AI Virtual Assistant Using LSTM for Natural Language Processing
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Solution Overview
Problem
Modern virtual assistants implemented using a rules-based approach are inflexible and unable to effectively address queries or commands that exceed the scope of predetermined rules, limiting their capability to provide meaningful responses to unrecognized or complex user inputs.
Innovation Solution
A system and method utilizing artificial intelligence and machine learning, specifically deep learning models like LSTM neural networks, for natural language processing, allowing the virtual assistant to comprehend and respond to various natural language inputs, including unstructured data, and evolve with user interactions without requiring additional programming.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a rules-based approach is used to implement a virtual assistant, then the system structure is simple and easy to understand, but the system cannot effectively address queries or commands that exceed the scope of predetermined rules
Solution Approach 1:
The patent replaces the mechanical rules-based system with a machine learning-based natural language processing system. Instead of using predetermined rules to match user inputs, the system employs deep learning models (LSTM neural networks) to automatically learn and comprehend natural language patterns, enabling the virtual assistant to handle complex and varied queries without explicit programming for each scenario.
2Adaptability or versatility
If machine learning models are used to enable the virtual assistant to comprehend natural language inputs, then the adaptability and versatility improve, but the device complexity and computational resources required increase
Solution Approach 1:
The patent implements preliminary action by pre-training the LSTM neural network models on large datasets before deployment. The machine learning models are trained in advance to learn natural language patterns, user behavior, and domain-specific knowledge, so that when deployed, the virtual assistant can immediately comprehend and respond to natural language inputs without requiring complex real-time computation for basic understanding.
3Adaptability or versatility
If a finite set of rules is used to drive the virtual assistant responses, then the system is easy to implement and maintain, but the system cannot evolve beyond the predefined rule set
Solution Approach 1:
The patent implements self-service by enabling the machine learning models to continuously learn and improve from user interactions. The LSTM neural networks automatically update their internal representations and knowledge bases based on new data, allowing the virtual assistant to evolve and adapt to new query types and user preferences without requiring manual rule updates or reprogramming by developers.
Data Source
AI summary
Systems and methods for implementing an artificially intelligent virtual assistant includes collecting a user query; using a competency classification machine learning model to generate a competency label for the user query; using a slot identification machine learning model to segment the text of the query and label each of the slots of the query; generating a slot value for each of the slots of the query; generating a handler for each of the slot values; and using the slot values to: identify an external data source relevant to the user query, fetch user data from the external data source, and apply one or more operations to the query to generate response data; and using the response data, to generate a response to the user query.