Satellite Imagery Pricing and Priority Weighting

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Solution Overview

Problem

Pricing and prioritization problems in satellite imagery collection systems face challenges in maximizing revenue while improving customer satisfaction, as they often involve scarce resources and complex constraints that can lead to increased computational difficulty and reduced productivity.

Innovation Solution

The systems and methods for selecting, pricing, and prioritizing imagery from a constellation of imaging satellites determine whether to accept or reject requests based on constellation load, calculate appropriate pricing, and prioritize requests using priority weights, balancing revenue generation with customer satisfaction by optimizing resource allocation and scheduling.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If imagery collection requests are accepted without strict constraints, then customer satisfaction improves, but resource scarcity and computational difficulty increase

Engineering Contradiction:

Improvecustomer satisfactionVSAvoidcomputational difficulty

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system performs preliminary actions by pre-calculating priority weights and pricing for imagery collection requests before actual satellite scheduling. It evaluates requests in advance based on user-specified time windows and constellation load projections, determining acceptance/rejection and pricing options beforehand. This preliminary evaluation reduces computational complexity during actual scheduling while maintaining customer satisfaction through accurate estimates and deadline adherence.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system dynamically adjusts pricing and priority weights based on real-time constellation load conditions. As satellite availability changes, the system recalculates priority weights and pricing options to balance revenue maximization with customer satisfaction. This dynamic adaptation allows the system to handle resource scarcity flexibly without increasing computational burden, as calculations are performed efficiently based on current state rather than exhaustive enumeration.

Inventive Principle:

Principle #15Dynamics

2Productivity

If revenue generation is maximized through strict resource constraints, then productivity improves, but customer satisfaction deteriorates

Engineering Contradiction:

Improverevenue generationVSAvoidcustomer satisfaction

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system incorporates feedback mechanisms where customers specify their acceptable time windows for imagery collection, and the system uses this feedback to calculate priority weights and pricing. The constellation load system provides feedback about available satellite capacity, and pricing is adjusted accordingly. This feedback loop ensures that revenue maximization does not compromise customer satisfaction, as pricing reflects both market conditions and customer-specific requirements.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system changes key parameters including priority weights, pricing, and acceptance criteria dynamically based on constellation load and request characteristics. By adjusting these parameters rather than using fixed constraints, the system can maximize revenue when resources are available while maintaining customer satisfaction when constraints apply. The priority weight parameter, in particular, allows flexible ordering of requests based on urgency and pricing rather than rigid temporal constraints.

Inventive Principle:

Principle #35Parameter changes

3Productivity

If imagery collection requests are rejected to manage constellation load, then resource allocation improves, but customer satisfaction deteriorates

Engineering Contradiction:

Improveresource allocationVSAvoidcustomer satisfaction

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system performs preliminary evaluation of requests against projected constellation load before final acceptance decisions. By assessing requests in advance and calculating priority weights based on user time windows and current load conditions, the system can make informed acceptance/rejection decisions that optimize resource allocation while maintaining customer satisfaction. Accurate preliminary estimates allow customers to understand acceptance probability and adjust expectations accordingly.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system introduces an intermediary pricing and prioritization layer between resource constraints and customer requests. Rather than directly rejecting requests based on load, the system uses pricing and priority weights as intermediaries to mediate the conflict. This intermediary mechanism allows the system to communicate resource scarcity to customers through pricing while maintaining polite and professional interactions, preserving customer satisfaction even when requests must be rejected.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS10325295B1Pricing of imagery and collection priority weighting

Publication Date: 2019.06.18 PLANET LABS PBC

AI summary

Systems and methods are provided for selecting, pricing, and prioritizing images obtained by a constellation of imaging satellites. The systems and methods presented can determine whether an imagery collection request should be accepted or rejected. If the imagery collection request is accepted, the systems and methods presented can determine an appropriate pricing option for the imagery collection request and how the imagery collection request should be prioritized in relation to other outstanding imagery collection requests.