System

Abstract

An object of a system according to an embodiment is to effectively prevent a special fraud by telephone.SOLUTION: A system includes a fraud detection part, a requirement proxy part, a safety transmission part, a power reception confirmation part, and a learning part. The fraud detector analyzes the source or content of the call to determine the likelihood of fraud. The requirements agency intercepts the requirements of the received telephone call on its behalf. The Safety Communicator communicates to the customer the safety or risk of the call. The power reception confirmation unit confirms power reception or rejection of the customer. The learning unit learns the latest fraud technique and responds in real time.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology lacks effective means to prevent special telephone fraud, leaving elderly people and others at risk of falling victim to fraud.

[0005] The system according to the embodiment aims to effectively prevent special frauds by telephone. [Means for solving the problem]

[0006] The system according to the embodiment comprises a fraud detection unit, a requirement proxy unit, a safety transmission unit, a call reception confirmation unit, and a learning unit. The fraud detection unit analyzes the call origin or content and determines the possibility of fraud. The requirement proxy unit listens to the requirements of the received call on behalf of the customer. The safety transmission unit informs the customer of the safety and risks of the call. The call reception confirmation unit confirms whether the customer accepts or rejects the call. The learning unit learns the latest fraud techniques and responds in real time. [Effects of the Invention]

[0007] The system according to the embodiment can effectively prevent special frauds by telephone. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The special fraud prevention system according to an embodiment of the present invention utilizes AI on a network to prevent special frauds committed over the phone. In this system, the cloud-based AI listens to and responds to requests on behalf of the customer, informs them of safety and risks, and confirms whether the call should be accepted or rejected. In the event of a rejection, the AI ​​responds on behalf of the customer, learns the latest fraud techniques, and responds in real time. This enables the special fraud prevention system to effectively prevent special frauds committed over the phone and ensure customer safety.

[0029] The special fraud prevention system according to the embodiment includes a fraud detection unit, a requirement proxy unit, a safety communication unit, a call reception confirmation unit, and a learning unit. The fraud detection unit analyzes the call origin or content to determine the possibility of fraud. For example, the fraud detection unit references a telephone number database to determine whether the number has been used in a fraudulent transaction in the past. The fraud detection unit also analyzes the call content in real time and issues a warning if there are signs of fraud. The requirement proxy unit listens to the caller's requirements. For example, the requirement proxy unit responds with a message such as, "Hello, this is an automated answering system. How can I help you?" to hear the caller's requirements. The requirement proxy unit also uses voice recognition technology to analyze the message and take appropriate action. The safety communication unit informs the customer of the safety and risks of the call. For example, the safety communication unit issues a warning such as, "This call may be fraudulent. Please be careful." The safety communication unit also provides specific examples of past fraud cases to enhance persuasiveness. The call reception confirmation unit confirms whether the customer accepts or rejects the call. For example, the call reception confirmation unit asks a question such as, "Do you want to accept this call or reject it?" and waits for the customer's response. The call reception confirmation unit also analyzes the customer's past call reception history and issues a warning if a specific pattern indicates a risk of fraud. The learning unit learns the latest fraud techniques and responds in real time. For example, the learning unit monitors fraud-related forums and social media on the Internet to learn new fraud techniques. The learning unit also analyzes past fraud cases and identifies fraud patterns. As a result, the special fraud prevention system according to the embodiment can effectively prevent special frauds over the phone and ensure the safety of customers. For example, the output unit notifies the customer of the risk of fraud before the customer answers the call. The warning message provides important information for the customer to recognize the risk of fraud and take appropriate action.

[0030] The fraud detection unit can refer to a database of telephone numbers to check whether a number has been used for fraud in the past. The fraud detection unit, for example, refers to a database of telephone numbers to check whether a number has been used for fraud in the past. For example, the fraud detection unit takes into account how often the database is updated and makes a judgment based on the most recent information. The fraud detection unit also evaluates the reliability of the database and gives priority to using highly reliable data. This allows phone numbers with a high probability of being fraudulent to be quickly identified.

[0031] The requirement agent unit can analyze the speaker's language and speech patterns to determine the possibility of fraud. The requirement agent unit, for example, analyzes the speaker's language and speech patterns to determine the possibility of fraud. For example, the requirement agent unit extracts the speaker's characteristics using voice analysis technology. The requirement agent unit also issues a warning if a specific phrase or language is a sign of fraud. The requirement agent unit also analyzes the speaker's tone and speed of voice to determine the possibility of fraud with high accuracy. In this way, by analyzing the speaker's language and speech patterns, the possibility of fraud can be determined with high accuracy.

[0032] The safety communication unit can increase persuasiveness when communicating risks to customers by specifically showing past fraud cases. For example, the safety communication unit can increase persuasiveness when communicating risks to customers by specifically showing past fraud cases. For example, the safety communication unit can provide specific examples such as, "Fraud has been committed using these methods in the past." The safety communication unit also clarifies the criteria for selecting fraud cases and provides highly reliable information to customers. In this way, by specifically showing past fraud cases, the safety communication unit can increase persuasiveness of risks to customers.

[0033] The call reception confirmation unit can analyze a customer's past call reception history and issue a warning if a specific pattern indicates a risk of fraud. The call reception confirmation unit, for example, analyzes a customer's past call reception history and issues a warning if a specific pattern indicates a risk of fraud. For example, the call reception confirmation unit analyzes data over a long period of time, taking into account the storage period of the history. The call reception confirmation unit also uses frequency analysis and anomaly detection algorithms to accurately determine the risk of fraud. As a result, by analyzing a customer's past call reception history, the risk of fraud can be accurately determined.

[0034] The learning unit monitors fraud-related forums and social networking sites on the Internet and can learn about the latest fraud techniques in real time. The learning unit, for example, monitors fraud-related forums and social networking sites on the Internet and can learn about the latest fraud techniques in real time. For example, the learning unit uses monitoring tools to collect fraud-related information. The learning unit also clarifies the types of social networking sites to target and collects a wide range of information. This allows the unit to learn about the latest fraud techniques in real time, allowing it to always be able to respond to the latest fraud techniques.

[0035] The learning unit can analyze past fraud cases, identify fraud patterns, and predict future fraud methods. The learning unit, for example, analyzes past fraud cases and identifies fraud patterns. For example, the learning unit clarifies how cases are collected and performs analysis based on highly reliable data. The learning unit also uses pattern mining technology to identify fraud patterns. In this way, by analyzing past fraud cases, it is possible to predict future fraud methods and take measures in advance.

[0036] The fraud detection unit can analyze geographic information of the call originator in real time to identify calls originating from areas with a high risk of fraud. For example, the fraud detection unit can analyze geographic information of the call originator in real time to identify calls originating from areas with a high risk of fraud. For example, the fraud detection unit can identify the location of the call originator using GPS data. The fraud detection unit can also identify high-risk areas using geographic risk assessment criteria. In this way, by analyzing the geographic information, calls originating from areas with a high risk of fraud can be identified.

[0037] The requirement agent can analyze the frequency of occurrence of specific phrases and keywords based on past call data and identify signs of fraud. The requirement agent analyzes the frequency of occurrence of specific phrases and keywords based on past call data, for example. For example, the requirement agent converts recorded call data into text and performs frequency analysis. The requirement agent also identifies signs of fraud using co-occurrence network analysis. This allows for highly accurate identification of signs of fraud by analyzing past call data.

[0038] The safety communication unit can analyze a customer's past call history and issue a warning if a specific pattern indicates a risk of fraud. For example, the safety communication unit analyzes a customer's past call history and issues a warning if a specific pattern indicates a risk of fraud. For example, the safety communication unit analyzes data over a long period of time, taking into account the storage period of the history. In addition, the safety communication unit uses frequency analysis and anomaly detection algorithms to determine the risk of fraud with high accuracy. As a result, by analyzing a customer's past call history, the risk of fraud can be determined with high accuracy.

[0039] The call reception confirmation unit can analyze the customer's answers and automatically recommend refusal if there is a high risk of fraud. The call reception confirmation unit, for example, analyzes the customer's answers and automatically recommends refusal if there is a high risk of fraud. For example, the call reception confirmation unit performs text analysis of the answers and recommends refusal if certain phrases or wording are indicative of fraud. The call reception confirmation unit also uses voice recognition technology to analyze the customer's answers. In this way, by analyzing the customer's answers, it can automatically recommend refusal if there is a high risk of fraud.

[0040] The learning unit can cooperate with other security systems to provide comprehensive fraud prevention measures. The learning unit, for example, cooperates with other security systems to provide comprehensive fraud prevention measures. For example, the learning unit cooperates with an email filtering system to block fraudulent emails. The learning unit also cooperates with a firewall to prevent unauthorized access. In this way, by cooperating with other security systems, comprehensive fraud prevention measures can be provided.

[0041] The learning unit can issue a warning to the user in real time when it detects a call with a high risk of fraud. For example, the learning unit issues a warning to the user in real time when it detects a call with a high risk of fraud. For example, the learning unit issues a warning if the risk of fraud is high based on the analysis results of the call content. The learning unit also evaluates the similarity with past fraud cases and issues a warning if the risk is high. This makes it possible to issue a warning to the user in real time when it detects a call with a high risk of fraud.

[0042] The call reception confirmation unit can propose the optimal option based on the customer's past behavioral patterns. The call reception confirmation unit proposes the optimal option based on the customer's past behavioral patterns, for example. For example, the call reception confirmation unit analyzes past behavioral history and performs behavior frequency analysis. The call reception confirmation unit also makes a similar proposal if the customer has rejected a call in the past because of a high risk of fraud. This makes it possible to propose the optimal option based on the customer's past behavioral patterns.

[0043] The call reception confirmation unit can take into account the customer's current situation. The call reception confirmation unit, for example, takes into account the customer's current situation. For example, the call reception confirmation unit determines whether the customer is busy based on schedule information. The call reception confirmation unit also grasps the customer's current situation using a real-time situation confirmation method. This allows the customer's current situation to be taken into account to optimally confirm whether to accept or reject the call.

[0044] The learning unit monitors fraud-related forums and social networking sites on the Internet and can learn about the latest fraud techniques in real time. The learning unit, for example, monitors fraud-related forums and social networking sites on the Internet and can learn about the latest fraud techniques in real time. For example, the learning unit uses monitoring tools to collect fraud-related information. The learning unit also clarifies the types of social networking sites to target and collects a wide range of information. This allows the unit to learn about the latest fraud techniques in real time, allowing it to always be able to respond to the latest fraud techniques.

[0045] The learning unit can analyze past fraud cases, identify fraud patterns, and predict future fraud methods. The learning unit, for example, analyzes past fraud cases and identifies fraud patterns. For example, the learning unit clarifies how cases are collected and performs analysis based on highly reliable data. The learning unit also uses pattern mining technology to identify fraud patterns. In this way, by analyzing past fraud cases, it is possible to predict future fraud methods and take measures in advance.

[0046] The elderly support department can analyze the elderly's past call history and issue a warning if a specific pattern indicates a risk of fraud. For example, the elderly support department can analyze the elderly's past call history and issue a warning if a specific pattern indicates a risk of fraud. For example, the elderly support department can analyze data over a long period of time, taking into account the storage period of the history. In addition, the elderly support department can use frequency analysis and anomaly detection algorithms to determine the risk of fraud with high accuracy. As a result, by analyzing the elderly's past call history, the risk of fraud can be determined with high accuracy.

[0047] The elderly support unit analyzes the elderly's voice tone and speaking style, and can automatically respond if there is a high risk of fraud. The elderly support unit analyzes the elderly's voice tone and speaking style, and can automatically respond if there is a high risk of fraud. For example, the elderly support unit analyzes the speaker's pitch and speed using a voice feature extraction method. The elderly support unit also extracts speaker features using voice analysis technology. In this way, by analyzing the elderly's voice tone and speaking style, it is possible to automatically respond if there is a high risk of fraud.

[0048] When providing voice support to an elderly person, the elderly support unit can propose the optimal response based on past call data. For example, when providing voice support to an elderly person, the elderly support unit proposes the optimal response based on past call data. For example, the elderly support unit analyzes past call data and issues a warning if a specific pattern indicates a risk of fraud. The elderly support unit also proposes the optimal response based on past call data. In this way, voice support for the elderly can be provided effectively by proposing the optimal response based on past call data.

[0049] The elderly support unit can send notifications to family members and caregivers in real time when providing voice support to an elderly person. For example, the elderly support unit sends notifications to family members and caregivers in real time when providing voice support to an elderly person. For example, the elderly support unit clarifies the format of the notification and sends the notification by an appropriate means. Furthermore, the elderly support unit specifies the content of the notification so that the family members and caregivers can respond quickly. This makes it possible to send notifications to family members and caregivers in real time when providing voice support to an elderly person.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The special fraud prevention system can further include a behavior analysis unit that analyzes user behavior patterns. The behavior analysis unit, for example, analyzes a user's past call history and internet usage history to identify behavior patterns that pose a high risk of fraud. For example, the behavior analysis unit identifies users who make frequent calls during specific time periods or who frequently visit specific websites, and determines that they are at high risk of fraud. The behavior analysis unit also issues a warning if there is a high risk of fraud based on the user's behavior patterns. In this way, by analyzing a user's behavior patterns, it is possible to determine the risk of fraud with high accuracy.

[0052] The special fraud prevention system can further include a health monitoring unit that monitors the user's health condition. The health monitoring unit measures, for example, the user's heart rate and blood pressure, and issues a warning if an abnormality is detected. The health monitoring unit also issues a warning if the risk of fraud is high based on the user's health condition. In this way, by monitoring the user's health condition, the risk of fraud can be determined with high accuracy.

[0053] The special fraud prevention system can further include a location information analysis unit that analyzes the user's location information. For example, the location information analysis unit determines that the risk of fraud is high when the user is in a specific area. Also, the location information analysis unit determines that the risk of fraud is low when the user is moving. This allows the user's location information to be analyzed in real time, making it possible to determine the risk of fraud with high accuracy.

[0054] The special fraud prevention system can further include a purchase history analysis unit that analyzes a user's purchase history. For example, the purchase history analysis unit determines that the risk of fraud is high if the user frequently purchases a particular product. The purchase history analysis unit also determines that the risk of fraud is low if the user frequently purchases from a particular store. This allows the user's purchase history to be analyzed in real time, making it possible to determine the risk of fraud with high accuracy.

[0055] The special fraud prevention system can further include a social media analysis unit that analyzes a user's social media activity. For example, the social media analysis unit determines that there is a high risk of fraud if the user frequently posts about a specific fraud. The social media analysis unit also determines that there is a high risk of fraud if the user participates in a specific fraud-related group. This allows the system to analyze a user's social media activity in real time and determine the risk of fraud with high accuracy.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The fraud detection unit analyzes the call source or content to determine the possibility of fraud. For example, it refers to a database of phone numbers to check whether the number has been used in a fraudulent transaction in the past. It also analyzes the call content in real time and issues a warning if there are signs of fraud. Step 2: The request agent listens to the incoming caller's request. For example, it responds by saying, "Hello, this is an automated answering system. How can I help you?" and listens to the caller's request. It also uses voice recognition technology to analyze the request and respond appropriately. Step 3: The Safety Communication Department informs customers about the safety and risks of the call. For example, they issue warnings such as, "This call may be a scam. Please be careful." They also provide specific examples of past fraud cases to make the message more persuasive. Step 4: The call confirmation unit checks whether the customer accepts or rejects the call. For example, it asks questions such as, "Do you want to accept or reject this call?" and waits for the customer's response. It also analyzes the customer's past call history and issues a warning if certain patterns indicate a risk of fraud. Step 5: The learning unit learns the latest fraud techniques and responds in real time. For example, it monitors fraud-related forums and social media to learn new fraud techniques. It also analyzes past fraud cases to identify fraud patterns.

[0058] (Example 2) The special fraud prevention system according to an embodiment of the present invention utilizes AI on a network to prevent special frauds committed over the phone. In this system, the cloud-based AI listens to and responds to requests on behalf of the customer, informs them of safety and risks, and confirms whether the call should be accepted or rejected. In the event of a rejection, the AI ​​responds on behalf of the customer, learns the latest fraud techniques, and responds in real time. This enables the special fraud prevention system to effectively prevent special frauds committed over the phone and ensure customer safety.

[0059] The special fraud prevention system according to the embodiment includes a fraud detection unit, a requirement proxy unit, a safety communication unit, a call reception confirmation unit, and a learning unit. The fraud detection unit analyzes the call origin or content to determine the possibility of fraud. For example, the fraud detection unit references a telephone number database to determine whether the number has been used in a fraudulent transaction in the past. The fraud detection unit also analyzes the call content in real time and issues a warning if there are signs of fraud. The requirement proxy unit listens to the caller's requirements. For example, the requirement proxy unit responds with a message such as, "Hello, this is an automated answering system. How can I help you?" to hear the caller's requirements. The requirement proxy unit also uses voice recognition technology to analyze the message and take appropriate action. The safety communication unit informs the customer of the safety and risks of the call. For example, the safety communication unit issues a warning such as, "This call may be fraudulent. Please be careful." The safety communication unit also provides specific examples of past fraud cases to enhance persuasiveness. The call reception confirmation unit confirms whether the customer accepts or rejects the call. For example, the call reception confirmation unit asks a question such as, "Do you want to accept this call or reject it?" and waits for the customer's response. The call reception confirmation unit also analyzes the customer's past call reception history and issues a warning if a specific pattern indicates a risk of fraud. The learning unit learns the latest fraud techniques and responds in real time. For example, the learning unit monitors fraud-related forums and social media on the Internet to learn new fraud techniques. The learning unit also analyzes past fraud cases and identifies fraud patterns. As a result, the special fraud prevention system according to the embodiment can effectively prevent special frauds over the phone and ensure the safety of customers. For example, the output unit notifies the customer of the risk of fraud before the customer answers the call. The warning message provides important information for the customer to recognize the risk of fraud and take appropriate action.

[0060] The fraud detection unit can refer to a database of telephone numbers to check whether a number has been used for fraud in the past. The fraud detection unit, for example, refers to a database of telephone numbers to check whether a number has been used for fraud in the past. For example, the fraud detection unit takes into account how often the database is updated and makes a judgment based on the most recent information. The fraud detection unit also evaluates the reliability of the database and gives priority to using highly reliable data. This allows phone numbers with a high probability of being fraudulent to be quickly identified.

[0061] The requirement agent unit can analyze the speaker's language and speech patterns to determine the possibility of fraud. The requirement agent unit, for example, analyzes the speaker's language and speech patterns to determine the possibility of fraud. For example, the requirement agent unit extracts the speaker's characteristics using voice analysis technology. The requirement agent unit also issues a warning if a specific phrase or language is a sign of fraud. The requirement agent unit also analyzes the speaker's tone and speed of voice to determine the possibility of fraud with high accuracy. In this way, by analyzing the speaker's language and speech patterns, the possibility of fraud can be determined with high accuracy.

[0062] The safety communication unit can increase persuasiveness when communicating risks to customers by specifically showing past fraud cases. For example, the safety communication unit can increase persuasiveness when communicating risks to customers by specifically showing past fraud cases. For example, the safety communication unit can provide specific examples such as, "Fraud has been committed using these methods in the past." The safety communication unit also clarifies the criteria for selecting fraud cases and provides highly reliable information to customers. In this way, by specifically showing past fraud cases, the safety communication unit can increase persuasiveness of risks to customers.

[0063] The call reception confirmation unit can analyze a customer's past call reception history and issue a warning if a specific pattern indicates a risk of fraud. The call reception confirmation unit, for example, analyzes a customer's past call reception history and issues a warning if a specific pattern indicates a risk of fraud. For example, the call reception confirmation unit analyzes data over a long period of time, taking into account the storage period of the history. The call reception confirmation unit also uses frequency analysis and anomaly detection algorithms to accurately determine the risk of fraud. As a result, by analyzing a customer's past call reception history, the risk of fraud can be accurately determined.

[0064] The learning unit monitors fraud-related forums and social networking sites on the Internet and can learn about the latest fraud techniques in real time. The learning unit, for example, monitors fraud-related forums and social networking sites on the Internet and can learn about the latest fraud techniques in real time. For example, the learning unit uses monitoring tools to collect fraud-related information. The learning unit also clarifies the types of social networking sites to target and collects a wide range of information. This allows the unit to learn about the latest fraud techniques in real time, allowing it to always be able to respond to the latest fraud techniques.

[0065] The learning unit can analyze past fraud cases, identify fraud patterns, and predict future fraud methods. The learning unit, for example, analyzes past fraud cases and identifies fraud patterns. For example, the learning unit clarifies how cases are collected and performs analysis based on highly reliable data. The learning unit also uses pattern mining technology to identify fraud patterns. In this way, by analyzing past fraud cases, it is possible to predict future fraud methods and take measures in advance.

[0066] When analyzing the contents of a call, the fraud detection unit analyzes the tone of the voice and the speaker's emotions, allowing it to determine the possibility of fraud with high accuracy. The fraud detection unit, for example, analyzes the contents of a call in real time and analyzes the tone of the voice and the speaker's emotions. For example, the fraud detection unit analyzes the speaker's pitch and speed using a voice feature extraction method. The fraud detection unit also detects changes in the speaker's emotions using an emotion recognition algorithm. In this way, by analyzing the tone of the voice and the speaker's emotions, it is possible to determine the possibility of fraud with high accuracy.

[0067] The fraud detection unit can analyze geographic information of the call originator in real time to identify calls originating from areas with a high risk of fraud. For example, the fraud detection unit can analyze geographic information of the call originator in real time to identify calls originating from areas with a high risk of fraud. For example, the fraud detection unit can identify the location of the call originator using GPS data. The fraud detection unit can also identify high-risk areas using geographic risk assessment criteria. In this way, by analyzing the geographic information, calls originating from areas with a high risk of fraud can be identified.

[0068] The fraud detection unit uses the emotion estimation function to analyze the emotions of the speaker during a call in real time, allowing early detection of signs of fraud. The fraud detection unit, for example, uses the emotion estimation function to analyze the emotions of the speaker during a call in real time. For example, the fraud detection unit uses voice emotion recognition technology to analyze the speaker's emotions. The fraud detection unit also uses a machine learning model to detect changes in emotions. In this way, by using the emotion estimation function, signs of fraud can be detected early.

[0069] The requirement agent can analyze the frequency of occurrence of specific phrases and keywords based on past call data and identify signs of fraud. The requirement agent analyzes the frequency of occurrence of specific phrases and keywords based on past call data, for example. For example, the requirement agent converts recorded call data into text and performs frequency analysis. The requirement agent also identifies signs of fraud using co-occurrence network analysis. This allows for highly accurate identification of signs of fraud by analyzing past call data.

[0070] The request agent unit uses the emotion estimation function to analyze the speaker's emotion in real time and can immediately issue a warning if there is a high possibility of fraud. The request agent unit, for example, uses the emotion estimation function to analyze the speaker's emotion in real time. For example, the request agent unit uses voice emotion recognition technology to analyze the speaker's emotion. The request agent unit also uses a machine learning model to detect changes in emotion. In this way, by using the emotion estimation function, it is possible to immediately issue a warning if there is a high possibility of fraud.

[0071] The safety communication unit can analyze a customer's past call history and issue a warning if a specific pattern indicates a risk of fraud. For example, the safety communication unit analyzes a customer's past call history and issues a warning if a specific pattern indicates a risk of fraud. For example, the safety communication unit analyzes data over a long period of time, taking into account the storage period of the history. In addition, the safety communication unit uses frequency analysis and anomaly detection algorithms to determine the risk of fraud with high accuracy. As a result, by analyzing a customer's past call history, the risk of fraud can be determined with high accuracy.

[0072] The safety communication unit can use the emotion estimation function to analyze the customer's emotions in real time and select the optimal timing for communicating the risk. The safety communication unit, for example, uses the emotion estimation function to analyze the customer's emotions in real time and select the optimal timing for communicating the risk. For example, the safety communication unit uses voice emotion recognition technology to analyze the customer's emotions. Furthermore, the safety communication unit detects changes in emotions using a machine learning model. In this way, the safety communication unit can select the optimal timing for communicating the risk by analyzing the customer's emotions in real time.

[0073] The call reception confirmation unit can analyze the customer's answers and automatically recommend refusal if there is a high risk of fraud. The call reception confirmation unit, for example, analyzes the customer's answers and automatically recommends refusal if there is a high risk of fraud. For example, the call reception confirmation unit performs text analysis of the answers and recommends refusal if certain phrases or wording are indicative of fraud. The call reception confirmation unit also uses voice recognition technology to analyze the customer's answers. In this way, by analyzing the customer's answers, it can automatically recommend refusal if there is a high risk of fraud.

[0074] The call reception confirmation unit uses an emotion estimation function to analyze the customer's emotions in real time and confirm whether to accept or reject the call at the optimal timing. The call reception confirmation unit uses, for example, the emotion estimation function to analyze the customer's emotions in real time and confirm whether to accept or reject the call at the optimal timing. For example, the call reception confirmation unit uses voice emotion recognition technology to analyze the customer's emotions. The call reception confirmation unit also uses a machine learning model to detect changes in emotions. In this way, by analyzing the customer's emotions in real time, the optimal timing to confirm whether to accept or reject the call can be selected.

[0075] The learning unit can cooperate with other security systems to provide comprehensive fraud prevention measures. The learning unit, for example, cooperates with other security systems to provide comprehensive fraud prevention measures. For example, the learning unit cooperates with an email filtering system to block fraudulent emails. The learning unit also cooperates with a firewall to prevent unauthorized access. In this way, by cooperating with other security systems, comprehensive fraud prevention measures can be provided.

[0076] The learning unit can issue a warning to the user in real time when it detects a call with a high risk of fraud. For example, the learning unit issues a warning to the user in real time when it detects a call with a high risk of fraud. For example, the learning unit issues a warning if the risk of fraud is high based on the analysis results of the call content. The learning unit also evaluates the similarity with past fraud cases and issues a warning if the risk is high. This makes it possible to issue a warning to the user in real time when it detects a call with a high risk of fraud.

[0077] The learning unit can use the emotion estimation function to send a message that emotionally reassures the user when a call with a high risk of fraud is detected. For example, the learning unit can use the emotion estimation function to send a message that emotionally reassures the user when a call with a high risk of fraud is detected. For example, the learning unit can use voice emotion recognition technology to analyze the user's emotions and send a message that reassures the user. The learning unit can also use a machine learning model to detect changes in emotions and send an appropriate message. This makes it possible to send a message that emotionally reassures the user when a call with a high risk of fraud is detected.

[0078] The call reception confirmation unit can propose the optimal option based on the customer's past behavioral patterns. The call reception confirmation unit proposes the optimal option based on the customer's past behavioral patterns, for example. For example, the call reception confirmation unit analyzes past behavioral history and performs behavior frequency analysis. The call reception confirmation unit also makes a similar proposal if the customer has rejected a call in the past because of a high risk of fraud. This makes it possible to propose the optimal option based on the customer's past behavioral patterns.

[0079] The call reception confirmation unit can take into account the customer's current situation. The call reception confirmation unit, for example, takes into account the customer's current situation. For example, the call reception confirmation unit determines whether the customer is busy based on schedule information. The call reception confirmation unit also grasps the customer's current situation using a real-time situation confirmation method. This allows the customer's current situation to be taken into account to optimally confirm whether to accept or reject the call.

[0080] The call reception confirmation unit can use the emotion estimation function to send a message that emotionally reassures the customer when the customer confirms whether to accept or reject the call. The call reception confirmation unit can, for example, use the emotion estimation function to send a message that emotionally reassures the customer when the customer confirms whether to accept or reject the call. For example, the call reception confirmation unit can use voice emotion recognition technology to analyze the customer's emotions and send a message that reassures the customer. The call reception confirmation unit can also use a machine learning model to detect changes in emotions and send an appropriate message. This makes it possible to send a message that emotionally reassures the customer when the customer confirms whether to accept or reject the call.

[0081] The learning unit monitors fraud-related forums and social networking sites on the Internet and can learn about the latest fraud techniques in real time. The learning unit, for example, monitors fraud-related forums and social networking sites on the Internet and can learn about the latest fraud techniques in real time. For example, the learning unit uses monitoring tools to collect fraud-related information. The learning unit also clarifies the types of social networking sites to target and collects a wide range of information. This allows the unit to learn about the latest fraud techniques in real time, allowing it to always be able to respond to the latest fraud techniques.

[0082] The learning unit can analyze past fraud cases, identify fraud patterns, and predict future fraud methods. The learning unit, for example, analyzes past fraud cases and identifies fraud patterns. For example, the learning unit clarifies how cases are collected and performs analysis based on highly reliable data. The learning unit also uses pattern mining technology to identify fraud patterns. In this way, by analyzing past fraud cases, it is possible to predict future fraud methods and take measures in advance.

[0083] The learning unit can use the emotion estimation function to detect calls with a high risk of fraud in real time and respond immediately. The learning unit can, for example, use the emotion estimation function to detect calls with a high risk of fraud in real time and respond immediately. For example, the learning unit can use voice emotion recognition technology to analyze the speaker's emotions and issue a warning if the risk of fraud is high. The learning unit can also use a machine learning model to detect changes in emotions and respond appropriately. This allows calls with a high risk of fraud to be detected in real time and responded immediately.

[0084] The elderly support department can analyze the elderly's past call history and issue a warning if a specific pattern indicates a risk of fraud. For example, the elderly support department can analyze the elderly's past call history and issue a warning if a specific pattern indicates a risk of fraud. For example, the elderly support department can analyze data over a long period of time, taking into account the storage period of the history. In addition, the elderly support department can use frequency analysis and anomaly detection algorithms to determine the risk of fraud with high accuracy. As a result, by analyzing the elderly's past call history, the risk of fraud can be determined with high accuracy.

[0085] The elderly support unit analyzes the elderly's voice tone and speaking style, and can automatically respond if there is a high risk of fraud. The elderly support unit analyzes the elderly's voice tone and speaking style, and can automatically respond if there is a high risk of fraud. For example, the elderly support unit analyzes the speaker's pitch and speed using a voice feature extraction method. The elderly support unit also extracts speaker features using voice analysis technology. In this way, by analyzing the elderly's voice tone and speaking style, it is possible to automatically respond if there is a high risk of fraud.

[0086] When providing voice support to an elderly person, the elderly support unit can propose the optimal response based on past call data. For example, when providing voice support to an elderly person, the elderly support unit proposes the optimal response based on past call data. For example, the elderly support unit analyzes past call data and issues a warning if a specific pattern indicates a risk of fraud. The elderly support unit also proposes the optimal response based on past call data. In this way, voice support for the elderly can be provided effectively by proposing the optimal response based on past call data.

[0087] The elderly support unit can send notifications to family members and caregivers in real time when providing voice support to an elderly person. For example, the elderly support unit sends notifications to family members and caregivers in real time when providing voice support to an elderly person. For example, the elderly support unit clarifies the format of the notification and sends the notification by an appropriate means. Furthermore, the elderly support unit specifies the content of the notification so that the family members and caregivers can respond quickly. This makes it possible to send notifications to family members and caregivers in real time when providing voice support to an elderly person.

[0088] The elderly support unit can use the emotion estimation function to send an emotionally reassuring message to the elderly when the elderly receives voice support. The elderly support unit, for example, uses the emotion estimation function to send an emotionally reassuring message to the elderly when the elderly receives voice support. For example, the elderly support unit uses voice emotion recognition technology to analyze the emotions of the elderly and send a reassuring message. The elderly support unit also uses a machine learning model to detect changes in emotions and send an appropriate message. This makes it possible to send an emotionally reassuring message to the elderly when the elderly receives voice support.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The special fraud prevention system can further include a behavior analysis unit that analyzes user behavior patterns. The behavior analysis unit, for example, analyzes a user's past call history and internet usage history to identify behavior patterns that pose a high risk of fraud. For example, the behavior analysis unit identifies users who make frequent calls during specific time periods or who frequently visit specific websites, and determines that they are at high risk of fraud. The behavior analysis unit also issues a warning if there is a high risk of fraud based on the user's behavior patterns. In this way, by analyzing a user's behavior patterns, it is possible to determine the risk of fraud with high accuracy.

[0091] The special fraud prevention system can further include an emotion response unit that estimates the user's emotion and adjusts the response based on the estimated emotion. For example, if the user is feeling anxious or scared, the emotion response unit sends a message to reassure the user. Also, if the user is feeling angry or irritated, the emotion response unit sends a message urging the user to stay calm. In this way, the risk of fraud can be reduced by analyzing the user's emotion in real time and taking appropriate action.

[0092] The special fraud prevention system can further include a health monitoring unit that monitors the user's health condition. The health monitoring unit measures, for example, the user's heart rate and blood pressure, and issues a warning if an abnormality is detected. The health monitoring unit also issues a warning if the risk of fraud is high based on the user's health condition. In this way, by monitoring the user's health condition, the risk of fraud can be determined with high accuracy.

[0093] The special fraud prevention system may further include a risk assessment unit that estimates the user's emotions and assesses the risk of fraud based on the estimated emotions. For example, the risk assessment unit may determine that the risk of fraud is high if the user feels anxious or scared. Alternatively, the risk assessment unit may determine that the risk of fraud is low if the user remains calm. This allows the user's emotions to be analyzed in real time, and the risk of fraud to be assessed with high accuracy.

[0094] The special fraud prevention system can further include a location information analysis unit that analyzes the user's location information. For example, the location information analysis unit determines that the risk of fraud is high when the user is in a specific area. Also, the location information analysis unit determines that the risk of fraud is low when the user is moving. This allows the user's location information to be analyzed in real time, making it possible to determine the risk of fraud with high accuracy.

[0095] The special fraud prevention system can further include a risk prediction unit that estimates the user's emotions and predicts the risk of fraud based on the estimated emotions. For example, the risk prediction unit predicts that the risk of fraud is high if the user is feeling anxious or scared. Furthermore, the risk prediction unit predicts that the risk of fraud is low if the user is calm. This allows the user's emotions to be analyzed in real time and the risk of fraud to be predicted with high accuracy.

[0096] The special fraud prevention system can further include a purchase history analysis unit that analyzes a user's purchase history. For example, the purchase history analysis unit determines that the risk of fraud is high if the user frequently purchases a particular product. The purchase history analysis unit also determines that the risk of fraud is low if the user frequently purchases from a particular store. This allows the user's purchase history to be analyzed in real time, making it possible to determine the risk of fraud with high accuracy.

[0097] The special fraud prevention system may further include a risk mitigation unit that estimates the user's emotions and reduces the risk of fraud based on the estimated emotions. For example, if the user feels anxious or scared, the risk mitigation unit sends a reassuring message. Also, if the user feels angry or irritated, the risk mitigation unit sends a message urging the user to stay calm. In this way, the user's emotions can be analyzed in real time and the risk of fraud can be reduced.

[0098] The special fraud prevention system can further include a social media analysis unit that analyzes a user's social media activity. For example, the social media analysis unit determines that there is a high risk of fraud if the user frequently posts about a specific fraud. The social media analysis unit also determines that there is a high risk of fraud if the user participates in a specific fraud-related group. This allows the system to analyze a user's social media activity in real time and determine the risk of fraud with high accuracy.

[0099] The special fraud prevention system can further include a risk management unit that estimates the user's emotions and manages the risk of fraud based on the estimated emotions. For example, if the user is feeling anxious or scared, the risk management unit determines that the risk of fraud is high and takes appropriate action. On the other hand, if the user is calm, the risk management unit determines that the risk of fraud is low and takes normal action. This makes it possible to analyze the user's emotions in real time and manage the risk of fraud with high accuracy.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The fraud detection unit analyzes the call source or content to determine the possibility of fraud. For example, it refers to a database of phone numbers to check whether the number has been used in a fraudulent transaction in the past. It also analyzes the call content in real time and issues a warning if there are signs of fraud. Step 2: The request agent listens to the incoming caller's request. For example, it responds by saying, "Hello, this is an automated answering system. How can I help you?" and listens to the caller's request. It also uses voice recognition technology to analyze the request and respond appropriately. Step 3: The Safety Communication Department informs customers about the safety and risks of the call. For example, they issue warnings such as, "This call may be a scam. Please be careful." They also provide specific examples of past fraud cases to make the message more persuasive. Step 4: The call confirmation unit checks whether the customer accepts or rejects the call. For example, it asks questions such as, "Do you want to accept or reject this call?" and waits for the customer's response. It also analyzes the customer's past call history and issues a warning if certain patterns indicate a risk of fraud. Step 5: The learning unit learns the latest fraud techniques and responds in real time. For example, it monitors fraud-related forums and social media to learn new fraud techniques. It also analyzes past fraud cases to identify fraud patterns.

[0102] 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.

[0103] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0104] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0108] 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.

[0109] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0115] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0116] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0117] 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.

[0118] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 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.

[0122] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0123] 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.

[0124] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0130] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0131] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0132] 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.

[0133] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0134] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0135] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0138] 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.

[0139] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0141] 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.

[0142] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0143] 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.

[0144] 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.

[0145] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0146] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0147] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0148] 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.

[0149] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0150] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0151] 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.

[0152] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0153] 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.

[0154] 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).

[0155] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0156] 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."

[0157] 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.

[0158] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0163] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0164] 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.

[0165] 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.

[0166] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0167] 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.

[0168] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0169] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a fraud detection unit that analyzes the source or content of the call to determine the possibility of fraud; A request proxy department that listens to the requests of incoming calls on behalf of the customer; A safety communication department that communicates safety and risks to customers over the phone; a call reception confirmation unit that confirms whether a customer accepts or rejects a call; A learning unit that learns the latest fraud techniques and responds in real time. A system characterized by:

2. The fraud detection unit Look up the phone number in a database to see if it has been used in a scam in the past 2. The system of claim 1.

3. The requirement agent unit Analyzes the speaker's language and speech patterns to determine the likelihood of fraud 2. The system of claim 1.

4. The safety transmission unit When communicating the risks to the customer, provide specific examples of past fraud cases to increase persuasiveness.

2. The system of claim 1.

5. The power reception confirmation unit Analyzing the customer's past call history and issuing a warning if certain patterns indicate a risk of fraud 2. The system of claim 1.

6. The learning unit Monitor fraud-related forums and social media sites to learn about the latest fraud techniques in real time.

2. The system of claim 1.

7. The fraud detection unit When analyzing call content, the tone of voice or speaker emotion can be analyzed to determine the likelihood of fraud with high accuracy.

2. The system of claim 1.

8. The requirement agent unit Analyzes speaker sentiment in real time and issues immediate warnings if fraud is likely.

2. The system of claim 1.

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