System

Abstract

An object of a system according to an embodiment is to analyze call content, detect a method of fraud, and notify a user of the fraud.SOLUTION: A system includes a call analysis unit, a fraud detection unit, and an alert transmission unit. The call analysis unit analyzes a call content. The fraud detection unit detects a method of fraud from the call content analyzed by the call analysis unit. The alert transmitter notifies the user of the possibility of fraud detected by the fraud detector.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to quickly and accurately detect fraudulent methods from the content of phone calls.

[0005] The system according to the embodiment aims to analyze the contents of a call, detect fraudulent methods, and notify the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a call analysis unit, a fraud detection unit, and an alert transmission unit. The call analysis unit analyzes the content of the call. The fraud detection unit detects fraudulent methods from the content of the call analyzed by the call analysis unit. The alert transmission unit notifies the user of the possibility of fraud detected by the fraud detection unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the contents of a call, detect fraudulent methods, and notify the user. [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 automatically analyzes the content of phone calls, and the AI ​​generation system detects fraudulent methods and notifies the user of possible fraud. This allows the fraud prevention system to analyze the content of phone calls in real time, detect fraudulent methods, and promptly notify the user.

[0029] The fraud prevention system according to the embodiment includes a call analysis unit, a fraud detection unit, and an alert transmission unit. The call analysis unit analyzes the content of the call. For example, the call analysis unit analyzes the audio data of the call in real time to detect specific phrases or patterns. The call analysis unit can also convert the content of the call into text data using voice recognition technology. For example, the call analysis unit analyzes the audio data and detects specific phrases such as "Please transfer money" or "Please handle this urgently." The fraud detection unit detects fraudulent methods from the call content analyzed by the call analysis unit. For example, the fraud detection unit compares the content with a database of past fraud cases to determine whether similar methods are being used. The fraud detection unit can also analyze the content of the call using a generation AI to evaluate the possibility of fraud. For example, the generation AI compares the content with a database of past fraud cases to detect fraudulent methods with high accuracy. The alert transmission unit notifies the user of the possibility of fraud detected by the fraud detection unit. For example, the alert transmission unit plays a voice message during the call such as, "This call may be fraudulent. Please be careful." The alert sending unit can also send a notification to the user's smartphone or PC to notify the user of a possible fraud. For example, the alert sending unit can send a push notification to the user's smartphone to notify the user of a possible fraud. This allows the fraud prevention system according to the embodiment to quickly detect fraudulent methods and notify the user, thereby preventing fraud before it occurs.

[0030] When analyzing the audio data of the call content, the call analysis unit can determine the possibility of fraud with higher accuracy based on parameters such as the speaker's tone of voice, speed, and pauses. When analyzing the audio data of the call content, the call analysis unit, for example, analyzes the speaker's tone of voice to detect tones that differ from normal conversation. For example, it detects a tone that sounds rushed or an unnaturally calm tone. The call analysis unit also analyzes the speed of the speaker's voice to detect speeds that are abnormally fast or slow compared to normal conversation. For example, if a fraudster is in a hurry, the speaker's voice speed often increases. The call analysis unit also analyzes the speaker's pauses to detect patterns that differ from normal conversation. For example, if a fraudster cannot answer a question immediately, there may be long pauses. This allows the possibility of fraud to be determined with higher accuracy.

[0031] The call analysis unit can also analyze the speaker's background sounds when analyzing the content of the call to assess the possibility of fraud. For example, when analyzing the content of the call, the call analysis unit analyzes the background sounds to detect sounds that are different from those in a normal home environment. For example, when a fraudster is calling from a call center, other call sounds may be heard in the background. The call analysis unit can also detect specific environmental sounds (e.g., traffic sounds or crowd sounds) through the analysis of background sounds to assess the possibility of fraud. For example, when a fraudster is calling from a public place, specific environmental sounds may be included in the background. The call analysis unit can also detect changes in background sounds when analyzing the content of the call to assess the possibility of fraud. For example, if a fraudster changes the location from which they are making a call, the background sounds may change. This allows the possibility of fraud to be assessed by analyzing the background sounds.

[0032] The call analysis unit can apply the analysis of call content not only to landlines or intercoms, but also to other communication methods such as video calls or chat apps. For example, the call analysis unit applies the analysis of call content to video calls, simultaneously analyzing video data and audio data. For example, it analyzes the speaker's facial expressions and gestures to evaluate the possibility of fraud. The call analysis unit also analyzes text data from chat apps to detect specific phrases and contexts. For example, if a phrase such as "Please transfer money" is included, it determines that there is a possibility of fraud. The call analysis unit also applies the analysis of call content to text communications such as emails and SMS to detect specific phrases and contexts. For example, if a phrase such as "Please respond quickly" is included, it determines that there is a possibility of fraud. This allows the system to handle a variety of communication methods.

[0033] The call analysis unit can compare the analysis results of the call content with the user's past call history to detect abnormal patterns. For example, the call analysis unit compares the analysis results of the call content with the user's past call history to detect abnormal patterns. For example, if there are an abnormally large number of requests for money compared to normal calls, it determines that there is a possibility of fraud. The call analysis unit also analyzes the user's past call history to detect specific patterns. For example, if there is a pattern similar to calls made by people who have been victims of fraud in the past, it determines that there is a possibility of fraud. The call analysis unit also compares the analysis results of the call content with the user's past call history to detect abnormal times of day or frequency. For example, if there are an abnormally large number of late-night calls compared to normal calls, it determines that there is a possibility of fraud. In this way, abnormal patterns can be detected by comparing with past call history.

[0034] In the fraud detection unit, the generation AI analyzes not only past fraud cases, but also news and social media posts, allowing it to learn the latest fraud methods. In the fraud detection unit, for example, the generation AI analyzes the latest news to learn the latest fraud methods. For example, it analyzes the details of recently reported fraud cases and adds the fraud methods to a database. In addition, in the fraud detection unit, the generation AI analyzes social media posts to learn the latest fraud methods. For example, it analyzes posts and comments related to fraud to detect new fraud methods. In addition, in the fraud detection unit, the generation AI collects news and social media data in real time to learn the latest fraud methods. For example, it analyzes fraud trends and adds new fraud methods to a database. In this way, by learning the latest fraud methods, the accuracy of fraud detection is improved.

[0035] The fraud detection unit uses the generation AI to understand the context of the call content and determine the possibility of fraud based on the entire context, not just specific phrases. For example, the fraud detection unit uses the generation AI to analyze the context of the call content and determine the possibility of fraud based on the entire context, not just specific phrases. For example, if the phrase "I would like you to transfer money" is included, the context before and after it is also analyzed. In addition, to understand the context of the call content, the generation AI uses natural language processing technology to analyze the entire context. For example, it understands the speaker's intention and purpose and determines the possibility of fraud. In addition, the fraud detection unit uses the generation AI to analyze the context of the call content and determine the possibility of fraud based on the entire context, not just specific phrases. For example, if the phrase "I would like you to respond quickly" is included, the context before and after it is also analyzed. This allows the possibility of fraud to be determined based on the entire context.

[0036] The fraud detection unit can detect fraudulent methods not only for landline phones or intercoms, but also for text communications such as emails and SMS. For example, the fraud detection unit applies fraudulent method detection to emails, analyzing specific phrases and context. For example, if a message contains a phrase such as "Please transfer money," it will determine that there is a possibility of fraud. The fraud detection unit also analyzes SMS text data to detect specific phrases and context. For example, if a message contains a phrase such as "Please respond quickly," it will determine that there is a possibility of fraud. In addition, to detect fraudulent methods in text communications such as emails and SMS, the generative AI uses natural language processing technology to analyze the entire context. For example, it understands the speaker's intention and purpose and determines the possibility of fraud. This allows the unit to support a variety of communication methods.

[0037] The fraud detection unit allows the generation AI to compare the user's past behavioral history and detect abnormal behavioral patterns. For example, the fraud detection unit analyzes the user's past behavioral history and detects abnormal behavioral patterns. For example, if there are abnormally many requests for money compared to normal behavior, it determines that there is a possibility of fraud. The fraud detection unit also analyzes the user's past behavioral history and detects specific patterns. For example, if there is a pattern similar to behavior that has resulted in past fraud, it determines that there is a possibility of fraud. The fraud detection unit also analyzes the user's past behavioral history and detects abnormal time periods and frequencies. For example, if there is abnormally many late-night activities compared to normal behavior, it determines that there is a possibility of fraud. This allows abnormal behavioral patterns to be detected by comparing them with past behavioral history.

[0038] When the alert sending unit detects the possibility of fraud, the generation AI learns the user's past response patterns and can issue an alert at the optimal timing. For example, the alert sending unit learns the user's past response patterns and issues an alert at the optimal timing when it detects the possibility of fraud. For example, if the user has ignored a fraud warning in the past, a stronger alert is issued. The alert sending unit also analyzes the user's past response patterns and issues an alert at the optimal timing when it detects the possibility of fraud. For example, if the user has accepted a fraud warning in the past, an alert is issued at a similar timing. The alert sending unit also learns the user's past response patterns and issues an alert at the optimal timing when it detects the possibility of fraud. For example, if the user has accepted a fraud warning in the past, an alert is issued at a similar timing. In this way, issuing an alert at the optimal timing can encourage the user to respond.

[0039] When the alert sending unit detects the possibility of fraud, the generation AI can select the optimal alert method by taking the user's current situation into consideration. For example, the alert sending unit analyzes the user's current location information and selects the optimal alert method when it detects the possibility of fraud. For example, if the user is at home, it issues an alert to a landline phone. The alert sending unit also analyzes the user's current time zone and selects the optimal alert method when it detects the possibility of fraud. For example, if there is a possibility of fraud late at night, it issues an alert to a smartphone. The alert sending unit also analyzes the user's current situation and selects the optimal alert method when it detects the possibility of fraud. For example, if the user is out, it issues an alert to a smartwatch. This makes it possible to select the optimal alert method according to the user's current situation.

[0040] The alert sending unit can send fraud prevention alerts not only to landlines and intercoms, but also to other devices such as smartwatches and smart speakers. For example, the alert sending unit can send fraud prevention alerts to a smartwatch, allowing the user to respond immediately. For example, it can issue a vibration alert to the smartwatch. The alert sending unit can also send fraud prevention alerts to a smart speaker, warning the user by voice. For example, it can play a voice message such as, "This call may be fraudulent. Please be careful." The alert sending unit can also send fraud prevention alerts to a smartphone, allowing the user to respond immediately. For example, it can send a push notification to the smartphone. This allows the device to be compatible with a variety of devices, allowing the user to respond immediately.

[0041] The alert sending unit can also simultaneously send anti-fraud alerts to the user's family and friends, allowing multiple people to deal with fraud. The alert sending unit, for example, can also simultaneously send anti-fraud alerts to the user's family and friends, allowing multiple people to deal with fraud. For example, it can send an alert to all family members by email. The alert sending unit can also simultaneously send anti-fraud alerts to the user's family and friends, allowing multiple people to deal with fraud. For example, it can send an alert to all family members by SMS. The alert sending unit can also simultaneously send anti-fraud alerts to the user's family and friends, allowing multiple people to deal with fraud. For example, it can send an alert to all family members by push notification on their smartphones. This allows multiple people to deal with fraud, thereby preventing fraud victims.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The fraud prevention system may further include a behavior analysis unit that learns the user's behavioral patterns. The behavior analysis unit analyzes the user's daily behavioral patterns and detects abnormal behavior. For example, if the user frequently visits places they normally don't go to or is active at times different from their usual hours, it can be determined that there is a possibility of fraud. The behavior analysis unit can also analyze the user's purchasing history and detect abnormally high-value purchases or frequent purchases. This allows for highly accurate determination of the possibility of fraud based on the user's behavioral patterns.

[0044] The fraud prevention system may further include a health analysis unit that monitors the user's health condition. The health analysis unit monitors the user's health data, such as heart rate, blood pressure, and stress level, in real time and detects abnormal changes. For example, if the user's heart rate increases suddenly, it may be determined that there is a possibility of fraud. The health analysis unit may also analyze the user's health data and issue a warning if the stress level is high. This makes it possible to determine the possibility of fraud based on the user's health condition.

[0045] The fraud prevention system may further include a social analysis unit that analyzes the user's social network. The social analysis unit analyzes the user's social networking accounts to detect potentially fraudulent messages or posts. For example, the social analysis unit may detect unnatural messages from the user's friends or posts containing potentially fraudulent links. The social analysis unit may also analyze the user's activity patterns on the social networking sites to detect abnormal activity. This allows the system to determine the possibility of fraud based on the user's social network.

[0046] The fraud prevention system may further include a location analysis unit that analyzes the user's location information. The location analysis unit monitors the user's current location information in real time and detects abnormal location fluctuations. For example, if the user is in a place they normally don't go to or if their location changes suddenly, it can be determined that there is a possibility of fraud. The location analysis unit can also analyze the user's past location history and detect abnormal patterns. This allows the possibility of fraud to be determined based on the user's location information.

[0047] The fraud prevention system may further include a device analysis unit that analyzes the user's device usage. The device analysis unit monitors the user's smartphone or PC usage in real time and detects abnormal usage patterns. For example, if an unusual application is frequently used or if an abnormal amount of data communication is occurring, it can determine that there is a possibility of fraud. The device analysis unit can also analyze the user's device usage history and detect abnormal patterns. This makes it possible to determine the possibility of fraud based on the user's device usage.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The call analysis unit analyzes the content of the call. For example, the call analysis unit analyzes the voice data of the call in real time and detects specific phrases and patterns. The call analysis unit can also convert the content of the call into text data using voice recognition technology. For example, the call analysis unit analyzes the voice data and detects specific phrases such as "I would like you to transfer money" or "I would like you to respond quickly." Step 2: The fraud detection unit detects fraudulent methods from the call content analyzed by the call analysis unit. For example, the fraud detection unit compares the content with a database of past fraud cases to see if similar methods are being used. The fraud detection unit can also use the generation AI to analyze the call content and evaluate the possibility of fraud. For example, the generation AI compares the content with a database of past fraud cases to detect fraudulent methods with high accuracy. Step 3: The alert sending unit notifies the user of the possibility of fraud detected by the fraud detection unit. For example, the alert sending unit plays a voice message during the call such as, "This call may be fraudulent. Please be careful." The alert sending unit can also send a notification to the user's smartphone or computer to notify them of the possibility of fraud. For example, the alert sending unit sends a push notification to the user's smartphone to notify them of the possibility of fraud.

[0050] (Example 2) The fraud prevention system according to an embodiment of the present invention automatically analyzes the content of phone calls, and the AI ​​generation system detects fraudulent methods and notifies the user of possible fraud. This allows the fraud prevention system to analyze the content of phone calls in real time, detect fraudulent methods, and promptly notify the user.

[0051] The fraud prevention system according to the embodiment includes a call analysis unit, a fraud detection unit, and an alert transmission unit. The call analysis unit analyzes the content of the call. For example, the call analysis unit analyzes the audio data of the call in real time to detect specific phrases or patterns. The call analysis unit can also convert the content of the call into text data using voice recognition technology. For example, the call analysis unit analyzes the audio data and detects specific phrases such as "Please transfer money" or "Please handle this urgently." The fraud detection unit detects fraudulent methods from the call content analyzed by the call analysis unit. For example, the fraud detection unit compares the content with a database of past fraud cases to determine whether similar methods are being used. The fraud detection unit can also analyze the content of the call using a generation AI to evaluate the possibility of fraud. For example, the generation AI compares the content with a database of past fraud cases to detect fraudulent methods with high accuracy. The alert transmission unit notifies the user of the possibility of fraud detected by the fraud detection unit. For example, the alert transmission unit plays a voice message during the call such as, "This call may be fraudulent. Please be careful." The alert sending unit can also send a notification to the user's smartphone or PC to notify the user of a possible fraud. For example, the alert sending unit can send a push notification to the user's smartphone to notify the user of a possible fraud. This allows the fraud prevention system according to the embodiment to quickly detect fraudulent methods and notify the user, thereby preventing fraud before it occurs.

[0052] When analyzing the audio data of the call content, the call analysis unit can determine the possibility of fraud with higher accuracy based on parameters such as the speaker's tone of voice, speed, and pauses. When analyzing the audio data of the call content, the call analysis unit, for example, analyzes the speaker's tone of voice to detect tones that differ from normal conversation. For example, it detects a tone that sounds rushed or an unnaturally calm tone. The call analysis unit also analyzes the speed of the speaker's voice to detect speeds that are abnormally fast or slow compared to normal conversation. For example, if a fraudster is in a hurry, the speaker's voice speed often increases. The call analysis unit also analyzes the speaker's pauses to detect patterns that differ from normal conversation. For example, if a fraudster cannot answer a question immediately, there may be long pauses. This allows the possibility of fraud to be determined with higher accuracy.

[0053] The call analysis unit can also analyze the speaker's background sounds when analyzing the content of the call to assess the possibility of fraud. For example, when analyzing the content of the call, the call analysis unit analyzes the background sounds to detect sounds that are different from those in a normal home environment. For example, when a fraudster is calling from a call center, other call sounds may be heard in the background. The call analysis unit can also detect specific environmental sounds (e.g., traffic sounds or crowd sounds) through the analysis of background sounds to assess the possibility of fraud. For example, when a fraudster is calling from a public place, specific environmental sounds may be included in the background. The call analysis unit can also detect changes in background sounds when analyzing the content of the call to assess the possibility of fraud. For example, if a fraudster changes the location from which they are making a call, the background sounds may change. This allows the possibility of fraud to be assessed by analyzing the background sounds.

[0054] The call analysis unit uses the emotion estimation function to estimate the speaker's emotions in real time and determine the possibility of fraud based on changes in emotions. For example, when analyzing the content of a call, the call analysis unit estimates emotions based on the tone and speed of the speaker's voice and detects changes in emotions. For example, if the speaker suddenly becomes angry or nervous, the possibility of fraud increases. The call analysis unit also uses the emotion estimation function to monitor the speaker's emotions in real time and determine the possibility of fraud based on changes in emotions. For example, if the speaker suddenly begins to feel anxious, there is a possibility of fraud. The call analysis unit also estimates the speaker's emotions and analyzes patterns of emotional changes when analyzing the content of a call. For example, if the speaker suddenly tries to suppress their emotions, the possibility of fraud increases. This makes it possible to determine the possibility of fraud based on changes in emotions.

[0055] The call analysis unit can apply the analysis of call content not only to landlines or intercoms, but also to other communication methods such as video calls or chat apps. For example, the call analysis unit applies the analysis of call content to video calls, simultaneously analyzing video data and audio data. For example, it analyzes the speaker's facial expressions and gestures to evaluate the possibility of fraud. The call analysis unit also analyzes text data from chat apps to detect specific phrases and contexts. For example, if a phrase such as "Please transfer money" is included, it determines that there is a possibility of fraud. The call analysis unit also applies the analysis of call content to text communications such as emails and SMS to detect specific phrases and contexts. For example, if a phrase such as "Please respond quickly" is included, it determines that there is a possibility of fraud. This allows the system to handle a variety of communication methods.

[0056] The call analysis unit can compare the analysis results of the call content with the user's past call history to detect abnormal patterns. For example, the call analysis unit compares the analysis results of the call content with the user's past call history to detect abnormal patterns. For example, if there are an abnormally large number of requests for money compared to normal calls, it determines that there is a possibility of fraud. The call analysis unit also analyzes the user's past call history to detect specific patterns. For example, if there is a pattern similar to calls made by people who have been victims of fraud in the past, it determines that there is a possibility of fraud. The call analysis unit also compares the analysis results of the call content with the user's past call history to detect abnormal times of day or frequency. For example, if there are an abnormally large number of late-night calls compared to normal calls, it determines that there is a possibility of fraud. In this way, abnormal patterns can be detected by comparing with past call history.

[0057] The call analysis unit can use the emotion estimation function to estimate the emotion of the speaker in real time and provide emotion-based advice in real time. The call analysis unit, for example, uses the emotion estimation function to estimate the emotion of the speaker during a call in real time and provide emotion-based advice. For example, if the speaker is feeling anxious, advice to reassure the speaker is provided. The call analysis unit also estimates the emotion of the speaker in analyzing the content of the call and provides emotion-based advice in real time. For example, if the speaker is feeling angry, advice to stay calm is provided. The call analysis unit also uses the emotion estimation function to monitor the emotion of the speaker during a call in real time and provide emotion-based advice. For example, if the speaker is nervous, advice to relax is provided. This makes it possible to provide emotion-based advice in real time.

[0058] In the fraud detection unit, the generation AI analyzes not only past fraud cases, but also news and social media posts, allowing it to learn the latest fraud methods. In the fraud detection unit, for example, the generation AI analyzes the latest news to learn the latest fraud methods. For example, it analyzes the details of recently reported fraud cases and adds the fraud methods to a database. In addition, in the fraud detection unit, the generation AI analyzes social media posts to learn the latest fraud methods. For example, it analyzes posts and comments related to fraud to detect new fraud methods. In addition, in the fraud detection unit, the generation AI collects news and social media data in real time to learn the latest fraud methods. For example, it analyzes fraud trends and adds new fraud methods to a database. In this way, by learning the latest fraud methods, the accuracy of fraud detection is improved.

[0059] The fraud detection unit uses the generation AI to understand the context of the call content and determine the possibility of fraud based on the entire context, not just specific phrases. For example, the fraud detection unit uses the generation AI to analyze the context of the call content and determine the possibility of fraud based on the entire context, not just specific phrases. For example, if the phrase "I would like you to transfer money" is included, the context before and after it is also analyzed. In addition, to understand the context of the call content, the generation AI uses natural language processing technology to analyze the entire context. For example, it understands the speaker's intention and purpose and determines the possibility of fraud. In addition, the fraud detection unit uses the generation AI to analyze the context of the call content and determine the possibility of fraud based on the entire context, not just specific phrases. For example, if the phrase "I would like you to respond quickly" is included, the context before and after it is also analyzed. This allows the possibility of fraud to be determined based on the entire context.

[0060] The fraud detection unit uses the emotion estimation function to analyze changes in the speaker's emotions and detect fraudulent methods from the pattern of emotional changes. The fraud detection unit, for example, uses the emotion estimation function to analyze changes in the speaker's emotions during a call in real time and detects fraudulent methods from the pattern of emotional changes. For example, if the speaker suddenly begins to feel anxious, it determines that there is a possibility of fraud. The fraud detection unit also analyzes the pattern of changes in the speaker's emotions in analyzing the content of the call and detects fraudulent methods. For example, if the speaker suddenly tries to suppress their emotions, it determines that there is a possibility of fraud. The fraud detection unit also uses the emotion estimation function to monitor changes in the speaker's emotions during a call in real time and detects fraudulent methods from the pattern of emotional changes. For example, if the speaker suddenly becomes angry or nervous, it determines that there is a possibility of fraud. In this way, it is possible to detect fraudulent methods from the pattern of emotional changes.

[0061] The fraud detection unit can detect fraudulent methods not only for landline phones or intercoms, but also for text communications such as emails and SMS. For example, the fraud detection unit applies fraudulent method detection to emails, analyzing specific phrases and context. For example, if a message contains a phrase such as "Please transfer money," it will determine that there is a possibility of fraud. The fraud detection unit also analyzes SMS text data to detect specific phrases and context. For example, if a message contains a phrase such as "Please respond quickly," it will determine that there is a possibility of fraud. In addition, to detect fraudulent methods in text communications such as emails and SMS, the generative AI uses natural language processing technology to analyze the entire context. For example, it understands the speaker's intention and purpose and determines the possibility of fraud. This allows the unit to support a variety of communication methods.

[0062] The fraud detection unit allows the generation AI to compare the user's past behavioral history and detect abnormal behavioral patterns. For example, the fraud detection unit analyzes the user's past behavioral history and detects abnormal behavioral patterns. For example, if there are abnormally many requests for money compared to normal behavior, it determines that there is a possibility of fraud. The fraud detection unit also analyzes the user's past behavioral history and detects specific patterns. For example, if there is a pattern similar to behavior that has resulted in past fraud, it determines that there is a possibility of fraud. The fraud detection unit also analyzes the user's past behavioral history and detects abnormal time periods and frequencies. For example, if there is abnormally many late-night activities compared to normal behavior, it determines that there is a possibility of fraud. This allows abnormal behavioral patterns to be detected by comparing them with past behavioral history.

[0063] The fraud detection unit can use the emotion estimation function to estimate the speaker's emotions and detect emotion-based fraud methods. The fraud detection unit, for example, uses the emotion estimation function to estimate the speaker's emotions during a call in real time and detect emotion-based fraud methods. For example, if the speaker feels anxious, it determines that there is a possibility of fraud. The fraud detection unit also estimates the speaker's emotions by analyzing the content of the call and detects emotion-based fraud methods. For example, if the speaker suddenly tries to suppress their emotions, it determines that there is a possibility of fraud. The fraud detection unit also uses the emotion estimation function to monitor the speaker's emotions during a call in real time and detect emotion-based fraud methods. For example, if the speaker suddenly becomes angry or nervous, it determines that there is a possibility of fraud. This makes it possible to detect emotion-based fraud methods.

[0064] When the alert sending unit detects the possibility of fraud, the generation AI learns the user's past response patterns and can issue an alert at the optimal timing. For example, the alert sending unit learns the user's past response patterns and issues an alert at the optimal timing when it detects the possibility of fraud. For example, if the user has ignored a fraud warning in the past, a stronger alert is issued. The alert sending unit also analyzes the user's past response patterns and issues an alert at the optimal timing when it detects the possibility of fraud. For example, if the user has accepted a fraud warning in the past, an alert is issued at a similar timing. The alert sending unit also learns the user's past response patterns and issues an alert at the optimal timing when it detects the possibility of fraud. For example, if the user has accepted a fraud warning in the past, an alert is issued at a similar timing. In this way, issuing an alert at the optimal timing can encourage the user to respond.

[0065] When the alert sending unit detects the possibility of fraud, the generation AI can select the optimal alert method by taking the user's current situation into consideration. For example, the alert sending unit analyzes the user's current location information and selects the optimal alert method when it detects the possibility of fraud. For example, if the user is at home, it issues an alert to a landline phone. The alert sending unit also analyzes the user's current time zone and selects the optimal alert method when it detects the possibility of fraud. For example, if there is a possibility of fraud late at night, it issues an alert to a smartphone. The alert sending unit also analyzes the user's current situation and selects the optimal alert method when it detects the possibility of fraud. For example, if the user is out, it issues an alert to a smartwatch. This makes it possible to select the optimal alert method according to the user's current situation.

[0066] The alert sending unit can use the emotion estimation function to monitor the user's emotional state in real time and issue an alert based on the emotion. The alert sending unit, for example, uses the emotion estimation function to monitor the user's emotional state in real time and issue an alert based on the emotion. For example, if the user is feeling anxious, it issues an alert to reassure the user. The alert sending unit also monitors the user's emotional state in real time and issues an alert based on the emotion. For example, if the user is feeling angry, it issues an alert to calm down. The alert sending unit also uses the emotion estimation function to monitor the user's emotional state in real time and issue an alert based on the emotion. For example, if the user is tense, it issues an alert to relax. In this way, by issuing an alert based on the user's emotional state, it is possible to promote an appropriate response.

[0067] The alert sending unit can send fraud prevention alerts not only to landlines and intercoms, but also to other devices such as smartwatches and smart speakers. For example, the alert sending unit can send fraud prevention alerts to a smartwatch, allowing the user to respond immediately. For example, it can issue a vibration alert to the smartwatch. The alert sending unit can also send fraud prevention alerts to a smart speaker, warning the user by voice. For example, it can play a voice message such as, "This call may be fraudulent. Please be careful." The alert sending unit can also send fraud prevention alerts to a smartphone, allowing the user to respond immediately. For example, it can send a push notification to the smartphone. This allows the device to be compatible with a variety of devices, allowing the user to respond immediately.

[0068] The alert sending unit can also simultaneously send anti-fraud alerts to the user's family and friends, allowing multiple people to deal with fraud. The alert sending unit, for example, can also simultaneously send anti-fraud alerts to the user's family and friends, allowing multiple people to deal with fraud. For example, it can send an alert to all family members by email. The alert sending unit can also simultaneously send anti-fraud alerts to the user's family and friends, allowing multiple people to deal with fraud. For example, it can send an alert to all family members by SMS. The alert sending unit can also simultaneously send anti-fraud alerts to the user's family and friends, allowing multiple people to deal with fraud. For example, it can send an alert to all family members by push notification on their smartphones. This allows multiple people to deal with fraud, thereby preventing fraud victims.

[0069] The alert sending unit can use the emotion estimation function to monitor the user's emotional state in real time and issue an alert based on the emotion. The alert sending unit, for example, uses the emotion estimation function to monitor the user's emotional state in real time and issue an alert based on the emotion. For example, if the user is feeling anxious, it issues an alert to reassure the user. The alert sending unit also monitors the user's emotional state in real time and issues an alert based on the emotion. For example, if the user is feeling angry, it issues an alert to calm down. The alert sending unit also uses the emotion estimation function to monitor the user's emotional state in real time and issue an alert based on the emotion. For example, if the user is tense, it issues an alert to relax. In this way, by issuing an alert based on the user's emotional state, it is possible to promote an appropriate response.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The fraud prevention system may further include a behavior analysis unit that learns the user's behavioral patterns. The behavior analysis unit analyzes the user's daily behavioral patterns and detects abnormal behavior. For example, if the user frequently visits places they normally don't go to or is active at times different from their usual hours, it can be determined that there is a possibility of fraud. The behavior analysis unit can also analyze the user's purchasing history and detect abnormally high-value purchases or frequent purchases. This allows for highly accurate determination of the possibility of fraud based on the user's behavioral patterns.

[0072] The fraud prevention system may further include a health analysis unit that monitors the user's health condition. The health analysis unit monitors the user's health data, such as heart rate, blood pressure, and stress level, in real time and detects abnormal changes. For example, if the user's heart rate increases suddenly, it may be determined that there is a possibility of fraud. The health analysis unit may also analyze the user's health data and issue a warning if the stress level is high. This makes it possible to determine the possibility of fraud based on the user's health condition.

[0073] The fraud prevention system may further include a social analysis unit that analyzes the user's social network. The social analysis unit analyzes the user's social networking accounts to detect potentially fraudulent messages or posts. For example, the social analysis unit may detect unnatural messages from the user's friends or posts containing potentially fraudulent links. The social analysis unit may also analyze the user's activity patterns on the social networking sites to detect abnormal activity. This allows the system to determine the possibility of fraud based on the user's social network.

[0074] The fraud prevention system may further include a location analysis unit that analyzes the user's location information. The location analysis unit monitors the user's current location information in real time and detects abnormal location fluctuations. For example, if the user is in a place they normally don't go to or if their location changes suddenly, it can be determined that there is a possibility of fraud. The location analysis unit can also analyze the user's past location history and detect abnormal patterns. This allows the possibility of fraud to be determined based on the user's location information.

[0075] The fraud prevention system may further include a device analysis unit that analyzes the user's device usage. The device analysis unit monitors the user's smartphone or PC usage in real time and detects abnormal usage patterns. For example, if an unusual application is frequently used or if an abnormal amount of data communication is occurring, it can determine that there is a possibility of fraud. The device analysis unit can also analyze the user's device usage history and detect abnormal patterns. This makes it possible to determine the possibility of fraud based on the user's device usage.

[0076] The fraud prevention system may further include an emotion analysis unit that estimates the user's emotions and determines the possibility of fraud based on the estimated emotions. The emotion analysis unit analyzes the user's tone of voice, speed, facial expressions, etc., to estimate emotions in real time. For example, if the user feels anxious, it can determine that there is a possibility of fraud. The emotion analysis unit can also analyze patterns of change in the user's emotions and detect fraudulent methods. This allows for highly accurate determination of the possibility of fraud based on the user's emotions.

[0077] The fraud prevention system may further include an emotion alert unit that estimates a user's emotion and issues an alert based on the estimated emotion. The emotion alert unit monitors the user's emotional state in real time and issues an alert based on the emotion. For example, if the user is feeling anxious, an alert to reassure the user may be issued. The emotion alert unit may also analyze the user's emotional state and issue an alert based on the emotion. This allows for providing an appropriate alert according to the user's emotion.

[0078] The fraud prevention system may further include an emotion detection unit that estimates the user's emotions and detects fraudulent methods based on the estimated emotions. The emotion detection unit analyzes the user's tone of voice, speed, facial expressions, etc., to estimate emotions in real time. For example, if the user suddenly begins to feel anxious, it can determine that there is a possibility of fraud. The emotion detection unit can also analyze patterns of change in the user's emotions to detect fraudulent methods. This allows for highly accurate detection of fraudulent methods based on the user's emotions.

[0079] The fraud prevention system may further include an emotion evaluation unit that estimates the user's emotions and evaluates the possibility of fraud based on the estimated emotions. The emotion evaluation unit analyzes the user's tone of voice, speed, facial expressions, etc., to estimate emotions in real time. For example, if the user is nervous, it can determine that there is a possibility of fraud. The emotion evaluation unit can also analyze patterns of change in the user's emotions and evaluate the possibility of fraud. This allows for a highly accurate evaluation of the possibility of fraud based on the user's emotions.

[0080] The fraud prevention system may further include an emotion detection unit that estimates the user's emotions and detects fraudulent methods based on the estimated emotions. The emotion detection unit analyzes the user's tone of voice, speed, facial expressions, etc., to estimate emotions in real time. For example, if the user suddenly begins to feel anxious, it can determine that there is a possibility of fraud. The emotion detection unit can also analyze patterns of change in the user's emotions to detect fraudulent methods. This allows for highly accurate detection of fraudulent methods based on the user's emotions.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The call analysis unit analyzes the content of the call. For example, the call analysis unit analyzes the voice data of the call in real time and detects specific phrases and patterns. The call analysis unit can also convert the content of the call into text data using voice recognition technology. For example, the call analysis unit analyzes the voice data and detects specific phrases such as "I would like you to transfer money" or "I would like you to respond quickly." Step 2: The fraud detection unit detects fraudulent methods from the call content analyzed by the call analysis unit. For example, the fraud detection unit compares the content with a database of past fraud cases to see if similar methods are being used. The fraud detection unit can also use the generation AI to analyze the call content and evaluate the possibility of fraud. For example, the generation AI compares the content with a database of past fraud cases to detect fraudulent methods with high accuracy. Step 3: The alert sending unit notifies the user of the possibility of fraud detected by the fraud detection unit. For example, the alert sending unit plays a voice message during the call such as, "This call may be fraudulent. Please be careful." The alert sending unit can also send a notification to the user's smartphone or computer to notify them of the possibility of fraud. For example, the alert sending unit sends a push notification to the user's smartphone to notify them of the possibility of fraud.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

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

[0119] The 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.

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

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

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

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a call analysis unit that analyzes the contents of the call; a fraud detection unit that detects fraudulent methods from the call content analyzed by the call analysis unit; an alert sending unit that notifies a user of the possibility of fraud detected by the fraud detection unit; system.

2. The call analysis unit When analyzing the voice data of the call, parameters such as the speaker's tone of voice, speed, and pauses are also taken into account to more accurately determine the possibility of fraud.

2. The system of claim 1.

3. The call analysis unit When analyzing the content of the call, the background noise of the speaker is also analyzed to assess the possibility of fraud.

2. The system of claim 1.

4. The call analysis unit Estimate the speaker's emotions in real time and determine the likelihood of fraud based on changes in the emotions.

2. The system of claim 1.

5. The call analysis unit Apply the analysis of call content to other means of communication, such as video calls or chat apps, as well as landlines or intercoms.

2. The system of claim 1.

6. The call analysis unit The results of the analysis of the call content are compared with the user's past call history to detect any abnormal patterns.

2. The system of claim 1.

7. The call analysis unit Estimate the speaker's emotions in real time and provide advice based on the emotions in real time.

2. The system of claim 1.

8. The fraud detection unit The generative AI analyzes not only past fraud cases but also news and posts on the aforementioned social media to learn the latest fraud methods.

2. The system of claim 1.

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