Truist Bank (20240232765). AUDIO SIGNAL PROCESSING AND DYNAMIC NATURAL LANGUAGE UNDERSTANDING simplified abstract
Contents
AUDIO SIGNAL PROCESSING AND DYNAMIC NATURAL LANGUAGE UNDERSTANDING
Organization Name
Inventor(s)
Bjorn Austraat of New York NY (US)
AUDIO SIGNAL PROCESSING AND DYNAMIC NATURAL LANGUAGE UNDERSTANDING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240232765 titled 'AUDIO SIGNAL PROCESSING AND DYNAMIC NATURAL LANGUAGE UNDERSTANDING
Simplified Explanation: The patent application describes systems and methods that receive and process natural language input derived from an audio signal in real-time. The input is interpreted using AI models to identify risk elements and assign a risk score based on the inherent risk of the input.
- Trained AI models are used to perform automatic speech recognition (ASR) and natural language understanding (NLU) on the unstructured data from the audio signal.
- Risk elements are identified in the natural language input, and a risk score is dynamically adjusted based on additional risk elements found during the input.
- Risk analysis is conducted by comparing the risk score to a predetermined threshold to determine the level of risk associated with the input.
Key Features and Innovation:
- Real-time processing of natural language input derived from audio signals.
- Dynamic interpretation of the input using AI models for ASR and NLU.
- Identification of risk elements and assignment of a risk score based on the input.
- Dynamic adjustment of the risk score based on additional risk elements found during the input.
- Risk analysis to determine the level of risk associated with the input.
Potential Applications:
- Security and surveillance systems.
- Customer service and support applications.
- Compliance monitoring in financial services.
- Risk assessment in healthcare settings.
- Legal and law enforcement applications.
Problems Solved:
- Efficient processing of unstructured data from audio signals.
- Real-time identification of risk elements in natural language input.
- Dynamic adjustment of risk scores based on additional risk elements.
- Enhanced risk analysis for improved decision-making.
Benefits:
- Improved accuracy in identifying risk elements.
- Real-time risk assessment for timely interventions.
- Enhanced security and compliance measures.
- Streamlined decision-making processes.
- Increased efficiency in processing audio-derived data.
Commercial Applications: Title: Real-time Risk Analysis System for Audio-derived Natural Language Input This technology can be utilized in various industries such as security, customer service, finance, healthcare, and law enforcement for enhanced risk assessment and decision-making processes. The market implications include improved security measures, compliance monitoring, and operational efficiency.
Questions about Real-time Risk Analysis System for Audio-derived Natural Language Input: 1. How does the system dynamically adjust the risk score based on additional risk elements found during the natural language input? 2. What are the potential challenges in implementing this technology in real-time applications?
Frequently Updated Research: Stay updated on advancements in AI models for ASR and NLU to enhance the accuracy and efficiency of risk analysis systems for audio-derived natural language input.
Original Abstract Submitted
systems and methods receive, from a user device through a communication channel, and process, in real-time, a natural language input comprising unstructured data that is derived from an audio signal. the natural language input is dynamically interpreted, the interpreting including applying the unstructured data to trained ai models that (i) perform asr to generate textual data and (ii) contextualize the textual data using a nlu model. based thereon, a risk element from the natural language input is identified, and a risk score is assigned that ranks inherent risk of the natural language input. the risk score is dynamically adjusted based on identifying additional risk element(s) during the natural language input and is based on an aggregation of the risk element and the additional risk element(s). risk analysis is performed on the natural language input and includes comparing the risk score to a threshold.
