Package Routing Using Damaged Label Recovery and Match Algorithms
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Solution Overview
Problem
Existing systems struggle to efficiently identify and route packages with damaged shipping labels within a shipping network, leading to difficulties in determining the correct destination for such packages.
Innovation Solution
A method involving a combination of identification algorithms and machine learning models is used to match packages with damaged labels to their undamaged images, utilizing characteristics like weight, color, dimensions, and expected delivery routes, to accurately identify and route the packages through the shipping network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional label-based identification is used for packages, then routing is straightforward and automated, but packages with damaged labels cannot be identified or routed correctly
Solution Approach 1:
The patent introduces an intermediary identification system that uses package characteristics (weight, dimensions, color, shape) as mediators to link damaged packages with their corresponding undamaged reference images. This intermediary approach enables identification without relying on damaged labels, resolving the contradiction between reliable identification and adaptability to label damage.
Solution Approach 2:
The system creates and stores reference copies of packages with intact labels before they enter the shipping network. These copies serve as templates for matching against damaged packages later, enabling identification through characteristic comparison rather than label reading, thus maintaining reliability while adapting to label damage.
2Measurement precision
If comprehensive package data collection and analysis is performed to identify damaged packages, then identification accuracy improves, but computational resources and processing time increase
Solution Approach 1:
The identification process is segmented into distinct stages: data collection (weight, dimensions, color), candidate generation based on characteristic matching, and final verification using reference images. This segmentation allows the system to achieve high accuracy through multiple focused steps rather than one complex operation, managing computational complexity while maintaining precision.
Solution Approach 2:
Package reference images and characteristics are captured and stored in advance before labels are damaged. This preliminary action creates a ready-to-use database for rapid matching when damage occurs, reducing real-time computational requirements while maintaining high identification accuracy through pre-processed data.
Data Source
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AI summary
Methods, systems, and computer-readable storage media for identification and routing of a first package within a shipping network that has a damaged shipping label is described. First data for the first package with the damaged label is obtained. Second data for a plurality of packages within the shipping network is obtained. A particular package from the plurality of packages that matches the first package is identified. The identification is performed by applying one or more identification algorithms to the first data and the second data. Applying the identification algorithms includes obtaining as output from each algorithm a subset of possible matches from among the plurality of 10 packages, the possible matches including packages identified by the one or more identification algorithms as having one or more characteristics similar to the first package. Information from a label of the particular package is used to route the first package through the shipping network.