Serverless Voice Application Context Mining
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Solution Overview
Problem
Current natural language systems lack contextual recall, provide unnatural responses, and are unable to reference prior utterances to identify known information, leading to limited conversational capabilities and 'walled garden' limitations in e-commerce interactions.
Innovation Solution
A system and method that utilize a computing device to receive application-specific and shared elements, identify user intent, and generate system utterances by integrating natural language understanding, context mining, and response generation components to provide contextual and coherent e-commerce interactions across multiple platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current natural language systems are used, then general queries can be handled, but contextual recall is very limited and conversations sound robotic
Solution Approach 1:
The system performs preliminary actions by extracting and storing contextual information from prior utterances before generating responses. The context mining component identifies entities, relationships, and semantic meaning from previous conversations and maintains this context in a knowledge base, enabling the system to reference and build upon prior interactions when generating responses.
Solution Approach 2:
The system implements feedback mechanisms where the response generation component receives feedback about the conversation context and adjusts its responses accordingly. The system continuously monitors the conversation flow, identifies contextual patterns, and uses this feedback to generate more natural and contextually appropriate responses that reference prior utterances.
2Adaptability or versatility
If current digital assistants are used, then specific tasks can be performed, but they exist in walled gardens with limited cross-system capability
Solution Approach 1:
The system achieves universality by creating a unified natural language processing platform that can handle multiple functions across different e-commerce systems. The shared elements framework allows the same NLP infrastructure to serve multiple applications, enabling cross-platform integration without requiring separate specialized systems for each e-commerce platform.
Solution Approach 2:
The system uses an intermediary framework that acts as a mediator between different e-commerce platforms and the natural language processing layer. This intermediary layer translates diverse platform-specific queries into unified internal representations, enabling seamless integration and communication across different systems through a common NLP interface.
3Reliability
If application-specific elements are integrated with shared elements, then contextual awareness is improved, but system complexity increases
Solution Approach 1:
The system applies segmentation by dividing the NLP system into distinct modular components: speech recognition, syntax processing, semantic processing, context mining, and response generation. Each component handles specific tasks independently, making the overall system more manageable and easier to integrate with different e-commerce applications while maintaining high contextual awareness through standardized interfaces.
Data Source
AI summary
Systems and methods for e-commerce systems using natural language understanding are described. A computing device is configured receive at least one application-specific element for a natural language conversation application, at least one shared element, and a user utterance, The system identifies at least one intent based on the at least one application-specific element and the at least one shared element and generates a system utterance based on the at least one application-specific element.