System

Abstract

A system is provided.SOLUTION: A system for preventing fraud implemented in a senior's communication terminal, comprising: means for matching an incoming call number against a list of known fraudulent numbers; means for monitoring call content in real-time and detecting specific fraudulent phrases; means for temporarily interrupting a call and issuing a warning if suspected fraudulent call content is detected; and means for recording call content and transmitting and storing the recorded data.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] Elderly people are particularly vulnerable to special frauds, and the number of fraud victims is increasing. While conventional prevention measures and educational activities have been effective to a certain extent, they remain insufficient as fraud methods continue to diversify. Against this background, new technological means are needed to dramatically reduce the risk of elderly people falling victim to fraud over the phone. The problem that this invention aims to solve is to detect signs of fraud in real time when elderly people are making calls, and effectively prevent fraud before it occurs. [Means for solving the problem]

[0005] To solve this problem, we provide a system that uses AI implemented in the elderly person's communication device. This system includes the following means.

[0006] 1. A means of checking the incoming number against a list of known fraudulent numbers.

[0007] 2. A means of monitoring call content in real time to detect specific fraudulent phrases.

[0008] 3. A means of temporarily interrupting a call and warning the senior if suspicious call content is detected.

[0009] 4. A means of recording the contents of calls and sending and storing the recording data on a server.

[0010] Furthermore, the system analyzes the content of calls, issues a warning if there is potential fraud, and then includes a means to resume or terminate the call as desired, as well as a means to automatically report relevant information to family members or the police if potential fraudulent activity is detected. This not only protects seniors from fraud, but also allows the system to continuously learn and adapt to new fraud methods.

[0011] The term "elderly" refers to people who are relatively older, above a certain age, usually 60 years of age or older.

[0012] A "communication terminal" is a device that can make calls and perform data communications, and includes landlines, mobile phones, smartphones, etc.

[0013] "Fraud" refers to any act that violates the law or social norms, particularly fraud or criminal acts committed with malicious intent.

[0014] A "system" refers to a set of technical structures that combine multiple elements and means to achieve a specific purpose.

[0015] "Called number" refers to a telephone number or identification number used to send a call or message to a communication terminal.

[0016] A "fraudulent number list" is a list of numbers associated with known fraudulent activity, including phone numbers that may be used to commit fraud.

[0017] "Matching" refers to the process of comparing two or more pieces of data or information to see if they match.

[0018] "Call content" refers to the voice data and information exchanged during a call.

[0019] "Real-time" refers to data processing and analysis being carried out immediately, with current information being processed without delay.

[0020] "Monitoring" refers to the act of constantly observing a specific object or situation in order to detect abnormalities or problems.

[0021] "Discovery" refers to the process of finding specific information or patterns.

[0022] A "phrase" refers to a part of a word or a short sentence made up of a combination of words.

[0023] "Recording" refers to the process of recording audio data and saving it in a format that can be played back at a later time.

[0024] "Retention" refers to the process of keeping data for an extended period of time and making it available for retrieval when needed.

[0025] "Warning" refers to a notice or alert intended to inform you of a particular risk or danger.

[0026] "Reporting" means communicating a problem or anomaly to a third party, especially to the appropriate authorities.

[0027] "Analysis" refers to the process of examining data or information in detail and extracting meaningful patterns or information from it. [Brief explanation of the drawings]

[0028] [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. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0029] 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.

[0030] First, the terms used in the following description will be explained.

[0031] 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, a 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), and an APU (Accelerated Processing Unit).

[0032] 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.

[0033] 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.

[0034] 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), Bluetooth (registered trademark), etc.

[0035] 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."

[0036] [First embodiment]

[0037] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0038] 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.

[0039] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0040] 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.

[0041] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0042] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0043] 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.

[0044] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0045] 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.

[0046] 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.

[0047] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0048] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0049] This invention relates to an AI-based fraud prevention system implemented in elderly users' communication devices. Specifically, the system monitors call content in real time and issues a warning if there are signs of fraud.

[0050] System Configuration

[0051] The system includes the following major components:

[0052] 1. Communication terminals: Communication devices such as landlines and mobile phones used by seniors.

[0053] 2. Server: A centralized device that hosts the AI ​​model, matches incoming numbers, stores data, and performs analysis.

[0054] 3. AI Police Program: A software module installed on communication devices that monitors call content in real time and detects signs of fraudulent activity.

[0055] System Operation

[0056] 1. Operation when receiving a call

[0057] When a user receives a call on their communication device, the device reads the phone number. The device sends the incoming number to a server, which checks it against a list of known fraud numbers. If the server matches the number as a fraudulent number, the device automatically rejects the call. If there is no match, the call proceeds as normal.

[0058] 2. Call monitoring

[0059] Once a call is initiated, the device's AI police program monitors the call in real time, converting the audio data into text and searching for specific fraudulent phrases and patterns, such as "Please tell me your account number" or "Please send the money now."

[0060] 3. Fraud detection and warning

[0061] If signs of fraud are detected, the device will pause the call and display a warning message to the user, such as "Possible fraud. Do you want to continue?" If the user chooses to continue, the call will resume but remain alerted. If the user chooses to end the call, the device will end the call and save the recording.

[0062] 4. Reporting and Data Retention

[0063] Once the call is over, the recording is sent to a server where it is stored, and the server analyzes this data and reports details of the fraudulent activity to family members or the police, including the date and time of the call, the content of the call, and any characteristics of the fraudulent activity that were detected.

[0064] Specific examples

[0065] Suppose a user receives a call on their mobile phone. The device sends the incoming number to a server and checks it against a list of fraudulent numbers. After verifying that it is not a fraudulent number, the call begins. If, during the call, the caller says, "Your son has been in a traffic accident. We urgently need money," the device's AI police program detects this phrase, temporarily suspends the call, and displays a warning. If the user ends the call, the recording and call log are sent to a server, and family members and the police are notified. This reduces the risk of elderly people becoming victims of fraud.

[0066] The above is a specific embodiment for carrying out the present invention. This system is expected to enable elderly people to use the telephone safely and protect them from fraudulent activity.

[0067] The processing flow will be explained below.

[0068] Step 1:

[0069] The user turns on their home landline or mobile phone. The device activates the AI ​​police program and enters fraud prevention mode.

[0070] Step 2:

[0071] The device detects an incoming call, acquires the incoming number, and notifies the user by ringing.

[0072] Step 3:

[0073] The device sends the incoming number to a server, which checks it against a list of known fraud numbers.

[0074] Step 4:

[0075] The server returns the result of matching the incoming number to the terminal. If the matched result matches the fraudulent number, the terminal will automatically reject the call. If the matched result does not match the fraudulent number, proceed to the next step.

[0076] Step 5:

[0077] When the user presses the call button, the device starts the call, and at the same time, the device's AI police program starts recording the audio data.

[0078] Step 6:

[0079] The device's AI police program converts calls into text in real time and monitors for fraudulent phrases and patterns.

[0080] Step 7:

[0081] If the device detects signs of fraudulent activity, it will temporarily halt the call and display a warning message, such as "Possible fraud. Do you want to continue the call?"

[0082] Step 8:

[0083] After receiving the warning message, the user can choose to continue or end the call. If the user chooses to continue the call, the terminal will resume the call and continue monitoring. If the user chooses to end the call, the terminal will proceed to the next step.

[0084] Step 9:

[0085] When the device ends the call, it saves the recording data and sends the recording data and call log to the server.

[0086] Step 10:

[0087] The server analyzes the recordings and creates a detailed report of the misconduct, which it then uses to notify family members or the police.

[0088] Step 11:

[0089] The server uses the collected data to train the AI ​​model and delivers updates to the device to improve the accuracy of the next fraud detection.

[0090] The above are the specific processing steps for carrying out the present invention, which can reduce the risk of elderly people becoming victims of fraud and can be expected to have a further preventative effect.

[0091] Example 1

[0092] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0093] Communication devices used by elderly people are at high risk of becoming victims of fraud and other illegal activities. To solve this problem, a system is needed that can detect fraudulent activities in real time when a call is received or during a call and respond quickly. In addition, if fraudulent activities are suspected, it is necessary to issue a warning to the user and, if necessary, interrupt the call.

[0094] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0095] In this invention, the server includes a means for checking the incoming number against known fraudulent number data, a means for monitoring communication content in real time and detecting specific fraudulent phrases, and a means for temporarily interrupting the call and issuing a warning when communication content suspected of fraud is detected. This makes it possible to quickly detect fraudulent activity on communication terminals used by elderly people and take appropriate warnings and countermeasures.

[0096] "Electronic devices for the elderly" refers to devices with communication functions that are primarily used by the elderly, such as telephones, mobile phones, smartphones, tablets, and landlines.

[0097] "Fraud prevention system" refers to an integrated system that has functions to prevent fraud and other fraudulent activities on elderly people's communication devices.

[0098] The "called number" refers to the telephone number that was called to the communication terminal.

[0099] "Known fraudulent number data" refers to a list of phone numbers that have been identified in the past as being associated with fraud or other fraudulent activity.

[0100] "Means of matching" refers to the process of comparing the incoming number with known fraudulent number data to see if there is a match.

[0101] "Communication content" refers to the audio information exchanged with the other party during a call.

[0102] "Means of monitoring in real time" refers to technology that analyzes the content of ongoing communications almost simultaneously.

[0103] "Specific fraudulent phrases" refers to specific words or phrases that suggest fraud or other fraudulent activity.

[0104] "Means of detection" refers to technology that detects specific fraudulent phrases in communications.

[0105] "Means to temporarily interrupt the call and issue a warning" refers to a feature that pauses the call if fraudulent activity is suspected and warns the user of possible fraudulent activity.

[0106] "Means of recording" refers to the technology that records the contents of a call as digital audio data.

[0107] "Transmission and storage means" refers to the process of transmitting the recorded data to a server and storing that data long-term.

[0108] "Means of analysis" refers to technology that analyzes the contents of recorded communications and determines whether or not there is any fraudulent activity.

[0109] "Means for reporting" refers to the ability to notify family members or public authorities of relevant information when fraudulent activity is detected.

[0110] This invention is a system implemented in the electronic devices of elderly people to prevent fraudulent activities. Specifically, the system monitors communication content in real time and issues a warning if there is any sign of fraudulent activity.

[0111] System Configuration

[0112] The system includes the following major components:

[0113] 1. Communication devices: Electronic devices such as telephones and mobile phones used by the elderly.

[0114] 2. Server: A centralized device that hosts the AI ​​model, matches incoming numbers, stores data, and performs analysis.

[0115] 3. AI Police Program: A software module installed on communication devices that monitors communication content in real time and detects signs of fraudulent activity.

[0116] System Operation

[0117] Incoming call behavior

[0118] When a user receives a call on their communication device, the device reads the phone number. The device then sends the retrieved phone number to the server and checks it against known fraud numbers. If the server finds that the number is a fraudulent number, the device automatically rejects the call. If there is no match, the call proceeds as normal.

[0119] In-call monitoring

[0120] When a user initiates a call, the device's AI police program captures the call in real time as audio data. The device converts this audio data into text using transcription software (e.g., Google® Cloud Speech-to-Text). The device then analyzes the text data to detect specific fraudulent phrases and patterns, such as "Please tell me your account number" or "Please transfer the money now."

[0121] Fraud detection and warning

[0122] If signs of fraud are detected, the device will pause the call and display a warning message to the user, such as "Possible fraud. Do you want to continue?" If the user chooses to continue the call, it will resume but remain alerted. If the user chooses to end the call, the device will end the call and save the recording.

[0123] Reporting and Data Retention

[0124] Once the call is over, the device sends the recorded conversation to a server, which stores the data and analyzes it. It then reports details of the fraudulent activity to family members or authorities, including the date and time of the call, the content of the call, and the characteristics of the fraudulent activity detected.

[0125] Specific examples

[0126] Consider an example of when a user receives a call on their mobile phone. The device sends the incoming number to a server and checks it against a scam list. After verifying that it is not a scam number, the call begins. If the caller says, "Your son has been in a traffic accident. We urgently need money," the device's AI police program detects this phrase, pauses the call, and displays a warning. If the user ends the call, the recording and call log are sent to a server, and family members and public authorities are notified. This reduces the risk of elderly people becoming victims of fraud.

[0127] Prompt statement

[0128] Here are some example prompts to input to a generative AI model:

[0129] Please explain the specific operation and processing steps of the system that uses AI technology to prevent fraud on phones used by seniors. Please give us more details on what happens when a call comes in and during a call.

[0130] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0131] Step 1:

[0132] When a user receives a call on a communication terminal, the terminal acquires the called number. The input is the incoming call signal received by the communication terminal, and the output is the called number information. The terminal sends the acquired phone number to the server. Specifically, the terminal transmits the called number to the server as digital data in packet format.

[0133] Step 2:

[0134] The server checks the received phone number against known fraudulent number data. The input is the incoming number sent from the terminal, and the output is information indicating whether or not the number matches the fraudulent number. The server checks whether the number matches the list of fraudulent numbers stored in the database, and if so, returns that information to the terminal. Specifically, the server performs an index search on the number and returns the result.

[0135] Step 3:

[0136] The terminal receives the information returned by the server and automatically rejects the call if there is a match. The input is a notification from the server that the fraud number matches, and the output is a control signal to reject the call. Specifically, the terminal aborts the call connection process and displays a message to the user that the call has been rejected. If there is no match, the call will be initiated normally.

[0137] Step 4:

[0138] When a user initiates a call, the device's AI police program captures the call content as audio data in real time. The input is the call audio data, and the output is a recorded audio file. The device converts this audio data into text using transcription software (e.g., Google Cloud Speech-to-Text). Specifically, the audio data is streamed and converted to text in the cloud.

[0139] Step 5:

[0140] The device then analyzes the converted text data, searching for specific fraudulent phrases and patterns. The input is the converted text of the call, and the output is the phrase detection results. Specifically, natural language processing algorithms are used to detect phrases such as "Please tell me your account number" or "Please transfer the money now."

[0141] Step 6:

[0142] If signs of fraud are detected, the terminal will suspend the call and display a warning message to the user. The input is the result of fraud phrase detection, and the output is the display of a warning message. Specifically, the terminal will display "Possible fraud. Do you want to continue the call?" on the screen.

[0143] Step 7:

[0144] If the user wants to continue the call, the terminal resumes the call but remains alert. The input is the user's selection, and the output is the call resumption control. Specifically, if the user presses the "Yes" button on the screen, the terminal resumes the call connection and continues monitoring.

[0145] Step 8:

[0146] When the user ends the call, the device ends the call and saves the recorded data. The input is the user's selection, and the output is saving the recorded data. Specifically, when the user ends the call, the device disconnects the call and saves the recorded audio data locally or in cloud storage.

[0147] Step 9:

[0148] When the call ends, the device sends the recorded call content to the server. The input is the recorded call data, and the output is the call data sent to the server. Specifically, the recorded data is encrypted and sent to the server via a secure channel.

[0149] Step 10:

[0150] The server stores the received data and analyzes it. The input is the call data sent from the device, and the output is an analysis report. Specifically, the server uses voice recognition and natural language processing to analyze the data and determine whether or not there is any fraudulent activity.

[0151] Step 11:

[0152] Based on the analysis results, the server reports details of fraudulent activity to family members or public institutions. The input is the analysis report, and the output is a report message. Specifically, the report message is sent via email or SMS, containing the date and time of the call, the content of the call, and the characteristics of the detected fraudulent activity.

[0153] (Application example 1)

[0154] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0155] Protecting the elderly from fraud and other fraudulent activities is an important issue. However, conventional communication devices lack the capabilities to monitor call content in real time or detect signs of fraud, leaving the elderly at high risk of being scammed. It is also difficult for them to quickly report suspected fraudulent calls to family members or the police. Therefore, an effective system is needed to ensure safe communication for the elderly and protect them from fraud.

[0156] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0157] In this invention, the server includes means for checking incoming numbers against a list of known fraudulent numbers, means for monitoring call content in real time and detecting specific fraudulent phrases, means for temporarily suspending the call and issuing a warning when suspicious call content is detected, means for recording the call content and transmitting and saving the recording data, means for converting voice data to text and searching for specific phrases in real time, means for notifying a user terminal of a warning when signs of fraud are detected, and means for saving the call data and related information in a remote data storage device. This effectively protects elderly people from fraud when communicating and enables relevant parties to be promptly notified of necessary information.

[0158] "Elderly communication devices" are communication devices such as telephones, mobile phones, and smartphones used by elderly people.

[0159] A "known fraudulent number list" is a list of phone numbers that have been confirmed to be involved in fraud or other fraudulent activity.

[0160] "Means for checking" refers to the function of the communication terminal to check the incoming call number received against a list of known fraud numbers.

[0161] "Means for monitoring call content in real time" refers to technology that can instantly analyze voice data during a call and detect specific phrases that indicate fraudulent activity.

[0162] "Specific fraudulent phrases" are specific words or expressions that can be identified as indicative of fraudulent activity.

[0163] "Means for detecting call content suspected of fraudulent activity" refers to technology that determines whether a call contains content suspected of fraudulent activity.

[0164] "Means to temporarily interrupt the call and issue a warning" refers to a function that temporarily halts the call and displays a warning message to the user if signs of fraudulent activity are detected during the call.

[0165] "Means for recording the contents of phone calls and transmitting and storing the recorded data" refers to a system for recording the audio data of phone calls and transmitting and storing it on a central server or cloud service, etc.

[0166] "Means for converting voice data into text" refers to technology that has the function of automatically converting voices during a call into text information.

[0167] "Means for searching for specific phrases in real time" refers to technology that instantly finds specific phrases related to fraudulent activity from within the text of a call.

[0168] "Means for sending a warning to a user terminal" refers to technology capable of immediately sending a warning to a user's communication terminal when signs of fraudulent activity are detected.

[0169] "Means for storing call data and related information in a remote data storage device" refers to a mechanism for storing call content and other related information in a remote location such as a remote server.

[0170] This invention is a fraud prevention system implemented in elderly users' communication devices, providing real-time call monitoring and automatic alerting functions. The system aims to protect elderly users from fraud and other fraudulent activities and enable them to communicate safely.

[0171] Key components of the system

[0172] communication terminal

[0173] Communication devices used by seniors (telephones, mobile phones, smartphones, etc.).

[0174] An AI police program has been implemented to monitor voice data in real time and detect specific abusive phrases.

[0175] server

[0176] It is a centralized device that performs incoming number verification, fraud data storage and analysis.

[0177] The main software and services used include AWS (registered trademark) (Amazon Web Services) EC2, AWS Lambda, and AWS S3.

[0178] AI Police Program

[0179] A software module implemented in a communications terminal.

[0180] Use the Google Speech-to-Text API to convert speech to text and search for specific phrases in real time.

[0181] If it detects signs of fraudulent activity, it will pause the call and display a warning to the user.

[0182] System Operation

[0183] Incoming call behavior

[0184] When a communication device receives a call, it first reads the incoming number. The read number is sent to a server and checked against a list of known fraudulent numbers. If the number matches a fraudulent number, the device automatically rejects the call and displays a warning to the user. If there is no match, the call proceeds as normal.

[0185] In-call monitoring

[0186] Once a call begins, the AI ​​police program on the communication device monitors the call in real time. The audio data is converted to text using the Google Speech-to-Text API and searched for fraudulent phrases and patterns. If signs of fraud are detected, the call is paused and a warning is issued to the user, such as a message saying, "This may be a scam. Do you want to continue?"

[0187] Call recording and data storage

[0188] Calls are recorded, and if fraudulent activity is detected, the recordings are sent to an AWS S3 bucket for storage, which can then be automatically reported to the appropriate parties (family members or police).

[0189] Specific examples

[0190] When a call comes in to a communication device, it works as follows: The incoming number is read and sent to the server, where it is checked against a list of fraudulent numbers. If it is not a fraudulent number, the call begins. If, during the call, the caller says, "My son has been in a traffic accident. I need money urgently," the AI ​​police program on the device detects this phrase, temporarily suspends the call, and issues a warning to the user. The user can then choose to end or resume the call. The call is also recorded, and the necessary information is sent to the relevant parties.

[0191] Prompt Sentence Examples

[0192] "Your son was in a car accident. I need money urgently."

[0193] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0194] Step 1:

[0195] The communication terminal receives an incoming call. It reads the incoming number and sends it to the server. The input is the incoming number notified to the communication terminal, and the output is the transmission of the number to the server. Specifically, the communication terminal's incoming call sensor is activated, the number is saved in its internal memory, and then the number is sent to the server.

[0196] Step 2:

[0197] The server compares the incoming number it receives with a list of known fraudulent numbers. The input is the incoming number sent from the communication terminal, and the output is the comparison result. Specifically, it matches the number with the list of fraudulent numbers stored in the server's database. A detection algorithm determines whether the number is fraudulent and sends the result back to the communication terminal.

[0198] Step 3:

[0199] The communication terminal receives the matching result from the server. If it matches a fraudulent number, it automatically rejects the call and displays a warning to the user. The input is the matching result from the server, and the output is to display a warning to the user or allow the call to proceed. The specific operation is to issue a command to reject the call and display a warning message on the display.

[0200] Step 4:

[0201] If the server determines that the number is not a fraudulent number, the call starts as normal. The input is the server's determination that the number is not a fraudulent number, and the output is the establishment of the call. Specifically, the communication terminal issues permission to start the call, and full-duplex communication begins.

[0202] Step 5:

[0203] During a call, the communication device's AI police program monitors the call content in real time. It converts the voice data into text and searches for fraudulent phrases. The input is the voice data during the call, and the output is the text of the call and the results of the fraudulent phrase detection. Specifically, it uses the Google Speech-to-Text API to convert the voice data into text, and then searches that text using a pattern matching algorithm.

[0204] Step 6:

[0205] If the AI ​​police program detects signs of fraud, it will suspend the call and issue a warning to the user. The input is the result of detecting fraudulent phrases, and the output is to suspend the call and display a warning to the user. Specifically, the phrase detection algorithm detects signs of fraud, issues a call suspension command, and displays a warning message on the display saying, "Possible fraud. Do you want to continue the call?"

[0206] Step 7:

[0207] When the user receives the warning message, they can choose to resume or end the call. The input is the user's choice, and the output is to resume or end the call. Specifically, the user selects "Continue" or "End" on the warning message screen, and based on that selection, the system issues a command to resume the call or ends the call.

[0208] Step 8:

[0209] When the call ends, the AI ​​police program records the call and sends the data to a server for storage. The input is the call end event and the recorded data, and the output is data storage on the server. Specifically, the recorded call data is uploaded to an AWS S3 bucket and a storage log is recorded in the database.

[0210] Step 9:

[0211] The server analyzes the stored data and automatically notifies relevant parties (family members or the police) of details of the fraudulent activity. The input is the recorded data and information on the detected fraudulent activity, and the output is a notification to the relevant parties. Specifically, a data analysis algorithm evaluates the recorded data and notifies the relevant parties via email or SMS.

[0212] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0213] This invention relates to a fraud prevention system that uses AI and an emotion engine implemented in elderly users' communication devices. Specifically, the system monitors call content in real time, and if there are signs of fraud or if the user's emotional state is recognized, it issues a warning and takes appropriate measures.

[0214] System Configuration

[0215] The system includes the following major components:

[0216] 1. Communication terminals: Communication devices such as landlines and mobile phones used by seniors.

[0217] 2. Server: A centralized device that hosts the AI ​​model and emotion engine, matches incoming numbers, stores data, and analyzes it.

[0218] 3. AI Police Program: A software module installed on communication devices that monitors call content in real time and detects signs of fraudulent activity.

[0219] 4. Emotion Engine: A module that recognizes and analyzes the user's emotional state in real time.

[0220] System Operation

[0221] 1. Operation when receiving a call

[0222] When a user receives a call on their communication device, the device retrieves the phone number. The device sends the incoming number to a server, which checks it against a list of known fraud numbers. If the server matches the number as a fraudulent number, the device automatically rejects the call. If there is no match, the call proceeds as normal.

[0223] 2. Call monitoring

[0224] Once a call is initiated, the device's AI police program monitors the call in real time, converting the audio data into text and searching for specific fraudulent phrases and patterns. An emotion engine also analyzes the user's tone of voice and voice patterns to recognize their emotional state in real time.

[0225] 3. Fraud detection and warning

[0226] If signs of fraud are detected or the emotion engine determines that the user's emotion is anxiety or nervousness, the device will pause the call and display a warning message, such as "This is a possible scam. Do you want to continue?" or "I sense anxiety in your voice. This is a possible scam. Do you want to continue?" If the user chooses to continue the call, the device resumes the call but remains alert. If the user chooses to end the call, the device proceeds to the next step.

[0227] 4. Reporting and Data Retention

[0228] Once the call ends, the recorded conversation and emotional data are sent to a server, which analyzes the data and creates a detailed report of the misconduct. This information can then be used to notify family members or the police. Specific details include the date and time of the call, the content of the conversation, the user's emotional state, and any characteristics of the misconduct detected.

[0229] Specific examples

[0230] Suppose a user receives a call on their mobile phone. The device sends the incoming number to a server and checks it against a list of scam numbers. After confirming that it is not a scam, the call begins. During the call, if the caller says, "Your son has been in a traffic accident. I urgently need money," the device's AI police program detects this phrase. At the same time, the emotion engine detects signs of anxiety in the user's voice. The device pauses the call and displays two warning messages: "This may be a scam. Do you want to continue?" and "Your voice sounds anxious. Do you want to continue?" If the user chooses to end the call, the recording and emotion data are sent to the server, which analyzes them and notifies family members or the police. This strengthens prevention measures to prevent elderly people from falling victim to fraud.

[0231] The above is a concrete example of how to implement this invention. This system is expected to enable elderly people to use the telephone safely and protect them from fraudulent activity. By combining it with an emotion engine, even greater crime prevention effects can be expected.

[0232] The processing flow will be explained below.

[0233] Step 1:

[0234] The user turns on their home landline or mobile phone. The device activates the AI ​​police program and emotion engine and goes into fraud prevention mode.

[0235] Step 2:

[0236] The device detects an incoming call, acquires the incoming number, and notifies the user by ringing.

[0237] Step 3:

[0238] The device sends the incoming number to a server, which checks it against a list of known fraud numbers.

[0239] Step 4:

[0240] The server returns the result of the incoming number matching to the terminal. If the matched number matches a fraudulent number, the terminal automatically rejects the call. If the number does not match a fraudulent number, the call is accepted as normal.

[0241] Step 5:

[0242] When the user presses the call button, the device starts the call, and at the same time, the device's AI police program starts recording the audio data.

[0243] Step 6:

[0244] The device's AI police program converts calls into text in real time and monitors for fraudulent phrases and patterns, while its emotion engine analyzes the user's tone of voice and voice patterns to recognize their emotional state in real time.

[0245] Step 7:

[0246] If the device detects signs of fraudulent activity or if the emotion engine determines that the user is feeling anxious or nervous, it will temporarily interrupt the call and display a warning message, such as "This is a possible scam. Do you want to continue?" and "I sense anxiety in your voice. Do you want to continue?"

[0247] Step 8:

[0248] After receiving the warning message, the user can choose to continue or end the call. If the user chooses to continue the call, the terminal will resume the call and continue monitoring. If the user chooses to end the call, the terminal will proceed to the next step.

[0249] Step 9:

[0250] When the device ends the call, it saves the recording data and emotion data, and then sends the recording data and call record to the server.

[0251] Step 10:

[0252] The server analyzes the received recording and emotional data and creates a detailed report of the misconduct. Once the report is complete, the server notifies family members or the police, including the date and time of the call, the content of the call, the emotional state, and the characteristics of the detected misconduct.

[0253] Step 11:

[0254] The server uses the collected data to train the AI ​​model and emotion engine, and delivers updates to the device to improve the accuracy of fraud and emotion detection next time.

[0255] The above are the specific processing steps for implementing this invention. This reduces the risk of elderly people becoming victims of fraud, and further prevention effects can be expected. The incorporation of an emotion engine allows for quick and accurate responses even in situations where the user feels particularly anxious or tense.

[0256] Example 2

[0257] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0258] Seniors are increasingly at risk of falling victim to telephone fraud. These frauds are often sophisticated and difficult to prevent simply by warning users. Therefore, there is a need for systems that enable seniors to use the telephone more safely. It is also important to more accurately detect signs of fraud by monitoring not only the content of calls but also the user's emotional state.

[0259] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for comparing the incoming number with a list of known fraudulent numbers, means for monitoring the content of the call in real time and detecting specific fraudulent phrases, means for converting voice data into text and detecting signs of fraud, means for recognizing the user's emotional state and detecting emotions such as anxiety and tension, means for temporarily interrupting the call and issuing a warning when the call content is suspected of fraud or an abnormal emotional state is detected, and means for recording the call content and transmitting and saving the recording data and emotional data. This reduces the risk of elderly people becoming victims of telephone fraud and enables them to use the phone with peace of mind.

[0260] The "incoming number verification means" is a means having a function of verifying the telephone number of an incoming call to a communication terminal against a list of known fraud numbers.

[0261] The "real-time monitoring means" is a means having a function of monitoring the contents of calls in real time at a communication terminal and detecting specific fraudulent phrases.

[0262] The "voice data to text conversion means" is a means having a function of converting voice data acquired during a call into text data and analyzing it.

[0263] The "emotion recognition means" is a means having a function of analyzing the tone and voice pattern of the user's voice during a call and recognizing the user's emotional state in real time.

[0264] The "warning issuing means" is a means having a function of temporarily interrupting the call and displaying a warning message to the user when the content of the call is suspected to be fraudulent or an abnormal emotional state is detected.

[0265] The "recorded data transmission means" is a means having a function of transmitting the recorded contents of the call and emotional data to a server at the end of the call and storing them.

[0266] This invention is a system that prevents fraudulent activities using AI and an emotion engine implemented in elderly users' communication devices. Specifically, it monitors call content in real time, and if there are signs of fraudulent activities or if it recognizes the user's emotional state, it issues a warning and takes appropriate measures. The system configuration and a specific embodiment are described below.

[0267] System Configuration

[0268] The system includes the following major components:

[0269] 1. Communication terminals: Communication devices such as landlines and mobile phones used by seniors.

[0270] 2. Server: A centralized device that hosts the AI ​​model and emotion engine, matches incoming numbers, stores data, and analyzes it.

[0271] 3. AI Police Program: A software module installed on communication devices that monitors call content in real time and detects signs of fraudulent activity.

[0272] 4. Emotion Engine: A module that recognizes and analyzes the user's emotional state in real time.

[0273] System Operation

[0274] Processing when an incoming call arrives

[0275] When a user receives a call on their communication device, the device captures the phone number and sends it to a server to check against a list of known fraudulent numbers. The server checks the number against the fraudulent number list, and if there is a match, the device automatically rejects the call. If there is no match, the device initiates a normal call to the user.

[0276] Call Initiation and Monitoring

[0277] Once a call is initiated, the device's AI police program begins converting the voice data into text, analyzing it to detect specific fraudulent phrases and patterns, while an emotion engine analyzes the user's tone of voice to recognize their emotional state.

[0278] Fraud detection and warning

[0279] If the device detects signs of fraudulent activity, it will pause the call and display a warning message to the user. It will also display a warning message if the emotion engine determines the user's emotional state to be "anxious" or "nervous." If the user chooses to continue the call, the device resumes the call but remains alert. If the user chooses to end the call, it proceeds to the next step.

[0280] Call end notification and data storage

[0281] After the call ends, the device sends the recorded conversation and emotional data to a server. The server analyzes the received data and creates a detailed report of the fraudulent activity. This report includes the date and time of the call, the other party's phone number, the conversation content (transcribed into text), the user's emotional state, and the fraudulent phrases detected. The server then uses this report to notify the user's family or the police.

[0282] Specific examples

[0283] Consider a scenario in which a user receives a call on their mobile phone. After verifying that the phone number is not on a scam number list, the call begins. If, during the call, the caller says, "Your son has been in a traffic accident. I urgently need money," the device's AI police program converts this phrase into text, analyzes it, and detects fraudulent phrases. At the same time, the emotion engine detects signs of anxiety in the user's voice. The device temporarily suspends the call and warns, "This may be a scam. Do you want to continue?" and "I sense anxiety in your voice. Do you want to continue?" When the user ends the call, the recording and emotion data are sent to a server, which analyzes them and notifies family members or the police.

[0284] Example prompts to be input to the generative AI model

[0285] "Please explain a system that analyzes the content of calls received on communication devices used by elderly people in real time and detects signs of fraudulent activity. In particular, please include the process flow for analyzing the user's emotional state using an emotion engine and detecting an abnormality."

[0286] In this way, we will strengthen prevention measures to prevent elderly people from becoming victims of fraud. This system is expected to provide an environment in which elderly people can use communications safely. By combining it with an emotion engine, we can expect even greater crime prevention effects.

[0287] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0288] Specific flow of system program processing

[0289] Step 1:

[0290] A user receives a call on their communication terminal. The terminal captures the incoming number and sends it to a server. The server checks the received phone number against a list of known fraud numbers.

[0291] Input: Dialed number

[0292] Action: Server checks against list of known fraud numbers

[0293] Output: If it is not a fraudulent number, send the result to the terminal

[0294] Example: A user receives a call and the number is "123-456-7890." The device sends this number to the server, which checks it against a list of fraudulent numbers.

[0295] Step 2:

[0296] If the server recognizes the number as a scam, the device will automatically reject the call. If the number is not a scam, the device will initiate the call.

[0297] Input: Fraudulent number matching result

[0298] Processing: Determine whether the number is a fraudulent number

[0299] Output: The device decides whether to initiate or reject the call

[0300] Example: The device initiates a call to the user because the scam number did not match.

[0301] Step 3:

[0302] When a call is initiated, the device's AI police program begins converting the voice data into text, which the device then sends to a server for analysis, detecting specific fraudulent phrases.

[0303] Input: Audio data from the call

[0304] Processing: Convert the audio data into text and send it to the server

[0305] Output: Text of the call

[0306] Example: A conversation during a phone call, "Hello, I'd like to check as there is a possibility of fraud," is converted into text and sent to the server.

[0307] Step 4:

[0308] At the same time, the emotion engine begins to analyze the user's tone of voice to recognize the user's emotional state, and also analyzes the emotion data sent to the server.

[0309] Input: Audio tone during a call

[0310] Processing: Recognizing and analyzing emotional states

[0311] Output: User's emotional state data

[0312] Example: If the user's tone of voice is rising, the emotion engine will analyze it and detect anxiety.

[0313] Step 5:

[0314] If the device detects signs of fraudulent activity or abnormal emotional states such as anxiety or tension, it will pause the call and display a warning message.

[0315] Input: Analyzed call content, emotional state data

[0316] Processing: Detecting fraud or abnormal emotions

[0317] Output: Pause the call and display a warning message

[0318] Example: The AI ​​police program detects the fraudulent phrase "I need money urgently" during a call, and the emotion engine recognizes the anxiety and warns, "This may be a scam. Do you want to continue the call?"

[0319] Step 6:

[0320] If the user wants to continue the call, the terminal resumes the call but continues to monitor. If the user wants to end the call, proceed to the next step.

[0321] Input: User's choice (continue or end call)

[0322] Processing: Prepare to resume call or save data

[0323] Output: Continue or end the call

[0324] Example: If the user chooses to end the call, end the call and move on.

[0325] Step 7:

[0326] After the call ends, the device sends the recorded conversation and emotional data to a server, which analyzes the data and creates a detailed report of the abuse.

[0327] Input: Recorded call data, emotion data

[0328] Processing: Analyze call content and emotion data, and create detailed reports

[0329] Output: Analysis report

[0330] Example: After a call ends, the recording and emotional data are sent to a server, which analyzes it and compiles a detailed report of any mistakes or misbehavior phrases.

[0331] Step 8:

[0332] Based on the report created, the server automatically notifies the user's family and the police.

[0333] Input: Parsed report

[0334] Action: Report Notification

[0335] Output: Report to family and police

[0336] Example: The server automatically generates a report and sends a notification to the user's family or the police saying, "Fraud was detected on this call."

[0337] The above is a concrete process flow and details of the operations at each step. This allows elderly people to use the telephone with peace of mind and is protected from fraudulent activity.

[0338] (Application example 2)

[0339] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0340] When elderly people use communication devices, they are at increased risk of telephone fraud and other fraudulent activities. It is particularly difficult to detect fraudulent activities, and it is difficult for elderly people themselves to recognize the risks and respond appropriately. Therefore, there is a need for a system that can effectively protect elderly people from fraud and other fraudulent activities and allow them to use communication devices with peace of mind. Furthermore, it is necessary to perform more accurate fraud detection by analyzing the user's emotional state.

[0341] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for comparing the incoming number with a list of known fraudulent numbers, means for monitoring the call content in real time and detecting specific fraudulent phrases, means including an emotion engine for analyzing detected signs of fraud and the user's emotional state, means for temporarily suspending the call and issuing a warning when the call content or change in the emotional state that suggests fraud is detected, means for resuming or terminating the call after the warning according to the user's selection, and means for recording the call content and transmitting and saving the recording data and emotion data. This provides a high level of protection for elderly people from fraud and fraudulent activities, allowing them to use communication terminals with peace of mind.

[0342] "Elderly people" refers to elderly users of communication terminals.

[0343] "Communication terminals" refers to devices such as landlines and mobile phones used by seniors.

[0344] "Fraud" refers to the use of false information or methods to commit a fraudulent act.

[0345] "Fraud" refers to illegal activities carried out with the intent of defrauding seniors of money or information.

[0346] "Emotion engine" refers to a software module for recognizing and analyzing a user's emotional state in real time.

[0347] "Means for checking incoming numbers against a list of known fraudulent numbers" refers to a function that checks incoming calls to a communications terminal against a list of fraudulent numbers.

[0348] "Means for monitoring call content in real time" refers to the ability to analyze voice data during a call in real time and detect fraudulent phrases and patterns.

[0349] "Means for detecting specific fraudulent phrases" refers to the ability to convert call audio into text and search for specific phrases that may indicate fraudulent activity.

[0350] "Warning means" refers to the ability to present a warning message to the user when fraudulent activity is suspected or when the user's emotional state changes.

[0351] "Means to temporarily interrupt a call" refers to the ability to temporarily halt a call when fraudulent activity or a change in emotional state is detected.

[0352] "Means for recording telephone conversations" refers to the function of saving telephone conversations made on a communication terminal as audio data.

[0353] "Means for transmitting and storing recorded data and emotional data" refers to the function of transmitting the recorded voice data and analyzed emotional data to a server and storing them after the call ends.

[0354] The "means for resuming or terminating a call according to the user's choice" refers to a function that determines whether to resume or terminate a call based on the user's judgment after receiving a warning message.

[0355] This section describes an embodiment of the present invention. This invention is a fraud prevention system that uses AI and an emotion engine implemented in elderly users' communication devices. Specifically, the system monitors call content in real time, and if it detects signs of fraud or recognizes the user's emotional state, it issues a warning and takes appropriate measures.

[0356] System Configuration

[0357] The system includes the following main components:

[0358] 1. Communication terminals: Devices such as landlines and mobile phones used by seniors that perform functions such as verifying incoming numbers, detecting fraud, and recording calls.

[0359] 2. Server: Hosts the AI ​​model and emotion engine, matches incoming numbers, stores data, and analyzes it. Specific software used includes Tensorflow® and Azure® Cognitive Services.

[0360] 3. AI Police Program: A module implemented in communication devices that monitors call content in real time and detects signs of fraudulent activity.

[0361] 4. Emotion Engine: A module that recognizes and analyzes the user's emotional state in real time.

[0362] System Operation

[0363] The server performs the following actions:

[0364] 1. Called number matching:

[0365] When a call comes in, the communication device acquires the phone number and sends it to the server. The server checks it against a list of fraudulent numbers, and if there is a match, the communication device rejects the call.

[0366] 2. Call monitoring:

[0367] Once a call is initiated, the device's AI police program monitors the call in real time, converting the audio into text and detecting specific fraudulent phrases and patterns. An emotion engine analyzes the user's tone of voice and voice patterns to recognize their emotional state.

[0368] 3. Fraud detection and warning:

[0369] If the device detects signs of fraud or an altered emotional state in the user, it will pause the call and display a warning message, such as "This is a possible scam. Do you want to continue?" or "I sense anxiety in your voice. Do you want to continue?"

[0370] 4. Reporting and Data Retention:

[0371] Once the call is over, the recorded conversation and emotional data are sent to a server, which analyzes the data and creates a detailed report of the fraudulent activity. Based on this information, the server can then notify family members or the police.

[0372] Specific examples

[0373] For example:

[0374] Suppose a user receives a call on their mobile phone. The communication device sends the incoming number to a server and checks it against a list of scam numbers. After confirming that it is not a scam, the call begins. During the call, the caller might say, "Your son has been in a traffic accident. I urgently need money." In this case, the communication device's AI police program detects this phrase. At the same time, the emotion engine detects signs of anxiety in the user's voice. The communication device temporarily suspends the call and displays two warning messages: "This may be a scam. Do you want to continue the call?" and "I sense anxiety in your voice. Do you want to continue the call?" If the user chooses to end the call, the recording and emotion data are sent to the server, which analyzes them and notifies family members or the police.

[0375] Prompt Sentence Examples

[0376] "Your son was in a car accident. I need money urgently."

[0377] Output: Signs of fraud

[0378] Sentiment analysis: "We detected anxiety in your voice"

[0379] In this way, it is possible to strengthen prevention measures to prevent seniors from becoming victims of fraud.

[0380] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0381] Step 1:

[0382] Called number matching

[0383] Input: When a call is received at the communication terminal, the incoming call number is obtained.

[0384] Operation: The communication device sends the acquired incoming number to the server, which checks the number against a list of known fraud numbers.

[0385] Output: Returns the result of whether the number is fraudulent or not.

[0386] Specific operation: The server checks the database to see if the number is on the fraudulent number list. If so, it sends an instruction to the communication terminal to automatically reject the call.

[0387] Step 2:

[0388] Real-time call monitoring

[0389] Input: If the number is not a scam number, the call is initiated and the call audio data is input.

[0390] How it works: The communication device's AI police program recognizes the contents of the call in real time and converts it into text data.

[0391] Output: Transcribed transcript of the call.

[0392] What it does: The communication device uses speech recognition software (e.g., Azure Cognitive Services) to convert speech to text.

[0393] Step 3:

[0394] Fraudulent Phrase Detection

[0395] Input: Transcribed call transcript.

[0396] How it works: The server uses a generative AI model (e.g., TensorFlow) to analyze text data and detect indicators of fraud and specific phrases.

[0397] Output: Results of whether there are any signs of fraud.

[0398] How it works: The server inputs text data into an AI model to determine whether it contains fraudulent phrases.

[0399] Step 4:

[0400] Emotional state analysis

[0401] Input: Transcribed call content and audio data.

[0402] How it works: The communication device uses an emotion engine to analyze the user's emotional state in real time.

[0403] Output: The result of the user's emotional state (e.g., anxiety, tension).

[0404] Specific operation: The communication device analyzes the voice data and interprets the user's emotional state using an emotion engine (e.g., Emotion API for Azure Cognitive Services).

[0405] Step 5:

[0406] Issuance of a warning

[0407] Input: Signs of cheating or results of a change in emotional state.

[0408] How it works: If the communication device suspects fraudulent activity or if the user seems anxious or nervous, it will temporarily suspend the call and display a warning message.

[0409] Output: A warning message to the user.

[0410] Specific action: Display a message such as "This may be a scam. Do you want to continue the call?" or "Your voice sounds unsafe. Do you want to continue the call?"

[0411] Step 6:

[0412] User Selection

[0413] Input: User's choice in response to the warning message (Resume or Quit).

[0414] Action: The communication terminal resumes or terminates the call depending on the user's selection.

[0415] Output: Resume or end the call.

[0416] Specific operation: If the user selects "Resume", the call continues; if the user selects "End", the call ends.

[0417] Step 7:

[0418] Reporting and Data Retention

[0419] Input: Recording data and emotion data when the call ends.

[0420] How it works: The communication device sends recorded voice and emotion data to a server, which analyzes it and creates a detailed report of the misconduct.

[0421] Output: Detailed report and notification.

[0422] Specific operation: The recorded data and emotional data are sent to a server, which analyzes them and automatically notifies family members or the police based on the results.

[0423] 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.

[0424] 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> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0425] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0426] [Second embodiment]

[0427] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0428] 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.

[0429] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0430] 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.

[0431] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0432] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0433] 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.

[0434] 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.

[0435] 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 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.

[0436] 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.

[0437] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0438] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0439] This invention relates to an AI-based fraud prevention system implemented in elderly users' communication devices. Specifically, the system monitors call content in real time and issues a warning if there are signs of fraud.

[0440] System Configuration

[0441] The system includes the following major components:

[0442] 1. Communication terminals: Communication devices such as landlines and mobile phones used by seniors.

[0443] 2. Server: A centralized device that hosts the AI ​​model, matches incoming numbers, stores data, and performs analysis.

[0444] 3. AI Police Program: A software module installed on communication devices that monitors call content in real time and detects signs of fraudulent activity.

[0445] System Operation

[0446] 1. Operation when receiving a call

[0447] When a user receives a call on their communication device, the device reads the phone number. The device sends the incoming number to a server, which checks it against a list of known fraud numbers. If the server matches the number as a fraudulent number, the device automatically rejects the call. If there is no match, the call proceeds as normal.

[0448] 2. Call monitoring

[0449] Once a call is initiated, the device's AI police program monitors the call in real time, converting the audio data into text and searching for specific fraudulent phrases and patterns, such as "Please tell me your account number" or "Please send the money now."

[0450] 3. Fraud detection and warning

[0451] If signs of fraud are detected, the device will pause the call and display a warning message to the user, such as "Possible fraud. Do you want to continue?" If the user chooses to continue, the call will resume but remain alerted. If the user chooses to end the call, the device will end the call and save the recording.

[0452] 4. Reporting and Data Retention

[0453] Once the call is over, the recording is sent to a server where it is stored, and the server analyzes this data and reports details of the fraudulent activity to family members or the police, including the date and time of the call, the content of the call, and any characteristics of the fraudulent activity that were detected.

[0454] Specific examples

[0455] Suppose a user receives a call on their mobile phone. The device sends the incoming number to a server and checks it against a list of fraudulent numbers. After verifying that it is not a fraudulent number, the call begins. If, during the call, the caller says, "Your son has been in a traffic accident. We urgently need money," the device's AI police program detects this phrase, temporarily suspends the call, and displays a warning. If the user ends the call, the recording and call log are sent to a server, and family members and the police are notified. This reduces the risk of elderly people becoming victims of fraud.

[0456] The above is a specific embodiment for carrying out the present invention. This system is expected to enable elderly people to use the telephone safely and protect them from fraudulent activity.

[0457] The processing flow will be explained below.

[0458] Step 1:

[0459] The user turns on their home landline or mobile phone. The device activates the AI ​​police program and enters fraud prevention mode.

[0460] Step 2:

[0461] The device detects an incoming call, acquires the incoming number, and notifies the user by ringing.

[0462] Step 3:

[0463] The device sends the incoming number to a server, which checks it against a list of known fraud numbers.

[0464] Step 4:

[0465] The server returns the result of matching the incoming number to the terminal. If the matched result matches the fraudulent number, the terminal will automatically reject the call. If the matched result does not match the fraudulent number, proceed to the next step.

[0466] Step 5:

[0467] When the user presses the call button, the device starts the call, and at the same time, the device's AI police program starts recording the audio data.

[0468] Step 6:

[0469] The device's AI police program converts calls into text in real time and monitors for fraudulent phrases and patterns.

[0470] Step 7:

[0471] If the device detects signs of fraudulent activity, it will temporarily halt the call and display a warning message, such as "Possible fraud. Do you want to continue the call?"

[0472] Step 8:

[0473] After receiving the warning message, the user can choose to continue or end the call. If the user chooses to continue the call, the terminal will resume the call and continue monitoring. If the user chooses to end the call, the terminal will proceed to the next step.

[0474] Step 9:

[0475] When the device ends the call, it saves the recording data and sends the recording data and call log to the server.

[0476] Step 10:

[0477] The server analyzes the recordings and creates a detailed report of the misconduct, which it then uses to notify family members or the police.

[0478] Step 11:

[0479] The server uses the collected data to train the AI ​​model and delivers updates to the device to improve the accuracy of the next fraud detection.

[0480] The above are the specific processing steps for carrying out the present invention, which can reduce the risk of elderly people becoming victims of fraud and can be expected to have a further preventative effect.

[0481] Example 1

[0482] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0483] Communication devices used by elderly people are at high risk of becoming victims of fraud and other illegal activities. To solve this problem, a system is needed that can detect fraudulent activities in real time when a call is received or during a call and respond quickly. In addition, if fraudulent activities are suspected, it is necessary to issue a warning to the user and, if necessary, interrupt the call.

[0484] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0485] In this invention, the server includes a means for checking the incoming number against known fraudulent number data, a means for monitoring communication content in real time and detecting specific fraudulent phrases, and a means for temporarily interrupting the call and issuing a warning when communication content suspected of fraud is detected. This makes it possible to quickly detect fraudulent activity on communication terminals used by elderly people and take appropriate warnings and countermeasures.

[0486] "Electronic devices for the elderly" refers to devices with communication functions that are primarily used by the elderly, such as telephones, mobile phones, smartphones, tablets, and landlines.

[0487] "Fraud prevention system" refers to an integrated system that has functions to prevent fraud and other fraudulent activities on elderly people's communication devices.

[0488] The "called number" refers to the telephone number that was called to the communication terminal.

[0489] "Known fraudulent number data" refers to a list of phone numbers that have been identified in the past as being associated with fraud or other fraudulent activity.

[0490] "Means of matching" refers to the process of comparing the incoming number with known fraudulent number data to see if there is a match.

[0491] "Communication content" refers to the audio information exchanged with the other party during a call.

[0492] "Means of monitoring in real time" refers to technology that analyzes the content of ongoing communications almost simultaneously.

[0493] "Specific fraudulent phrases" refers to specific words or phrases that suggest fraud or other fraudulent activity.

[0494] "Means of detection" refers to technology that detects specific fraudulent phrases in communications.

[0495] "Means to temporarily interrupt the call and issue a warning" refers to a feature that pauses the call if fraudulent activity is suspected and warns the user of possible fraudulent activity.

[0496] "Means of recording" refers to the technology that records the contents of a call as digital audio data.

[0497] "Transmission and storage means" refers to the process of transmitting the recorded data to a server and storing that data long-term.

[0498] "Means of analysis" refers to technology that analyzes the contents of recorded communications and determines whether or not there is any fraudulent activity.

[0499] "Means for reporting" refers to the ability to notify family members or public authorities of relevant information when fraudulent activity is detected.

[0500] This invention is a system implemented in the electronic devices of elderly people to prevent fraudulent activities. Specifically, the system monitors communication content in real time and issues a warning if there is any sign of fraudulent activity.

[0501] System Configuration

[0502] The system includes the following major components:

[0503] 1. Communication devices: Electronic devices such as telephones and mobile phones used by the elderly.

[0504] 2. Server: A centralized device that hosts the AI ​​model, matches incoming numbers, stores data, and performs analysis.

[0505] 3. AI Police Program: A software module installed on communication devices that monitors communication content in real time and detects signs of fraudulent activity.

[0506] System Operation

[0507] Incoming call behavior

[0508] When a user receives a call on their communication device, the device reads the phone number. The device then sends the retrieved phone number to the server and checks it against known fraud numbers. If the server finds that the number is a fraudulent number, the device automatically rejects the call. If there is no match, the call proceeds as normal.

[0509] In-call monitoring

[0510] When a user initiates a call, the device's AI police program captures the call in real time as audio data. The device converts this audio data into text using transcription software (e.g., Google Cloud Speech-to-Text). The device then analyzes the text data to detect specific fraudulent phrases and patterns, such as "Please tell me your account number" or "Please transfer the money now."

[0511] Fraud detection and warning

[0512] If signs of fraud are detected, the device will pause the call and display a warning message to the user, such as "Possible fraud. Do you want to continue?" If the user chooses to continue the call, it will resume but remain alerted. If the user chooses to end the call, the device will end the call and save the recording.

[0513] Reporting and Data Retention

[0514] Once the call is over, the device sends the recorded conversation to a server, which stores the data and analyzes it. It then reports details of the fraudulent activity to family members or authorities, including the date and time of the call, the content of the call, and the characteristics of the fraudulent activity detected.

[0515] Specific examples

[0516] Consider an example of when a user receives a call on their mobile phone. The device sends the incoming number to a server and checks it against a scam list. After verifying that it is not a scam number, the call begins. If the caller says, "Your son has been in a traffic accident. We urgently need money," the device's AI police program detects this phrase, pauses the call, and displays a warning. If the user ends the call, the recording and call log are sent to a server, and family members and public authorities are notified. This reduces the risk of elderly people becoming victims of fraud.

[0517] Prompt statement

[0518] Here are some example prompts to input to a generative AI model:

[0519] Please explain the specific operation and processing steps of the system that uses AI technology to prevent fraud on phones used by seniors. Please give us more details on what happens when a call comes in and during a call.

[0520] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0521] Step 1:

[0522] When a user receives a call on a communication terminal, the terminal acquires the called number. The input is the incoming call signal received by the communication terminal, and the output is the called number information. The terminal sends the acquired phone number to the server. Specifically, the terminal transmits the called number to the server as digital data in packet format.

[0523] Step 2:

[0524] The server checks the received phone number against known fraudulent number data. The input is the incoming number sent from the terminal, and the output is information indicating whether or not the number matches the fraudulent number. The server checks whether the number matches the list of fraudulent numbers stored in the database, and if so, returns that information to the terminal. Specifically, the server performs an index search on the number and returns the result.

[0525] Step 3:

[0526] The terminal receives the information returned by the server and automatically rejects the call if there is a match. The input is a notification from the server that the fraud number matches, and the output is a control signal to reject the call. Specifically, the terminal aborts the call connection process and displays a message to the user that the call has been rejected. If there is no match, the call will be initiated normally.

[0527] Step 4:

[0528] When a user initiates a call, the device's AI police program captures the call content as audio data in real time. The input is the call audio data, and the output is a recorded audio file. The device converts this audio data into text using transcription software (e.g., Google Cloud Speech-to-Text). Specifically, the audio data is streamed and converted to text in the cloud.

[0529] Step 5:

[0530] The device then analyzes the converted text data, searching for specific fraudulent phrases and patterns. The input is the converted text of the call, and the output is the phrase detection results. Specifically, natural language processing algorithms are used to detect phrases such as "Please tell me your account number" or "Please transfer the money now."

[0531] Step 6:

[0532] If signs of fraud are detected, the terminal will suspend the call and display a warning message to the user. The input is the result of fraud phrase detection, and the output is the display of a warning message. Specifically, the terminal will display "Possible fraud. Do you want to continue the call?" on the screen.

[0533] Step 7:

[0534] If the user wants to continue the call, the terminal resumes the call but remains alert. The input is the user's selection, and the output is the call resumption control. Specifically, if the user presses the "Yes" button on the screen, the terminal resumes the call connection and continues monitoring.

[0535] Step 8:

[0536] When the user ends the call, the device ends the call and saves the recorded data. The input is the user's selection, and the output is saving the recorded data. Specifically, when the user ends the call, the device disconnects the call and saves the recorded audio data locally or in cloud storage.

[0537] Step 9:

[0538] When the call ends, the device sends the recorded call content to the server. The input is the recorded call data, and the output is the call data sent to the server. Specifically, the recorded data is encrypted and sent to the server via a secure channel.

[0539] Step 10:

[0540] The server stores the received data and analyzes it. The input is the call data sent from the device, and the output is an analysis report. Specifically, the server uses voice recognition and natural language processing to analyze the data and determine whether or not there is any fraudulent activity.

[0541] Step 11:

[0542] Based on the analysis results, the server reports details of fraudulent activity to family members or public institutions. The input is the analysis report, and the output is a report message. Specifically, the report message is sent via email or SMS, containing the date and time of the call, the content of the call, and the characteristics of the detected fraudulent activity.

[0543] (Application example 1)

[0544] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0545] Protecting the elderly from fraud and other fraudulent activities is an important issue. However, conventional communication devices lack the capabilities to monitor call content in real time or detect signs of fraud, leaving the elderly at high risk of being scammed. It is also difficult for them to quickly report suspected fraudulent calls to family members or the police. Therefore, an effective system is needed to ensure safe communication for the elderly and protect them from fraud.

[0546] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0547] In this invention, the server includes means for checking incoming numbers against a list of known fraudulent numbers, means for monitoring call content in real time and detecting specific fraudulent phrases, means for temporarily suspending the call and issuing a warning when suspicious call content is detected, means for recording the call content and transmitting and saving the recording data, means for converting voice data to text and searching for specific phrases in real time, means for notifying a user terminal of a warning when signs of fraud are detected, and means for saving the call data and related information in a remote data storage device. This effectively protects elderly people from fraud when communicating and enables relevant parties to be promptly notified of necessary information.

[0548] "Elderly communication devices" are communication devices such as telephones, mobile phones, and smartphones used by elderly people.

[0549] A "known fraudulent number list" is a list of phone numbers that have been confirmed to be involved in fraud or other fraudulent activity.

[0550] "Means for checking" refers to the function of the communication terminal to check the incoming call number received against a list of known fraud numbers.

[0551] "Means for monitoring call content in real time" refers to technology that can instantly analyze voice data during a call and detect specific phrases that indicate fraudulent activity.

[0552] "Specific fraudulent phrases" are specific words or expressions that can be identified as indicative of fraudulent activity.

[0553] "Means for detecting call content suspected of fraudulent activity" refers to technology that determines whether a call contains content suspected of fraudulent activity.

[0554] "Means to temporarily interrupt the call and issue a warning" refers to a function that temporarily halts the call and displays a warning message to the user if signs of fraudulent activity are detected during the call.

[0555] "Means for recording the contents of phone calls and transmitting and storing the recorded data" refers to a system for recording the audio data of phone calls and transmitting and storing it on a central server or cloud service, etc.

[0556] "Means for converting voice data into text" refers to technology that has the function of automatically converting voices during a call into text information.

[0557] "Means for searching for specific phrases in real time" refers to technology that instantly finds specific phrases related to fraudulent activity from within the text of a call.

[0558] "Means for sending a warning to a user terminal" refers to technology capable of immediately sending a warning to a user's communication terminal when signs of fraudulent activity are detected.

[0559] "Means for storing call data and related information in a remote data storage device" refers to a mechanism for storing call content and other related information in a remote location such as a remote server.

[0560] This invention is a fraud prevention system implemented in elderly users' communication devices, providing real-time call monitoring and automatic alerting functions. The system aims to protect elderly users from fraud and other fraudulent activities and enable them to communicate safely.

[0561] Key components of the system

[0562] communication terminal

[0563] Communication devices used by seniors (telephones, mobile phones, smartphones, etc.).

[0564] An AI police program has been implemented to monitor voice data in real time and detect specific abusive phrases.

[0565] server

[0566] It is a centralized device that performs incoming number verification, fraud data storage and analysis.

[0567] The main software and services used include AWS (Amazon Web Services) EC2, AWS Lambda, and AWS S3.

[0568] AI Police Program

[0569] A software module implemented in a communications terminal.

[0570] Use the Google Speech-to-Text API to convert speech to text and search for specific phrases in real time.

[0571] If it detects signs of fraudulent activity, it will pause the call and display a warning to the user.

[0572] System Operation

[0573] Incoming call behavior

[0574] When a communication device receives a call, it first reads the incoming number. The read number is sent to a server and checked against a list of known fraudulent numbers. If the number matches a fraudulent number, the device automatically rejects the call and displays a warning to the user. If there is no match, the call proceeds as normal.

[0575] In-call monitoring

[0576] Once a call begins, the AI ​​police program on the communication device monitors the call in real time. The audio data is converted to text using the Google Speech-to-Text API and searched for fraudulent phrases and patterns. If signs of fraud are detected, the call is paused and a warning is issued to the user, such as a message saying, "This may be a scam. Do you want to continue?"

[0577] Call recording and data storage

[0578] Calls are recorded, and if fraudulent activity is detected, the recordings are sent to an AWS S3 bucket for storage, which can then be automatically reported to the appropriate parties (family members or police).

[0579] Specific examples

[0580] When a call comes in to a communication device, it works as follows: The incoming number is read and sent to the server, where it is checked against a list of fraudulent numbers. If it is not a fraudulent number, the call begins. If, during the call, the caller says, "My son has been in a traffic accident. I need money urgently," the AI ​​police program on the device detects this phrase, temporarily suspends the call, and issues a warning to the user. The user can then choose to end or resume the call. The call is also recorded, and the necessary information is sent to the relevant parties.

[0581] Prompt Sentence Examples

[0582] "Your son was in a car accident. I need money urgently."

[0583] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0584] Step 1:

[0585] The communication terminal receives an incoming call. It reads the incoming number and sends it to the server. The input is the incoming number notified to the communication terminal, and the output is the transmission of the number to the server. Specifically, the communication terminal's incoming call sensor is activated, the number is saved in its internal memory, and then the number is sent to the server.

[0586] Step 2:

[0587] The server compares the incoming number it receives with a list of known fraudulent numbers. The input is the incoming number sent from the communication terminal, and the output is the comparison result. Specifically, it matches the number with the list of fraudulent numbers stored in the server's database. A detection algorithm determines whether the number is fraudulent and sends the result back to the communication terminal.

[0588] Step 3:

[0589] The communication terminal receives the matching result from the server. If it matches a fraudulent number, it automatically rejects the call and displays a warning to the user. The input is the matching result from the server, and the output is to display a warning to the user or allow the call to proceed. The specific operation is to issue a command to reject the call and display a warning message on the display.

[0590] Step 4:

[0591] If the server determines that the number is not a fraudulent number, the call starts as normal. The input is the server's determination that the number is not a fraudulent number, and the output is the establishment of the call. Specifically, the communication terminal issues permission to start the call, and full-duplex communication begins.

[0592] Step 5:

[0593] During a call, the communication device's AI police program monitors the call content in real time. It converts the voice data into text and searches for fraudulent phrases. The input is the voice data during the call, and the output is the text of the call and the results of the fraudulent phrase detection. Specifically, it uses the Google Speech-to-Text API to convert the voice data into text, and then searches that text using a pattern matching algorithm.

[0594] Step 6:

[0595] If the AI ​​police program detects signs of fraud, it will suspend the call and issue a warning to the user. The input is the result of detecting fraudulent phrases, and the output is to suspend the call and display a warning to the user. Specifically, the phrase detection algorithm detects signs of fraud, issues a call suspension command, and displays a warning message on the display saying, "Possible fraud. Do you want to continue the call?"

[0596] Step 7:

[0597] When the user receives the warning message, they can choose to resume or end the call. The input is the user's choice, and the output is to resume or end the call. Specifically, the user selects "Continue" or "End" on the warning message screen, and based on that selection, the system issues a command to resume the call or ends the call.

[0598] Step 8:

[0599] When the call ends, the AI ​​police program records the call and sends the data to a server for storage. The input is the call end event and the recorded data, and the output is data storage on the server. Specifically, the recorded call data is uploaded to an AWS S3 bucket and a storage log is recorded in the database.

[0600] Step 9:

[0601] The server analyzes the stored data and automatically notifies relevant parties (family members or the police) of details of the fraudulent activity. The input is the recorded data and information on the detected fraudulent activity, and the output is a notification to the relevant parties. Specifically, a data analysis algorithm evaluates the recorded data and notifies the relevant parties via email or SMS.

[0602] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0603] This invention relates to a fraud prevention system that uses AI and an emotion engine implemented in elderly users' communication devices. Specifically, the system monitors call content in real time, and if there are signs of fraud or if the user's emotional state is recognized, it issues a warning and takes appropriate measures.

[0604] System Configuration

[0605] The system includes the following major components:

[0606] 1. Communication terminals: Communication devices such as landlines and mobile phones used by seniors.

[0607] 2. Server: A centralized device that hosts the AI ​​model and emotion engine, matches incoming numbers, stores data, and analyzes it.

[0608] 3. AI Police Program: A software module installed on communication devices that monitors call content in real time and detects signs of fraudulent activity.

[0609] 4. Emotion Engine: A module that recognizes and analyzes the user's emotional state in real time.

[0610] System Operation

[0611] 1. Operation when receiving a call

[0612] When a user receives a call on their communication device, the device retrieves the phone number. The device sends the incoming number to a server, which checks it against a list of known fraud numbers. If the server matches the number as a fraudulent number, the device automatically rejects the call. If there is no match, the call proceeds as normal.

[0613] 2. Call monitoring

[0614] Once a call is initiated, the device's AI police program monitors the call in real time, converting the audio data into text and searching for specific fraudulent phrases and patterns. An emotion engine also analyzes the user's tone of voice and voice patterns to recognize their emotional state in real time.

[0615] 3. Fraud detection and warning

[0616] If signs of fraud are detected or the emotion engine determines that the user's emotion is anxiety or nervousness, the device will pause the call and display a warning message, such as "This is a possible scam. Do you want to continue?" or "I sense anxiety in your voice. This is a possible scam. Do you want to continue?" If the user chooses to continue the call, the device resumes the call but remains alert. If the user chooses to end the call, the device proceeds to the next step.

[0617] 4. Reporting and Data Retention

[0618] Once the call ends, the recorded conversation and emotional data are sent to a server, which analyzes the data and creates a detailed report of the misconduct. This information can then be used to notify family members or the police. Specific details include the date and time of the call, the content of the conversation, the user's emotional state, and any characteristics of the misconduct detected.

[0619] Specific examples

[0620] Suppose a user receives a call on their mobile phone. The device sends the incoming number to a server and checks it against a list of scam numbers. After confirming that it is not a scam, the call begins. During the call, if the caller says, "Your son has been in a traffic accident. I urgently need money," the device's AI police program detects this phrase. At the same time, the emotion engine detects signs of anxiety in the user's voice. The device pauses the call and displays two warning messages: "This may be a scam. Do you want to continue?" and "Your voice sounds anxious. Do you want to continue?" If the user chooses to end the call, the recording and emotion data are sent to the server, which analyzes them and notifies family members or the police. This strengthens prevention measures to prevent elderly people from falling victim to fraud.

[0621] The above is a concrete example of how to implement this invention. This system is expected to enable elderly people to use the telephone safely and protect them from fraudulent activity. By combining it with an emotion engine, even greater crime prevention effects can be expected.

[0622] The processing flow will be explained below.

[0623] Step 1:

[0624] The user turns on their home landline or mobile phone. The device activates the AI ​​police program and emotion engine and goes into fraud prevention mode.

[0625] Step 2:

[0626] The device detects an incoming call, acquires the incoming number, and notifies the user by ringing.

[0627] Step 3:

[0628] The device sends the incoming number to a server, which checks it against a list of known fraud numbers.

[0629] Step 4:

[0630] The server returns the result of the incoming number matching to the terminal. If the matched number matches a fraudulent number, the terminal automatically rejects the call. If the number does not match a fraudulent number, the call is accepted as normal.

[0631] Step 5:

[0632] When the user presses the call button, the device starts the call, and at the same time, the device's AI police program starts recording the audio data.

[0633] Step 6:

[0634] The device's AI police program converts calls into text in real time and monitors for fraudulent phrases and patterns, while its emotion engine analyzes the user's tone of voice and voice patterns to recognize their emotional state in real time.

[0635] Step 7:

[0636] If the device detects signs of fraudulent activity or if the emotion engine determines that the user is feeling anxious or nervous, it will temporarily interrupt the call and display a warning message, such as "This is a possible scam. Do you want to continue?" and "I sense anxiety in your voice. Do you want to continue?"

[0637] Step 8:

[0638] After receiving the warning message, the user can choose to continue or end the call. If the user chooses to continue the call, the terminal will resume the call and continue monitoring. If the user chooses to end the call, the terminal will proceed to the next step.

[0639] Step 9:

[0640] When the device ends the call, it saves the recording data and emotion data, and then sends the recording data and call record to the server.

[0641] Step 10:

[0642] The server analyzes the received recording and emotional data and creates a detailed report of the misconduct. Once the report is complete, the server notifies family members or the police, including the date and time of the call, the content of the call, the emotional state, and the characteristics of the detected misconduct.

[0643] Step 11:

[0644] The server uses the collected data to train the AI ​​model and emotion engine, and delivers updates to the device to improve the accuracy of fraud and emotion detection next time.

[0645] The above are the specific processing steps for implementing this invention. This reduces the risk of elderly people becoming victims of fraud, and further prevention effects can be expected. The incorporation of an emotion engine allows for quick and accurate responses even in situations where the user feels particularly anxious or tense.

[0646] Example 2

[0647] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0648] Seniors are increasingly at risk of falling victim to telephone fraud. These frauds are often sophisticated and difficult to prevent simply by warning users. Therefore, there is a need for systems that enable seniors to use the telephone more safely. It is also important to more accurately detect signs of fraud by monitoring not only the content of calls but also the user's emotional state.

[0649] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for comparing the incoming number with a list of known fraudulent numbers, means for monitoring the content of the call in real time and detecting specific fraudulent phrases, means for converting voice data into text and detecting signs of fraud, means for recognizing the user's emotional state and detecting emotions such as anxiety and tension, means for temporarily interrupting the call and issuing a warning when the call content is suspected of fraud or an abnormal emotional state is detected, and means for recording the call content and transmitting and saving the recording data and emotional data. This reduces the risk of elderly people becoming victims of telephone fraud and enables them to use the phone with peace of mind.

[0650] The "incoming number verification means" is a means having a function of verifying the telephone number of an incoming call to a communication terminal against a list of known fraud numbers.

[0651] The "real-time monitoring means" is a means having a function of monitoring the contents of calls in real time at a communication terminal and detecting specific fraudulent phrases.

[0652] The "voice data to text conversion means" is a means having a function of converting voice data acquired during a call into text data and analyzing it.

[0653] The "emotion recognition means" is a means having a function of analyzing the tone and voice pattern of the user's voice during a call and recognizing the user's emotional state in real time.

[0654] The "warning issuing means" is a means having a function of temporarily interrupting the call and displaying a warning message to the user when the content of the call is suspected to be fraudulent or an abnormal emotional state is detected.

[0655] The "recorded data transmission means" is a means having a function of transmitting the recorded contents of the call and emotional data to a server at the end of the call and storing them.

[0656] This invention is a system that prevents fraudulent activities using AI and an emotion engine implemented in elderly users' communication devices. Specifically, it monitors call content in real time, and if there are signs of fraudulent activities or if it recognizes the user's emotional state, it issues a warning and takes appropriate measures. The system configuration and a specific embodiment are described below.

[0657] System Configuration

[0658] The system includes the following major components:

[0659] 1. Communication terminals: Communication devices such as landlines and mobile phones used by seniors.

[0660] 2. Server: A centralized device that hosts the AI ​​model and emotion engine, matches incoming numbers, stores data, and analyzes it.

[0661] 3. AI Police Program: A software module installed on communication devices that monitors call content in real time and detects signs of fraudulent activity.

[0662] 4. Emotion Engine: A module that recognizes and analyzes the user's emotional state in real time.

[0663] System Operation

[0664] Processing when an incoming call arrives

[0665] When a user receives a call on their communication device, the device captures the phone number and sends it to a server to check against a list of known fraudulent numbers. The server checks the number against the fraudulent number list, and if there is a match, the device automatically rejects the call. If there is no match, the device initiates a normal call to the user.

[0666] Call Initiation and Monitoring

[0667] Once a call is initiated, the device's AI police program begins converting the voice data into text, analyzing it to detect specific fraudulent phrases and patterns, while an emotion engine analyzes the user's tone of voice to recognize their emotional state.

[0668] Fraud detection and warning

[0669] If the device detects signs of fraudulent activity, it will pause the call and display a warning message to the user. It will also display a warning message if the emotion engine determines the user's emotional state to be "anxious" or "nervous." If the user chooses to continue the call, the device resumes the call but remains alert. If the user chooses to end the call, it proceeds to the next step.

[0670] Call end notification and data storage

[0671] After the call ends, the device sends the recorded conversation and emotional data to a server. The server analyzes the received data and creates a detailed report of the fraudulent activity. This report includes the date and time of the call, the other party's phone number, the conversation content (transcribed into text), the user's emotional state, and the fraudulent phrases detected. The server then uses this report to notify the user's family or the police.

[0672] Specific examples

[0673] Consider a scenario in which a user receives a call on their mobile phone. After verifying that the phone number is not on a scam number list, the call begins. If, during the call, the caller says, "Your son has been in a traffic accident. I urgently need money," the device's AI police program converts this phrase into text, analyzes it, and detects fraudulent phrases. At the same time, the emotion engine detects signs of anxiety in the user's voice. The device temporarily suspends the call and warns, "This may be a scam. Do you want to continue?" and "I sense anxiety in your voice. Do you want to continue?" When the user ends the call, the recording and emotion data are sent to a server, which analyzes them and notifies family members or the police.

[0674] Example prompts to be input to the generative AI model

[0675] "Please explain a system that analyzes the content of calls received on communication devices used by elderly people in real time and detects signs of fraudulent activity. In particular, please include the process flow for analyzing the user's emotional state using an emotion engine and detecting an abnormality."

[0676] In this way, we will strengthen prevention measures to prevent elderly people from becoming victims of fraud. This system is expected to provide an environment in which elderly people can use communications safely. By combining it with an emotion engine, we can expect even greater crime prevention effects.

[0677] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0678] Specific flow of system program processing

[0679] Step 1:

[0680] A user receives a call on their communication terminal. The terminal captures the incoming number and sends it to a server. The server checks the received phone number against a list of known fraud numbers.

[0681] Input: Dialed number

[0682] Action: Server checks against list of known fraud numbers

[0683] Output: If it is not a fraudulent number, send the result to the terminal

[0684] Example: A user receives a call and the number is "123-456-7890." The device sends this number to the server, which checks it against a list of fraudulent numbers.

[0685] Step 2:

[0686] If the server recognizes the number as a scam, the device will automatically reject the call. If the number is not a scam, the device will initiate the call.

[0687] Input: Fraudulent number matching result

[0688] Processing: Determine whether the number is a fraudulent number

[0689] Output: The device decides whether to initiate or reject the call

[0690] Example: The device initiates a call to the user because the scam number did not match.

[0691] Step 3:

[0692] When a call is initiated, the device's AI police program begins converting the voice data into text, which the device then sends to a server for analysis, detecting specific fraudulent phrases.

[0693] Input: Audio data from the call

[0694] Processing: Convert the audio data into text and send it to the server

[0695] Output: Text of the call

[0696] Example: A conversation during a phone call, "Hello, I'd like to check as there is a possibility of fraud," is converted into text and sent to the server.

[0697] Step 4:

[0698] At the same time, the emotion engine begins to analyze the user's tone of voice to recognize the user's emotional state, and also analyzes the emotion data sent to the server.

[0699] Input: Audio tone during a call

[0700] Processing: Recognizing and analyzing emotional states

[0701] Output: User's emotional state data

[0702] Example: If the user's tone of voice is rising, the emotion engine will analyze it and detect anxiety.

[0703] Step 5:

[0704] If the device detects signs of fraudulent activity or abnormal emotional states such as anxiety or tension, it will pause the call and display a warning message.

[0705] Input: Analyzed call content, emotional state data

[0706] Processing: Detecting fraud or abnormal emotions

[0707] Output: Pause the call and display a warning message

[0708] Example: The AI ​​police program detects the fraudulent phrase "I need money urgently" during a call, and the emotion engine recognizes the anxiety and warns, "This may be a scam. Do you want to continue the call?"

[0709] Step 6:

[0710] If the user wants to continue the call, the terminal resumes the call but continues to monitor. If the user wants to end the call, proceed to the next step.

[0711] Input: User's choice (continue or end call)

[0712] Processing: Prepare to resume call or save data

[0713] Output: Continue or end the call

[0714] Example: If the user chooses to end the call, end the call and move on.

[0715] Step 7:

[0716] After the call ends, the device sends the recorded conversation and emotional data to a server, which analyzes the data and creates a detailed report of the abuse.

[0717] Input: Recorded call data, emotion data

[0718] Processing: Analyze call content and emotion data, and create detailed reports

[0719] Output: Analysis report

[0720] Example: After a call ends, the recording and emotional data are sent to a server, which analyzes it and compiles a detailed report of any mistakes or misbehavior phrases.

[0721] Step 8:

[0722] Based on the report created, the server automatically notifies the user's family and the police.

[0723] Input: Parsed report

[0724] Action: Report Notification

[0725] Output: Report to family and police

[0726] Example: The server automatically generates a report and sends a notification to the user's family or the police saying, "Fraud was detected on this call."

[0727] The above is a concrete process flow and details of the operations at each step. This allows elderly people to use the telephone with peace of mind and is protected from fraudulent activity.

[0728] (Application example 2)

[0729] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0730] When elderly people use communication devices, they are at increased risk of telephone fraud and other fraudulent activities. It is particularly difficult to detect fraudulent activities, and it is difficult for elderly people themselves to recognize the risks and respond appropriately. Therefore, there is a need for a system that can effectively protect elderly people from fraud and other fraudulent activities and allow them to use communication devices with peace of mind. Furthermore, it is necessary to perform more accurate fraud detection by analyzing the user's emotional state.

[0731] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for comparing the incoming number with a list of known fraudulent numbers, means for monitoring the call content in real time and detecting specific fraudulent phrases, means including an emotion engine for analyzing detected signs of fraud and the user's emotional state, means for temporarily suspending the call and issuing a warning when the call content or change in the emotional state that suggests fraud is detected, means for resuming or terminating the call after the warning according to the user's selection, and means for recording the call content and transmitting and saving the recording data and emotion data. This provides a high level of protection for elderly people from fraud and fraudulent activities, allowing them to use communication terminals with peace of mind.

[0732] "Elderly people" refers to elderly users of communication terminals.

[0733] "Communication terminals" refers to devices such as landlines and mobile phones used by seniors.

[0734] "Fraud" refers to the use of false information or methods to commit a fraudulent act.

[0735] "Fraud" refers to illegal activities carried out with the intent of defrauding seniors of money or information.

[0736] "Emotion engine" refers to a software module for recognizing and analyzing a user's emotional state in real time.

[0737] "Means for checking incoming numbers against a list of known fraudulent numbers" refers to a function that checks incoming calls to a communications terminal against a list of fraudulent numbers.

[0738] "Means for monitoring call content in real time" refers to the ability to analyze voice data during a call in real time and detect fraudulent phrases and patterns.

[0739] "Means for detecting specific fraudulent phrases" refers to the ability to convert call audio into text and search for specific phrases that may indicate fraudulent activity.

[0740] "Warning means" refers to the ability to present a warning message to the user when fraudulent activity is suspected or when the user's emotional state changes.

[0741] "Means to temporarily interrupt a call" refers to the ability to temporarily halt a call when fraudulent activity or a change in emotional state is detected.

[0742] "Means for recording telephone conversations" refers to the function of saving telephone conversations made on a communication terminal as audio data.

[0743] "Means for transmitting and storing recorded data and emotional data" refers to the function of transmitting the recorded voice data and analyzed emotional data to a server and storing them after the call ends.

[0744] The "means for resuming or terminating a call according to the user's choice" refers to a function that determines whether to resume or terminate a call based on the user's judgment after receiving a warning message.

[0745] This section describes an embodiment of the present invention. This invention is a fraud prevention system that uses AI and an emotion engine implemented in elderly users' communication devices. Specifically, the system monitors call content in real time, and if it detects signs of fraud or recognizes the user's emotional state, it issues a warning and takes appropriate measures.

[0746] System Configuration

[0747] The system includes the following main components:

[0748] 1. Communication terminals: Devices such as landlines and mobile phones used by seniors that perform functions such as verifying incoming numbers, detecting fraud, and recording calls.

[0749] 2. Server: Hosts the AI ​​model and emotion engine, matches incoming numbers, stores and analyzes data, and uses software such as TensorFlow and Azure Cognitive Services.

[0750] 3. AI Police Program: A module implemented in communication devices that monitors call content in real time and detects signs of fraudulent activity.

[0751] 4. Emotion Engine: A module that recognizes and analyzes the user's emotional state in real time.

[0752] System Operation

[0753] The server performs the following actions:

[0754] 1. Called number matching:

[0755] When a call comes in, the communication device acquires the phone number and sends it to the server. The server checks it against a list of fraudulent numbers, and if there is a match, the communication device rejects the call.

[0756] 2. Call monitoring:

[0757] Once a call is initiated, the device's AI police program monitors the call in real time, converting the audio into text and detecting specific fraudulent phrases and patterns. An emotion engine analyzes the user's tone of voice and voice patterns to recognize their emotional state.

[0758] 3. Fraud detection and warning:

[0759] If the device detects signs of fraud or an altered emotional state in the user, it will pause the call and display a warning message, such as "This is a possible scam. Do you want to continue?" or "I sense anxiety in your voice. Do you want to continue?"

[0760] 4. Reporting and Data Retention:

[0761] Once the call is over, the recorded conversation and emotional data are sent to a server, which analyzes the data and creates a detailed report of the fraudulent activity. Based on this information, the server can then notify family members or the police.

[0762] Specific examples

[0763] For example:

[0764] Suppose a user receives a call on their mobile phone. The communication device sends the incoming number to a server and checks it against a list of scam numbers. After confirming that it is not a scam, the call begins. During the call, the caller might say, "Your son has been in a traffic accident. I urgently need money." In this case, the communication device's AI police program detects this phrase. At the same time, the emotion engine detects signs of anxiety in the user's voice. The communication device temporarily suspends the call and displays two warning messages: "This may be a scam. Do you want to continue the call?" and "I sense anxiety in your voice. Do you want to continue the call?" If the user chooses to end the call, the recording and emotion data are sent to the server, which analyzes them and notifies family members or the police.

[0765] Prompt Sentence Examples

[0766] "Your son was in a car accident. I need money urgently."

[0767] Output: Signs of fraud

[0768] Sentiment analysis: "We detected anxiety in your voice"

[0769] In this way, it is possible to strengthen prevention measures to prevent seniors from becoming victims of fraud.

[0770] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0771] Step 1:

[0772] Called number matching

[0773] Input: When a call is received at the communication terminal, the incoming call number is obtained.

[0774] Operation: The communication device sends the acquired incoming number to the server, which checks the number against a list of known fraud numbers.

[0775] Output: Returns the result of whether the number is fraudulent or not.

[0776] Specific operation: The server checks the database to see if the number is on the fraudulent number list. If so, it sends an instruction to the communication terminal to automatically reject the call.

[0777] Step 2:

[0778] Real-time call monitoring

[0779] Input: If the number is not a scam number, the call is initiated and the call audio data is input.

[0780] How it works: The communication device's AI police program recognizes the contents of the call in real time and converts it into text data.

[0781] Output: Transcribed transcript of the call.

[0782] What it does: The communication device uses speech recognition software (e.g., Azure Cognitive Services) to convert speech to text.

[0783] Step 3:

[0784] Fraudulent Phrase Detection

[0785] Input: Transcribed call transcript.

[0786] How it works: The server uses a generative AI model (e.g., TensorFlow) to analyze text data and detect indicators of fraud and specific phrases.

[0787] Output: Results of whether there are any signs of fraud.

[0788] How it works: The server inputs text data into an AI model to determine whether it contains fraudulent phrases.

[0789] Step 4:

[0790] Emotional state analysis

[0791] Input: Transcribed call content and audio data.

[0792] How it works: The communication device uses an emotion engine to analyze the user's emotional state in real time.

[0793] Output: The result of the user's emotional state (e.g., anxiety, tension).

[0794] Specific operation: The communication device analyzes the voice data and interprets the user's emotional state using an emotion engine (e.g., Emotion API for Azure Cognitive Services).

[0795] Step 5:

[0796] Issuance of a warning

[0797] Input: Signs of cheating or results of a change in emotional state.

[0798] How it works: If the communication device suspects fraudulent activity or if the user seems anxious or nervous, it will temporarily suspend the call and display a warning message.

[0799] Output: A warning message to the user.

[0800] Specific action: Display a message such as "This may be a scam. Do you want to continue the call?" or "Your voice sounds unsafe. Do you want to continue the call?"

[0801] Step 6:

[0802] User Selection

[0803] Input: User's choice in response to the warning message (Resume or Quit).

[0804] Action: The communication terminal resumes or terminates the call depending on the user's selection.

[0805] Output: Resume or end the call.

[0806] Specific operation: If the user selects "Resume", the call continues; if the user selects "End", the call ends.

[0807] Step 7:

[0808] Reporting and Data Retention

[0809] Input: Recording data and emotion data when the call ends.

[0810] How it works: The communication device sends recorded voice and emotion data to a server, which analyzes it and creates a detailed report of the misconduct.

[0811] Output: Detailed report and notification.

[0812] Specific operation: The recorded data and emotional data are sent to a server, which analyzes them and automatically notifies family members or the police based on the results.

[0813] 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.

[0814] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0815] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0816] [Third embodiment]

[0817] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0818] 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.

[0819] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0820] 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.

[0821] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0822] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0823] 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.

[0824] 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.

[0825] 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 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.

[0826] 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.

[0827] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0828] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0829] This invention relates to an AI-based fraud prevention system implemented in elderly users' communication devices. Specifically, the system monitors call content in real time and issues a warning if there are signs of fraud.

[0830] System Configuration

[0831] The system includes the following major components:

[0832] 1. Communication terminals: Communication devices such as landlines and mobile phones used by seniors.

[0833] 2. Server: A centralized device that hosts the AI ​​model, matches incoming numbers, stores data, and performs analysis.

[0834] 3. AI Police Program: A software module installed on communication devices that monitors call content in real time and detects signs of fraudulent activity.

[0835] System Operation

[0836] 1. Operation when receiving a call

[0837] When a user receives a call on their communication device, the device reads the phone number. The device sends the incoming number to a server, which checks it against a list of known fraud numbers. If the server matches the number as a fraudulent number, the device automatically rejects the call. If there is no match, the call proceeds as normal.

[0838] 2. Call monitoring

[0839] Once a call is initiated, the device's AI police program monitors the call in real time, converting the audio data into text and searching for specific fraudulent phrases and patterns, such as "Please tell me your account number" or "Please send the money now."

[0840] 3. Fraud detection and warning

[0841] If signs of fraud are detected, the device will pause the call and display a warning message to the user, such as "Possible fraud. Do you want to continue?" If the user chooses to continue, the call will resume but remain alerted. If the user chooses to end the call, the device will end the call and save the recording.

[0842] 4. Reporting and Data Retention

[0843] Once the call is over, the recording is sent to a server where it is stored, and the server analyzes this data and reports details of the fraudulent activity to family members or the police, including the date and time of the call, the content of the call, and any characteristics of the fraudulent activity that were detected.

[0844] Specific examples

[0845] Suppose a user receives a call on their mobile phone. The device sends the incoming number to a server and checks it against a list of fraudulent numbers. After verifying that it is not a fraudulent number, the call begins. If, during the call, the caller says, "Your son has been in a traffic accident. We urgently need money," the device's AI police program detects this phrase, temporarily suspends the call, and displays a warning. If the user ends the call, the recording and call log are sent to a server, and family members and the police are notified. This reduces the risk of elderly people becoming victims of fraud.

[0846] The above is a specific embodiment for carrying out the present invention. This system is expected to enable elderly people to use the telephone safely and protect them from fraudulent activity.

[0847] The processing flow will be explained below.

[0848] Step 1:

[0849] The user turns on their home landline or mobile phone. The device activates the AI ​​police program and enters fraud prevention mode.

[0850] Step 2:

[0851] The device detects an incoming call, acquires the incoming number, and notifies the user by ringing.

[0852] Step 3:

[0853] The device sends the incoming number to a server, which checks it against a list of known fraud numbers.

[0854] Step 4:

[0855] The server returns the result of matching the incoming number to the terminal. If the matched result matches the fraudulent number, the terminal will automatically reject the call. If the matched result does not match the fraudulent number, proceed to the next step.

[0856] Step 5:

[0857] When the user presses the call button, the device starts the call, and at the same time, the device's AI police program starts recording the audio data.

[0858] Step 6:

[0859] The device's AI police program converts calls into text in real time and monitors for fraudulent phrases and patterns.

[0860] Step 7:

[0861] If the device detects signs of fraudulent activity, it will temporarily halt the call and display a warning message, such as "Possible fraud. Do you want to continue the call?"

[0862] Step 8:

[0863] After receiving the warning message, the user can choose to continue or end the call. If the user chooses to continue the call, the terminal will resume the call and continue monitoring. If the user chooses to end the call, the terminal will proceed to the next step.

[0864] Step 9:

[0865] When the device ends the call, it saves the recording data and sends the recording data and call log to the server.

[0866] Step 10:

[0867] The server analyzes the recordings and creates a detailed report of the misconduct, which it then uses to notify family members or the police.

[0868] Step 11:

[0869] The server uses the collected data to train the AI ​​model and delivers updates to the device to improve the accuracy of the next fraud detection.

[0870] The above are the specific processing steps for carrying out the present invention, which can reduce the risk of elderly people becoming victims of fraud and can be expected to have a further preventative effect.

[0871] Example 1

[0872] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0873] Communication devices used by elderly people are at high risk of becoming victims of fraud and other illegal activities. To solve this problem, a system is needed that can detect fraudulent activities in real time when a call is received or during a call and respond quickly. In addition, if fraudulent activities are suspected, it is necessary to issue a warning to the user and, if necessary, interrupt the call.

[0874] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0875] In this invention, the server includes a means for checking the incoming number against known fraudulent number data, a means for monitoring communication content in real time and detecting specific fraudulent phrases, and a means for temporarily interrupting the call and issuing a warning when communication content suspected of fraud is detected. This makes it possible to quickly detect fraudulent activity on communication terminals used by elderly people and take appropriate warnings and countermeasures.

[0876] "Electronic devices for the elderly" refers to devices with communication functions that are primarily used by the elderly, such as telephones, mobile phones, smartphones, tablets, and landlines.

[0877] "Fraud prevention system" refers to an integrated system that has functions to prevent fraud and other fraudulent activities on elderly people's communication devices.

[0878] The "called number" refers to the telephone number that was called to the communication terminal.

[0879] "Known fraudulent number data" refers to a list of phone numbers that have been identified in the past as being associated with fraud or other fraudulent activity.

[0880] "Means of matching" refers to the process of comparing the incoming number with known fraudulent number data to see if there is a match.

[0881] "Communication content" refers to the audio information exchanged with the other party during a call.

[0882] "Means of monitoring in real time" refers to technology that analyzes the content of ongoing communications almost simultaneously.

[0883] "Specific fraudulent phrases" refers to specific words or phrases that suggest fraud or other fraudulent activity.

[0884] "Means of detection" refers to technology that detects specific fraudulent phrases in communications.

[0885] "Means to temporarily interrupt the call and issue a warning" refers to a feature that pauses the call if fraudulent activity is suspected and warns the user of possible fraudulent activity.

[0886] "Means of recording" refers to the technology that records the contents of a call as digital audio data.

[0887] "Transmission and storage means" refers to the process of transmitting the recorded data to a server and storing that data long-term.

[0888] "Means of analysis" refers to technology that analyzes the contents of recorded communications and determines whether or not there is any fraudulent activity.

[0889] "Means for reporting" refers to the ability to notify family members or public authorities of relevant information when fraudulent activity is detected.

[0890] This invention is a system implemented in the electronic devices of elderly people to prevent fraudulent activities. Specifically, the system monitors communication content in real time and issues a warning if there is any sign of fraudulent activity.

[0891] System Configuration

[0892] The system includes the following major components:

[0893] 1. Communication devices: Electronic devices such as telephones and mobile phones used by the elderly.

[0894] 2. Server: A centralized device that hosts the AI ​​model, matches incoming numbers, stores data, and performs analysis.

[0895] 3. AI Police Program: A software module installed on communication devices that monitors communication content in real time and detects signs of fraudulent activity.

[0896] System Operation

[0897] Incoming call behavior

[0898] When a user receives a call on their communication device, the device reads the phone number. The device then sends the retrieved phone number to the server and checks it against known fraud numbers. If the server finds that the number is a fraudulent number, the device automatically rejects the call. If there is no match, the call proceeds as normal.

[0899] In-call monitoring

[0900] When a user initiates a call, the device's AI police program captures the call in real time as audio data. The device converts this audio data into text using transcription software (e.g., Google Cloud Speech-to-Text). The device then analyzes the text data to detect specific fraudulent phrases and patterns, such as "Please tell me your account number" or "Please transfer the money now."

[0901] Fraud detection and warning

[0902] If signs of fraud are detected, the device will pause the call and display a warning message to the user, such as "Possible fraud. Do you want to continue?" If the user chooses to continue the call, it will resume but remain alerted. If the user chooses to end the call, the device will end the call and save the recording.

[0903] Reporting and Data Retention

[0904] Once the call is over, the device sends the recorded conversation to a server, which stores the data and analyzes it. It then reports details of the fraudulent activity to family members or authorities, including the date and time of the call, the content of the call, and the characteristics of the fraudulent activity detected.

[0905] Specific examples

[0906] Consider an example of when a user receives a call on their mobile phone. The device sends the incoming number to a server and checks it against a scam list. After verifying that it is not a scam number, the call begins. If the caller says, "Your son has been in a traffic accident. We urgently need money," the device's AI police program detects this phrase, pauses the call, and displays a warning. If the user ends the call, the recording and call log are sent to a server, and family members and public authorities are notified. This reduces the risk of elderly people becoming victims of fraud.

[0907] Prompt statement

[0908] Here are some example prompts to input to a generative AI model:

[0909] Please explain the specific operation and processing steps of the system that uses AI technology to prevent fraud on phones used by seniors. Please give us more details on what happens when a call comes in and during a call.

[0910] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0911] Step 1:

[0912] When a user receives a call on a communication terminal, the terminal acquires the called number. The input is the incoming call signal received by the communication terminal, and the output is the called number information. The terminal sends the acquired phone number to the server. Specifically, the terminal transmits the called number to the server as digital data in packet format.

[0913] Step 2:

[0914] The server checks the received phone number against known fraudulent number data. The input is the incoming number sent from the terminal, and the output is information indicating whether or not the number matches the fraudulent number. The server checks whether the number matches the list of fraudulent numbers stored in the database, and if so, returns that information to the terminal. Specifically, the server performs an index search on the number and returns the result.

[0915] Step 3:

[0916] The terminal receives the information returned by the server and automatically rejects the call if there is a match. The input is a notification from the server that the fraud number matches, and the output is a control signal to reject the call. Specifically, the terminal aborts the call connection process and displays a message to the user that the call has been rejected. If there is no match, the call will be initiated normally.

[0917] Step 4:

[0918] When a user initiates a call, the device's AI police program captures the call content as audio data in real time. The input is the call audio data, and the output is a recorded audio file. The device converts this audio data into text using transcription software (e.g., Google Cloud Speech-to-Text). Specifically, the audio data is streamed and converted to text in the cloud.

[0919] Step 5:

[0920] The device then analyzes the converted text data, searching for specific fraudulent phrases and patterns. The input is the converted text of the call, and the output is the phrase detection results. Specifically, natural language processing algorithms are used to detect phrases such as "Please tell me your account number" or "Please transfer the money now."

[0921] Step 6:

[0922] If signs of fraud are detected, the terminal will suspend the call and display a warning message to the user. The input is the result of fraud phrase detection, and the output is the display of a warning message. Specifically, the terminal will display "Possible fraud. Do you want to continue the call?" on the screen.

[0923] Step 7:

[0924] If the user wants to continue the call, the terminal resumes the call but remains alert. The input is the user's selection, and the output is the call resumption control. Specifically, if the user presses the "Yes" button on the screen, the terminal resumes the call connection and continues monitoring.

[0925] Step 8:

[0926] When the user ends the call, the device ends the call and saves the recorded data. The input is the user's selection, and the output is saving the recorded data. Specifically, when the user ends the call, the device disconnects the call and saves the recorded audio data locally or in cloud storage.

[0927] Step 9:

[0928] When the call ends, the device sends the recorded call content to the server. The input is the recorded call data, and the output is the call data sent to the server. Specifically, the recorded data is encrypted and sent to the server via a secure channel.

[0929] Step 10:

[0930] The server stores the received data and analyzes it. The input is the call data sent from the device, and the output is an analysis report. Specifically, the server uses voice recognition and natural language processing to analyze the data and determine whether or not there is any fraudulent activity.

[0931] Step 11:

[0932] Based on the analysis results, the server reports details of fraudulent activity to family members or public institutions. The input is the analysis report, and the output is a report message. Specifically, the report message is sent via email or SMS, containing the date and time of the call, the content of the call, and the characteristics of the detected fraudulent activity.

[0933] (Application example 1)

[0934] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0935] Protecting the elderly from fraud and other fraudulent activities is an important issue. However, conventional communication devices lack the capabilities to monitor call content in real time or detect signs of fraud, leaving the elderly at high risk of being scammed. It is also difficult for them to quickly report suspected fraudulent calls to family members or the police. Therefore, an effective system is needed to ensure safe communication for the elderly and protect them from fraud.

[0936] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0937] In this invention, the server includes means for checking incoming numbers against a list of known fraudulent numbers, means for monitoring call content in real time and detecting specific fraudulent phrases, means for temporarily suspending the call and issuing a warning when suspicious call content is detected, means for recording the call content and transmitting and saving the recording data, means for converting voice data to text and searching for specific phrases in real time, means for notifying a user terminal of a warning when signs of fraud are detected, and means for saving the call data and related information in a remote data storage device. This effectively protects elderly people from fraud when communicating and enables relevant parties to be promptly notified of necessary information.

[0938] "Elderly communication devices" are communication devices such as telephones, mobile phones, and smartphones used by elderly people.

[0939] A "known fraudulent number list" is a list of phone numbers that have been confirmed to be involved in fraud or other fraudulent activity.

[0940] "Means for checking" refers to the function of the communication terminal to check the incoming call number received against a list of known fraud numbers.

[0941] "Means for monitoring call content in real time" refers to technology that can instantly analyze voice data during a call and detect specific phrases that indicate fraudulent activity.

[0942] "Specific fraudulent phrases" are specific words or expressions that can be identified as indicative of fraudulent activity.

[0943] "Means for detecting call content suspected of fraudulent activity" refers to technology that determines whether a call contains content suspected of fraudulent activity.

[0944] "Means to temporarily interrupt the call and issue a warning" refers to a function that temporarily halts the call and displays a warning message to the user if signs of fraudulent activity are detected during the call.

[0945] "Means for recording the contents of phone calls and transmitting and storing the recorded data" refers to a system for recording the audio data of phone calls and transmitting and storing it on a central server or cloud service, etc.

[0946] "Means for converting voice data into text" refers to technology that has the function of automatically converting voices during a call into text information.

[0947] "Means for searching for specific phrases in real time" refers to technology that instantly finds specific phrases related to fraudulent activity from within the text of a call.

[0948] "Means for sending a warning to a user terminal" refers to technology capable of immediately sending a warning to a user's communication terminal when signs of fraudulent activity are detected.

[0949] "Means for storing call data and related information in a remote data storage device" refers to a mechanism for storing call content and other related information in a remote location such as a remote server.

[0950] This invention is a fraud prevention system implemented in elderly users' communication devices, providing real-time call monitoring and automatic alerting functions. The system aims to protect elderly users from fraud and other fraudulent activities and enable them to communicate safely.

[0951] Key components of the system

[0952] communication terminal

[0953] Communication devices used by seniors (telephones, mobile phones, smartphones, etc.).

[0954] An AI police program has been implemented to monitor voice data in real time and detect specific abusive phrases.

[0955] server

[0956] It is a centralized device that performs incoming number verification, fraud data storage and analysis.

[0957] The main software and services used include AWS (Amazon Web Services) EC2, AWS Lambda, and AWS S3.

[0958] AI Police Program

[0959] A software module implemented in a communications terminal.

[0960] Use the Google Speech-to-Text API to convert speech to text and search for specific phrases in real time.

[0961] If it detects signs of fraudulent activity, it will pause the call and display a warning to the user.

[0962] System Operation

[0963] Incoming call behavior

[0964] When a communication device receives a call, it first reads the incoming number. The read number is sent to a server and checked against a list of known fraudulent numbers. If the number matches a fraudulent number, the device automatically rejects the call and displays a warning to the user. If there is no match, the call proceeds as normal.

[0965] In-call monitoring

[0966] Once a call begins, the AI ​​police program on the communication device monitors the call in real time. The audio data is converted to text using the Google Speech-to-Text API and searched for fraudulent phrases and patterns. If signs of fraud are detected, the call is paused and a warning is issued to the user, such as a message saying, "This may be a scam. Do you want to continue?"

[0967] Call recording and data storage

[0968] Calls are recorded, and if fraudulent activity is detected, the recordings are sent to an AWS S3 bucket for storage, which can then be automatically reported to the appropriate parties (family members or police).

[0969] Specific examples

[0970] When a call comes in to a communication device, it works as follows: The incoming number is read and sent to the server, where it is checked against a list of fraudulent numbers. If it is not a fraudulent number, the call begins. If, during the call, the caller says, "My son has been in a traffic accident. I need money urgently," the AI ​​police program on the device detects this phrase, temporarily suspends the call, and issues a warning to the user. The user can then choose to end or resume the call. The call is also recorded, and the necessary information is sent to the relevant parties.

[0971] Prompt Sentence Examples

[0972] "Your son was in a car accident. I need money urgently."

[0973] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0974] Step 1:

[0975] The communication terminal receives an incoming call. It reads the incoming number and sends it to the server. The input is the incoming number notified to the communication terminal, and the output is the transmission of the number to the server. Specifically, the communication terminal's incoming call sensor is activated, the number is saved in its internal memory, and then the number is sent to the server.

[0976] Step 2:

[0977] The server compares the incoming number it receives with a list of known fraudulent numbers. The input is the incoming number sent from the communication terminal, and the output is the comparison result. Specifically, it matches the number with the list of fraudulent numbers stored in the server's database. A detection algorithm determines whether the number is fraudulent and sends the result back to the communication terminal.

[0978] Step 3:

[0979] The communication terminal receives the matching result from the server. If it matches a fraudulent number, it automatically rejects the call and displays a warning to the user. The input is the matching result from the server, and the output is to display a warning to the user or allow the call to proceed. The specific operation is to issue a command to reject the call and display a warning message on the display.

[0980] Step 4:

[0981] If the server determines that the number is not a fraudulent number, the call starts as normal. The input is the server's determination that the number is not a fraudulent number, and the output is the establishment of the call. Specifically, the communication terminal issues permission to start the call, and full-duplex communication begins.

[0982] Step 5:

[0983] During a call, the communication device's AI police program monitors the call content in real time. It converts the voice data into text and searches for fraudulent phrases. The input is the voice data during the call, and the output is the text of the call and the results of the fraudulent phrase detection. Specifically, it uses the Google Speech-to-Text API to convert the voice data into text, and then searches that text using a pattern matching algorithm.

[0984] Step 6:

[0985] If the AI ​​police program detects signs of fraud, it will suspend the call and issue a warning to the user. The input is the result of detecting fraudulent phrases, and the output is to suspend the call and display a warning to the user. Specifically, the phrase detection algorithm detects signs of fraud, issues a call suspension command, and displays a warning message on the display saying, "Possible fraud. Do you want to continue the call?"

[0986] Step 7:

[0987] When the user receives the warning message, they can choose to resume or end the call. The input is the user's choice, and the output is to resume or end the call. Specifically, the user selects "Continue" or "End" on the warning message screen, and based on that selection, the system issues a command to resume the call or ends the call.

[0988] Step 8:

[0989] When the call ends, the AI ​​police program records the call and sends the data to a server for storage. The input is the call end event and the recorded data, and the output is data storage on the server. Specifically, the recorded call data is uploaded to an AWS S3 bucket and a storage log is recorded in the database.

[0990] Step 9:

[0991] The server analyzes the stored data and automatically notifies relevant parties (family members or the police) of details of the fraudulent activity. The input is the recorded data and information on the detected fraudulent activity, and the output is a notification to the relevant parties. Specifically, a data analysis algorithm evaluates the recorded data and notifies the relevant parties via email or SMS.

[0992] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0993] This invention relates to a fraud prevention system that uses AI and an emotion engine implemented in elderly users' communication devices. Specifically, the system monitors call content in real time, and if there are signs of fraud or if the user's emotional state is recognized, it issues a warning and takes appropriate measures.

[0994] System Configuration

[0995] The system includes the following major components:

[0996] 1. Communication terminals: Communication devices such as landlines and mobile phones used by seniors.

[0997] 2. Server: A centralized device that hosts the AI ​​model and emotion engine, matches incoming numbers, stores data, and analyzes it.

[0998] 3. AI Police Program: A software module installed on communication devices that monitors call content in real time and detects signs of fraudulent activity.

[0999] 4. Emotion Engine: A module that recognizes and analyzes the user's emotional state in real time.

[1000] System Operation

[1001] 1. Operation when receiving a call

[1002] When a user receives a call on their communication device, the device retrieves the phone number. The device sends the incoming number to a server, which checks it against a list of known fraud numbers. If the server matches the number as a fraudulent number, the device automatically rejects the call. If there is no match, the call proceeds as normal.

[1003] 2. Call monitoring

[1004] Once a call is initiated, the device's AI police program monitors the call in real time, converting the audio data into text and searching for specific fraudulent phrases and patterns. An emotion engine also analyzes the user's tone of voice and voice patterns to recognize their emotional state in real time.

[1005] 3. Fraud detection and warning

[1006] If signs of fraud are detected or the emotion engine determines that the user's emotion is anxiety or nervousness, the device will pause the call and display a warning message, such as "This is a possible scam. Do you want to continue?" or "I sense anxiety in your voice. This is a possible scam. Do you want to continue?" If the user chooses to continue the call, the device resumes the call but remains alert. If the user chooses to end the call, the device proceeds to the next step.

[1007] 4. Reporting and Data Retention

[1008] Once the call ends, the recorded conversation and emotional data are sent to a server, which analyzes the data and creates a detailed report of the misconduct. This information can then be used to notify family members or the police. Specific details include the date and time of the call, the content of the conversation, the user's emotional state, and any characteristics of the misconduct detected.

[1009] Specific examples

[1010] Suppose a user receives a call on their mobile phone. The device sends the incoming number to a server and checks it against a list of scam numbers. After confirming that it is not a scam, the call begins. During the call, if the caller says, "Your son has been in a traffic accident. I urgently need money," the device's AI police program detects this phrase. At the same time, the emotion engine detects signs of anxiety in the user's voice. The device pauses the call and displays two warning messages: "This may be a scam. Do you want to continue?" and "Your voice sounds anxious. Do you want to continue?" If the user chooses to end the call, the recording and emotion data are sent to the server, which analyzes them and notifies family members or the police. This strengthens prevention measures to prevent elderly people from falling victim to fraud.

[1011] The above is a concrete example of how to implement this invention. This system is expected to enable elderly people to use the telephone safely and protect them from fraudulent activity. By combining it with an emotion engine, even greater crime prevention effects can be expected.

[1012] The processing flow will be explained below.

[1013] Step 1:

[1014] The user turns on their home landline or mobile phone. The device activates the AI ​​police program and emotion engine and goes into fraud prevention mode.

[1015] Step 2:

[1016] The device detects an incoming call, acquires the incoming number, and notifies the user by ringing.

[1017] Step 3:

[1018] The device sends the incoming number to a server, which checks it against a list of known fraud numbers.

[1019] Step 4:

[1020] The server returns the result of the incoming number matching to the terminal. If the matched number matches a fraudulent number, the terminal automatically rejects the call. If the number does not match a fraudulent number, the call is accepted as normal.

[1021] Step 5:

[1022] When the user presses the call button, the device starts the call, and at the same time, the device's AI police program starts recording the audio data.

[1023] Step 6:

[1024] The device's AI police program converts calls into text in real time and monitors for fraudulent phrases and patterns, while its emotion engine analyzes the user's tone of voice and voice patterns to recognize their emotional state in real time.

[1025] Step 7:

[1026] If the device detects signs of fraudulent activity or if the emotion engine determines that the user is feeling anxious or nervous, it will temporarily interrupt the call and display a warning message, such as "This is a possible scam. Do you want to continue?" and "I sense anxiety in your voice. Do you want to continue?"

[1027] Step 8:

[1028] After receiving the warning message, the user can choose to continue or end the call. If the user chooses to continue the call, the terminal will resume the call and continue monitoring. If the user chooses to end the call, the terminal will proceed to the next step.

[1029] Step 9:

[1030] When the device ends the call, it saves the recording data and emotion data, and then sends the recording data and call record to the server.

[1031] Step 10:

[1032] The server analyzes the received recording and emotional data and creates a detailed report of the misconduct. Once the report is complete, the server notifies family members or the police, including the date and time of the call, the content of the call, the emotional state, and the characteristics of the detected misconduct.

[1033] Step 11:

[1034] The server uses the collected data to train the AI ​​model and emotion engine, and delivers updates to the device to improve the accuracy of fraud and emotion detection next time.

[1035] The above are the specific processing steps for implementing this invention. This reduces the risk of elderly people becoming victims of fraud, and further prevention effects can be expected. The incorporation of an emotion engine allows for quick and accurate responses even in situations where the user feels particularly anxious or tense.

[1036] Example 2

[1037] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1038] Seniors are increasingly at risk of falling victim to telephone fraud. These frauds are often sophisticated and difficult to prevent simply by warning users. Therefore, there is a need for systems that enable seniors to use the telephone more safely. It is also important to more accurately detect signs of fraud by monitoring not only the content of calls but also the user's emotional state.

[1039] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for comparing the incoming number with a list of known fraudulent numbers, means for monitoring the content of the call in real time and detecting specific fraudulent phrases, means for converting voice data into text and detecting signs of fraud, means for recognizing the user's emotional state and detecting emotions such as anxiety and tension, means for temporarily interrupting the call and issuing a warning when the call content is suspected of fraud or an abnormal emotional state is detected, and means for recording the call content and transmitting and saving the recording data and emotional data. This reduces the risk of elderly people becoming victims of telephone fraud and enables them to use the phone with peace of mind.

[1040] The "incoming number verification means" is a means having a function of verifying the telephone number of an incoming call to a communication terminal against a list of known fraud numbers.

[1041] The "real-time monitoring means" is a means having a function of monitoring the contents of calls in real time at a communication terminal and detecting specific fraudulent phrases.

[1042] The "voice data to text conversion means" is a means having a function of converting voice data acquired during a call into text data and analyzing it.

[1043] The "emotion recognition means" is a means having a function of analyzing the tone and voice pattern of the user's voice during a call and recognizing the user's emotional state in real time.

[1044] The "warning issuing means" is a means having a function of temporarily interrupting the call and displaying a warning message to the user when the content of the call is suspected to be fraudulent or an abnormal emotional state is detected.

[1045] The "recorded data transmission means" is a means having a function of transmitting the recorded contents of the call and emotional data to a server at the end of the call and storing them.

[1046] This invention is a system that prevents fraudulent activities using AI and an emotion engine implemented in elderly users' communication devices. Specifically, it monitors call content in real time, and if there are signs of fraudulent activities or if it recognizes the user's emotional state, it issues a warning and takes appropriate measures. The system configuration and a specific embodiment are described below.

[1047] System Configuration

[1048] The system includes the following major components:

[1049] 1. Communication terminals: Communication devices such as landlines and mobile phones used by seniors.

[1050] 2. Server: A centralized device that hosts the AI ​​model and emotion engine, matches incoming numbers, stores data, and analyzes it.

[1051] 3. AI Police Program: A software module installed on communication devices that monitors call content in real time and detects signs of fraudulent activity.

[1052] 4. Emotion Engine: A module that recognizes and analyzes the user's emotional state in real time.

[1053] System Operation

[1054] Processing when an incoming call arrives

[1055] When a user receives a call on their communication device, the device captures the phone number and sends it to a server to check against a list of known fraudulent numbers. The server checks the number against the fraudulent number list, and if there is a match, the device automatically rejects the call. If there is no match, the device initiates a normal call to the user.

[1056] Call Initiation and Monitoring

[1057] Once a call is initiated, the device's AI police program begins converting the voice data into text, analyzing it to detect specific fraudulent phrases and patterns, while an emotion engine analyzes the user's tone of voice to recognize their emotional state.

[1058] Fraud detection and warning

[1059] If the device detects signs of fraudulent activity, it will pause the call and display a warning message to the user. It will also display a warning message if the emotion engine determines the user's emotional state to be "anxious" or "nervous." If the user chooses to continue the call, the device resumes the call but remains alert. If the user chooses to end the call, it proceeds to the next step.

[1060] Call end notification and data storage

[1061] After the call ends, the device sends the recorded conversation and emotional data to a server. The server analyzes the received data and creates a detailed report of the fraudulent activity. This report includes the date and time of the call, the other party's phone number, the conversation content (transcribed into text), the user's emotional state, and the fraudulent phrases detected. The server then uses this report to notify the user's family or the police.

[1062] Specific examples

[1063] Consider a scenario in which a user receives a call on their mobile phone. After verifying that the phone number is not on a scam number list, the call begins. If, during the call, the caller says, "Your son has been in a traffic accident. I urgently need money," the device's AI police program converts this phrase into text, analyzes it, and detects fraudulent phrases. At the same time, the emotion engine detects signs of anxiety in the user's voice. The device temporarily suspends the call and warns, "This may be a scam. Do you want to continue?" and "I sense anxiety in your voice. Do you want to continue?" When the user ends the call, the recording and emotion data are sent to a server, which analyzes them and notifies family members or the police.

[1064] Example prompts to be input to the generative AI model

[1065] "Please explain a system that analyzes the content of calls received on communication devices used by elderly people in real time and detects signs of fraudulent activity. In particular, please include the process flow for analyzing the user's emotional state using an emotion engine and detecting an abnormality."

[1066] In this way, we will strengthen prevention measures to prevent elderly people from becoming victims of fraud. This system is expected to provide an environment in which elderly people can use communications safely. By combining it with an emotion engine, we can expect even greater crime prevention effects.

[1067] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1068] Specific flow of system program processing

[1069] Step 1:

[1070] A user receives a call on their communication terminal. The terminal captures the incoming number and sends it to a server. The server checks the received phone number against a list of known fraud numbers.

[1071] Input: Dialed number

[1072] Action: Server checks against list of known fraud numbers

[1073] Output: If it is not a fraudulent number, send the result to the terminal

[1074] Example: A user receives a call and the number is "123-456-7890." The device sends this number to the server, which checks it against a list of fraudulent numbers.

[1075] Step 2:

[1076] If the server recognizes the number as a scam, the device will automatically reject the call. If the number is not a scam, the device will initiate the call.

[1077] Input: Fraudulent number matching result

[1078] Processing: Determine whether the number is a fraudulent number

[1079] Output: The device decides whether to initiate or reject the call

[1080] Example: The device initiates a call to the user because the scam number did not match.

[1081] Step 3:

[1082] When a call is initiated, the device's AI police program begins converting the voice data into text, which the device then sends to a server for analysis, detecting specific fraudulent phrases.

[1083] Input: Audio data from the call

[1084] Processing: Convert the audio data into text and send it to the server

[1085] Output: Text of the call

[1086] Example: A conversation during a phone call, "Hello, I'd like to check as there is a possibility of fraud," is converted into text and sent to the server.

[1087] Step 4:

[1088] At the same time, the emotion engine begins to analyze the user's tone of voice to recognize the user's emotional state, and also analyzes the emotion data sent to the server.

[1089] Input: Audio tone during a call

[1090] Processing: Recognizing and analyzing emotional states

[1091] Output: User's emotional state data

[1092] Example: If the user's tone of voice is rising, the emotion engine will analyze it and detect anxiety.

[1093] Step 5:

[1094] If the device detects signs of fraudulent activity or abnormal emotional states such as anxiety or tension, it will pause the call and display a warning message.

[1095] Input: Analyzed call content, emotional state data

[1096] Processing: Detecting fraud or abnormal emotions

[1097] Output: Pause the call and display a warning message

[1098] Example: The AI ​​police program detects the fraudulent phrase "I need money urgently" during a call, and the emotion engine recognizes the anxiety and warns, "This may be a scam. Do you want to continue the call?"

[1099] Step 6:

[1100] If the user wants to continue the call, the terminal resumes the call but continues to monitor. If the user wants to end the call, proceed to the next step.

[1101] Input: User's choice (continue or end call)

[1102] Processing: Prepare to resume call or save data

[1103] Output: Continue or end the call

[1104] Example: If the user chooses to end the call, end the call and move on.

[1105] Step 7:

[1106] After the call ends, the device sends the recorded conversation and emotional data to a server, which analyzes the data and creates a detailed report of the abuse.

[1107] Input: Recorded call data, emotion data

[1108] Processing: Analyze call content and emotion data, and create detailed reports

[1109] Output: Analysis report

[1110] Example: After a call ends, the recording and emotional data are sent to a server, which analyzes it and compiles a detailed report of any mistakes or misbehavior phrases.

[1111] Step 8:

[1112] Based on the report created, the server automatically notifies the user's family and the police.

[1113] Input: Parsed report

[1114] Action: Report Notification

[1115] Output: Report to family and police

[1116] Example: The server automatically generates a report and sends a notification to the user's family or the police saying, "Fraud was detected on this call."

[1117] The above is a concrete process flow and details of the operations at each step. This allows elderly people to use the telephone with peace of mind and is protected from fraudulent activity.

[1118] (Application example 2)

[1119] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1120] When elderly people use communication devices, they are at increased risk of telephone fraud and other fraudulent activities. It is particularly difficult to detect fraudulent activities, and it is difficult for elderly people themselves to recognize the risks and respond appropriately. Therefore, there is a need for a system that can effectively protect elderly people from fraud and other fraudulent activities and allow them to use communication devices with peace of mind. Furthermore, it is necessary to perform more accurate fraud detection by analyzing the user's emotional state.

[1121] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for comparing the incoming number with a list of known fraudulent numbers, means for monitoring the call content in real time and detecting specific fraudulent phrases, means including an emotion engine for analyzing detected signs of fraud and the user's emotional state, means for temporarily suspending the call and issuing a warning when the call content or change in the emotional state that suggests fraud is detected, means for resuming or terminating the call after the warning according to the user's selection, and means for recording the call content and transmitting and saving the recording data and emotion data. This provides a high level of protection for elderly people from fraud and fraudulent activities, allowing them to use communication terminals with peace of mind.

[1122] "Elderly people" refers to elderly users of communication terminals.

[1123] "Communication terminals" refers to devices such as landlines and mobile phones used by seniors.

[1124] "Fraud" refers to the use of false information or methods to commit a fraudulent act.

[1125] "Fraud" refers to illegal activities carried out with the intent of defrauding seniors of money or information.

[1126] "Emotion engine" refers to a software module for recognizing and analyzing a user's emotional state in real time.

[1127] "Means for checking incoming numbers against a list of known fraudulent numbers" refers to a function that checks incoming calls to a communications terminal against a list of fraudulent numbers.

[1128] "Means for monitoring call content in real time" refers to the ability to analyze voice data during a call in real time and detect fraudulent phrases and patterns.

[1129] "Means for detecting specific fraudulent phrases" refers to the ability to convert call audio into text and search for specific phrases that may indicate fraudulent activity.

[1130] "Warning means" refers to the ability to present a warning message to the user when fraudulent activity is suspected or when the user's emotional state changes.

[1131] "Means to temporarily interrupt a call" refers to the ability to temporarily halt a call when fraudulent activity or a change in emotional state is detected.

[1132] "Means for recording telephone conversations" refers to the function of saving telephone conversations made on a communication terminal as audio data.

[1133] "Means for transmitting and storing recorded data and emotional data" refers to the function of transmitting the recorded voice data and analyzed emotional data to a server and storing them after the call ends.

[1134] The "means for resuming or terminating a call according to the user's choice" refers to a function that determines whether to resume or terminate a call based on the user's judgment after receiving a warning message.

[1135] This section describes an embodiment of the present invention. This invention is a fraud prevention system that uses AI and an emotion engine implemented in elderly users' communication devices. Specifically, the system monitors call content in real time, and if it detects signs of fraud or recognizes the user's emotional state, it issues a warning and takes appropriate measures.

[1136] System Configuration

[1137] The system includes the following main components:

[1138] 1. Communication terminals: Devices such as landlines and mobile phones used by seniors that perform functions such as verifying incoming numbers, detecting fraud, and recording calls.

[1139] 2. Server: Hosts the AI ​​model and emotion engine, matches incoming numbers, stores and analyzes data, and uses software such as TensorFlow and Azure Cognitive Services.

[1140] 3. AI Police Program: A module implemented in communication devices that monitors call content in real time and detects signs of fraudulent activity.

[1141] 4. Emotion Engine: A module that recognizes and analyzes the user's emotional state in real time.

[1142] System Operation

[1143] The server performs the following actions:

[1144] 1. Called number matching:

[1145] When a call comes in, the communication device acquires the phone number and sends it to the server. The server checks it against a list of fraudulent numbers, and if there is a match, the communication device rejects the call.

[1146] 2. Call monitoring:

[1147] Once a call is initiated, the device's AI police program monitors the call in real time, converting the audio into text and detecting specific fraudulent phrases and patterns. An emotion engine analyzes the user's tone of voice and voice patterns to recognize their emotional state.

[1148] 3. Fraud detection and warning:

[1149] If the device detects signs of fraud or an altered emotional state in the user, it will pause the call and display a warning message, such as "This is a possible scam. Do you want to continue?" or "I sense anxiety in your voice. Do you want to continue?"

[1150] 4. Reporting and Data Retention:

[1151] Once the call is over, the recorded conversation and emotional data are sent to a server, which analyzes the data and creates a detailed report of the fraudulent activity. Based on this information, the server can then notify family members or the police.

[1152] Specific examples

[1153] For example:

[1154] Suppose a user receives a call on their mobile phone. The communication device sends the incoming number to a server and checks it against a list of scam numbers. After confirming that it is not a scam, the call begins. During the call, the caller might say, "Your son has been in a traffic accident. I urgently need money." In this case, the communication device's AI police program detects this phrase. At the same time, the emotion engine detects signs of anxiety in the user's voice. The communication device temporarily suspends the call and displays two warning messages: "This may be a scam. Do you want to continue the call?" and "I sense anxiety in your voice. Do you want to continue the call?" If the user chooses to end the call, the recording and emotion data are sent to the server, which analyzes them and notifies family members or the police.

[1155] Prompt Sentence Examples

[1156] "Your son was in a car accident. I need money urgently."

[1157] Output: Signs of fraud

[1158] Sentiment analysis: "We detected anxiety in your voice"

[1159] In this way, it is possible to strengthen prevention measures to prevent seniors from becoming victims of fraud.

[1160] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1161] Step 1:

[1162] Called number matching

[1163] Input: When a call is received at the communication terminal, the incoming call number is obtained.

[1164] Operation: The communication device sends the acquired incoming number to the server, which checks the number against a list of known fraud numbers.

[1165] Output: Returns the result of whether the number is fraudulent or not.

[1166] Specific operation: The server checks the database to see if the number is on the fraudulent number list. If so, it sends an instruction to the communication terminal to automatically reject the call.

[1167] Step 2:

[1168] Real-time call monitoring

[1169] Input: If the number is not a scam number, the call is initiated and the call audio data is input.

[1170] How it works: The communication device's AI police program recognizes the contents of the call in real time and converts it into text data.

[1171] Output: Transcribed transcript of the call.

[1172] What it does: The communication device uses speech recognition software (e.g., Azure Cognitive Services) to convert speech to text.

[1173] Step 3:

[1174] Fraudulent Phrase Detection

[1175] Input: Transcribed call transcript.

[1176] How it works: The server uses a generative AI model (e.g., TensorFlow) to analyze text data and detect indicators of fraud and specific phrases.

[1177] Output: Results of whether there are any signs of fraud.

[1178] How it works: The server inputs text data into an AI model to determine whether it contains fraudulent phrases.

[1179] Step 4:

[1180] Emotional state analysis

[1181] Input: Transcribed call content and audio data.

[1182] How it works: The communication device uses an emotion engine to analyze the user's emotional state in real time.

[1183] Output: The result of the user's emotional state (e.g., anxiety, tension).

[1184] Specific operation: The communication device analyzes the voice data and interprets the user's emotional state using an emotion engine (e.g., Emotion API for Azure Cognitive Services).

[1185] Step 5:

[1186] Issuance of a warning

[1187] Input: Signs of cheating or results of a change in emotional state.

[1188] How it works: If the communication device suspects fraudulent activity or if the user seems anxious or nervous, it will temporarily suspend the call and display a warning message.

[1189] Output: A warning message to the user.

[1190] Specific action: Display a message such as "This may be a scam. Do you want to continue the call?" or "Your voice sounds unsafe. Do you want to continue the call?"

[1191] Step 6:

[1192] User Selection

[1193] Input: User's choice in response to the warning message (Resume or Quit).

[1194] Action: The communication terminal resumes or terminates the call depending on the user's selection.

[1195] Output: Resume or end the call.

[1196] Specific operation: If the user selects "Resume", the call continues; if the user selects "End", the call ends.

[1197] Step 7:

[1198] Reporting and Data Retention

[1199] Input: Recording data and emotion data when the call ends.

[1200] How it works: The communication device sends recorded voice and emotion data to a server, which analyzes it and creates a detailed report of the misconduct.

[1201] Output: Detailed report and notification.

[1202] Specific operation: The recorded data and emotional data are sent to a server, which analyzes them and automatically notifies family members or the police based on the results.

[1203] 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.

[1204] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1205] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1206] [Fourth embodiment]

[1207] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1208] 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.

[1209] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[1210] 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.

[1211] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1212] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1213] 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.

[1214] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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.

[1215] 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.

[1216] 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 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.

[1217] 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.

[1218] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1219] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1220] This invention relates to an AI-based fraud prevention system implemented in elderly users' communication devices. Specifically, the system monitors call content in real time and issues a warning if there are signs of fraud.

[1221] System Configuration

[1222] The system includes the following major components:

[1223] 1. Communication terminals: Communication devices such as landlines and mobile phones used by seniors.

[1224] 2. Server: A centralized device that hosts the AI ​​model, matches incoming numbers, stores data, and performs analysis.

[1225] 3. AI Police Program: A software module installed on communication devices that monitors call content in real time and detects signs of fraudulent activity.

[1226] System Operation

[1227] 1. Operation when receiving a call

[1228] When a user receives a call on their communication device, the device reads the phone number. The device sends the incoming number to a server, which checks it against a list of known fraud numbers. If the server matches the number as a fraudulent number, the device automatically rejects the call. If there is no match, the call proceeds as normal.

[1229] 2. Call monitoring

[1230] Once a call is initiated, the device's AI police program monitors the call in real time, converting the audio data into text and searching for specific fraudulent phrases and patterns, such as "Please tell me your account number" or "Please send the money now."

[1231] 3. Fraud detection and warning

[1232] If signs of fraud are detected, the device will pause the call and display a warning message to the user, such as "Possible fraud. Do you want to continue?" If the user chooses to continue, the call will resume but remain alerted. If the user chooses to end the call, the device will end the call and save the recording.

[1233] 4. Reporting and Data Retention

[1234] Once the call is over, the recording is sent to a server where it is stored, and the server analyzes this data and reports details of the fraudulent activity to family members or the police, including the date and time of the call, the content of the call, and any characteristics of the fraudulent activity that were detected.

[1235] Specific examples

[1236] Suppose a user receives a call on their mobile phone. The device sends the incoming number to a server and checks it against a list of fraudulent numbers. After verifying that it is not a fraudulent number, the call begins. If, during the call, the caller says, "Your son has been in a traffic accident. We urgently need money," the device's AI police program detects this phrase, temporarily suspends the call, and displays a warning. If the user ends the call, the recording and call log are sent to a server, and family members and the police are notified. This reduces the risk of elderly people becoming victims of fraud.

[1237] The above is a specific embodiment for carrying out the present invention. This system is expected to enable elderly people to use the telephone safely and protect them from fraudulent activity.

[1238] The processing flow will be explained below.

[1239] Step 1:

[1240] The user turns on their home landline or mobile phone. The device activates the AI ​​police program and enters fraud prevention mode.

[1241] Step 2:

[1242] The device detects an incoming call, acquires the incoming number, and notifies the user by ringing.

[1243] Step 3:

[1244] The device sends the incoming number to a server, which checks it against a list of known fraud numbers.

[1245] Step 4:

[1246] The server returns the result of matching the incoming number to the terminal. If the matched result matches the fraudulent number, the terminal will automatically reject the call. If the matched result does not match the fraudulent number, proceed to the next step.

[1247] Step 5:

[1248] When the user presses the call button, the device starts the call, and at the same time, the device's AI police program starts recording the audio data.

[1249] Step 6:

[1250] The device's AI police program converts calls into text in real time and monitors for fraudulent phrases and patterns.

[1251] Step 7:

[1252] If the device detects signs of fraudulent activity, it will temporarily halt the call and display a warning message, such as "Possible fraud. Do you want to continue the call?"

[1253] Step 8:

[1254] After receiving the warning message, the user can choose to continue or end the call. If the user chooses to continue the call, the terminal will resume the call and continue monitoring. If the user chooses to end the call, the terminal will proceed to the next step.

[1255] Step 9:

[1256] When the device ends the call, it saves the recording data and sends the recording data and call log to the server.

[1257] Step 10:

[1258] The server analyzes the recordings and creates a detailed report of the misconduct, which it then uses to notify family members or the police.

[1259] Step 11:

[1260] The server uses the collected data to train the AI ​​model and delivers updates to the device to improve the accuracy of the next fraud detection.

[1261] The above are the specific processing steps for carrying out the present invention, which can reduce the risk of elderly people becoming victims of fraud and can be expected to have a further preventative effect.

[1262] Example 1

[1263] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1264] Communication devices used by elderly people are at high risk of becoming victims of fraud and other illegal activities. To solve this problem, a system is needed that can detect fraudulent activities in real time when a call is received or during a call and respond quickly. In addition, if fraudulent activities are suspected, it is necessary to issue a warning to the user and, if necessary, interrupt the call.

[1265] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1266] In this invention, the server includes a means for checking the incoming number against known fraudulent number data, a means for monitoring communication content in real time and detecting specific fraudulent phrases, and a means for temporarily interrupting the call and issuing a warning when communication content suspected of fraud is detected. This makes it possible to quickly detect fraudulent activity on communication terminals used by elderly people and take appropriate warnings and countermeasures.

[1267] "Electronic devices for the elderly" refers to devices with communication functions that are primarily used by the elderly, such as telephones, mobile phones, smartphones, tablets, and landlines.

[1268] "Fraud prevention system" refers to an integrated system that has functions to prevent fraud and other fraudulent activities on elderly people's communication devices.

[1269] The "called number" refers to the telephone number that was called to the communication terminal.

[1270] "Known fraudulent number data" refers to a list of phone numbers that have been identified in the past as being associated with fraud or other fraudulent activity.

[1271] "Means of matching" refers to the process of comparing the incoming number with known fraudulent number data to see if there is a match.

[1272] "Communication content" refers to the audio information exchanged with the other party during a call.

[1273] "Means of monitoring in real time" refers to technology that analyzes the content of ongoing communications almost simultaneously.

[1274] "Specific fraudulent phrases" refers to specific words or phrases that suggest fraud or other fraudulent activity.

[1275] "Means of detection" refers to technology that detects specific fraudulent phrases in communications.

[1276] "Means to temporarily interrupt the call and issue a warning" refers to a feature that pauses the call if fraudulent activity is suspected and warns the user of possible fraudulent activity.

[1277] "Means of recording" refers to the technology that records the contents of a call as digital audio data.

[1278] "Transmission and storage means" refers to the process of transmitting the recorded data to a server and storing that data long-term.

[1279] "Means of analysis" refers to technology that analyzes the contents of recorded communications and determines whether or not there is any fraudulent activity.

[1280] "Means for reporting" refers to the ability to notify family members or public authorities of relevant information when fraudulent activity is detected.

[1281] This invention is a system implemented in the electronic devices of elderly people to prevent fraudulent activities. Specifically, the system monitors communication content in real time and issues a warning if there is any sign of fraudulent activity.

[1282] System Configuration

[1283] The system includes the following major components:

[1284] 1. Communication devices: Electronic devices such as telephones and mobile phones used by the elderly.

[1285] 2. Server: A centralized device that hosts the AI ​​model, matches incoming numbers, stores data, and performs analysis.

[1286] 3. AI Police Program: A software module installed on communication devices that monitors communication content in real time and detects signs of fraudulent activity.

[1287] System Operation

[1288] Incoming call behavior

[1289] When a user receives a call on their communication device, the device reads the phone number. The device then sends the retrieved phone number to the server and checks it against known fraud numbers. If the server finds that the number is a fraudulent number, the device automatically rejects the call. If there is no match, the call proceeds as normal.

[1290] In-call monitoring

[1291] When a user initiates a call, the device's AI police program captures the call in real time as audio data. The device converts this audio data into text using transcription software (e.g., Google Cloud Speech-to-Text). The device then analyzes the text data to detect specific fraudulent phrases and patterns, such as "Please tell me your account number" or "Please transfer the money now."

[1292] Fraud detection and warning

[1293] If signs of fraud are detected, the device will pause the call and display a warning message to the user, such as "Possible fraud. Do you want to continue?" If the user chooses to continue the call, it will resume but remain alerted. If the user chooses to end the call, the device will end the call and save the recording.

[1294] Reporting and Data Retention

[1295] Once the call is over, the device sends the recorded conversation to a server, which stores the data and analyzes it. It then reports details of the fraudulent activity to family members or authorities, including the date and time of the call, the content of the call, and the characteristics of the fraudulent activity detected.

[1296] Specific examples

[1297] Consider an example of when a user receives a call on their mobile phone. The device sends the incoming number to a server and checks it against a scam list. After verifying that it is not a scam number, the call begins. If the caller says, "Your son has been in a traffic accident. We urgently need money," the device's AI police program detects this phrase, pauses the call, and displays a warning. If the user ends the call, the recording and call log are sent to a server, and family members and public authorities are notified. This reduces the risk of elderly people becoming victims of fraud.

[1298] Prompt statement

[1299] Here are some example prompts to input to a generative AI model:

[1300] Please explain the specific operation and processing steps of the system that uses AI technology to prevent fraud on phones used by seniors. Please give us more details on what happens when a call comes in and during a call.

[1301] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1302] Step 1:

[1303] When a user receives a call on a communication terminal, the terminal acquires the called number. The input is the incoming call signal received by the communication terminal, and the output is the called number information. The terminal sends the acquired phone number to the server. Specifically, the terminal transmits the called number to the server as digital data in packet format.

[1304] Step 2:

[1305] The server checks the received phone number against known fraudulent number data. The input is the incoming number sent from the terminal, and the output is information indicating whether or not the number matches the fraudulent number. The server checks whether the number matches the list of fraudulent numbers stored in the database, and if so, returns that information to the terminal. Specifically, the server performs an index search on the number and returns the result.

[1306] Step 3:

[1307] The terminal receives the information returned by the server and automatically rejects the call if there is a match. The input is a notification from the server that the fraud number matches, and the output is a control signal to reject the call. Specifically, the terminal aborts the call connection process and displays a message to the user that the call has been rejected. If there is no match, the call will be initiated normally.

[1308] Step 4:

[1309] When a user initiates a call, the device's AI police program captures the call content as audio data in real time. The input is the call audio data, and the output is a recorded audio file. The device converts this audio data into text using transcription software (e.g., Google Cloud Speech-to-Text). Specifically, the audio data is streamed and converted to text in the cloud.

[1310] Step 5:

[1311] The device then analyzes the converted text data, searching for specific fraudulent phrases and patterns. The input is the converted text of the call, and the output is the phrase detection results. Specifically, natural language processing algorithms are used to detect phrases such as "Please tell me your account number" or "Please transfer the money now."

[1312] Step 6:

[1313] If signs of fraud are detected, the terminal will suspend the call and display a warning message to the user. The input is the result of fraud phrase detection, and the output is the display of a warning message. Specifically, the terminal will display "Possible fraud. Do you want to continue the call?" on the screen.

[1314] Step 7:

[1315] If the user wants to continue the call, the terminal resumes the call but remains alert. The input is the user's selection, and the output is the call resumption control. Specifically, if the user presses the "Yes" button on the screen, the terminal resumes the call connection and continues monitoring.

[1316] Step 8:

[1317] When the user ends the call, the device ends the call and saves the recorded data. The input is the user's selection, and the output is saving the recorded data. Specifically, when the user ends the call, the device disconnects the call and saves the recorded audio data locally or in cloud storage.

[1318] Step 9:

[1319] When the call ends, the device sends the recorded call content to the server. The input is the recorded call data, and the output is the call data sent to the server. Specifically, the recorded data is encrypted and sent to the server via a secure channel.

[1320] Step 10:

[1321] The server stores the received data and analyzes it. The input is the call data sent from the device, and the output is an analysis report. Specifically, the server uses voice recognition and natural language processing to analyze the data and determine whether or not there is any fraudulent activity.

[1322] Step 11:

[1323] Based on the analysis results, the server reports details of fraudulent activity to family members or public institutions. The input is the analysis report, and the output is a report message. Specifically, the report message is sent via email or SMS, containing the date and time of the call, the content of the call, and the characteristics of the detected fraudulent activity.

[1324] (Application example 1)

[1325] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1326] Protecting the elderly from fraud and other fraudulent activities is an important issue. However, conventional communication devices lack the capabilities to monitor call content in real time or detect signs of fraud, leaving the elderly at high risk of being scammed. It is also difficult for them to quickly report suspected fraudulent calls to family members or the police. Therefore, an effective system is needed to ensure safe communication for the elderly and protect them from fraud.

[1327] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1328] In this invention, the server includes means for checking incoming numbers against a list of known fraudulent numbers, means for monitoring call content in real time and detecting specific fraudulent phrases, means for temporarily suspending the call and issuing a warning when suspicious call content is detected, means for recording the call content and transmitting and saving the recording data, means for converting voice data to text and searching for specific phrases in real time, means for notifying a user terminal of a warning when signs of fraud are detected, and means for saving the call data and related information in a remote data storage device. This effectively protects elderly people from fraud when communicating and enables relevant parties to be promptly notified of necessary information.

[1329] "Elderly communication devices" are communication devices such as telephones, mobile phones, and smartphones used by elderly people.

[1330] A "known fraudulent number list" is a list of phone numbers that have been confirmed to be involved in fraud or other fraudulent activity.

[1331] "Means for checking" refers to the function of the communication terminal to check the incoming call number received against a list of known fraud numbers.

[1332] "Means for monitoring call content in real time" refers to technology that can instantly analyze voice data during a call and detect specific phrases that indicate fraudulent activity.

[1333] "Specific fraudulent phrases" are specific words or expressions that can be identified as indicative of fraudulent activity.

[1334] "Means for detecting call content suspected of fraudulent activity" refers to technology that determines whether a call contains content suspected of fraudulent activity.

[1335] "Means to temporarily interrupt the call and issue a warning" refers to a function that temporarily halts the call and displays a warning message to the user if signs of fraudulent activity are detected during the call.

[1336] "Means for recording the contents of phone calls and transmitting and storing the recorded data" refers to a system for recording the audio data of phone calls and transmitting and storing it on a central server or cloud service, etc.

[1337] "Means for converting voice data into text" refers to technology that has the function of automatically converting voices during a call into text information.

[1338] "Means for searching for specific phrases in real time" refers to technology that instantly finds specific phrases related to fraudulent activity from within the text of a call.

[1339] "Means for sending a warning to a user terminal" refers to technology capable of immediately sending a warning to a user's communication terminal when signs of fraudulent activity are detected.

[1340] "Means for storing call data and related information in a remote data storage device" refers to a mechanism for storing call content and other related information in a remote location such as a remote server.

[1341] This invention is a fraud prevention system implemented in elderly users' communication devices, providing real-time call monitoring and automatic alerting functions. The system aims to protect elderly users from fraud and other fraudulent activities and enable them to communicate safely.

[1342] Key components of the system

[1343] communication terminal

[1344] Communication devices used by seniors (telephones, mobile phones, smartphones, etc.).

[1345] An AI police program has been implemented to monitor voice data in real time and detect specific abusive phrases.

[1346] server

[1347] It is a centralized device that performs incoming number verification, fraud data storage and analysis.

[1348] The main software and services used include AWS (Amazon Web Services) EC2, AWS Lambda, and AWS S3.

[1349] AI Police Program

[1350] A software module implemented in a communications terminal.

[1351] Use the Google Speech-to-Text API to convert speech to text and search for specific phrases in real time.

[1352] If it detects signs of fraudulent activity, it will pause the call and display a warning to the user.

[1353] System Operation

[1354] Incoming call behavior

[1355] When a communication device receives a call, it first reads the incoming number. The read number is sent to a server and checked against a list of known fraudulent numbers. If the number matches a fraudulent number, the device automatically rejects the call and displays a warning to the user. If there is no match, the call proceeds as normal.

[1356] In-call monitoring

[1357] Once a call begins, the AI ​​police program on the communication device monitors the call in real time. The audio data is converted to text using the Google Speech-to-Text API and searched for fraudulent phrases and patterns. If signs of fraud are detected, the call is paused and a warning is issued to the user, such as a message saying, "This may be a scam. Do you want to continue?"

[1358] Call recording and data storage

[1359] Calls are recorded, and if fraudulent activity is detected, the recordings are sent to an AWS S3 bucket for storage, which can then be automatically reported to the appropriate parties (family members or police).

[1360] Specific examples

[1361] When a call comes in to a communication device, it works as follows: The incoming number is read and sent to the server, where it is checked against a list of fraudulent numbers. If it is not a fraudulent number, the call begins. If, during the call, the caller says, "My son has been in a traffic accident. I need money urgently," the AI ​​police program on the device detects this phrase, temporarily suspends the call, and issues a warning to the user. The user can then choose to end or resume the call. The call is also recorded, and the necessary information is sent to the relevant parties.

[1362] Prompt Sentence Examples

[1363] "Your son was in a car accident. I need money urgently."

[1364] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1365] Step 1:

[1366] The communication terminal receives an incoming call. It reads the incoming number and sends it to the server. The input is the incoming number notified to the communication terminal, and the output is the transmission of the number to the server. Specifically, the communication terminal's incoming call sensor is activated, the number is saved in its internal memory, and then the number is sent to the server.

[1367] Step 2:

[1368] The server compares the incoming number it receives with a list of known fraudulent numbers. The input is the incoming number sent from the communication terminal, and the output is the comparison result. Specifically, it matches the number with the list of fraudulent numbers stored in the server's database. A detection algorithm determines whether the number is fraudulent and sends the result back to the communication terminal.

[1369] Step 3:

[1370] The communication terminal receives the matching result from the server. If it matches a fraudulent number, it automatically rejects the call and displays a warning to the user. The input is the matching result from the server, and the output is to display a warning to the user or allow the call to proceed. The specific operation is to issue a command to reject the call and display a warning message on the display.

[1371] Step 4:

[1372] If the server determines that the number is not a fraudulent number, the call starts as normal. The input is the server's determination that the number is not a fraudulent number, and the output is the establishment of the call. Specifically, the communication terminal issues permission to start the call, and full-duplex communication begins.

[1373] Step 5:

[1374] During a call, the communication device's AI police program monitors the call content in real time. It converts the voice data into text and searches for fraudulent phrases. The input is the voice data during the call, and the output is the text of the call and the results of the fraudulent phrase detection. Specifically, it uses the Google Speech-to-Text API to convert the voice data into text, and then searches that text using a pattern matching algorithm.

[1375] Step 6:

[1376] If the AI ​​police program detects signs of fraud, it will suspend the call and issue a warning to the user. The input is the result of detecting fraudulent phrases, and the output is to suspend the call and display a warning to the user. Specifically, the phrase detection algorithm detects signs of fraud, issues a call suspension command, and displays a warning message on the display saying, "Possible fraud. Do you want to continue the call?"

[1377] Step 7:

[1378] When the user receives the warning message, they can choose to resume or end the call. The input is the user's choice, and the output is to resume or end the call. Specifically, the user selects "Continue" or "End" on the warning message screen, and based on that selection, the system issues a command to resume the call or ends the call.

[1379] Step 8:

[1380] When the call ends, the AI ​​police program records the call and sends the data to a server for storage. The input is the call end event and the recorded data, and the output is data storage on the server. Specifically, the recorded call data is uploaded to an AWS S3 bucket and a storage log is recorded in the database.

[1381] Step 9:

[1382] The server analyzes the stored data and automatically notifies relevant parties (family members or the police) of details of the fraudulent activity. The input is the recorded data and information on the detected fraudulent activity, and the output is a notification to the relevant parties. Specifically, a data analysis algorithm evaluates the recorded data and notifies the relevant parties via email or SMS.

[1383] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1384] This invention relates to a fraud prevention system that uses AI and an emotion engine implemented in elderly users' communication devices. Specifically, the system monitors call content in real time, and if there are signs of fraud or if the user's emotional state is recognized, it issues a warning and takes appropriate measures.

[1385] System Configuration

[1386] The system includes the following major components:

[1387] 1. Communication terminals: Communication devices such as landlines and mobile phones used by seniors.

[1388] 2. Server: A centralized device that hosts the AI ​​model and emotion engine, matches incoming numbers, stores data, and analyzes it.

[1389] 3. AI Police Program: A software module installed on communication devices that monitors call content in real time and detects signs of fraudulent activity.

[1390] 4. Emotion Engine: A module that recognizes and analyzes the user's emotional state in real time.

[1391] System Operation

[1392] 1. Operation when receiving a call

[1393] When a user receives a call on their communication device, the device retrieves the phone number. The device sends the incoming number to a server, which checks it against a list of known fraud numbers. If the server matches the number as a fraudulent number, the device automatically rejects the call. If there is no match, the call proceeds as normal.

[1394] 2. Call monitoring

[1395] Once a call is initiated, the device's AI police program monitors the call in real time, converting the audio data into text and searching for specific fraudulent phrases and patterns. An emotion engine also analyzes the user's tone of voice and voice patterns to recognize their emotional state in real time.

[1396] 3. Fraud detection and warning

[1397] If signs of fraud are detected or the emotion engine determines that the user's emotion is anxiety or nervousness, the device will pause the call and display a warning message, such as "This is a possible scam. Do you want to continue?" or "I sense anxiety in your voice. This is a possible scam. Do you want to continue?" If the user chooses to continue the call, the device resumes the call but remains alert. If the user chooses to end the call, the device proceeds to the next step.

[1398] 4. Reporting and Data Retention

[1399] Once the call ends, the recorded conversation and emotional data are sent to a server, which analyzes the data and creates a detailed report of the misconduct. This information can then be used to notify family members or the police. Specific details include the date and time of the call, the content of the conversation, the user's emotional state, and any characteristics of the misconduct detected.

[1400] Specific examples

[1401] Suppose a user receives a call on their mobile phone. The device sends the incoming number to a server and checks it against a list of scam numbers. After confirming that it is not a scam, the call begins. During the call, if the caller says, "Your son has been in a traffic accident. I urgently need money," the device's AI police program detects this phrase. At the same time, the emotion engine detects signs of anxiety in the user's voice. The device pauses the call and displays two warning messages: "This may be a scam. Do you want to continue?" and "Your voice sounds anxious. Do you want to continue?" If the user chooses to end the call, the recording and emotion data are sent to the server, which analyzes them and notifies family members or the police. This strengthens prevention measures to prevent elderly people from falling victim to fraud.

[1402] The above is a concrete example of how to implement this invention. This system is expected to enable elderly people to use the telephone safely and protect them from fraudulent activity. By combining it with an emotion engine, even greater crime prevention effects can be expected.

[1403] The processing flow will be explained below.

[1404] Step 1:

[1405] The user turns on their home landline or mobile phone. The device activates the AI ​​police program and emotion engine and goes into fraud prevention mode.

[1406] Step 2:

[1407] The device detects an incoming call, acquires the incoming number, and notifies the user by ringing.

[1408] Step 3:

[1409] The device sends the incoming number to a server, which checks it against a list of known fraud numbers.

[1410] Step 4:

[1411] The server returns the result of the incoming number matching to the terminal. If the matched number matches a fraudulent number, the terminal automatically rejects the call. If the number does not match a fraudulent number, the call is accepted as normal.

[1412] Step 5:

[1413] When the user presses the call button, the device starts the call, and at the same time, the device's AI police program starts recording the audio data.

[1414] Step 6:

[1415] The device's AI police program converts calls into text in real time and monitors for fraudulent phrases and patterns, while its emotion engine analyzes the user's tone of voice and voice patterns to recognize their emotional state in real time.

[1416] Step 7:

[1417] If the device detects signs of fraudulent activity or if the emotion engine determines that the user is feeling anxious or nervous, it will temporarily interrupt the call and display a warning message, such as "This is a possible scam. Do you want to continue?" and "I sense anxiety in your voice. Do you want to continue?"

[1418] Step 8:

[1419] After receiving the warning message, the user can choose to continue or end the call. If the user chooses to continue the call, the terminal will resume the call and continue monitoring. If the user chooses to end the call, the terminal will proceed to the next step.

[1420] Step 9:

[1421] When the device ends the call, it saves the recording data and emotion data, and then sends the recording data and call record to the server.

[1422] Step 10:

[1423] The server analyzes the received recording and emotional data and creates a detailed report of the misconduct. Once the report is complete, the server notifies family members or the police, including the date and time of the call, the content of the call, the emotional state, and the characteristics of the detected misconduct.

[1424] Step 11:

[1425] The server uses the collected data to train the AI ​​model and emotion engine, and delivers updates to the device to improve the accuracy of fraud and emotion detection next time.

[1426] The above are the specific processing steps for implementing this invention. This reduces the risk of elderly people becoming victims of fraud, and further prevention effects can be expected. The incorporation of an emotion engine allows for quick and accurate responses even in situations where the user feels particularly anxious or tense.

[1427] Example 2

[1428] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1429] Seniors are increasingly at risk of falling victim to telephone fraud. These frauds are often sophisticated and difficult to prevent simply by warning users. Therefore, there is a need for systems that enable seniors to use the telephone more safely. It is also important to more accurately detect signs of fraud by monitoring not only the content of calls but also the user's emotional state.

[1430] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for comparing the incoming number with a list of known fraudulent numbers, means for monitoring the content of the call in real time and detecting specific fraudulent phrases, means for converting voice data into text and detecting signs of fraud, means for recognizing the user's emotional state and detecting emotions such as anxiety and tension, means for temporarily interrupting the call and issuing a warning when the call content is suspected of fraud or an abnormal emotional state is detected, and means for recording the call content and transmitting and saving the recording data and emotional data. This reduces the risk of elderly people becoming victims of telephone fraud and enables them to use the phone with peace of mind.

[1431] The "incoming number verification means" is a means having a function of verifying the telephone number of an incoming call to a communication terminal against a list of known fraud numbers.

[1432] The "real-time monitoring means" is a means having a function of monitoring the contents of calls in real time at a communication terminal and detecting specific fraudulent phrases.

[1433] The "voice data to text conversion means" is a means having a function of converting voice data acquired during a call into text data and analyzing it.

[1434] The "emotion recognition means" is a means having a function of analyzing the tone and voice pattern of the user's voice during a call and recognizing the user's emotional state in real time.

[1435] The "warning issuing means" is a means having a function of temporarily interrupting the call and displaying a warning message to the user when the content of the call is suspected to be fraudulent or an abnormal emotional state is detected.

[1436] The "recorded data transmission means" is a means having a function of transmitting the recorded contents of the call and emotional data to a server at the end of the call and storing them.

[1437] This invention is a system that prevents fraudulent activities using AI and an emotion engine implemented in elderly users' communication devices. Specifically, it monitors call content in real time, and if there are signs of fraudulent activities or if it recognizes the user's emotional state, it issues a warning and takes appropriate measures. The system configuration and a specific embodiment are described below.

[1438] System Configuration

[1439] The system includes the following major components:

[1440] 1. Communication terminals: Communication devices such as landlines and mobile phones used by seniors.

[1441] 2. Server: A centralized device that hosts the AI ​​model and emotion engine, matches incoming numbers, stores data, and analyzes it.

[1442] 3. AI Police Program: A software module installed on communication devices that monitors call content in real time and detects signs of fraudulent activity.

[1443] 4. Emotion Engine: A module that recognizes and analyzes the user's emotional state in real time.

[1444] System Operation

[1445] Processing when an incoming call arrives

[1446] When a user receives a call on their communication device, the device captures the phone number and sends it to a server to check against a list of known fraudulent numbers. The server checks the number against the fraudulent number list, and if there is a match, the device automatically rejects the call. If there is no match, the device initiates a normal call to the user.

[1447] Call Initiation and Monitoring

[1448] Once a call is initiated, the device's AI police program begins converting the voice data into text, analyzing it to detect specific fraudulent phrases and patterns, while an emotion engine analyzes the user's tone of voice to recognize their emotional state.

[1449] Fraud detection and warning

[1450] If the device detects signs of fraudulent activity, it will pause the call and display a warning message to the user. It will also display a warning message if the emotion engine determines the user's emotional state to be "anxious" or "nervous." If the user chooses to continue the call, the device resumes the call but remains alert. If the user chooses to end the call, it proceeds to the next step.

[1451] Call end notification and data storage

[1452] After the call ends, the device sends the recorded conversation and emotional data to a server. The server analyzes the received data and creates a detailed report of the fraudulent activity. This report includes the date and time of the call, the other party's phone number, the conversation content (transcribed into text), the user's emotional state, and the fraudulent phrases detected. The server then uses this report to notify the user's family or the police.

[1453] Specific examples

[1454] Consider a scenario in which a user receives a call on their mobile phone. After verifying that the phone number is not on a scam number list, the call begins. If, during the call, the caller says, "Your son has been in a traffic accident. I urgently need money," the device's AI police program converts this phrase into text, analyzes it, and detects fraudulent phrases. At the same time, the emotion engine detects signs of anxiety in the user's voice. The device temporarily suspends the call and warns, "This may be a scam. Do you want to continue?" and "I sense anxiety in your voice. Do you want to continue?" When the user ends the call, the recording and emotion data are sent to a server, which analyzes them and notifies family members or the police.

[1455] Example prompts to be input to the generative AI model

[1456] "Please explain a system that analyzes the content of calls received on communication devices used by elderly people in real time and detects signs of fraudulent activity. In particular, please include the process flow for analyzing the user's emotional state using an emotion engine and detecting an abnormality."

[1457] In this way, we will strengthen prevention measures to prevent elderly people from becoming victims of fraud. This system is expected to provide an environment in which elderly people can use communications safely. By combining it with an emotion engine, we can expect even greater crime prevention effects.

[1458] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1459] Specific flow of system program processing

[1460] Step 1:

[1461] A user receives a call on their communication terminal. The terminal captures the incoming number and sends it to a server. The server checks the received phone number against a list of known fraud numbers.

[1462] Input: Dialed number

[1463] Action: Server checks against list of known fraud numbers

[1464] Output: If it is not a fraudulent number, send the result to the terminal

[1465] Example: A user receives a call and the number is "123-456-7890." The device sends this number to the server, which checks it against a list of fraudulent numbers.

[1466] Step 2:

[1467] If the server recognizes the number as a scam, the device will automatically reject the call. If the number is not a scam, the device will initiate the call.

[1468] Input: Fraudulent number matching result

[1469] Processing: Determine whether the number is a fraudulent number

[1470] Output: The device decides whether to initiate or reject the call

[1471] Example: The device initiates a call to the user because the scam number did not match.

[1472] Step 3:

[1473] When a call is initiated, the device's AI police program begins converting the voice data into text, which the device then sends to a server for analysis, detecting specific fraudulent phrases.

[1474] Input: Audio data from the call

[1475] Processing: Convert the audio data into text and send it to the server

[1476] Output: Text of the call

[1477] Example: A conversation during a phone call, "Hello, I'd like to check as there is a possibility of fraud," is converted into text and sent to the server.

[1478] Step 4:

[1479] At the same time, the emotion engine begins to analyze the user's tone of voice to recognize the user's emotional state, and also analyzes the emotion data sent to the server.

[1480] Input: Audio tone during a call

[1481] Processing: Recognizing and analyzing emotional states

[1482] Output: User's emotional state data

[1483] Example: If the user's tone of voice is rising, the emotion engine will analyze it and detect anxiety.

[1484] Step 5:

[1485] If the device detects signs of fraudulent activity or abnormal emotional states such as anxiety or tension, it will pause the call and display a warning message.

[1486] Input: Analyzed call content, emotional state data

[1487] Processing: Detecting fraud or abnormal emotions

[1488] Output: Pause the call and display a warning message

[1489] Example: The AI ​​police program detects the fraudulent phrase "I need money urgently" during a call, and the emotion engine recognizes the anxiety and warns, "This may be a scam. Do you want to continue the call?"

[1490] Step 6:

[1491] If the user wants to continue the call, the terminal resumes the call but continues to monitor. If the user wants to end the call, proceed to the next step.

[1492] Input: User's choice (continue or end call)

[1493] Processing: Prepare to resume call or save data

[1494] Output: Continue or end the call

[1495] Example: If the user chooses to end the call, end the call and move on.

[1496] Step 7:

[1497] After the call ends, the device sends the recorded conversation and emotional data to a server, which analyzes the data and creates a detailed report of the abuse.

[1498] Input: Recorded call data, emotion data

[1499] Processing: Analyze call content and emotion data, and create detailed reports

[1500] Output: Analysis report

[1501] Example: After a call ends, the recording and emotional data are sent to a server, which analyzes it and compiles a detailed report of any mistakes or misbehavior phrases.

[1502] Step 8:

[1503] Based on the report created, the server automatically notifies the user's family and the police.

[1504] Input: Parsed report

[1505] Action: Report Notification

[1506] Output: Report to family and police

[1507] Example: The server automatically generates a report and sends a notification to the user's family or the police saying, "Fraud was detected on this call."

[1508] The above is a concrete process flow and details of the operations at each step. This allows elderly people to use the telephone with peace of mind and is protected from fraudulent activity.

[1509] (Application example 2)

[1510] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1511] When elderly people use communication devices, they are at increased risk of telephone fraud and other fraudulent activities. It is particularly difficult to detect fraudulent activities, and it is difficult for elderly people themselves to recognize the risks and respond appropriately. Therefore, there is a need for a system that can effectively protect elderly people from fraud and other fraudulent activities and allow them to use communication devices with peace of mind. Furthermore, it is necessary to perform more accurate fraud detection by analyzing the user's emotional state.

[1512] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for comparing the incoming number with a list of known fraudulent numbers, means for monitoring the call content in real time and detecting specific fraudulent phrases, means including an emotion engine for analyzing detected signs of fraud and the user's emotional state, means for temporarily suspending the call and issuing a warning when the call content or change in the emotional state that suggests fraud is detected, means for resuming or terminating the call after the warning according to the user's selection, and means for recording the call content and transmitting and saving the recording data and emotion data. This provides a high level of protection for elderly people from fraud and fraudulent activities, allowing them to use communication terminals with peace of mind.

[1513] "Elderly people" refers to elderly users of communication terminals.

[1514] "Communication terminals" refers to devices such as landlines and mobile phones used by seniors.

[1515] "Fraud" refers to the use of false information or methods to commit a fraudulent act.

[1516] "Fraud" refers to illegal activities carried out with the intent of defrauding seniors of money or information.

[1517] "Emotion engine" refers to a software module for recognizing and analyzing a user's emotional state in real time.

[1518] "Means for checking incoming numbers against a list of known fraudulent numbers" refers to a function that checks incoming calls to a communications terminal against a list of fraudulent numbers.

[1519] "Means for monitoring call content in real time" refers to the ability to analyze voice data during a call in real time and detect fraudulent phrases and patterns.

[1520] "Means for detecting specific fraudulent phrases" refers to the ability to convert call audio into text and search for specific phrases that may indicate fraudulent activity.

[1521] "Warning means" refers to the ability to present a warning message to the user when fraudulent activity is suspected or when the user's emotional state changes.

[1522] "Means to temporarily interrupt a call" refers to the ability to temporarily halt a call when fraudulent activity or a change in emotional state is detected.

[1523] "Means for recording telephone conversations" refers to the function of saving telephone conversations made on a communication terminal as audio data.

[1524] "Means for transmitting and storing recorded data and emotional data" refers to the function of transmitting the recorded voice data and analyzed emotional data to a server and storing them after the call ends.

[1525] The "means for resuming or terminating a call according to the user's choice" refers to a function that determines whether to resume or terminate a call based on the user's judgment after receiving a warning message.

[1526] This section describes an embodiment of the present invention. This invention is a fraud prevention system that uses AI and an emotion engine implemented in elderly users' communication devices. Specifically, the system monitors call content in real time, and if it detects signs of fraud or recognizes the user's emotional state, it issues a warning and takes appropriate measures.

[1527] System Configuration

[1528] The system includes the following main components:

[1529] 1. Communication terminals: Devices such as landlines and mobile phones used by seniors that perform functions such as verifying incoming numbers, detecting fraud, and recording calls.

[1530] 2. Server: Hosts the AI ​​model and emotion engine, matches incoming numbers, stores and analyzes data, and uses software such as TensorFlow and Azure Cognitive Services.

[1531] 3. AI Police Program: A module implemented in communication devices that monitors call content in real time and detects signs of fraudulent activity.

[1532] 4. Emotion Engine: A module that recognizes and analyzes the user's emotional state in real time.

[1533] System Operation

[1534] The server performs the following actions:

[1535] 1. Called number matching:

[1536] When a call comes in, the communication device acquires the phone number and sends it to the server. The server checks it against a list of fraudulent numbers, and if there is a match, the communication device rejects the call.

[1537] 2. Call monitoring:

[1538] Once a call is initiated, the device's AI police program monitors the call in real time, converting the audio into text and detecting specific fraudulent phrases and patterns. An emotion engine analyzes the user's tone of voice and voice patterns to recognize their emotional state.

[1539] 3. Fraud detection and warning:

[1540] If the device detects signs of fraud or an altered emotional state in the user, it will pause the call and display a warning message, such as "This is a possible scam. Do you want to continue?" or "I sense anxiety in your voice. Do you want to continue?"

[1541] 4. Reporting and Data Retention:

[1542] Once the call is over, the recorded conversation and emotional data are sent to a server, which analyzes the data and creates a detailed report of the fraudulent activity. Based on this information, the server can then notify family members or the police.

[1543] Specific examples

[1544] For example:

[1545] Suppose a user receives a call on their mobile phone. The communication device sends the incoming number to a server and checks it against a list of scam numbers. After confirming that it is not a scam, the call begins. During the call, the caller might say, "Your son has been in a traffic accident. I urgently need money." In this case, the communication device's AI police program detects this phrase. At the same time, the emotion engine detects signs of anxiety in the user's voice. The communication device temporarily suspends the call and displays two warning messages: "This may be a scam. Do you want to continue the call?" and "I sense anxiety in your voice. Do you want to continue the call?" If the user chooses to end the call, the recording and emotion data are sent to the server, which analyzes them and notifies family members or the police.

[1546] Prompt Sentence Examples

[1547] "Your son was in a car accident. I need money urgently."

[1548] Output: Signs of fraud

[1549] Sentiment analysis: "We detected anxiety in your voice"

[1550] In this way, it is possible to strengthen prevention measures to prevent seniors from becoming victims of fraud.

[1551] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1552] Step 1:

[1553] Called number matching

[1554] Input: When a call is received at the communication terminal, the incoming call number is obtained.

[1555] Operation: The communication device sends the acquired incoming number to the server, which checks the number against a list of known fraud numbers.

[1556] Output: Returns the result of whether the number is fraudulent or not.

[1557] Specific operation: The server checks the database to see if the number is on the fraudulent number list. If so, it sends an instruction to the communication terminal to automatically reject the call.

[1558] Step 2:

[1559] Real-time call monitoring

[1560] Input: If the number is not a scam number, the call is initiated and the call audio data is input.

[1561] How it works: The communication device's AI police program recognizes the contents of the call in real time and converts it into text data.

[1562] Output: Transcribed transcript of the call.

[1563] What it does: The communication device uses speech recognition software (e.g., Azure Cognitive Services) to convert speech to text.

[1564] Step 3:

[1565] Fraudulent Phrase Detection

[1566] Input: Transcribed call transcript.

[1567] How it works: The server uses a generative AI model (e.g., TensorFlow) to analyze text data and detect indicators of fraud and specific phrases.

[1568] Output: Results of whether there are any signs of fraud.

[1569] How it works: The server inputs text data into an AI model to determine whether it contains fraudulent phrases.

[1570] Step 4:

[1571] Emotional state analysis

[1572] Input: Transcribed call content and audio data.

[1573] How it works: The communication device uses an emotion engine to analyze the user's emotional state in real time.

[1574] Output: The result of the user's emotional state (e.g., anxiety, tension).

[1575] Specific operation: The communication device analyzes the voice data and interprets the user's emotional state using an emotion engine (e.g., Emotion API for Azure Cognitive Services).

[1576] Step 5:

[1577] Issuance of a warning

[1578] Input: Signs of cheating or results of a change in emotional state.

[1579] How it works: If the communication device suspects fraudulent activity or if the user seems anxious or nervous, it will temporarily suspend the call and display a warning message.

[1580] Output: A warning message to the user.

[1581] Specific action: Display a message such as "This may be a scam. Do you want to continue the call?" or "Your voice sounds unsafe. Do you want to continue the call?"

[1582] Step 6:

[1583] User Selection

[1584] Input: User's choice in response to the warning message (Resume or Quit).

[1585] Action: The communication terminal resumes or terminates the call depending on the user's selection.

[1586] Output: Resume or end the call.

[1587] Specific operation: If the user selects "Resume", the call continues; if the user selects "End", the call ends.

[1588] Step 7:

[1589] Reporting and Data Retention

[1590] Input: Recording data and emotion data when the call ends.

[1591] How it works: The communication device sends recorded voice and emotion data to a server, which analyzes it and creates a detailed report of the misconduct.

[1592] Output: Detailed report and notification.

[1593] Specific operation: The recorded data and emotional data are sent to a server, which analyzes them and automatically notifies family members or the police based on the results.

[1594] 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.

[1595] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1596] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1597] 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.

[1598] FIG. 9 is a diagram illustrating 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 actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect 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.

[1599] 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.

[1600] 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).

[1601] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, 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 indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1602] 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."

[1603] 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.

[1604] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1605] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1606] 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.

[1607] 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.

[1608] 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.

[1609] The hardware resource for executing a specific process can be any of the following processors: An example of a processor 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. Another example of a processor is 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.

[1610] The hardware resource that executes the specific processing 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 processing may be a single processor.

[1611] 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.

[1612] 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.

[1613] 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.

[1614] 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.

[1615] The following is further disclosed regarding the above embodiment.

[1616] (Claim 1)

[1617] A system that is implemented in a communication terminal of an elderly person and prevents fraudulent activities,

[1618] means for checking the incoming number against a list of known fraudulent numbers;

[1619] A means of monitoring call content in real time to detect specific fraudulent phrases;

[1620] A means for temporarily interrupting a call and issuing a warning when a call content that is suspected to be fraudulent is detected;

[1621] A means for recording the contents of the call and transmitting and storing the recording data;

[1622] A system including:

[1623] (Claim 2)

[1624] 10. The system of claim 1, further comprising means for analyzing the content of the call and issuing an alert for possible fraudulent activity, and then resuming or terminating the call as selected.

[1625] (Claim 3)

[1626] 10. The system of claim 1, further comprising means for automatically reporting relevant information to family members or police when a call content that may be fraudulent is detected.

[1627] "Example 1"

[1628] (Claim 1)

[1629] A system implemented in an electronic device of an elderly person to prevent fraudulent activity, comprising:

[1630] means for matching the called number with known fraud number data;

[1631] A means of monitoring communications in real time to detect specific abusive phrases;

[1632] A means for temporarily interrupting a call and issuing a warning when communication content suspected of fraudulent activity is detected;

[1633] A means for recording the contents of communications and transmitting and storing the recording data;

[1634] A system including:

[1635] (Claim 2)

[1636] 10. The system of claim 1, further comprising means for analyzing the content of the communication, issuing an alert for possible fraudulent activity, and then resuming or terminating the call as selected.

[1637] (Claim 3)

[1638] 10. The system of claim 1, further comprising means for automatically reporting relevant information to family members or public authorities when communication content that may be fraudulent is detected.

[1639] "Application Example 1"

[1640] (Claim 1)

[1641] A system that is implemented in a communication terminal of an elderly person and prevents fraudulent activities,

[1642] means for checking the incoming number against a list of known fraudulent numbers;

[1643] A means of monitoring call content in real time to detect specific fraudulent phrases;

[1644] A means for temporarily interrupting a call and issuing a warning when a call content that is suspected to be fraudulent is detected;

[1645] A means for recording the contents of the call and transmitting and storing the recording data;

[1646] A means for issuing a warning to a user device when a sign of fraudulent activity is detected;

[1647] A means to convert voice data into text and search for specific phrases in real time;

[1648] means for storing the call data and related information in a remote data storage device;

[1649] A system including:

[1650] (Claim 2)

[1651] 10. The system of claim 1, further comprising means for analyzing the content of the call and issuing an alert for possible fraudulent activity, and then resuming or terminating the call as selected.

[1652] (Claim 3)

[1653] 10. The system of claim 1, further comprising means for automatically reporting relevant information to a parent or a police officer when a call content that may be fraudulent is detected.

[1654] "Example 2: Combining Emotion Engines"

[1655] (Claim 1)

[1656] A system that is implemented in a communication terminal of an elderly person and prevents fraudulent activities,

[1657] means for checking the incoming number against a list of known fraudulent numbers;

[1658] A means of monitoring call content in real time to detect specific fraudulent phrases;

[1659] a means for converting the voice data into text and detecting indicators of fraud;

[1660] means for recognizing the user's emotional state and detecting emotions such as anxiety or tension;

[1661] means for temporarily interrupting a call and issuing a warning when suspicious call content or an abnormal emotional state is detected;

[1662] A means for recording the contents of the call and transmitting and storing the recording data and emotion data;

[1663] A system including:

[1664] (Claim 2)

[1665] 10. The system of claim 1, further comprising means for analyzing the content of the call and issuing an alert for possible fraudulent activity, and then resuming or terminating the call as selected.

[1666] (Claim 3)

[1667] 10. The system of claim 1, further comprising means for automatically reporting relevant information to family members or police when potentially fraudulent call content or an abnormal emotional state is detected.

[1668] "Application example 2 when combining emotion engines"

[1669] (Claim 1)

[1670] A system that is implemented in a communication terminal of an elderly person and prevents fraudulent activities,

[1671] means for checking the incoming number against a list of known fraudulent numbers;

[1672] A means of monitoring call content in real time to detect specific fraudulent phrases;

[1673] means for analyzing the detected indications of fraudulent activity and the emotional state of the user, the emotional engine comprising:

[1674] means for temporarily interrupting a call and issuing a warning when a change in call content or emotional state that suggests fraud is detected;

[1675] means for resuming or terminating the call after the alert, at the user's option;

[1676] A means for recording the contents of the call and transmitting and storing the recording data and emotion data;

[1677] A system including:

[1678] (Claim 2)

[1679] 10. The system according to claim 1, further comprising means for automatically reporting relevant information to family members or the police when potentially fraudulent call content and changes in the user's emotions are detected.

[1680] (Claim 3)

[1681] 10. The system of claim 1, further comprising means for analyzing emotions in real time based on call content using a generative AI model for identifying user emotions. [Explanation of symbols]

[1682] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A system that is implemented in a communication terminal of an elderly person and prevents fraudulent activities, means for checking the incoming number against a list of known fraudulent numbers; A means of monitoring call content in real time to detect specific fraudulent phrases; A means for temporarily interrupting a call and issuing a warning when a call content that is suspected to be fraudulent is detected; A means for recording the contents of the call and transmitting and storing the recording data; A system including:

2. 10. The system of claim 1, further comprising means for analyzing the content of the call and issuing a warning of possible fraudulent activity, and then resuming or terminating the call as selected.

3. 10. The system according to claim 1, further comprising means for automatically reporting relevant information to family members or the police when a call content that may be fraudulent is detected.

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