Item Similarity Modeling via Image Embedding Conversion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional methods for correlating item data, particularly in competitive analysis, are slow, error-prone, and often obsolete by the time they are produced, leading to misinforming decision-makers and inefficient resource usage due to manual searches and comparisons.
Innovation Solution
A system that converts item images to image embeddings using a computer-modeled embedding layer, compares these embeddings with reference embeddings, and calculates similarity scores to perform responsive actions such as creating associations or updating models based on confidence scores, thereby automating the correlation process and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual user searches and comparisons are used for item correlation, then users can identify item associations, but the process is slow and error-prone, making results obsolete by the time they are produced
Solution Approach 1:
The patent replaces manual mechanical comparison processes with automated image embedding and text correlation models. Image data is converted to embeddings via neural networks, and automated algorithms compute similarity scores, eliminating the need for manual searching and comparison while dramatically reducing processing time and errors.
Solution Approach 2:
The system performs self-service by automatically generating item correlations without requiring user intervention. The correlation models autonomously process images and text data, compute similarity scores, and generate associations, allowing the system to serve itself rather than relying on manual user operations.
2Loss of information
If large amounts of product and image information are processed, then comprehensive item correlations can be identified, but it becomes difficult to identify relevant images and increases system resource usage
Solution Approach 1:
The patent extracts only the most relevant features from large amounts of product and image information. Image embeddings capture essential visual characteristics, and text correlation models extract key textual features, filtering out irrelevant data while preserving critical information for accurate correlation.
Solution Approach 2:
The system transforms raw images and text into standardized parameter representations (embeddings and similarity scores). This parameter transformation reduces data complexity by converting unstructured large-volume data into compact, comparable numerical representations that are easier to process while retaining essential information.
3Reliability
If users manually differentiate between items, then associations can be established, but users may incorrectly differentiate or fail to differentiate, leading to misleading associations and resource waste
Solution Approach 1:
The patent implements feedback mechanisms where confidence scores from correlation models are continuously evaluated. High-confidence associations are automatically accepted, while low-confidence results trigger re-evaluation or user review, creating a feedback loop that improves reliability while maintaining appropriate automation levels.
Solution Approach 2:
The system introduces automated correlation models as intermediaries between raw item data and final associations. These models act as mediators that objectively compare items using learned patterns, removing human bias and error while maintaining reliability through confidence scoring and model validation.
4Productivity
If conventional manual methods are used for competitive analysis, then item comparisons can be performed, but the process requires significant user input and produces results that misinform decision-makers
Solution Approach 1:
The patent replaces manual competitive analysis processes with automated image and text correlation systems. Neural network-based embedding models and correlation algorithms automatically perform comparisons at scale, dramatically increasing productivity while improving accuracy through consistent, objective computational methods free from human error.
Data Source
AI summary
Systems and methods for correlating item data are disclosed. A system for correlating item data may include a memory storing instructions and at least one processor configured to execute instructions to perform operations including: receiving text and image data associated with a reference item from a remote device; converting, using a computer-modeled embedding layer, at least one image to an image embedding; comparing the image embedding to reference embeddings stored in a database; selecting a subset of the candidate item text as candidate text data based on the comparison; selecting a subset of the candidate item images as candidate image data based on the comparison; selecting a text correlation model; determining a first similarity score; selecting an image correlation model; determining a second similarity score; calculating a confidence score based on the first and second similarity scores; and performing a responsive action based on the calculated confidence score.