Machine-Learning Quote Generation from Live Inventory Data
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Solution Overview
Problem
The manual process of generating quotes for building products is time-consuming and often fails to account for rapidly changing market conditions, leading to erroneous and suboptimal quotes due to human estimation and lack of data-based recommendations.
Innovation Solution
Utilizing machine learning algorithms to automatically generate quotes for building products by analyzing factors such as available inventory, forecasted demand, and customer pricing scenarios, with dynamic quote expiration times and one-click purchase options.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual quote generation by brokers is used, then human expertise and intuition can be applied, but the process is time-consuming and fails to account for rapidly changing market conditions
Solution Approach 1:
The patent replaces the manual mechanical process of broker quote generation with an automated machine learning system. The ML model processes market data, inventory levels, and pricing scenarios automatically, eliminating the time-consuming manual analysis while maintaining or improving quote accuracy through data-driven insights.
Solution Approach 2:
The system enables self-service quote generation where the automated platform independently analyzes market conditions, calculates pricing scenarios, and generates quotes without human intervention. This allows the system to rapidly adapt to changing market conditions while reducing dependency on manual broker analysis.
2Measurement precision
If manual estimation methods are used, then simplicity in operation is maintained, but measurement precision and data-based recommendations are insufficient
Solution Approach 1:
The patent segments the complex pricing decision into multiple independent components: market data collection, inventory analysis, pricing scenario generation, and ML-based optimization. Each component processes specific data types independently, improving measurement precision while managing system complexity through modular architecture.
Solution Approach 2:
The system dynamically changes multiple parameters including pricing scenarios, inventory levels, market conditions, and customer alternatives. The ML model evaluates how changes in these parameters affect optimal pricing, enabling precise measurement of pricing accuracy through systematic parameter variation and analysis.
3Productivity
If automated quote generation is implemented, then productivity and speed are improved, but the system complexity increases
Solution Approach 1:
The patent creates a universal automated platform that handles multiple functions: data collection from various sources, inventory management, pricing scenario analysis, and quote generation. This multi-functional system improves overall productivity by consolidating previously separate manual processes into a single automated workflow.
Solution Approach 2:
The machine learning model serves as an intermediary between raw market data and final pricing decisions. It processes complex inputs including inventory levels, market conditions, and customer alternatives, transforming them into optimized pricing recommendations. This intermediary layer manages system complexity by abstracting the computational complexity from the user interface.
Data Source
AI summary
Embodiments provide for dynamic generation and updating of quote data objects using machine learning. An example apparatus is configured to receive a quote request and parse the quote request to extract one or more quote parameters. The example apparatus is further configured to determine, based at least in part the one or more quote parameters and by querying a plurality of repositories that are each updated periodically or in real-time, available inventory associated with the product identifier, and to determine a set of alternative quote values for the product identifier. The example apparatus is further configured to generate, using a first trained machine learning model and based at least in part on the available inventory and the set of alternative quote values, a first quote feature for the quote request and a confidence score, and to cause transmission of a quote data object to a user device.