Proof-of-Delivery Image Scoring for Package Placement Accuracy

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Solution Overview

Problem

Manual review of proof-of-delivery images for package delivery compliance is time-consuming and prone to human error, struggling to scale with increasing delivery volumes and handle diverse delivery scenarios.

Innovation Solution

Utilize machine-learning models, particularly large language models, to analyze proof-of-delivery images and corresponding delivery instructions, generating automated package delivery scores and explanations.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If manual review of proof-of-delivery images is used, then delivery compliance can be evaluated, but the process is time-consuming and cannot scale with increasing delivery volumes

Engineering Contradiction:

Improvedelivery evaluation throughputVSAvoidreview time per delivery

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The patent replaces the manual mechanical review process with an automated machine learning system that uses computer vision to analyze proof-of-delivery images. The ML model automatically detects packages, verifies placement compliance, and generates delivery scores without human intervention, thereby increasing throughput and eliminating time loss associated with manual review.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If manual review processes are used, then delivery compliance can be assessed, but the process is prone to human error and inconsistency

Engineering Contradiction:

Improvedelivery evaluation accuracyVSAvoidreview process complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system employs self-service through automated machine learning models that independently analyze proof-of-delivery images and determine compliance without human intervention. The ML models consistently apply predefined criteria to evaluate package placement, eliminating human error and inconsistency while maintaining high reliability across large volumes of deliveries.

Inventive Principle:

Principle #25Self-service

3Productivity

If automated machine learning evaluation is implemented, then productivity and consistency are improved, but system complexity increases

Engineering Contradiction:

Improvedelivery evaluation throughputVSAvoidsystem architecture complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent segments the delivery evaluation system into distinct functional modules: an image processing component that receives and preprocesses proof-of-delivery images, a machine learning model component that performs package detection and compliance evaluation, and a scoring component that generates delivery scores. This modular segmentation manages system complexity while maintaining high productivity through automated processing.

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS20250371485A1Machine learning models to evaluate placement accuracy from image data

Publication Date: 2025.12.04 VEHO TECH INC

AI summary

A method may include identifying a package to be transported to a location based on a data structure including an identifier of the package, querying a database using the identifier of the package to retrieve placement instructions for the package, retrieving a placement image for the package in the location using a media identifier indicating a cloud storage location, executing a request generation engine to generate a request for a machine-learning model using the placement instructions and the placement image for the package, transmitting the generated request to the machine-learning model, receiving a response from the machine-learning model including a package placement score based on a position of the package, and transmitting the package placement score to a mobile device of a transport agent corresponding to the package.