI converts the voice data into text using voice recognition technology. The generation AI can also analyze the caller's speech using natural language processing technology. Furthermore, the generation AI can estimate the caller's emotional state from the voice data. Step 3: The caller information analysis unit evaluates the caller's identity and trustworthiness based on the information analyzed by the generation AI. For example, the caller information analysis unit uses voiceprint analysis technology to analyze the caller's voiceprint and compare it with past call history. The caller information analysis unit can also analyze the caller's tone and speed of voice to detect emotions such as nervousness or impatience. Furthermore, the caller information analysis unit uses emotion estimation functionality to analyze the caller's emotional state and evaluate the possibility of fraud. Step 4: The fraud detection unit determines the possibility of fraud based on the information evaluated by the caller information analysis unit. For example, the fraud detection unit may use fraud pattern recognition technology to analyze the caller's statements and determine whether there is a high possibility of fraud. The fraud detection unit may also evaluate the caller's credibility using risk assessment criteria. Furthermore, the fraud detection unit may refer to a database of past fraud cases and compare the caller's statements with those. Step 5: The user setting learning unit learns the user's setting information and past report data. For example, if a user sets a particular caller as trustworthy, the user setting learning unit learns that information and determines that the caller can be trusted for future calls. The user setting learning unit can also analyze the user's report data and learn fraud patterns to improve detection accuracy. Furthermore, the user setting learning unit can analyze the user's setting information and customize the fraud detection algorithm to suit each individual user. Step 6: The real-time advice unit provides advice in real time based on the information determined by the fraud detection unit. For example, the real-time advice unit analyzes the content of statements made during a call and provides advice in real time based on specific keywords or phrases. The real-time advice unit can also analyze audio data during a call and provide advice based on the caller's tone and speed of voice. Furthermore, the real-time advice unit can use an emotion estimation function to analyze the caller's emotional state during a call and provide appropriate advice to the user. Step 7: The emergency call unit makes an emergency call if the fraud detection unit determines that there is a high possibility of fraud. For example, the emergency call unit analyzes the caller's voice data when making an emergency call and saves it as evidence of fraud. The emergency call unit can also automatically obtain the user's location information when making an emergency call and notify the police or family. Furthermore, the emergency call unit can use an emotion estimation function to analyze the user's emotional state when making an emergency call and provide an appropriate response.

[0080] 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.

[0081] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0082] 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.

[0083] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0084] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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).

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0114] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] In the robot 414, 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 robot 414 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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."

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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. [Explanation of symbols]

[0147] 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. Audio glasses and Generative AI and A caller information analysis unit; a fraud detection unit; a user setting learning unit; Real-time advice department, an emergency reporting unit; The audio glasses are Capture the caller's voice, The generated AI is Analyzing the caller's voice acquired by the audio glasses; The caller information analysis unit Evaluate the sender's identity and credibility based on the information analyzed by the generating AI, The fraud detection unit determining the possibility of fraud based on the information evaluated by the caller information analysis unit; The user setting learning unit Learn user settings and past report data, The real-time advice unit providing real-time advice based on the information determined by the fraud detection unit; The emergency notification unit If the fraud detection unit determines that there is a high possibility of fraud, an emergency call is made. A system characterized by:

2. The caller information analysis unit Analyzing the caller's voiceprint and matching it with call history to assess the caller's consistency 2. The system of claim 1.

3. The caller information analysis unit Analyzing the tone and speed of the caller's voice to detect emotions such as nervousness or impatience and evaluate the caller's credibility 2. The system of claim 1.

4. The caller information analysis unit Analyzing the caller's emotional state to assess the likelihood of fraud 2. The system of claim 1.

5. The caller information analysis unit Apply caller information analysis to video calls to evaluate the credibility of the caller based on both the video and audio.

2. The system of claim 1.

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