Multimodal Package Search for Lost Items With Damaged Labels
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Solution Overview
Problem
Existing package tracking systems struggle with image retrieval of high-value lost packages in large-scale handling systems, particularly when package labels are missing, damaged, or obscured, and often lack cross-modal retrieval capabilities using textual descriptions.
Innovation Solution
A multimodal object finder model utilizing neural networks for generating image and text embeddings, enabling efficient cross-modal retrieval by performing similarity searches between text and image embeddings, even in challenging scenarios with occlusions or missing tags.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If text-based matching or barcode/RFID tagging is used for package identification, then the system can identify packages with intact labels, but the system fails when labels are missing, damaged, or obscured
Solution Approach 1:
The patent introduces an intermediary element - a textual description of the package - that mediates between the damaged physical package and the identification system. Instead of directly matching barcodes or RFID tags on potentially damaged packages, the system uses text descriptions that customers provide about their lost packages to query the image database, bridging the gap when traditional identification methods fail
Solution Approach 2:
The patent replaces the mechanical/physical identification system (barcodes, RFID tags, physical labels) with an information-based system using text descriptions and image embeddings. This substitution allows the system to identify packages based on visual appearance and descriptive text rather than relying on physical tags that can be damaged or missing
2Measurement precision
If manual searching is performed to locate lost packages, then the system can examine individual packages, but the process becomes time-consuming and prone to human error
Solution Approach 1:
The patent replaces manual human searching with an automated computer-based system that uses image embeddings and text similarity search. The system automatically processes images, generates embeddings, and performs similarity comparisons, eliminating the time-consuming and error-prone manual inspection process while maintaining or improving identification accuracy
Solution Approach 2:
The patent transforms the search problem from a manual visual inspection task into a computational similarity search problem in embedding space. By converting images and text descriptions into numerical vectors and performing mathematical similarity comparisons, the system achieves rapid automated search that is both fast and accurate
3Adaptability or versatility
If image recognition techniques are used for package retrieval, then the system can identify packages visually, but the system lacks cross-modal retrieval capability using textual descriptions
Solution Approach 1:
The patent merges previously separate unimodal systems (image recognition and text processing) into a unified multimodal system. By combining image embeddings with text embeddings in a shared vector space, the system enables cross-modal retrieval where text queries can find matching images and vice versa, creating a more versatile identification system without excessive complexity
Data Source
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AI summary
A first object out of a plurality of objects that have passed an object handling system is identified. To that end, image embeddings of images of the plurality of objects are generated by a multimodal object finder model that comprises at least one neural network. A first object description text describing the first object is passed to the multimodal object finder model to generate a text embedding. Using a similarity search, a most similar image embedding among the image embeddings that is most similar to the text embedding is found, and a corresponding image and/or a text is output.