On-Device AI Verification of Packaging-Removed Deliveries
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Solution Overview
Problem
Conventional delivery verification systems require significant network bandwidth and time due to server-based AI model processing, and often fail to accurately identify delivery objects, leading to inefficient and delayed corrective actions for incorrect deliveries.
Innovation Solution
Implementing computationally efficient AI models, such as FRCNN and SSD, on user devices for real-time delivery verification, including packaging detection and object counting, to provide immediate feedback and reduce network bandwidth usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If server-based AI model processing is used for delivery verification, then verification accuracy can be maintained, but network bandwidth consumption increases and processing time is delayed
Solution Approach 1:
The patent extracts the AI model processing capability from the server environment and embeds it directly into the driver's mobile device. This allows the delivery verification process to run locally on the device, eliminating the need to transmit image data to and from the server, thus reducing network bandwidth consumption while maintaining verification accuracy through local object detection and packaging identification.
Solution Approach 2:
The patent transitions the processing dimension from centralized server-based computation to distributed edge computing on the mobile device. This dimensional shift in where AI processing occurs enables real-time verification without network dependency, resolving the contradiction between maintaining accuracy and reducing bandwidth usage.
2Measurement precision
If server-based AI model processing is used for delivery verification, then comprehensive analysis can be performed, but processing time increases导致delayed corrective actions
Solution Approach 1:
The patent implements preliminary action by pre-installing and pre-configuring AI models on the driver's mobile device before delivery tasks begin. This allows the system to perform verification immediately when delivery images are captured, without waiting for server processing, thus reducing verification time while maintaining comprehensive analysis capabilities through local object detection and packaging identification.
Solution Approach 2:
The patent skips the network transmission step entirely by performing all AI processing locally on the device. This eliminates the time delay associated with uploading images to the server and waiting for processing results, enabling immediate verification and faster corrective actions while maintaining verification accuracy through local computation.
3Productivity
If multiple AI models are implemented on user devices for real-time verification, then processing speed increases, but device computational requirements increase
Solution Approach 1:
The patent segments the verification process into multiple specialized AI models that run on the device: one model for detecting delivery objects, another for identifying packaging, and a third for counting objects. This segmentation allows each model to be optimized for its specific task, improving overall processing speed while managing computational requirements through task specialization rather than requiring a single complex model.
Solution Approach 2:
The patent implements multi-functionality by designing AI models that can perform multiple verification tasks (object detection, packaging identification, and counting) within a unified local processing framework on the mobile device. This universal approach enables real-time verification across different delivery scenarios without requiring separate systems, balancing processing speed with manageable device computational requirements.
Data Source
AI summary
Artificial intelligence (AI) models for verifying packing removed deliveries are described herein. In an example, a computer system receives image data corresponding to a portion of a delivery location. The computer system determines an indication of at least one delivery object in the portion. The computer system inputs the indication into a first AI model trained for detecting entity-associated packaging associated with the at least one delivery object. The computer system receives, from the first AI model, an output of whether the at least one delivery object includes the entity-associated packaging. The computer system causes a first presentation about the output to be provided at a device.