Conversational NLP Feedback Loop for Accurate Intent Prediction
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Solution Overview
Problem
Existing natural language processing systems struggle to accurately interpret nuanced language, such as sarcasm, leading to inaccuracies in understanding user intents.
Innovation Solution
A computing system that trains a natural language processing model using a feedback loop to iteratively predict key statements and adjust weights based on user conversation data, improving the predictability of user intents by generating pre-filled or fillable forms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional natural language processing methods are used, then the system can process language data, but the accuracy in interpreting nuanced language (such as sarcasm) deteriorates
Solution Approach 1:
The patent implements a feedback loop where the NLP model's predictions are evaluated against actual user intents, and the model weights are iteratively adjusted based on prediction errors. This continuous feedback mechanism improves the model's accuracy in interpreting nuanced language while maintaining reliable user intent understanding through systematic error correction.
Solution Approach 2:
The patent dynamically adjusts the weights of different features and parameters in the NLP model during training iterations. By changing these parameters based on feedback from prediction accuracy, the system optimizes its ability to interpret nuanced language while maintaining overall reliability through controlled parameter evolution.
2Measurement precision
If the natural language processing model is trained with more iterations and adjustments, then the predictability of user intents improves, but the computational complexity and training time increase
Solution Approach 1:
The patent applies partial action by iteratively adjusting only the necessary model weights based on prediction errors rather than retraining the entire model from scratch. This approach achieves improved predictability through targeted weight modifications while reducing unnecessary computational overhead from complete retraining cycles.
Solution Approach 2:
The system performs preliminary actions by pre-processing conversation data into structured formats and pre-defining the feedback loop mechanisms before actual training begins. This preliminary preparation reduces computational complexity during the iterative training process by having data and evaluation frameworks ready in advance.
3Reliability
If the system processes more conversation data to improve training accuracy, then the model's understanding of user intents improves, but the data processing time and computational resources increase
Solution Approach 1:
The patent segments the training process into iterative cycles where subsets of conversation data are processed in batches. This segmentation allows the system to handle large volumes of data efficiently by processing them in manageable chunks while maintaining reliable model understanding through cumulative learning across iterations.
Solution Approach 2:
The system maintains continuous useful action by implementing an ongoing feedback loop that continuously processes new conversation data and immediately applies learned insights to improve predictions. This continuous processing minimizes idle time between data ingestion and model improvement, efficiently utilizing computational resources.
Data Source
AI summary
Systems and methods iteratively train, using training data, a natural language processing (NLP) model to interpret conversational input during a conversation between an agent and a user by predicting key statements to be used in the prediction of a user intent, the training comparing outputs to a target variable during each iteration and adjusting parameters of the NLP model during each iteration to improve predictability of the user intent from the conversational input. Real-time conversational data is transmitted to the NLP model and the trained NLP algorithm derives key statements predicted to indicate intents and predicts one or more user intents based on the data from the conversation. One or more pre-filled forms predicted to effectuate the one or more user intents is generated, the pre-filled forms including generated text derived from information from the data of the conversation, and the form is transmitted to an agent device.