Drone Delivery Location Text Extraction for Privacy-Preserving Proof
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Solution Overview
Problem
The increasing prevalence of package delivery by drones raises concerns about privacy, as recipients may not want image data stored or transmitted, and image data may be insufficient or inefficient for confirming delivery completion, leading to additional operational costs and potential data loss.
Innovation Solution
Generating textual descriptions of delivery locations based on aerial images using a machine learning model, which anonymizes visual information and requires less memory, providing a filtered representation of the delivery location.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image data is used to confirm delivery completion, then delivery confirmation accuracy is improved, but privacy concerns and operational costs increase
Solution Approach 1:
The patent extracts only the essential location information from the full aerial image using machine learning models, separating the useful delivery confirmation data from the unnecessary environmental details. This extraction process generates textual descriptions that convey delivery accuracy without exposing privacy-sensitive visual information about the delivery location's environment.
Solution Approach 2:
The patent creates a textual representation (copy) of the delivery location information derived from the aerial image. This textual copy contains the essential location data needed for delivery confirmation but omits the visual privacy-sensitive details, effectively replacing the full image with a privacy-preserving alternative that maintains delivery confirmation functionality.
2Reliability
If image data is stored and transmitted for delivery confirmation, then delivery proof is improved, but storage and transmission costs increase
Solution Approach 1:
The system extracts only the essential location information from the full aerial image, transforming it into a compact textual description. This extracted information maintains the delivery proof functionality while dramatically reducing the data volume that needs to be stored and transmitted, as only the necessary location details are retained rather than the complete image data.
Solution Approach 2:
The patent changes the data representation parameter from full-resolution image data to compressed textual descriptions. This parameter transformation reduces the data size and storage/transmission requirements while preserving the essential information needed for delivery confirmation, effectively optimizing the balance between proof reliability and data efficiency.
3Measurement precision
If full aerial images are used for delivery confirmation, then location accuracy is improved, but data loss from compression and transmission increases
Solution Approach 1:
The system performs preliminary processing of the aerial image through machine learning models to generate textual descriptions before the data needs to be transmitted or stored. This preliminary extraction and transformation of essential location information into text format prevents subsequent data loss that would occur during compression and transmission of full images, as the critical location data is already isolated and optimized for efficient transmission.
Data Source
AI summary
A computer-implemented method includes obtaining an aerial image representing an object in an environment and providing the aerial image as input to a machine learning model. Based on the aerial image, and using the machine learning model, a textual description of a location of the object in the environment is generated and the textual description of the location of the object is outputted.