Demand Model for Dynamic Vehicle Pricing

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Solution Overview

Problem

Conventional internet-based systems for vehicle purchase or lease transactions struggle to determine pricing that meets both consumer demands and system operator profitability, leading to unprofitable operations due to competing interests between sellers and consumers.

Innovation Solution

A demand model is implemented within the automotive data processing system to determine consumer-facing prices based on historical transaction data, using machine learning to analyze consumer and vehicle attributes, ensuring prices align with desired conversion rates and profitability goals, thereby allowing system operators to acquire vehicles that meet their business objectives.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If conventional internet-based systems set vehicle prices based on seller prices or market averages, then sellers can maintain their pricing strategies, but system operators cannot ensure desired profitability and return on transactions

Engineering Contradiction:

Improvesystem operator profitabilityVSAvoidpricing system complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system transforms the pricing approach by changing the key parameter from seller-centric pricing to consumer-demand-centric pricing. The demand model calculates a consumer-facing price based on consumer attributes, vehicle attributes, and desired conversion rates, fundamentally altering how prices are determined to ensure system operator profitability while accounting for consumer willingness to pay

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The demand model acts as an intermediary between seller pricing and consumer purchasing decisions. It takes seller price as input along with consumer and vehicle attributes, processes this information through machine learning, and outputs an optimized consumer-facing price that balances seller interests, consumer affordability, and system operator profitability requirements

Inventive Principle:

Principle #24Intermediary (Mediator)

2Reliability

If system operators acquire vehicles at prices that ensure desired return, then profitability is improved, but the ability to meet consumer payment needs may be compromised

Engineering Contradiction:

Improvedesired return achievementVSAvoidconsumer payment flexibility

Core Design Contradiction:

ReliabilityVSAdaptability or versatility

Solution Approach 1:

The system dynamically adjusts the consumer-facing price based on consumer-specific parameters such as credit score, income, and payment history. This allows the same vehicle to be priced differently for different consumers, enabling the system to meet desired return thresholds while adapting to each consumer's payment capacity and needs

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The pricing system is made dynamic by continuously adjusting prices based on real-time consumer attributes, vehicle attributes, and market conditions. The demand model recalculates optimal prices for each consumer-vehicle pairing, allowing the system to adapt pricing to match both profitability goals and individual consumer payment capabilities rather than using static pricing

Inventive Principle:

Principle #15Dynamics

3Measurement precision

If the system processes large quantities of historical transaction data to accurately determine consumer interest, then pricing accuracy is improved, but processing time and computational resources increase

Engineering Contradiction:

Improveconsumer interest determination accuracyVSAvoiddata processing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs preliminary actions by pre-processing and storing historical transaction data in structured formats before it is needed for pricing decisions. Consumer attributes, vehicle attributes, and historical transaction patterns are pre-analyzed and organized, so when a pricing decision is needed, the demand model can quickly query pre-computed results rather than processing raw data from scratch

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system creates simplified copies or representations of complex historical data patterns through the trained machine learning model. Once the demand model is trained on extensive historical data, it captures the essential patterns and relationships in a compact form that can quickly predict consumer interest and optimal pricing without requiring access to or processing of the entire historical dataset for each pricing decision

Inventive Principle:

Principle #26Copying

Data Source

PatentUS11361335B2Machine learning engine for demand-based pricing

Publication Date: 2022.06.14 FAIR IP LLC

AI summary

Systems, methods and products for determining a consumer-facing price using a demand model that is generated based on historical transactional data. One embodiment comprises a method implemented in a pricing module of an automotive data processing system. Data that identifies a consumer (or consumer group) and a vehicle type are received and the demand model is accessed to generate a payment corresponding to the attributes of the consumer and the attributes of the vehicle type. The demand model may be implemented in a machine learning engine that maintains a set of weights β used in a predictive demand function. The weights are adjusted by the machine learning engine to minimize a loss function which measures deviation of demand estimated by the predictive demand function from the demand indicated by a set of historical transaction data.