Semantic Processing for Product Identification in Unstructured Content
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Solution Overview
Problem
Existing systems fail to effectively identify and present product references within user-generated content, such as text messages, emails, and social media posts, which often lack structured formatting and may not be intended for commercial purposes.
Innovation Solution
A semantic processing system that analyzes user-generated content using named-entity recognition techniques and content filters to identify product references, assigning confidence scores and presenting relevant product links or advertisements to users, involving a user device, semantic processing server, and optional front-end and proxy backend servers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If semantic processing is applied to user-generated content to identify product references, then product identification accuracy is improved, but processing complexity increases
Solution Approach 1:
The semantic processing system divides the content analysis into distinct modules: content filtering to prune irrelevant text, named-entity recognition to identify potential product mentions, and confidence scoring to rank results. This segmentation allows each component to specialize in one aspect of the problem, improving overall accuracy while managing complexity through modular design.
Solution Approach 2:
The patent introduces an intermediary confidence scoring mechanism that bridges the gap between raw text analysis and final product identification. This intermediary layer evaluates the likelihood that identified entities are actual product references, filtering out false positives and improving identification accuracy without requiring complete reanalysis of the entire content.
2Productivity
If content filters are used to prune user-generated content, then processing efficiency is improved, but information loss increases
Solution Approach 1:
The content filtering process applies partial action by selectively pruning only the most obviously irrelevant content while preserving ambiguous or potentially relevant passages. This approach processes less content overall (improving efficiency) but maintains enough information to allow subsequent named-entity recognition and confidence scoring to identify product references that might otherwise be filtered out.
Data Source
AI summary
This disclosure describes systems, methods, and computer-readable media related to semantic processing of content for product identification. Content may be received from a user device. The content may be processed based at least in part on one or more content filters. At least a portion of the processed content may be analyzed with named-entity recognition to identify one or more product references. A confidence score associated with each of the one or more product references may be calculated. Data associated with the one or more product references may be obtained. The data associated with the one or more product references may be transmitted for presentation on the user device.