System

Abstract

An object of a system according to an embodiment is to automatically determine the possibility of special fraud and issue a warning.SOLUTION: A system includes a voice data acquisition unit, a determination unit, and a warning unit. The voice data acquisition unit acquires voice data. The determination unit analyzes the voice data acquired by the voice data acquisition unit and determines the possibility of a special fraud. The warning unit issues a warning based on the possibility of a special fraud determined by the determination unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology had the problem of making it difficult to identify the possibility of special fraud in real time and issue a warning.

[0005] The system according to the embodiment aims to automatically determine the possibility of special fraud and issue a warning. [Means for solving the problem]

[0006] The system according to the embodiment includes a voice data acquisition unit, a determination unit, and a warning unit. The voice data acquisition unit acquires voice data. The determination unit analyzes the voice data acquired by the voice data acquisition unit and determines the possibility of special fraud. The warning unit issues a warning based on the possibility of special fraud determined by the determination unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically determine the possibility of special fraud and issue a warning. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The special fraud detection system according to the embodiment of the present invention is a system that automatically detects the possibility of special fraud from the content of a conversation and issues a warning. By automatically detecting the possibility of special fraud and issuing a warning, the special fraud detection system can reduce the number of victims.

[0029] The special fraud detection system according to the embodiment includes a voice data acquisition unit, a determination unit, and a warning unit. The voice data acquisition unit acquires voice data. For example, the voice data acquisition unit acquires recorded data from a telephone call. The voice data acquisition unit can also acquire real-time voice streams. The voice data acquisition unit can also acquire voice data using a smartphone microphone. The determination unit analyzes the voice data acquired by the voice data acquisition unit to determine the possibility of special fraud. For example, the determination unit converts the voice data into text and analyzes the content. The determination unit can also perform detailed analysis of voice features such as the speaker's tone of voice, speed, and pauses to identify patterns that increase the possibility of fraud. The determination unit can also use an emotion estimation function to estimate the speaker's emotion during a conversation in real time and detect emotional changes that increase the possibility of fraud. For example, the determination unit uses voice recognition technology to convert the voice data into text. The determination unit uses natural language processing technology to analyze the voice data. The determination unit estimates the speaker's emotion using voice emotion recognition technology. The warning unit issues a warning based on the possibility of special fraud determined by the determination unit. For example, the warning unit sends the warning through a smartphone notification. The warning unit can also send the warning through email. The warning unit can also issue a voice warning. For example, the warning unit sends the warning using a smartphone notification function. The warning unit sends the warning using an email server. The warning unit issues a voice warning using voice synthesis technology. As a result, the special fraud detection system according to the embodiment can automatically determine the possibility of special fraud and issue a warning, thereby reducing the number of victims.

[0030] The determination unit can convert voice data into text and analyze its content. For example, when recording voice data, the determination unit simultaneously collects surrounding environmental and background sounds. For example, it analyzes music and noise playing in the background of a phone call to identify factors that increase the likelihood of fraud. The determination unit also analyzes the background sounds contained in the recorded voice data to determine whether specific patterns or noises are signs of fraud. For example, if a specific acoustic pattern is associated with a fraudulent method, it detects that pattern. The determination unit also identifies factors that increase the likelihood of fraud by analyzing environmental sounds. For example, if a specific environmental sound matches a fraud scenario, it detects that sound and issues a warning. In this way, by converting voice data into text and analyzing its content, it is possible to more accurately determine the possibility of special fraud.

[0031] The determination unit can identify patterns that increase the likelihood of fraud by analyzing in detail voice features such as the speaker's tone of voice, speed, and pauses. The determination unit can, for example, analyze in detail the speaker's tone of voice, speed, and pauses to identify voice patterns that increase the likelihood of fraud. For example, if a hurried speaking tone or unnatural pauses are signs of fraud, the determination unit detects these patterns. The determination unit can also analyze voice features to identify patterns that increase the likelihood of fraud. For example, if the tone of voice changes suddenly or the speaker appears nervous, the determination unit detects these characteristics and issues a warning. The determination unit can also analyze the speaker's speed of voice and pauses to identify patterns that increase the likelihood of fraud. For example, if the speaker speaks in a hurried manner or without pauses, the determination unit detects these characteristics and issues a warning. In this way, by analyzing the speaker's voice features in detail, it is possible to identify patterns that increase the likelihood of fraud.

[0032] The determination unit can extend the recording and analysis of voice data to the content of text messages and chats, thereby determining the possibility of fraud from multiple angles. The determination unit, for example, can extend the recording and analysis of voice data to the content of text messages and chats. For example, it analyzes the content of text messages to determine the possibility of fraud. The determination unit can also analyze the content of chats to determine the possibility of fraud from multiple angles. For example, it can analyze the context and keywords of chats to detect signs of fraud. The determination unit can also analyze the content of text messages and chats to determine the possibility of fraud from multiple angles. For example, it can analyze the context and keywords of text messages to detect signs of fraud. In this way, by analyzing not only voice data but also the content of text messages and chats, it is possible to determine the possibility of fraud from multiple angles.

[0033] The judgment unit simultaneously collects the speaker's facial recognition data when recording audio data, allowing the generation AI to perform an integrated analysis of the voice and facial expression. For example, the judgment unit simultaneously collects the speaker's facial recognition data when recording audio data. For example, it may use a camera to capture a photo of the speaker's face and analyze the data. The judgment unit also performs an integrated analysis of the audio data and facial recognition data to determine the possibility of fraud. For example, it may analyze the speaker's facial expression and tone of voice to detect signs of fraud. The judgment unit also uses facial recognition data to analyze the speaker's facial expression and determine the possibility of fraud. For example, if the speaker appears nervous or has an unnatural facial expression, it analyzes the data and issues a warning. This allows for more accurate determination of the possibility of fraud by performing an integrated analysis of the audio data and facial recognition data.

[0034] The judgment unit can compare the results with a database of past fraud cases and detect similar patterns. For example, the generation AI compares the results with a database of past fraud cases and detects similar patterns. For example, it detects keywords and phrases that match past fraud cases. The judgment unit also has the generation AI detect similar patterns based on the fraud case database. For example, it analyzes contexts and scenarios that match past fraud cases. The judgment unit also has the generation AI compare the results with a database of past fraud cases and detects similar patterns. For example, it analyzes voice features and speaker tone that match past fraud cases. This makes it possible to detect similar patterns by comparing the results with a database of past fraud cases and increase the likelihood of fraud.

[0035] When determining the possibility of fraud, the determination unit takes into account the context of the conversation and the speaker's background information (age, occupation, etc.), allowing for more accurate determination. For example, when determining the possibility of fraud, the determination unit takes into account the context of the conversation. For example, it analyzes the flow of the conversation and the speaker's intentions to detect signs of fraud. The determination unit also takes into account the speaker's background information (age, occupation, etc.) to determine the possibility of fraud. For example, it identifies patterns of fraud that target elderly people. The determination unit also performs an integrated analysis of the conversation context and the speaker's background information to determine the possibility of fraud. For example, it detects patterns of fraud targeting specific occupations or age groups. In this way, by taking into account the conversation context and the speaker's background information, it is possible to more accurately determine the possibility of fraud.

[0036] The judgment unit can apply the algorithm that determines the possibility of special fraud to other frauds (e.g., phishing fraud and investment fraud), thereby building a comprehensive fraud prevention system. The judgment unit, for example, applies the algorithm that determines the possibility of special fraud to phishing fraud. For example, it analyzes the contents of emails and websites to detect signs of fraud. The judgment unit can also build an algorithm that determines the possibility of investment fraud, thereby building a comprehensive fraud prevention system. For example, it can analyze the contents of investment projects to detect signs of fraud. The judgment unit can also develop an algorithm that can be applied to other frauds (e.g., phishing fraud and investment fraud), thereby building a comprehensive fraud prevention system. For example, it can analyze different fraud patterns to detect signs of fraud. This makes it possible to build a comprehensive fraud prevention system that can deal not only with special frauds, but also with other frauds.

[0037] The judgment unit can link the judgment result with the user's credit score and risk profile to perform an individual risk assessment. The judgment unit, for example, links the judgment result with the user's credit score to perform an individual risk assessment. For example, it determines that a user with a low credit score has a high risk of fraud. The judgment unit also links the judgment result with the user's risk profile to perform an individual risk assessment. For example, it determines that a user who has a history of being a victim of fraud has a high risk of fraud. The judgment unit also analyzes the judgment result in an integrated manner with the user's credit score and risk profile to perform an individual risk assessment. For example, it evaluates the risk of fraud based on the credit score and risk profile. In this way, by linking the judgment result with the user's credit score and risk profile, an individual risk assessment can be performed.

[0038] The warning unit can send a warning at the optimal timing and in the optimal manner based on the user's past behavioral history and response patterns. The warning unit, for example, analyzes the user's past behavioral history and sends a warning at the optimal timing. For example, if a user is at high risk of fraud during a specific time period, the warning is sent during that time period. The warning unit also takes into account the user's response pattern and sends a warning in the optimal manner. For example, if a user is more likely to respond to smartphone notifications than emails, the warning is sent preferentially. The warning unit also comprehensively analyzes the user's past behavioral history and response patterns and sends a warning at the optimal timing and in the optimal manner. For example, if a user is at high risk of fraud on a specific day of the week, the warning is sent on that day. In this way, the warning can be sent at the optimal timing and in the optimal manner by taking into account the user's past behavioral history and response patterns.

[0039] The warning unit can have the generation AI automatically generate the content of the warning message and provide it in a format that is easiest for the user to understand. The warning unit, for example, uses the generation AI to automatically generate the content of the warning message. For example, if there is a high risk of fraud, it generates a message that includes specific countermeasures. The warning unit also provides the warning message in a format that is easiest for the user to understand. For example, it generates a message using concise and clear language. The warning unit also has the generation AI learn the user's past response patterns and automatically generate the optimal warning message. For example, it generates a new message based on the format of a message that the user found easy to understand in the past. This allows the generation AI to automatically generate a warning message and provide it in a format that is easiest for the user to understand, thereby increasing the effectiveness of the warning.

[0040] The alert unit can extend the transmission of the alert to at least one other device, such as a smartwatch or a smart speaker, to provide the alert through multiple channels. For example, the alert unit can extend the transmission of the alert to a smartwatch, allowing the user to check the alert at their fingertips. For example, the alert can be sent using a notification function of the smartwatch. The alert unit can also provide an audio alert using a smart speaker. For example, if the user is at home, the alert can be transmitted via audio through the smart speaker. The alert unit can also build a system that provides alerts using multiple devices. For example, the alert can be sent to all of a smartphone, a smartwatch, and a smart speaker, allowing the user to check the alert on any device. In this way, the alert can be extended to multiple devices, allowing the user to check the alert on any device.

[0041] The warning unit can customize the warning message to suit the user's language or culture, thereby supporting international use. For example, the warning unit customizes the warning message to suit the user's language. For example, the warning is provided in the user's native language, such as English, French, or Chinese. The warning unit also customizes the warning message to suit the user's culture. For example, the message is generated using expressions and examples that take cultural background into consideration. The warning unit also makes the warning message multilingual to support international use. For example, the warning is provided in a language selected by the user, thereby supporting users from different cultural backgrounds. In this way, the warning message can be customized to suit the user's language or culture, thereby supporting international use.

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

[0043] The determination unit can analyze a user's past behavioral history and identify patterns that increase the likelihood of fraud. For example, it can analyze the behavioral patterns of users who have fallen victim to fraud in the past and issue a warning if a similar pattern occurs again. The determination unit can also identify cases where the risk of fraud is higher during specific time periods or situations based on the user's behavioral history. For example, if the risk of fraud is higher during a specific time period, it issues a warning during that time period. The determination unit can also analyze a user's behavioral history and identify specific behavioral patterns that increase the risk of fraud. For example, it issues a warning if a specific website is visited. In this way, by analyzing a user's past behavioral history, it is possible to identify patterns that increase the likelihood of fraud and issue more effective warnings.

[0044] The determination unit can analyze the characteristics of the user's voice and compare it with an individual voiceprint database to determine the possibility of fraud. For example, a voiceprint database of users can be constructed and voiceprint patterns that are likely to be fraudulent can be identified. The determination unit can also determine whether a specific voiceprint pattern is a sign of fraud based on the voiceprint database. For example, if a specific voiceprint pattern matches a past fraud case, the determination unit detects the pattern and issues a warning. The determination unit can also use the voiceprint database to analyze the characteristics of the user's voice and determine the possibility of fraud. For example, if a specific voiceprint pattern is associated with a fraudulent method, the determination unit detects the pattern and issues a warning. In this way, the use of the voiceprint database can more accurately determine the possibility of fraud.

[0045] The determination unit can analyze a user's behavioral patterns and identify behaviors that increase the likelihood of fraud. For example, if a user frequently visits a specific website, the determination unit can detect that behavior and issue a warning. The determination unit can also identify cases where the risk of fraud is higher during specific times of the day or in specific situations based on the user's behavioral patterns. For example, if the risk of fraud is higher during a specific time of day, the determination unit can issue a warning during that time of day. The determination unit can also analyze a user's behavioral patterns and identify specific behaviors that increase the risk of fraud. For example, the determination unit can issue a warning when a specific application is used. In this way, by analyzing a user's behavioral patterns, it is possible to identify behaviors that increase the likelihood of fraud and issue more effective warnings.

[0046] The determination unit can analyze the user's location information and identify locations that increase the possibility of fraud. For example, if there is a high risk of fraud in a specific area, a warning is issued to users in that area. The determination unit can also identify cases where there is a high risk of fraud in a specific location based on the user's location information. For example, if there is a high risk of fraud in a specific store or facility, a warning is issued to users in that location. The determination unit can also analyze the user's location information and identify specific locations where there is a high risk of fraud. For example, if there is a high risk of fraud at a specific event venue, a warning is issued to users in that location. In this way, by analyzing the user's location information, it is possible to identify locations that increase the possibility of fraud and issue more effective warnings.

[0047] The determination unit can analyze a user's social media activity and identify posts and messages that increase the likelihood of fraud. For example, it can detect posts that contain specific keywords or phrases and issue a warning. The determination unit can also determine whether a specific post or message is a sign of fraud based on the user's social media activity. For example, it can detect posts that contain specific links or images and issue a warning. The determination unit can also analyze social media activity and identify specific posts and messages that increase the risk of fraud. For example, if a message from a specific account is related to a fraudulent method, it can detect that message and issue a warning. In this way, by analyzing social media activity, it is possible to identify posts and messages that increase the likelihood of fraud and issue more effective warnings.

[0048] The determination unit can analyze a user's purchase history and identify purchase patterns that increase the likelihood of fraud. For example, if a specific product or service is frequently purchased, the determination unit can detect that pattern and issue a warning. The determination unit can also determine whether a specific purchase pattern is a sign of fraud based on the user's purchase history. For example, if purchases at a specific store or online shop are associated with fraudulent methods, the determination unit can detect that pattern and issue a warning. The determination unit can also analyze the purchase history and identify specific purchase patterns that increase the risk of fraud. For example, if purchases made during a specific time period or situation increase the risk of fraud, the determination unit can detect that pattern and issue a warning. In this way, by analyzing the purchase history, it is possible to identify purchase patterns that increase the likelihood of fraud and issue more effective warnings.

[0049] The judgment unit can analyze the content of a user's email and identify emails that increase the likelihood of fraud. For example, it detects emails that contain specific keywords or phrases and issues a warning. The judgment unit can also determine whether a specific email is a sign of fraud based on the content of the user's email. For example, it detects emails that contain specific links or attachments and issues a warning. The judgment unit can also analyze the content of emails and identify specific emails that increase the risk of fraud. For example, if an email from a specific sender is related to a fraudulent method, it detects that email and issues a warning. In this way, by analyzing the content of emails, it is possible to identify emails that increase the likelihood of fraud and issue more effective warnings.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The voice data acquisition unit acquires voice data. For example, the voice data can be acquired using recorded data from a telephone call, a real-time voice stream, or a smartphone microphone. Step 2: The determination unit analyzes the voice data acquired by the voice data acquisition unit and determines the possibility of special fraud. For example, it converts the voice data into text and analyzes its content. It can also perform detailed analysis of voice characteristics such as the speaker's tone of voice, speed, and pauses to identify patterns that increase the possibility of fraud. Furthermore, it can use an emotion estimation function to estimate the speaker's emotions during a conversation in real time and detect emotional changes that increase the possibility of fraud. Step 3: The warning unit issues a warning based on the possibility of special fraud determined by the judgment unit. For example, the warning can be sent via smartphone notification, email, or voice alert.

[0052] (Example 2) The special fraud detection system according to the embodiment of the present invention is a system that automatically detects the possibility of special fraud from the content of a conversation and issues a warning. By automatically detecting the possibility of special fraud and issuing a warning, the special fraud detection system can reduce the number of victims.

[0053] The special fraud detection system according to the embodiment includes a voice data acquisition unit, a determination unit, and a warning unit. The voice data acquisition unit acquires voice data. For example, the voice data acquisition unit acquires recorded data from a telephone call. The voice data acquisition unit can also acquire real-time voice streams. The voice data acquisition unit can also acquire voice data using a smartphone microphone. The determination unit analyzes the voice data acquired by the voice data acquisition unit to determine the possibility of special fraud. For example, the determination unit converts the voice data into text and analyzes the content. The determination unit can also perform detailed analysis of voice features such as the speaker's tone of voice, speed, and pauses to identify patterns that increase the possibility of fraud. The determination unit can also use an emotion estimation function to estimate the speaker's emotion during a conversation in real time and detect emotional changes that increase the possibility of fraud. For example, the determination unit uses voice recognition technology to convert the voice data into text. The determination unit uses natural language processing technology to analyze the voice data. The determination unit estimates the speaker's emotion using voice emotion recognition technology. The warning unit issues a warning based on the possibility of special fraud determined by the determination unit. For example, the warning unit sends the warning through a smartphone notification. The warning unit can also send the warning through email. The warning unit can also issue a voice warning. For example, the warning unit sends the warning using a smartphone notification function. The warning unit sends the warning using an email server. The warning unit issues a voice warning using voice synthesis technology. As a result, the special fraud detection system according to the embodiment can automatically determine the possibility of special fraud and issue a warning, thereby reducing the number of victims.

[0054] The determination unit can convert voice data into text and analyze its content. For example, when recording voice data, the determination unit simultaneously collects surrounding environmental and background sounds. For example, it analyzes music and noise playing in the background of a phone call to identify factors that increase the likelihood of fraud. The determination unit also analyzes the background sounds contained in the recorded voice data to determine whether specific patterns or noises are signs of fraud. For example, if a specific acoustic pattern is associated with a fraudulent method, it detects that pattern. The determination unit also identifies factors that increase the likelihood of fraud by analyzing environmental sounds. For example, if a specific environmental sound matches a fraud scenario, it detects that sound and issues a warning. In this way, by converting voice data into text and analyzing its content, it is possible to more accurately determine the possibility of special fraud.

[0055] The determination unit can identify patterns that increase the likelihood of fraud by analyzing in detail voice features such as the speaker's tone of voice, speed, and pauses. The determination unit can, for example, analyze in detail the speaker's tone of voice, speed, and pauses to identify voice patterns that increase the likelihood of fraud. For example, if a hurried speaking tone or unnatural pauses are signs of fraud, the determination unit detects these patterns. The determination unit can also analyze voice features to identify patterns that increase the likelihood of fraud. For example, if the tone of voice changes suddenly or the speaker appears nervous, the determination unit detects these characteristics and issues a warning. The determination unit can also analyze the speaker's speed of voice and pauses to identify patterns that increase the likelihood of fraud. For example, if the speaker speaks in a hurried manner or without pauses, the determination unit detects these characteristics and issues a warning. In this way, by analyzing the speaker's voice features in detail, it is possible to identify patterns that increase the likelihood of fraud.

[0056] The determination unit uses the emotion estimation function to estimate the emotion of a speaker during a conversation in real time, and can detect emotional changes that increase the likelihood of fraud. The determination unit, for example, uses the emotion estimation function to estimate the emotion of a speaker during a conversation in real time. For example, if the speaker is nervous or angry, the determination unit detects that emotion and issues a warning. The determination unit also analyzes emotional changes during a conversation in real time and identifies emotional patterns that increase the likelihood of fraud. For example, if the speaker suddenly becomes anxious, the determination unit detects that emotional change and issues a warning. The determination unit also uses the emotion estimation function to monitor the speaker's emotion in real time and detects emotional changes that increase the likelihood of fraud. For example, if the speaker suddenly becomes nervous, the determination unit detects that emotional change and issues a warning. In this way, by using the emotion estimation function, emotional changes that increase the likelihood of fraud can be detected in real time.

[0057] The determination unit can extend the recording and analysis of voice data to the content of text messages and chats, thereby determining the possibility of fraud from multiple angles. The determination unit, for example, can extend the recording and analysis of voice data to the content of text messages and chats. For example, it analyzes the content of text messages to determine the possibility of fraud. The determination unit can also analyze the content of chats to determine the possibility of fraud from multiple angles. For example, it can analyze the context and keywords of chats to detect signs of fraud. The determination unit can also analyze the content of text messages and chats to determine the possibility of fraud from multiple angles. For example, it can analyze the context and keywords of text messages to detect signs of fraud. In this way, by analyzing not only voice data but also the content of text messages and chats, it is possible to determine the possibility of fraud from multiple angles.

[0058] The judgment unit simultaneously collects the speaker's facial recognition data when recording audio data, allowing the generation AI to perform an integrated analysis of the voice and facial expression. For example, the judgment unit simultaneously collects the speaker's facial recognition data when recording audio data. For example, it may use a camera to capture a photo of the speaker's face and analyze the data. The judgment unit also performs an integrated analysis of the audio data and facial recognition data to determine the possibility of fraud. For example, it may analyze the speaker's facial expression and tone of voice to detect signs of fraud. The judgment unit also uses facial recognition data to analyze the speaker's facial expression and determine the possibility of fraud. For example, if the speaker appears nervous or has an unnatural facial expression, it analyzes the data and issues a warning. This allows for more accurate determination of the possibility of fraud by performing an integrated analysis of the audio data and facial recognition data.

[0059] The determination unit can use the emotion estimation function to estimate the speaker's emotion during recording and provide feedback in real time to elicit positive emotions. The determination unit, for example, uses the emotion estimation function to estimate the speaker's emotion during recording. For example, if the speaker is nervous, it detects that emotion and provides positive feedback. The determination unit also analyzes the speaker's emotion in real time during recording and provides feedback to elicit positive emotions. For example, if the speaker is feeling anxious, it detects that emotion and sends an encouraging message. The determination unit also uses the emotion estimation function to estimate the speaker's emotion during recording and provide feedback in real time to elicit positive emotions. For example, if the speaker is nervous, it detects that emotion and encourages the speaker to relax. In this way, by estimating the speaker's emotion during recording and providing feedback to elicit positive emotions, the speaker's psychological burden can be reduced.

[0060] The judgment unit can compare the results with a database of past fraud cases and detect similar patterns. For example, the generation AI compares the results with a database of past fraud cases and detects similar patterns. For example, it detects keywords and phrases that match past fraud cases. The judgment unit also has the generation AI detect similar patterns based on the fraud case database. For example, it analyzes contexts and scenarios that match past fraud cases. The judgment unit also has the generation AI compare the results with a database of past fraud cases and detects similar patterns. For example, it analyzes voice features and speaker tone that match past fraud cases. This makes it possible to detect similar patterns by comparing the results with a database of past fraud cases and increase the likelihood of fraud.

[0061] When determining the possibility of fraud, the determination unit takes into account the context of the conversation and the speaker's background information (age, occupation, etc.), allowing for more accurate determination. For example, when determining the possibility of fraud, the determination unit takes into account the context of the conversation. For example, it analyzes the flow of the conversation and the speaker's intentions to detect signs of fraud. The determination unit also takes into account the speaker's background information (age, occupation, etc.) to determine the possibility of fraud. For example, it identifies patterns of fraud that target elderly people. The determination unit also performs an integrated analysis of the conversation context and the speaker's background information to determine the possibility of fraud. For example, it detects patterns of fraud targeting specific occupations or age groups. In this way, by taking into account the conversation context and the speaker's background information, it is possible to more accurately determine the possibility of fraud.

[0062] The determination unit can use the emotion estimation function to analyze changes in emotions during a conversation and identify emotion patterns that increase the likelihood of fraud. The determination unit, for example, uses the emotion estimation function to analyze changes in emotions during a conversation. For example, if a speaker suddenly becomes nervous, the determination unit detects this change in emotion and determines the possibility of fraud. The determination unit also analyzes changes in emotions during a conversation in real time and identifies emotion patterns that increase the likelihood of fraud. For example, if a speaker suddenly feels anxious, the determination unit detects this change in emotion and issues a warning. The determination unit also uses the emotion estimation function to analyze changes in emotions during a conversation and identify emotion patterns that increase the likelihood of fraud. For example, if a speaker suddenly becomes angry, the determination unit detects this change in emotion and issues a warning. In this way, by using the emotion estimation function, emotion patterns that increase the likelihood of fraud can be identified.

[0063] The judgment unit can apply the algorithm that determines the possibility of special fraud to other frauds (e.g., phishing fraud and investment fraud), thereby building a comprehensive fraud prevention system. The judgment unit, for example, applies the algorithm that determines the possibility of special fraud to phishing fraud. For example, it analyzes the contents of emails and websites to detect signs of fraud. The judgment unit can also build an algorithm that determines the possibility of investment fraud, thereby building a comprehensive fraud prevention system. For example, it can analyze the contents of investment projects to detect signs of fraud. The judgment unit can also develop an algorithm that can be applied to other frauds (e.g., phishing fraud and investment fraud), thereby building a comprehensive fraud prevention system. For example, it can analyze different fraud patterns to detect signs of fraud. This makes it possible to build a comprehensive fraud prevention system that can deal not only with special frauds, but also with other frauds.

[0064] The judgment unit can link the judgment result with the user's credit score and risk profile to perform an individual risk assessment. The judgment unit, for example, links the judgment result with the user's credit score to perform an individual risk assessment. For example, it determines that a user with a low credit score has a high risk of fraud. The judgment unit also links the judgment result with the user's risk profile to perform an individual risk assessment. For example, it determines that a user who has a history of being a victim of fraud has a high risk of fraud. The judgment unit also analyzes the judgment result in an integrated manner with the user's credit score and risk profile to perform an individual risk assessment. For example, it evaluates the risk of fraud based on the credit score and risk profile. In this way, by linking the judgment result with the user's credit score and risk profile, an individual risk assessment can be performed.

[0065] The determination unit can use the emotion estimation function to automatically generate an emotional support message for the user based on the determination result, thereby providing a sense of security. The determination unit, for example, uses the emotion estimation function to automatically generate an emotional support message for the user based on the determination result. For example, if the risk of fraud is high, the determination unit sends a message that provides a sense of security. The determination unit also automatically generates an emotional support message for the user based on the determination result, thereby providing a sense of security. For example, if the risk of fraud is low, the determination unit also uses the emotion estimation function to automatically generate an emotional support message for the user based on the determination result, thereby providing a sense of security. For example, if the risk of fraud is medium, the determination unit sends a message that warns the user. In this way, by using the emotion estimation function, an emotional support message for the user can be automatically generated, thereby providing a sense of security.

[0066] The warning unit can send a warning at the optimal timing and in the optimal manner based on the user's past behavioral history and response patterns. The warning unit, for example, analyzes the user's past behavioral history and sends a warning at the optimal timing. For example, if a user is at high risk of fraud during a specific time period, the warning is sent during that time period. The warning unit also takes into account the user's response pattern and sends a warning in the optimal manner. For example, if a user is more likely to respond to smartphone notifications than emails, the warning is sent preferentially. The warning unit also comprehensively analyzes the user's past behavioral history and response patterns and sends a warning at the optimal timing and in the optimal manner. For example, if a user is at high risk of fraud on a specific day of the week, the warning is sent on that day. In this way, the warning can be sent at the optimal timing and in the optimal manner by taking into account the user's past behavioral history and response patterns.

[0067] The warning unit can have the generation AI automatically generate the content of the warning message and provide it in a format that is easiest for the user to understand. The warning unit, for example, uses the generation AI to automatically generate the content of the warning message. For example, if there is a high risk of fraud, it generates a message that includes specific countermeasures. The warning unit also provides the warning message in a format that is easiest for the user to understand. For example, it generates a message using concise and clear language. The warning unit also has the generation AI learn the user's past response patterns and automatically generate the optimal warning message. For example, it generates a new message based on the format of a message that the user found easy to understand in the past. This allows the generation AI to automatically generate a warning message and provide it in a format that is easiest for the user to understand, thereby increasing the effectiveness of the warning.

[0068] The warning unit can use the emotion estimation function to monitor the emotional reaction of a user who receives the warning in real time and send an additional support message as needed. The warning unit, for example, uses the emotion estimation function to monitor the emotional reaction of a user who receives the warning in real time. For example, if the user is feeling anxious, the warning unit detects that emotion and sends an additional support message. The warning unit also analyzes the user's emotional reaction in real time and sends an additional support message as needed. For example, if the user is feeling surprised by the warning, the warning unit detects that emotion and sends a message providing a sense of security. The warning unit also uses the emotion estimation function to monitor the emotional reaction of a user who receives the warning and sends an additional support message as needed. For example, if the user is feeling doubtful about the warning, the warning unit detects that emotion and sends a message including a detailed explanation. In this way, by using the emotion estimation function, the user's emotional reaction can be monitored in real time and an additional support message can be sent as needed.

[0069] The alert unit can extend the transmission of the alert to at least one other device, such as a smartwatch or a smart speaker, to provide the alert through multiple channels. For example, the alert unit can extend the transmission of the alert to a smartwatch, allowing the user to check the alert at their fingertips. For example, the alert can be sent using a notification function of the smartwatch. The alert unit can also provide an audio alert using a smart speaker. For example, if the user is at home, the alert can be transmitted via audio through the smart speaker. The alert unit can also build a system that provides alerts using multiple devices. For example, the alert can be sent to all of a smartphone, a smartwatch, and a smart speaker, allowing the user to check the alert on any device. In this way, the alert can be extended to multiple devices, allowing the user to check the alert on any device.

[0070] The warning unit can customize the warning message to suit the user's language or culture, thereby supporting international use. For example, the warning unit customizes the warning message to suit the user's language. For example, the warning is provided in the user's native language, such as English, French, or Chinese. The warning unit also customizes the warning message to suit the user's culture. For example, the message is generated using expressions and examples that take cultural background into consideration. The warning unit also makes the warning message multilingual to support international use. For example, the warning is provided in a language selected by the user, thereby supporting users from different cultural backgrounds. In this way, the warning message can be customized to suit the user's language or culture, thereby supporting international use.

[0071] The warning unit can use the emotion estimation function to adjust the content of the warning message to match the emotional state of the user and provide the most effective warning. The warning unit, for example, uses the emotion estimation function to adjust the content of the warning message to match the emotional state of the user. For example, if the user is feeling anxious, it sends a message that alleviates the emotion. The warning unit also analyzes the emotional state of the user in real time and provides the most effective warning message. For example, if the user is feeling surprised by the warning, it sends a message that alleviates the emotion. The warning unit also uses the emotion estimation function to adjust the content of the warning message to match the emotional state of the user and provide the most effective warning. For example, if the user is feeling doubtful about the warning, it sends a message that alleviates the emotion. In this way, by using the emotion estimation function, the most effective warning can be provided that matches the emotional state of the user.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The determination unit can analyze a user's past behavioral history and identify patterns that increase the likelihood of fraud. For example, it can analyze the behavioral patterns of users who have fallen victim to fraud in the past and issue a warning if a similar pattern occurs again. The determination unit can also identify cases where the risk of fraud is higher during specific time periods or situations based on the user's behavioral history. For example, if the risk of fraud is higher during a specific time period, it issues a warning during that time period. The determination unit can also analyze a user's behavioral history and identify specific behavioral patterns that increase the risk of fraud. For example, it issues a warning if a specific website is visited. In this way, by analyzing a user's past behavioral history, it is possible to identify patterns that increase the likelihood of fraud and issue more effective warnings.

[0074] The determination unit can analyze the characteristics of the user's voice and compare it with an individual voiceprint database to determine the possibility of fraud. For example, a voiceprint database of users can be constructed and voiceprint patterns that are likely to be fraudulent can be identified. The determination unit can also determine whether a specific voiceprint pattern is a sign of fraud based on the voiceprint database. For example, if a specific voiceprint pattern matches a past fraud case, the determination unit detects the pattern and issues a warning. The determination unit can also use the voiceprint database to analyze the characteristics of the user's voice and determine the possibility of fraud. For example, if a specific voiceprint pattern is associated with a fraudulent method, the determination unit detects the pattern and issues a warning. In this way, the use of the voiceprint database can more accurately determine the possibility of fraud.

[0075] The determination unit can estimate the user's emotions and determine the possibility of fraud based on the estimated emotions. For example, if the user feels anxious, the determination unit detects that emotion and determines that there is a high risk of fraud. The determination unit can also analyze changes in the user's emotions in real time and identify emotional patterns that increase the possibility of fraud. For example, if the user suddenly becomes nervous, the determination unit detects that change in emotion and issues a warning. The determination unit can also use the emotion estimation function to monitor the user's emotions in real time and detect emotional changes that increase the possibility of fraud. For example, if the user suddenly feels anxious, the determination unit detects that change in emotion and issues a warning. In this way, by using the emotion estimation function, emotional changes that increase the possibility of fraud can be detected in real time.

[0076] The determination unit can analyze the user's past emotional data and identify emotional patterns that increase the likelihood of fraud. For example, it can analyze emotional data from when the user was previously a victim of fraud and issue a warning if a similar emotional pattern occurs again. The determination unit can also determine whether a specific emotional pattern is a sign of fraud based on the user's emotional data. For example, if a specific emotional pattern matches a past fraud case, it detects that pattern and issues a warning. The determination unit can also use the emotional data to analyze changes in the user's emotions and identify emotional patterns that increase the likelihood of fraud. For example, if a specific emotional pattern is related to a fraudulent method, it detects that pattern and issues a warning. In this way, the use of emotional data can more accurately determine the likelihood of fraud.

[0077] The determination unit can estimate the user's emotions and determine the possibility of fraud based on the estimated emotions. For example, if the user feels anxious, the determination unit detects that emotion and determines that there is a high risk of fraud. The determination unit can also analyze changes in the user's emotions in real time and identify emotional patterns that increase the possibility of fraud. For example, if the user suddenly becomes nervous, the determination unit detects that change in emotion and issues a warning. The determination unit can also use the emotion estimation function to monitor the user's emotions in real time and detect emotional changes that increase the possibility of fraud. For example, if the user suddenly feels anxious, the determination unit detects that change in emotion and issues a warning. In this way, by using the emotion estimation function, emotional changes that increase the possibility of fraud can be detected in real time.

[0078] The determination unit can analyze a user's behavioral patterns and identify behaviors that increase the likelihood of fraud. For example, if a user frequently visits a specific website, the determination unit can detect that behavior and issue a warning. The determination unit can also identify cases where the risk of fraud is higher during specific times of the day or in specific situations based on the user's behavioral patterns. For example, if the risk of fraud is higher during a specific time of day, the determination unit can issue a warning during that time of day. The determination unit can also analyze a user's behavioral patterns and identify specific behaviors that increase the risk of fraud. For example, the determination unit can issue a warning when a specific application is used. In this way, by analyzing a user's behavioral patterns, it is possible to identify behaviors that increase the likelihood of fraud and issue more effective warnings.

[0079] The determination unit can analyze the user's location information and identify locations that increase the possibility of fraud. For example, if there is a high risk of fraud in a specific area, a warning is issued to users in that area. The determination unit can also identify cases where there is a high risk of fraud in a specific location based on the user's location information. For example, if there is a high risk of fraud in a specific store or facility, a warning is issued to users in that location. The determination unit can also analyze the user's location information and identify specific locations where there is a high risk of fraud. For example, if there is a high risk of fraud at a specific event venue, a warning is issued to users in that location. In this way, by analyzing the user's location information, it is possible to identify locations that increase the possibility of fraud and issue more effective warnings.

[0080] The determination unit can analyze a user's social media activity and identify posts and messages that increase the likelihood of fraud. For example, it can detect posts that contain specific keywords or phrases and issue a warning. The determination unit can also determine whether a specific post or message is a sign of fraud based on the user's social media activity. For example, it can detect posts that contain specific links or images and issue a warning. The determination unit can also analyze social media activity and identify specific posts and messages that increase the risk of fraud. For example, if a message from a specific account is related to a fraudulent method, it can detect that message and issue a warning. In this way, by analyzing social media activity, it is possible to identify posts and messages that increase the likelihood of fraud and issue more effective warnings.

[0081] The determination unit can analyze a user's purchase history and identify purchase patterns that increase the likelihood of fraud. For example, if a specific product or service is frequently purchased, the determination unit can detect that pattern and issue a warning. The determination unit can also determine whether a specific purchase pattern is a sign of fraud based on the user's purchase history. For example, if purchases at a specific store or online shop are associated with fraudulent methods, the determination unit can detect that pattern and issue a warning. The determination unit can also analyze the purchase history and identify specific purchase patterns that increase the risk of fraud. For example, if purchases made during a specific time period or situation increase the risk of fraud, the determination unit can detect that pattern and issue a warning. In this way, by analyzing the purchase history, it is possible to identify purchase patterns that increase the likelihood of fraud and issue more effective warnings.

[0082] The judgment unit can analyze the content of a user's email and identify emails that increase the likelihood of fraud. For example, it detects emails that contain specific keywords or phrases and issues a warning. The judgment unit can also determine whether a specific email is a sign of fraud based on the content of the user's email. For example, it detects emails that contain specific links or attachments and issues a warning. The judgment unit can also analyze the content of emails and identify specific emails that increase the risk of fraud. For example, if an email from a specific sender is related to a fraudulent method, it detects that email and issues a warning. In this way, by analyzing the content of emails, it is possible to identify emails that increase the likelihood of fraud and issue more effective warnings.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The voice data acquisition unit acquires voice data. For example, the voice data can be acquired using recorded data from a telephone call, a real-time voice stream, or a smartphone microphone. Step 2: The determination unit analyzes the voice data acquired by the voice data acquisition unit and determines the possibility of special fraud. For example, it converts the voice data into text and analyzes its content. It can also perform detailed analysis of voice characteristics such as the speaker's tone of voice, speed, and pauses to identify patterns that increase the possibility of fraud. Furthermore, it can use an emotion estimation function to estimate the speaker's emotions during a conversation in real time and detect emotional changes that increase the possibility of fraud. Step 3: The warning unit issues a warning based on the possibility of special fraud determined by the judgment unit. For example, the warning can be sent via smartphone notification, email, or voice alert.

[0085] 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.

[0086] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0087] 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0098] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0113] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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."

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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]

[0152] 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 voice data acquisition unit that acquires voice data; a determination unit that analyzes the voice data acquired by the voice data acquisition unit and determines whether it is a special fraud; a warning unit that issues a warning based on the possibility of special fraud determined by the determination unit. A system characterized by:

2. The determination unit Converting the audio data into text and analyzing the content 2. The system of claim 1.

3. The determination unit Detailed analysis of speech characteristics such as speaker tone, speed and pauses to identify patterns that increase the likelihood of fraud 2. The system of claim 1.

4. The determination unit Estimates speaker emotions in real time during a conversation and detects emotional changes that increase the likelihood of fraud 2. The system of claim 1.

5. The determination unit Expanding voice recording and analysis to text messages and chat content to identify potential fraud from multiple angles 2. The system of claim 1.

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