Multimodal Package Similarity Search for Lost Item Identification
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Solution Overview
Problem
Conventional package tracking systems struggle with identifying lost packages due to limitations in image retrieval based on visual appearance, especially when package labels are missing or damaged, and lack the capability for cross-modal retrieval using textual descriptions, leading to time-consuming and error-prone manual searches.
Innovation Solution
A multimodal object finder model using neural networks for generating image and text embeddings, enabling cross-modal similarity search to identify lost packages efficiently, even with missing or obscured labels, by integrating visual and textual data into a coherent embedding space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional barcodes, RFID tags, or text-based labels are used for package identification, then package tracking is simplified, but the system fails when labels are missing, damaged, or obscured
Solution Approach 1:
The patent introduces an intermediary multimodal neural network model that bridges visual and textual data representations. This model generates embeddings from both image data and text descriptions, enabling the system to identify packages through visual appearance when labels are damaged or missing, while maintaining compatibility with traditional label-based systems
Solution Approach 2:
The system implements a universal package identification approach by integrating multiple data modalities (visual images and text descriptions) into a single search framework. The multimodal neural network can process both traditional label information and visual characteristics, making the system adaptable to various package identification scenarios regardless of label condition
2Ease of operation
If manual searching is employed to identify lost packages, then flexibility in search is maintained, but the process becomes time-consuming and error-prone
Solution Approach 1:
The patent replaces manual mechanical searching with an automated computational system. The multimodal neural network performs automated similarity search by comparing embeddings of package images and descriptions, eliminating human intervention in the search process while maintaining flexible search capabilities through text-based queries
Solution Approach 2:
The system enables self-service package identification where the multimodal neural network automatically processes search queries, compares package data, and returns results without human intervention. The automated embedding generation and similarity computation perform the search function that would otherwise require manual inspection
3Measurement precision
If image recognition techniques are used for package retrieval, then visual appearance matching is improved, but cross-modal retrieval using textual descriptions is lacking
Solution Approach 1:
The patent merges image recognition and text-based search capabilities into a unified multimodal neural network framework. The model simultaneously generates embeddings from both image data and text descriptions, enabling cross-modal retrieval where text queries can search through visual package data and vice versa, while maintaining high visual matching accuracy
Data Source
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 includes 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.