System

Abstract

An object of the system according to the embodiment is to detect a sign of fraud in real time during a call and issue a warning to a user.SOLUTION: A system includes a call analysis unit, a warning unit, and a recording unit. The call analysis unit analyzes a call content in real time. The warning unit issues a warning to the user based on the sign of fraud detected by the call analysis unit. The recording unit records call contents and analyzes the call contents later.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 technologies do not adequately detect signs of fraud in real time during a call and warn the user, and there is room for improvement.

[0005] The system according to the embodiment aims to detect signs of fraud in real time during a call and issue a warning to the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a call analysis unit, a warning unit, and a recording unit. The call analysis unit analyzes call content in real time. The warning unit issues a warning to a user based on signs of fraud detected by the call analysis unit. The recording unit records the call content for later analysis. [Effects of the Invention]

[0007] An embodiment of the system can detect fraudulent activity in real time during a call and alert 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 detection system according to an embodiment of the present invention is a system that analyzes the contents of phone calls in real time, detects signs of fraud, and issues a warning to the user. As a result, the fraud detection system can prevent fraud damage by detecting signs of "it's me, it's me" fraud in real time and issuing a warning to the user.

[0029] The fraud detection system according to the embodiment includes a call analysis unit, a warning unit, and a recording unit. The call analysis unit analyzes the content of the call in real time. For example, the generation AI analyzes audio data during a call in real time to detect signs of fraud from specific phrases and the overall context. The generation AI can also analyze background and environmental sounds during a call to detect signs of fraud. Furthermore, the generation AI uses an emotion estimation function to analyze changes in the speaker's emotions during a call and identify moments when the possibility of fraud increases. For example, the generation AI detects specific phrases during a call, such as "Please transfer money" or "Please handle this quickly," and determines the possibility of fraud. The generation AI also analyzes the entire context of the call to understand not only specific phrases but also the flow of the conversation and the speaker's intention. The generation AI also analyzes background and environmental sounds during a call to detect unnatural sounds and typical fraud patterns. The warning unit issues a warning to the user based on the signs of fraud detected by the call analysis unit. For example, the warning unit plays a voice message during the call, such as "This call may be fraudulent. Please be careful." The warning unit can also display a warning message on the user's smartphone. Furthermore, if the warning unit detects signs of fraud, it plays a voice message suggesting specific countermeasures to the user. For example, it can play a message such as, "Always check with your family before transferring money." The recording unit records the contents of the call and analyzes them later. For example, the generation AI converts the contents of the call into text and highlights parts that may be fraudulent. The recording unit can also compare the analysis results of the call with the user's past call history to detect abnormal patterns. Furthermore, the recording unit automatically saves the records of the call in the user's cloud storage for later access. In this way, the fraud detection system according to the embodiment can detect signs of "I'm your son" fraud in real time and issue a warning to the user, thereby preventing fraud damage. For example, by detecting signs of fraud during a call and receiving a warning, the user can become more vigilant against fraud. Furthermore, by recording the contents of the call and analyzing them in detail later, the risk of fraud can be assessed and appropriate measures can be taken.

[0030] The call analysis unit can determine the possibility of fraud based not only on specific phrases but also on the entire context of the call. For example, the generation AI analyzes the context of the call to understand not only specific phrases but also the flow of the conversation and the speaker's intention. For example, if the phrase "Please handle this quickly" is used during a call, the context before and after is analyzed to determine the possibility of fraud. The call content is also analyzed, and the possibility of fraud is assessed based on the frequency and timing of specific phrases. For example, if the phrase "Please transfer money" is repeated multiple times, it is determined that there is a high possibility of fraud. Furthermore, the generation AI understands the context of the call and analyzes the speaker's intentions and emotions to determine the possibility of fraud. For example, if the speaker suddenly changes their tone of voice or becomes emotional, this change is detected and the possibility of fraud is assessed. This enables more accurate fraud detection by determining the possibility of fraud based on the entire context of the call.

[0031] The call analysis unit can detect signs of fraud by analyzing background or environmental sounds in addition to the voice data during a call. The call analysis unit, for example, analyzes background or environmental sounds in addition to the voice data during a call to detect signs of fraud. For example, if unnatural background sounds are heard during a call, the sounds are analyzed to evaluate the possibility of fraud. The call analysis unit also analyzes environmental sounds during a call to evaluate the reliability of the call. For example, if background sounds heard during a call differ from a normal home environment, the sounds are analyzed to determine the possibility of fraud. Furthermore, the call analysis unit performs an integrated analysis of the voice data and background sounds during a call to detect signs of fraud. For example, if environmental sounds heard during a call match a typical pattern of fraud, the possibility of fraud is evaluated based on that information. In this way, by analyzing background and environmental sounds during a call, signs of fraud can be detected more accurately.

[0032] The call analysis unit can also apply the analysis of call content to the content of text messages or chats to detect signs of fraud. The call analysis unit, for example, applies call content analysis technology to the content of text messages and chats to detect signs of fraud. For example, if a text message contains a phrase such as "Please transfer money," the message is analyzed to determine the possibility of fraud. The unit also analyzes the content of text messages and chats in real time to detect signs of fraud. For example, if a phrase such as "Please respond quickly" appears during a chat, the context is analyzed to evaluate the possibility of fraud. Furthermore, the call content analysis technology can also be applied to the content of text messages and chats to build a system that detects signs of fraud. For example, the content of text messages and chats can be analyzed to issue a warning if there is a possibility of fraud. In this way, by applying analysis to the content of text messages and chats, signs of fraud can be detected over a wide range.

[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. The call analysis unit, for example, compares the analysis results of the call content with the user's past call history to detect abnormal patterns. For example, if a phrase or tone not found in the past call history appears, the abnormality is detected and the possibility of fraud is evaluated. The call analysis unit also analyzes the user's past call history to learn normal call patterns. For example, if content that differs from the normal call pattern appears, the abnormality is detected and the possibility of fraud is determined. Furthermore, a system is constructed that compares the analysis results of the call content with the past call history to detect abnormal patterns. For example, if content that differs from the past call history appears, the abnormality is detected and a warning is issued. This makes it easier to detect abnormal patterns by comparing with the past call history.

[0034] The warning unit can play a voice message suggesting specific countermeasures to the user when it detects signs of fraud. For example, the warning unit plays a voice message suggesting specific countermeasures to the user when it detects signs of fraud. For example, it plays a message such as, "This call may be fraudulent. Be sure to check with your family before transferring money." Furthermore, a system is constructed in which, when it detects signs of fraud, it plays a voice message suggesting specific countermeasures to the user. For example, it plays a message such as, "If you receive a suspicious call, contact the police immediately." Furthermore, when it detects signs of fraud, it plays a voice message suggesting specific countermeasures to the user. For example, it plays a message such as, "This call may be fraudulent. Please be careful." In this way, by playing a voice message suggesting specific countermeasures, the user can take appropriate action.

[0035] If the warning unit detects signs of fraud, it can cooperate with the user's smart home devices and issue a warning. For example, if the warning unit detects signs of fraud, it cooperates with the user's smart home devices to build a system that issues a warning. For example, a message such as "There is a possibility of fraud. Please be careful" is played from a smart speaker. Also, if signs of fraud are detected, it cooperates with the user's smart home devices and issues a warning. For example, a warning message is displayed on a smartphone. Furthermore, if signs of fraud are detected, it cooperates with the user's smart home devices and issues a warning. For example, a smart light flashes to issue a warning. In this way, by cooperating with smart home devices, it is possible to issue warnings to the user from multiple angles.

[0036] When recording the contents of a call, the generation AI can automatically summarize the important parts and provide them to the user. The recording unit, for example, builds a system in which the recording unit records the contents of a call and the generation AI automatically summarizes the important parts. For example, it extracts important phrases and information that come up during the call and provides this to the user as a summary. In addition, the recording unit records the contents of a call and the generation AI automatically summarizes the important parts. For example, it extracts the main points and important information from the call and provides this to the user in text format. Furthermore, a system is developed in which the recording unit records the contents of a call and the generation AI automatically summarizes the important parts. For example, it extracts important information that comes up during the call and provides this to the user as a summary. In this way, the important parts of the call can be automatically summarized, allowing the user to efficiently grasp the information.

[0037] The recording unit can compare the analysis results of the call content with the user's past behavioral patterns to identify any abnormalities. The recording unit, for example, builds a system that compares the analysis results of the call content with the user's past behavioral patterns to identify any abnormalities. For example, if a pattern different from past call content appears, the abnormality is detected and the user is notified. The recording unit also analyzes the user's past behavioral patterns and compares them with the analysis results of the call content. For example, if content different from the normal behavioral pattern appears, the abnormality is detected and the possibility of fraud is evaluated. Furthermore, a system is developed that compares the analysis results of the call content with past behavioral patterns to identify any abnormalities. For example, if content different from past behavioral patterns appears, the abnormality is detected and a warning is issued. This makes it easier to identify any abnormalities by comparing with past behavioral patterns.

[0038] The recording unit can automatically save a record of the call content in the user's cloud storage so that it can be accessed later. The recording unit, for example, builds a system that automatically saves a record of the call content in the user's cloud storage. For example, after the call ends, it is automatically saved in the cloud storage. Also, the record of the call content is automatically saved in the cloud storage so that the user can access it later. For example, the call content is saved in text format in the cloud storage. Furthermore, a system is developed that automatically saves a record of the call content in the cloud storage. For example, after the call ends, it is automatically saved in the cloud storage so that the user can access it later. In this way, saving the call content in the cloud storage makes it easier for the user to access it later.

[0039] The recording unit can synchronize the analysis results of the call content with the user's other digital devices to provide integrated fraud prevention measures. The recording unit, for example, builds a system that synchronizes the analysis results of the call content with the user's other digital devices. For example, it synchronizes with a smartphone or tablet to provide integrated fraud prevention measures. The analysis results of the call content are also synchronized with other digital devices to allow the user to access them from any device. For example, the analysis results of the call content are stored in the cloud to allow access from multiple devices. Furthermore, a system is developed that synchronizes the analysis results of the call content with the user's other digital devices. For example, it synchronizes with a smart watch or smart speaker to provide integrated fraud prevention measures. This makes it possible to provide integrated fraud prevention measures by synchronizing the analysis results of the call content with other digital devices.

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

[0041] The fraud detection system can further include a learning unit that learns the user's behavioral patterns. The learning unit analyzes the user's call history and behavioral patterns to learn normal behavioral patterns. For example, it learns what time of day the user usually makes calls and what phrases the user frequently uses. The learning unit can also detect abnormal behavior based on the user's behavioral patterns. For example, if a call is made at a time different from the user's normal behavioral patterns, it detects the abnormality and evaluates the possibility of fraud. Furthermore, the learning unit can evaluate the risk of fraud based on the user's behavioral patterns and notify the warning unit. In this way, by learning the user's behavioral patterns, more accurate fraud detection is possible.

[0042] The fraud detection system may further include a location information acquisition unit that acquires user location information. The location information acquisition unit acquires location information from the user's smartphone or GPS device and compares it with the content of the call. For example, if the user is making a call from a location other than their usual place of residence, the abnormality is detected and the possibility of fraud is assessed. The location information acquisition unit can also assess the risk of fraud based on the user's location information and notify the warning unit. For example, if the user is in an area where fraud is prevalent, the possibility of fraud can be assessed based on that information. Furthermore, the location information acquisition unit can customize a warning message based on the user's location information. This allows for more accurate assessment of the risk of fraud by utilizing location information.

[0043] The fraud detection system may further include a reliability evaluation unit that evaluates the reliability of the caller's telephone number and past call history. The reliability evaluation unit evaluates reliability by analyzing the caller's telephone number and past call history. For example, if the caller's telephone number has been associated with fraud in the past, the possibility of fraud is evaluated based on that information. The reliability evaluation unit may also notify the warning unit based on the caller's reliability. For example, if a call is made from a caller with low reliability, the warning may be issued based on that information. Furthermore, the reliability evaluation unit may suggest specific countermeasures to the user based on the caller's reliability. In this way, by evaluating the caller's reliability, the risk of fraud can be more accurately assessed.

[0044] The fraud detection system may further include a translation unit that translates the content of a user's call in real time. The translation unit translates the content of the call in real time and provides it to the user. For example, the translation unit may translate the content of a call in a foreign language into the user's native language to detect signs of fraud. The translation unit may also assess the possibility of fraud based on the translation result of the call content. For example, if the translated content of the call contains signs of fraud, the translation unit may assess the possibility of fraud based on that information. The translation unit may also notify the warning unit based on the translation result of the call content. In this way, by translating the content of the call, the risk of fraud can be assessed more comprehensively.

[0045] The fraud detection system may further include a database for analyzing the content of a user's calls and detecting signs of fraud. The database stores past fraud cases and typical fraud patterns and compares them with the content of the calls. For example, it detects specific phrases or patterns based on past fraud cases. The database may also assess the likelihood of fraud based on the results of the analysis of the call content. For example, if the call matches a typical fraud pattern registered in the database, it may assess the likelihood of fraud based on that information. Furthermore, the database may notify the warning unit based on the results of the analysis of the call content. This allows for more accurate assessment of the risk of fraud by utilizing the database.

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

[0047] Step 1: The call analysis unit analyzes the content of the call in real time. For example, the generation AI analyzes the voice data during the call in real time to detect signs of fraud from specific phrases and the overall context. The generation AI can also analyze background and environmental sounds during the call to detect signs of fraud. Furthermore, the generation AI uses emotion estimation to analyze changes in the speaker's emotions during the call and identify moments when the possibility of fraud increases. For example, the generation AI detects specific phrases during the call, such as "I want you to transfer money" or "Please respond quickly," and determines the possibility of fraud. The generation AI also analyzes the entire context of the call to understand not only specific phrases but also the flow of the conversation and the speaker's intention. The generation AI also analyzes background and environmental sounds during the call to detect unnatural sounds and typical patterns of fraud. Step 2: The warning unit issues a warning to the user based on the signs of fraud detected by the call analysis unit. For example, the warning unit may play a voice message during the call such as, "This call may be fraudulent. Please be careful." The warning unit may also display a warning message on the user's smartphone. Furthermore, if the warning unit detects signs of fraud, it may play a voice message suggesting specific countermeasures to the user. For example, it may play a message such as, "Always check with your family before transferring money." Step 3: The recording unit records the call content and analyzes it later. For example, the generative AI converts the call content into text and highlights potential fraudulent parts. The recording unit can also compare the analysis results of the call content with the user's past call history to detect abnormal patterns. The recording unit also automatically saves the call content recording to the user's cloud storage for later access.

[0048] (Example 2) The fraud detection system according to an embodiment of the present invention is a system that analyzes the contents of phone calls in real time, detects signs of fraud, and issues a warning to the user. As a result, the fraud detection system can prevent fraud damage by detecting signs of "it's me, it's me" fraud in real time and issuing a warning to the user.

[0049] The fraud detection system according to the embodiment includes a call analysis unit, a warning unit, and a recording unit. The call analysis unit analyzes the content of the call in real time. For example, the generation AI analyzes audio data during a call in real time to detect signs of fraud from specific phrases and the overall context. The generation AI can also analyze background and environmental sounds during a call to detect signs of fraud. Furthermore, the generation AI uses an emotion estimation function to analyze changes in the speaker's emotions during a call and identify moments when the possibility of fraud increases. For example, the generation AI detects specific phrases during a call, such as "Please transfer money" or "Please handle this quickly," and determines the possibility of fraud. The generation AI also analyzes the entire context of the call to understand not only specific phrases but also the flow of the conversation and the speaker's intention. The generation AI also analyzes background and environmental sounds during a call to detect unnatural sounds and typical fraud patterns. The warning unit issues a warning to the user based on the signs of fraud detected by the call analysis unit. For example, the warning unit plays a voice message during the call, such as "This call may be fraudulent. Please be careful." The warning unit can also display a warning message on the user's smartphone. Furthermore, if the warning unit detects signs of fraud, it plays a voice message suggesting specific countermeasures to the user. For example, it can play a message such as, "Always check with your family before transferring money." The recording unit records the contents of the call and analyzes them later. For example, the generation AI converts the contents of the call into text and highlights parts that may be fraudulent. The recording unit can also compare the analysis results of the call with the user's past call history to detect abnormal patterns. Furthermore, the recording unit automatically saves the records of the call in the user's cloud storage for later access. In this way, the fraud detection system according to the embodiment can detect signs of "I'm your son" fraud in real time and issue a warning to the user, thereby preventing fraud damage. For example, by detecting signs of fraud during a call and receiving a warning, the user can become more vigilant against fraud. Furthermore, by recording the contents of the call and analyzing them in detail later, the risk of fraud can be assessed and appropriate measures can be taken.

[0050] The call analysis unit can determine the possibility of fraud based not only on specific phrases but also on the entire context of the call. For example, the generation AI analyzes the context of the call to understand not only specific phrases but also the flow of the conversation and the speaker's intention. For example, if the phrase "Please handle this quickly" is used during a call, the context before and after is analyzed to determine the possibility of fraud. The call content is also analyzed, and the possibility of fraud is assessed based on the frequency and timing of specific phrases. For example, if the phrase "Please transfer money" is repeated multiple times, it is determined that there is a high possibility of fraud. Furthermore, the generation AI understands the context of the call and analyzes the speaker's intentions and emotions to determine the possibility of fraud. For example, if the speaker suddenly changes their tone of voice or becomes emotional, this change is detected and the possibility of fraud is assessed. This enables more accurate fraud detection by determining the possibility of fraud based on the entire context of the call.

[0051] The call analysis unit can detect signs of fraud by analyzing background or environmental sounds in addition to the voice data during a call. The call analysis unit, for example, analyzes background or environmental sounds in addition to the voice data during a call to detect signs of fraud. For example, if unnatural background sounds are heard during a call, the sounds are analyzed to evaluate the possibility of fraud. The call analysis unit also analyzes environmental sounds during a call to evaluate the reliability of the call. For example, if background sounds heard during a call differ from a normal home environment, the sounds are analyzed to determine the possibility of fraud. Furthermore, the call analysis unit performs an integrated analysis of the voice data and background sounds during a call to detect signs of fraud. For example, if environmental sounds heard during a call match a typical pattern of fraud, the possibility of fraud is evaluated based on that information. In this way, by analyzing background and environmental sounds during a call, signs of fraud can be detected more accurately.

[0052] The call analysis unit uses the emotion estimation function to analyze changes in the speaker's emotions during a call and can identify moments when the possibility of fraud increases. The call analysis unit, for example, uses the emotion estimation function to analyze changes in the speaker's emotions during a call in real time. For example, if the speaker suddenly becomes emotional, the change is detected and the possibility of fraud is assessed. The call analysis unit also analyzes changes in the speaker's emotions during a call and identifies moments when the possibility of fraud increases. For example, if the speaker suddenly changes the tone of their voice or starts using emotional expressions, the change is detected and the possibility of fraud is determined. The emotion estimation function also analyzes changes in the speaker's emotions during a call and detects signs of fraud. For example, if the speaker suddenly shows anger or impatience, the change is detected and the possibility of fraud is assessed. In this way, by analyzing changes in the speaker's emotions, the moment when the possibility of fraud increases can be identified.

[0053] The call analysis unit can also apply the analysis of call content to the content of text messages or chats to detect signs of fraud. The call analysis unit, for example, applies call content analysis technology to the content of text messages and chats to detect signs of fraud. For example, if a text message contains a phrase such as "Please transfer money," the message is analyzed to determine the possibility of fraud. The unit also analyzes the content of text messages and chats in real time to detect signs of fraud. For example, if a phrase such as "Please respond quickly" appears during a chat, the context is analyzed to evaluate the possibility of fraud. Furthermore, the call content analysis technology can also be applied to the content of text messages and chats to build a system that detects signs of fraud. For example, the content of text messages and chats can be analyzed to issue a warning if there is a possibility of fraud. In this way, by applying analysis to the content of text messages and chats, signs of fraud can be detected over a wide range.

[0054] The call analysis unit can compare the analysis results of the call content with the user's past call history to detect abnormal patterns. The call analysis unit, for example, compares the analysis results of the call content with the user's past call history to detect abnormal patterns. For example, if a phrase or tone not found in the past call history appears, the abnormality is detected and the possibility of fraud is evaluated. The call analysis unit also analyzes the user's past call history to learn normal call patterns. For example, if content that differs from the normal call pattern appears, the abnormality is detected and the possibility of fraud is determined. Furthermore, a system is constructed that compares the analysis results of the call content with the past call history to detect abnormal patterns. For example, if content that differs from the past call history appears, the abnormality is detected and a warning is issued. This makes it easier to detect abnormal patterns by comparing with the past call history.

[0055] The call analysis unit can use the emotion estimation function to analyze the emotion of a speaker during a call in real time and issue a warning to the user based on the emotion. The call analysis unit, for example, uses the emotion estimation function to analyze the emotion of a speaker during a call in real time and issue a warning to the user based on the emotion. For example, if the speaker suddenly becomes emotional, the change is detected and a warning is issued. Furthermore, a system is constructed that analyzes the emotion of a speaker during a call in real time and issues a warning based on the emotion. For example, if the speaker suddenly shows anger or impatience, the change is detected and a warning is issued. Furthermore, the emotion estimation function is used to analyze the emotion of a speaker during a call and issue a warning based on the emotion to the user. For example, if the speaker suddenly changes the tone of their voice or starts using emotional expressions, the change is detected and a warning is issued. In this way, by issuing a warning based on the speaker's emotion, the user can respond calmly.

[0056] The warning unit can play a voice message suggesting specific countermeasures to the user when it detects signs of fraud. For example, the warning unit plays a voice message suggesting specific countermeasures to the user when it detects signs of fraud. For example, it plays a message such as, "This call may be fraudulent. Be sure to check with your family before transferring money." Furthermore, a system is constructed in which, when it detects signs of fraud, it plays a voice message suggesting specific countermeasures to the user. For example, it plays a message such as, "If you receive a suspicious call, contact the police immediately." Furthermore, when it detects signs of fraud, it plays a voice message suggesting specific countermeasures to the user. For example, it plays a message such as, "This call may be fraudulent. Please be careful." In this way, by playing a voice message suggesting specific countermeasures, the user can take appropriate action.

[0057] If the warning unit detects signs of fraud, it can cooperate with the user's smart home devices and issue a warning. For example, if the warning unit detects signs of fraud, it cooperates with the user's smart home devices to build a system that issues a warning. For example, a message such as "There is a possibility of fraud. Please be careful" is played from a smart speaker. Also, if signs of fraud are detected, it cooperates with the user's smart home devices and issues a warning. For example, a warning message is displayed on a smartphone. Furthermore, if signs of fraud are detected, it cooperates with the user's smart home devices and issues a warning. For example, a smart light flashes to issue a warning. In this way, by cooperating with smart home devices, it is possible to issue warnings to the user from multiple angles.

[0058] When recording the contents of a call, the generation AI can automatically summarize the important parts and provide them to the user. The recording unit, for example, builds a system in which the recording unit records the contents of a call and the generation AI automatically summarizes the important parts. For example, it extracts important phrases and information that come up during the call and provides this to the user as a summary. In addition, the recording unit records the contents of a call and the generation AI automatically summarizes the important parts. For example, it extracts the main points and important information from the call and provides this to the user in text format. Furthermore, a system is developed in which the recording unit records the contents of a call and the generation AI automatically summarizes the important parts. For example, it extracts important information that comes up during the call and provides this to the user as a summary. In this way, the important parts of the call can be automatically summarized, allowing the user to efficiently grasp the information.

[0059] The recording unit can compare the analysis results of the call content with the user's past behavioral patterns to identify any abnormalities. The recording unit, for example, builds a system that compares the analysis results of the call content with the user's past behavioral patterns to identify any abnormalities. For example, if a pattern different from past call content appears, the abnormality is detected and the user is notified. The recording unit also analyzes the user's past behavioral patterns and compares them with the analysis results of the call content. For example, if content different from the normal behavioral pattern appears, the abnormality is detected and the possibility of fraud is evaluated. Furthermore, a system is developed that compares the analysis results of the call content with past behavioral patterns to identify any abnormalities. For example, if content different from past behavioral patterns appears, the abnormality is detected and a warning is issued. This makes it easier to identify any abnormalities by comparing with past behavioral patterns.

[0060] The recording unit can use the emotion estimation function to analyze the emotional tone of the call content and highlight parts that are likely to be fraudulent. The recording unit, for example, uses the emotion estimation function to build a system that analyzes the emotional tone of the call content and highlights parts that are likely to be fraudulent. For example, it highlights parts where the emotional tone suddenly changes during a call. The recording unit also analyzes the emotional tone of the call content and highlights parts that are likely to be fraudulent. For example, it detects and highlights parts where the speaker suddenly becomes emotional. Furthermore, a system is developed that uses the emotion estimation function to analyze the emotional tone of the call content and highlights parts that are likely to be fraudulent. For example, it detects and highlights parts where the emotional tone changes during a call. This makes it easier to identify parts that are likely to be fraudulent by analyzing the emotional tone.

[0061] The recording unit can automatically save a record of the call content in the user's cloud storage so that it can be accessed later. The recording unit, for example, builds a system that automatically saves a record of the call content in the user's cloud storage. For example, after the call ends, it is automatically saved in the cloud storage. Also, the record of the call content is automatically saved in the cloud storage so that the user can access it later. For example, the call content is saved in text format in the cloud storage. Furthermore, a system is developed that automatically saves a record of the call content in the cloud storage. For example, after the call ends, it is automatically saved in the cloud storage so that the user can access it later. In this way, saving the call content in the cloud storage makes it easier for the user to access it later.

[0062] The recording unit can synchronize the analysis results of the call content with the user's other digital devices to provide integrated fraud prevention measures. The recording unit, for example, builds a system that synchronizes the analysis results of the call content with the user's other digital devices. For example, it synchronizes with a smartphone or tablet to provide integrated fraud prevention measures. The analysis results of the call content are also synchronized with other digital devices to allow the user to access them from any device. For example, the analysis results of the call content are stored in the cloud to allow access from multiple devices. Furthermore, a system is developed that synchronizes the analysis results of the call content with the user's other digital devices. For example, it synchronizes with a smart watch or smart speaker to provide integrated fraud prevention measures. This makes it possible to provide integrated fraud prevention measures by synchronizing the analysis results of the call content with other digital devices.

[0063] The recording unit can use the emotion estimation function to analyze the emotional tone of the call content and provide emotion-based feedback to the user. The recording unit, for example, uses the emotion estimation function to build a system that analyzes the emotional tone of the call content and provides emotion-based feedback to the user. For example, it detects parts where the emotional tone changes during the call and provides feedback. The recording unit also analyzes the emotional tone of the call content and provides emotion-based feedback to the user. For example, it detects parts where the emotional tone suddenly changes during the call and provides feedback. Furthermore, a system is developed that uses the emotion estimation function to analyze the emotional tone of the call content and provide emotion-based feedback to the user. For example, it detects parts where the emotional tone changes during the call and provides feedback. In this way, emotion-based feedback can be provided to the user by analyzing the emotional tone.

[0064] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0065] The fraud detection system can further include a learning unit that learns the user's behavioral patterns. The learning unit analyzes the user's call history and behavioral patterns to learn normal behavioral patterns. For example, it learns what time of day the user usually makes calls and what phrases the user frequently uses. The learning unit can also detect abnormal behavior based on the user's behavioral patterns. For example, if a call is made at a time different from the user's normal behavioral patterns, it detects the abnormality and evaluates the possibility of fraud. Furthermore, the learning unit can evaluate the risk of fraud based on the user's behavioral patterns and notify the warning unit. In this way, by learning the user's behavioral patterns, more accurate fraud detection is possible.

[0066] The fraud detection system may further include a location information acquisition unit that acquires user location information. The location information acquisition unit acquires location information from the user's smartphone or GPS device and compares it with the content of the call. For example, if the user is making a call from a location other than their usual place of residence, the abnormality is detected and the possibility of fraud is assessed. The location information acquisition unit can also assess the risk of fraud based on the user's location information and notify the warning unit. For example, if the user is in an area where fraud is prevalent, the possibility of fraud can be assessed based on that information. Furthermore, the location information acquisition unit can customize a warning message based on the user's location information. This allows for more accurate assessment of the risk of fraud by utilizing location information.

[0067] The fraud detection system may further include a reliability evaluation unit that evaluates the reliability of the caller's telephone number and past call history. The reliability evaluation unit evaluates reliability by analyzing the caller's telephone number and past call history. For example, if the caller's telephone number has been associated with fraud in the past, the possibility of fraud is evaluated based on that information. The reliability evaluation unit may also notify the warning unit based on the caller's reliability. For example, if a call is made from a caller with low reliability, the warning may be issued based on that information. Furthermore, the reliability evaluation unit may suggest specific countermeasures to the user based on the caller's reliability. In this way, by evaluating the caller's reliability, the risk of fraud can be more accurately assessed.

[0068] The fraud detection system may further include a translation unit that translates the content of a user's call in real time. The translation unit translates the content of the call in real time and provides it to the user. For example, the translation unit may translate the content of a call in a foreign language into the user's native language to detect signs of fraud. The translation unit may also assess the possibility of fraud based on the translation result of the call content. For example, if the translated content of the call contains signs of fraud, the translation unit may assess the possibility of fraud based on that information. The translation unit may also notify the warning unit based on the translation result of the call content. In this way, by translating the content of the call, the risk of fraud can be assessed more comprehensively.

[0069] The fraud detection system may further include a database for analyzing the content of a user's calls and detecting signs of fraud. The database stores past fraud cases and typical fraud patterns and compares them with the content of the calls. For example, it detects specific phrases or patterns based on past fraud cases. The database may also assess the likelihood of fraud based on the results of the analysis of the call content. For example, if the call matches a typical fraud pattern registered in the database, it may assess the likelihood of fraud based on that information. Furthermore, the database may notify the warning unit based on the results of the analysis of the call content. This allows for more accurate assessment of the risk of fraud by utilizing the database.

[0070] The fraud detection system may further include an emotion analysis unit that estimates the user's emotions and analyzes the call content based on the estimated emotions. The emotion analysis unit analyzes the user's emotions during the call in real time to detect signs of fraud. For example, if the user suddenly feels anxious or scared, the emotion change is detected and the possibility of fraud is assessed. The emotion analysis unit can also analyze the emotional tone of the call content to identify moments when the possibility of fraud increases. For example, the possibility of fraud is assessed by detecting parts where the user suddenly becomes emotional. The emotion analysis unit can also notify the warning unit based on the emotional tone of the call content. This allows for a more accurate assessment of the risk of fraud by analyzing emotions.

[0071] The fraud detection system may further include an alert customization unit that estimates the user's emotions and customizes the alert message based on the estimated emotions. The alert customization unit analyzes the user's emotions during a call in real time and generates an appropriate alert message. For example, if the user is feeling anxious, the alert message is generated taking into consideration the user's emotions. The alert customization unit may also analyze the emotional tone of the call content and issue an alert to the user based on the emotions. For example, if the user suddenly becomes emotional, the alert customization unit may detect this change and issue an alert. The alert customization unit may also customize the alert message based on the emotional tone of the call content. This allows the user to respond calmly by providing an emotionally based alert message.

[0072] The fraud detection system may further include an emotion recording unit that estimates the user's emotions and records the call content based on the estimated emotions. The emotion recording unit analyzes the user's emotions during the call in real time and records changes in emotions. For example, if the user suddenly feels anxiety or fear, the emotion recording unit records the change in emotion. The emotion recording unit may also analyze the emotional tone of the call content and highlight parts that are likely to be fraudulent. For example, it may detect and highlight parts where the user suddenly becomes emotional. The emotion recording unit may also record the call content based on the emotional tone and analyze it later. By recording emotions, the risk of fraud can be more accurately assessed.

[0073] The fraud detection system may further include an emotion summarization unit that estimates the user's emotion and summarizes the call content based on the estimated emotion. The emotion summarization unit analyzes the user's emotion during the call in real time and summarizes important parts. For example, it summarizes parts where the user suddenly feels anxiety or fear. The emotion summarization unit may also analyze the emotional tone of the call content and summarize parts that are likely to be fraudulent. For example, it may detect and summarize parts where the user suddenly becomes emotional. Furthermore, the emotion summarization unit may summarize the call content based on the emotional tone and provide it to the user. In this way, by summarizing emotions, the risk of fraud can be more accurately assessed.

[0074] The fraud detection system may further include an emotion filtering unit that estimates the user's emotions and filters the call content based on the estimated emotions. The emotion filtering unit analyzes the user's emotions during the call in real time and filters out parts that are likely to be fraudulent. For example, it filters out parts where the user suddenly feels anxiety or fear. The emotion filtering unit may also analyze the emotional tone of the call content and filter out parts that are likely to be fraudulent. For example, it may detect and filter out parts where the user suddenly becomes emotional. The emotion filtering unit may also filter the call content based on the emotional tone and provide it to the user. In this way, filtering emotions allows for a more accurate assessment of the risk of fraud.

[0075] The processing flow of the second embodiment will be briefly explained below.

[0076] Step 1: The call analysis unit analyzes the content of the call in real time. For example, the generation AI analyzes the voice data during the call in real time to detect signs of fraud from specific phrases and the overall context. The generation AI can also analyze background and environmental sounds during the call to detect signs of fraud. Furthermore, the generation AI uses emotion estimation to analyze changes in the speaker's emotions during the call and identify moments when the possibility of fraud increases. For example, the generation AI detects specific phrases during the call, such as "I want you to transfer money" or "Please respond quickly," and determines the possibility of fraud. The generation AI also analyzes the entire context of the call to understand not only specific phrases but also the flow of the conversation and the speaker's intention. The generation AI also analyzes background and environmental sounds during the call to detect unnatural sounds and typical patterns of fraud. Step 2: The warning unit issues a warning to the user based on the signs of fraud detected by the call analysis unit. For example, the warning unit may play a voice message during the call such as, "This call may be fraudulent. Please be careful." The warning unit may also display a warning message on the user's smartphone. Furthermore, if the warning unit detects signs of fraud, it may play a voice message suggesting specific countermeasures to the user. For example, it may play a message such as, "Always check with your family before transferring money." Step 3: The recording unit records the call content and analyzes it later. For example, the generative AI converts the call content into text and highlights potential fraudulent parts. The recording unit can also compare the analysis results of the call content with the user's past call history to detect abnormal patterns. The recording unit also automatically saves the call content recording to the user's cloud storage for later access.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0081] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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).

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0090] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0096] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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).

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0105] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0121] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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."

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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]

[0144] 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 call content in real time; a warning unit that issues a warning to a user based on the fraud indications detected by the call analysis unit; a recording unit that records the contents of the call and analyzes them later. A system characterized by:

2. The call analysis unit Determine potential fraudulent activity based on the entire context of the call, not just specific phrases 2. The system of claim 1.

3. The call analysis unit Applying the analysis of the call content to the content of text messages or chats to detect indicators of fraud.

2. The system of claim 1.

4. The warning unit If the fraud indication is detected, a voice message is played to the user suggesting specific countermeasures.

2. The system of claim 1.

5. The recording unit When recording the call, the generation AI automatically summarizes the important parts and provides them to the user.

2. The system of claim 1.

6. The call analysis unit Analyzing changes in speaker emotion during a call to identify moments when fraud is more likely 2. The system of claim 1.

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