ML Model Configuration for Virtual Assistant Adaptability
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Solution Overview
Problem
Modern virtual assistants implemented with a rules-based approach lack flexibility to handle queries or commands outside their predetermined scope, limiting their ability to provide meaningful responses to users, and existing machine learning model training methods are inefficient.
Innovation Solution
A system utilizing deep machine learning models, such as LSTM neural networks, with natural language processing capabilities that can comprehend structured and unstructured input, evolve through interactions, and a machine learning model configuration and management console for rapid training, enabling flexible and intelligent responses without additional programming.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a rules-based approach is used to implement virtual assistants, then the system can provide responses for pointed or specific queries, but the system lacks flexibility to address queries outside the predetermined scope
Solution Approach 1:
The patent replaces the mechanical rules-based system with a machine learning-based system. Instead of using predetermined rules to handle queries, the system uses trained machine learning models that can automatically learn from data and adapt to handle a wide variety of queries without requiring explicit programming for each scenario.
Solution Approach 2:
The patent employs machine learning models that can change their internal parameters based on training data. The models adapt their decision boundaries and feature weights during training, allowing them to handle queries outside the predetermined scope by learning patterns from data rather than following fixed rules.
2Adaptability or versatility
If machine learning models are used to enhance virtual assistant capabilities, then the system can handle complex and unknown queries, but the training process is inefficient
Solution Approach 1:
The patent performs preliminary actions by pre-processing and preparing training data before it is used for model training. The system includes data processing components that clean, validate, and transform data into suitable formats, which significantly improves training efficiency and reduces the time required for model development.
Solution Approach 2:
The patent uses data augmentation techniques that create synthetic copies of training data by transforming existing data samples. This allows the system to train on larger and more diverse datasets without requiring additional real data collection, thereby improving training efficiency and model performance.
Data Source
AI summary
Systems and methods for intelligently training a machine learning model includes: configuring a machine learning (ML) training data request for a pre-existing machine learning classification model; transmitting the machine learning training data request to each of a plurality of external training data sources, wherein each of the plurality of external training data sources is different; collecting and storing the machine learning training data from each of the plurality of external training data sources; processing the collected machine learning training data using a predefined training data processing algorithm; and in response to processing the collected machine learning training data, deploying a subset of the collected machine learning training data into a live machine learning model.