Generative Search for Accurate Conversational Order Intent Mapping
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Solution Overview
Problem
Traditional conversational order management systems struggle with accurately understanding user intents, particularly in complex queries or large product catalogues, leading to misinterpretations, frustration, and increased operational costs due to manual intervention.
Innovation Solution
A system utilizing machine-learning and rule-based techniques to interpret user intents and adapt to changing product offerings, promotions, and customer preferences, enabling seamless integration with existing workflows and continuous learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional form-based interfaces are used for order management, then system simplicity is maintained, but user experience and operational efficiency deteriorate
Solution Approach 1:
The patent replaces traditional mechanical form-based interfaces with an AI-powered conversational interface that uses natural language processing. The system substitutes manual form filling with automated intent recognition and extraction, where users interact through natural conversation rather than filling out structured forms, thereby improving ease of operation while managing system complexity through intelligent automation
Solution Approach 2:
The system enables self-service order management by automatically interpreting user intents, extracting relevant information, and executing order operations without requiring manual form completion. The AI model processes user queries autonomously, performing tasks such as product search, cart management, and order placement automatically based on conversational input
2Measurement precision
If traditional conversational interfaces are used, then system simplicity is maintained, but intent understanding accuracy deteriorates
Solution Approach 1:
The patent replaces simple keyword-matching conversational interfaces with an AI-based intent recognition system that uses natural language processing models. The system substitutes basic pattern matching with sophisticated machine learning algorithms that can accurately interpret complex user intents, ambiguous queries, and contextual nuances, thereby significantly improving intent understanding accuracy
Solution Approach 2:
The system introduces an AI model as an intermediary between the user's natural language input and the order management system's processing logic. This intermediary layer translates diverse and ambiguous user queries into standardized intent representations, enabling accurate intent understanding while isolating the complexity of language processing from the core business logic
3Measurement precision
If machine-learning models are used for intent identification, then intent understanding accuracy is improved, but system complexity increases
Solution Approach 1:
The patent segments the intent identification process into multiple independent components: a first machine-learning model for high-level intent classification, a second model for item classification, and a third model for volume identification. This segmentation allows each model to specialize in specific tasks, improving overall accuracy while managing complexity through modular architecture where each component can be trained and optimized independently
Solution Approach 2:
The system employs a multi-functional AI platform that handles various tasks including intent identification, item classification, volume determination, and order execution through a unified machine-learning framework. This universal approach improves accuracy across multiple functions while managing complexity by sharing common infrastructure and training mechanisms across different tasks
4Productivity
If manual intervention is used to rectify errors, then system reliability is maintained through human control, but operational costs and time consumption increase
Solution Approach 1:
The system implements feedback mechanisms where the AI model continuously learns from user interactions and corrects its own errors through iterative refinement. The system provides real-time validation of extracted information, allows users to correct misunderstandings automatically, and uses these corrections to refine future intent recognition, thereby reducing error rates while maintaining high operational efficiency without requiring manual intervention
Data Source
AI summary
Systems and methods are disclosed for order management. One or more processors may receive a first data object containing text data related to user interactions. The processors may input the first data object into a machine-learning model configured to identify intents associated with the data. The processors may receive an identified intent from the model and generate a second data object based on the intent, including the intents, a prompt selected based on the intents, and a function selected based on the intents. The processors may input the second data object into another machine-learning model configured to identify item classifications. The processors may receive an item classification data object from the second model and perform actions related to the function based on the item classification data object.