Package Similarity Prediction Using Vector Space Analysis
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Solution Overview
Problem
Conventional package fingerprinting techniques are inflexible and rely on structured data comparison, making them inefficient for predicting package attributes and routing in inventory fulfillment systems, especially when dealing with new or varying package types.
Innovation Solution
A system that uses machine learning algorithms to generate vectors in a vector space of package attributes, combining visual and text data from images and sensors to identify similar packages without manual inspection, allowing for flexible updates based on new data and efficient retrieval of package information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional package fingerprinting techniques are used, then structured data comparison is performed, but the system becomes inflexible and inefficient for predicting package attributes
Solution Approach 1:
The patent transforms package attribute prediction from structured data comparison to vector space operations. Package attributes are represented as vectors in a multi-dimensional space, allowing flexible similarity calculations through mathematical operations. This parameter transformation enables the system to handle diverse package types uniformly while maintaining computational efficiency.
Solution Approach 2:
The patent replaces manual inspection and conventional fingerprinting methods with machine learning-based vector operations. The system uses trained models to automatically generate package vectors and perform similarity comparisons, eliminating the need for manual attribute extraction and structured data matching while improving both flexibility and accuracy.
2Productivity
If manual inspection is used to identify similar packages, then accurate predictions can be made, but the process becomes time-consuming and inefficient
Solution Approach 1:
The patent implements a self-service system where packages automatically generate their own vector representations and perform mutual similarity comparisons. The machine learning model autonomously processes package images and attributes, generating vectors and identifying similar packages without human intervention. This maintains prediction accuracy while dramatically improving processing throughput.
Solution Approach 2:
The patent creates vector copies of package attributes that preserve the essential characteristics needed for similarity determination. Instead of comparing raw images or detailed specifications, the system works with compressed vector representations that capture the most discriminative features, enabling fast comparisons without sacrificing prediction accuracy.
3Speed
If multiple inventory locations are maintained to reduce shipping time, then delivery speed improves, but the complexity and cost of operating facilities increases
Solution Approach 1:
The patent creates a universal package similarity prediction system that can be deployed at any facility location. The same machine learning model and vector space framework work across multiple locations, enabling consistent package routing decisions without requiring location-specific customization. This reduces operational complexity while maintaining the ability to optimize shipping times from multiple inventory locations.
Data Source
AI summary
Techniques for predicting a manufacturer and/or contents of a received package are described herein. Images of a package may be received from cameras. A visual vector for the package in a vector space of package attributes may be generated using the images. Physical attributes of the package may be received. A subset of candidate packages may be determined by comparing the physical attributes of a plurality of historically received packages to the physical attributes of the package. A ranking of the subset of candidate packages may be determined by identifying a distance in the vector space of the package attributes between a vector for the subset of candidate packages and the visual vector for the package. An identifier associated with a particular package of the ranked subset of candidate packages may be obtained. Data for the particular package may be retrieved from a database using the identifier.