20240040038. DETECTING SCAM CALLERS USING CONVERSATIONAL AGENT AND MACHINE LEARNING SYSTEMS AND METHODS simplified abstract (T-Mobile USA, Inc.)
Contents
DETECTING SCAM CALLERS USING CONVERSATIONAL AGENT AND MACHINE LEARNING SYSTEMS AND METHODS
Organization Name
Inventor(s)
Ovidiu Serban of Sammamish WA (US)
DETECTING SCAM CALLERS USING CONVERSATIONAL AGENT AND MACHINE LEARNING SYSTEMS AND METHODS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240040038 titled 'DETECTING SCAM CALLERS USING CONVERSATIONAL AGENT AND MACHINE LEARNING SYSTEMS AND METHODS
Simplified Explanation
The patent application describes systems and methods for detecting scam callers. It involves receiving call data, such as call audio, and using it to create a training dataset. A machine learning model is then trained using this dataset to detect indications of a scam caller in a phone call. Additionally, an interactive voice response (IVR) model is trained or configured using voice samples of a subscriber of a telecommunications service provider to simulate the speech and conversation of the subscriber. A conversational agent is generated using the IVR model and the trained machine learning model. This conversational agent receives a phone call, engages the caller in simulated conversation, and detects indications of whether the caller is a likely scam caller. If the caller is determined to be a likely scam caller, an alert can be generated and/or the call can be disconnected.
- Call data, such as call audio, is used to create a training dataset.
- A machine learning model is trained to detect indications of a scam caller in a phone call.
- An interactive voice response (IVR) model is trained or configured to simulate the speech and conversation of a subscriber.
- A conversational agent is generated using the IVR model and the trained machine learning model.
- The conversational agent engages a caller in simulated conversation and detects indications of whether the caller is a likely scam caller.
- If the caller is determined to be a likely scam caller, an alert can be generated and/or the call can be disconnected.
Potential applications of this technology:
- Enhancing call center operations by automatically detecting scam callers.
- Improving customer experience by reducing the number of scam calls received.
- Enhancing security and fraud prevention in telecommunications services.
Problems solved by this technology:
- Identifying scam callers and protecting individuals from fraudulent activities.
- Reducing the time and effort required to manually identify scam callers.
- Enhancing the overall security and trustworthiness of phone calls.
Benefits of this technology:
- Increased efficiency in identifying and handling scam callers.
- Improved customer satisfaction and trust in telecommunications services.
- Enhanced security and protection against fraudulent activities.
Original Abstract Submitted
systems and methods for detecting indications of a scam caller are disclosed. call data, such as call audio, is received and used to create a training dataset. using the training dataset, a machine learning model is trained to detect indications of a scam caller in a phone call. an interactive voice response (ivr) model is trained or configured, using voice samples of speech of a subscriber of a telecommunications service provider, to simulate speech and conversation of the subscriber. a conversational agent is generated using the ivr model and the trained machine learning model. the conversational agent receives a phone call, engages a caller in simulated conversation, and detects indications of whether the caller is a likely scam caller. if the caller is determined to be a likely scam caller, an alert can be generated and/or the call can be disconnected.
