UAV Delivery Abort Review Using VLM Image Similarity

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Solution Overview

Problem

Unmanned aerial vehicles (UAVs) frequently abort deliveries due to false positive obstacle detections, disrupting the user experience and requiring manual, latency-prone human intervention for review and decision-making, which does not scale well with increasing delivery volumes.

Innovation Solution

Implement a vision language model (VLM) and vector database system to automatically analyze delivery zone images, generate abort explanations, and determine delivery dispositions using similarity searches and in-context learning, enabling scalable and low-latency management of delivery aborts.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual human review is used for delivery aborts, then decision accuracy is maintained, but latency increases and scalability deteriorates

Engineering Contradiction:

Improvedecision accuracyVSAvoidlatency

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system enables automated self-review of delivery aborts through VLM-based classification, where the system independently evaluates abort cases without requiring manual human intervention for each decision, thereby reducing latency while maintaining accuracy through sophisticated automated reasoning

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system implements feedback mechanisms where delivery outcomes and customer feedback are fed back into the VLM training process, allowing the system to learn from actual delivery results and continuously improve its classification accuracy, resolving the latency-accuracy tradeoff through iterative improvement

Inventive Principle:

Principle #23Feedback

2Reliability

If manual human review is used for delivery aborts, then decision quality is maintained, but system scalability deteriorates

Engineering Contradiction:

Improvedecision qualityVSAvoidscalability

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The automated VLM-based review system performs self-service by independently handling large volumes of delivery abort cases without requiring proportional increases in human reviewers, enabling the system to scale with delivery volumes while maintaining consistent decision quality through replicated automated reasoning

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system changes the operational parameter from manual human processing to automated AI-based processing, transforming the scalability characteristics from linear (requiring more humans for more cases) to exponential (handling more cases with the same infrastructure through automated inference)

Inventive Principle:

Principle #35Parameter changes

3Productivity

If automated VLM review is implemented, then scalability improves and latency reduces, but system complexity increases

Engineering Contradiction:

ImprovescalabilityVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The VLM serves as an intermediary layer between the UAV delivery system and the review process, abstracting the complexity of automated reasoning into a dedicated component that handles classification decisions, thereby managing system complexity through modular architecture

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system uses copied and pre-trained VLM models that can be deployed efficiently, rather than building custom complex systems from scratch, reducing implementation complexity while maintaining scalability through model replication and inference optimization

Inventive Principle:

Principle #26Copying

4Productivity

If automated VLM review is implemented, then review speed increases, but measurement precision requirements increase

Engineering Contradiction:

Improvereview speedVSAvoiddetection accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The system uses feedback from delivery outcomes and customer feedback to continuously refine and retrain the VLM models, ensuring that automated review speed does not compromise detection accuracy, as the models adapt to real-world conditions and improve precision over time

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system performs preliminary actions by pre-processing and preparing training data from historical delivery outcomes before deployment, ensuring that the VLM models are pre-trained with high-precision detection capabilities, thereby maintaining measurement precision while enabling rapid automated review

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20260050864A1Scalable review of UAV delivery aborts

Publication Date: 2026.02.19 WING AVIATION LLC

AI summary

A technique for processing delivery aborts by a UAV delivery service includes: acquiring a delivery zone (DZ) image of a delivery destination including a DZ for a package being delivered to the delivery destination by a UAV; determining to abort a delivery mission for the package based upon the DZ image; converting the DZ image to a vector embedding; performing a similarity search on a vector database using the vector embedding, the vector database storing reference images of other delivery destinations indexed to reference vector embeddings and outcome attributes describing delivery outcomes associated with the reference images, and wherein the similarity search identifies a subset of the reference images deemed to have a threshold similarity to the DZ image; and prompting a vision language model with the DZ image and the subset of the reference images to provide an abort explanation or to determine a delivery disposition for the DZ.