Pricing Tool Using Exclusion Model for Low Latency Quotes
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Solution Overview
Problem
Traditional systems for generating price quotes in commercial goods and services industries face challenges such as inefficiency, high processing time, and user interaction difficulties due to the need to scan large databases for comparable product data, often resulting in unsorted and irrelevant information.
Innovation Solution
The method involves generating a set of comparable salable unit data based on a current date threshold, applying an exclusion model to determine the lowest corresponding price data, and transmitting this data for user notification and historical logging, utilizing transitory comparison data tables and adaptable exclusionary rules to streamline processing and improve usability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional systems scan large databases for comparable product data, then comprehensive pricing information is obtained, but processing time and computational resources increase significantly
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing pricing data in transitory comparison data tables before actual quote generation. Comparable salable unit data is pre-filtered and organized by date thresholds, allowing the system to retrieve relevant pricing information quickly without scanning entire databases during production operations.
Solution Approach 2:
The invention extracts only the necessary comparable salable unit data from large databases based on date thresholds and product criteria. By using transitory comparison data tables that store only relevant subsets of data, the system extracts and retains only the information needed for pricing decisions, eliminating the need to process entire databases.
2Reliability
If traditional systems scan large databases for comparable product data, then comprehensive pricing information is obtained, but memory resources are consumed excessively
Solution Approach 1:
The system extracts and stores only the necessary comparable salable unit data in transitory comparison data tables. By filtering data based on date thresholds and product criteria beforehand, the system retains only the subset of information needed for pricing decisions, significantly reducing memory consumption compared to storing or processing entire databases.
Solution Approach 2:
The pricing data is segmented into transitory comparison data tables that contain only relevant subsets of information. This segmentation divides the large database into manageable, purpose-specific segments that can be stored and processed efficiently in memory without requiring excessive resources.
3Reliability
If traditional systems provide unsorted pricing data from database scans, then comprehensive data coverage is achieved, but data interpretability and usability decrease
Solution Approach 1:
The system performs preliminary sorting and filtering of comparable salable unit data before presentation to users. Data is pre-organized by date thresholds, product criteria, and price relevance, so that when pricing information is retrieved, it is already sorted and ready for immediate interpretation without requiring additional user processing.
Solution Approach 2:
Transitory comparison data tables serve as intermediaries between the large database and the user interface. These tables pre-process and organize data, acting as a mediator that transforms comprehensive but unsorted database contents into sorted, interpretable pricing information that is easy for users to understand and act upon.
Data Source
AI summary
Methods and systems for determining a set of lowest corresponding price data related to a salable unit are disclosed herein. An example method includes receiving an input indicative of the salable unit, the input including a current date. The example method further includes generating a set of comparable salable unit data based on the input. Each respective comparable salable unit data in the set of comparable salable unit data includes a respective prior date within a date threshold from the current date. The example method further includes determining the set of lowest corresponding price data by applying an exclusion model to the set of comparable salable unit data, and transmitting a notification of the set of lowest corresponding price data for display to a user. The example method further includes storing the set of lowest corresponding price data into an historical transaction log.