Client-Side Markup Tool for Product Reference Tagging
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Solution Overview
Problem
Existing systems fail to effectively identify and link product references in unstructured text, particularly those that are variants or syntactically unconnected with a product's title, which hinders monetization opportunities and user convenience in electronic marketplaces.
Innovation Solution
A client-side markup tool analyzes and identifies product references in published content, allowing users to manually tag or auto-insert hyperlinks to product pages, enabling seamless linking of product mentions to respective electronic marketplace pages, even across different URLs and article instances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing systems are used to identify product references, then basic product linking is achieved, but product references that are variants or syntactically unconnected with product titles cannot be effectively identified
Solution Approach 1:
The patent introduces an intermediary component (product reference identification system) that bridges the gap between unstructured text and product database. This intermediary uses natural language processing and semantic analysis to translate variant product references into identifiable product identifiers, enabling accurate linking even when the reference doesn't exactly match the product title.
Solution Approach 2:
The system changes the parameters of product reference identification by moving from exact string matching to semantic similarity measurement. It employs techniques like word embedding, semantic role labeling, and contextual analysis to evaluate the meaning and intent behind various product reference formulations, thereby identifying variants and syntactically unconnected references.
2Measurement precision
If manual tagging of product references is implemented, then accurate product linking is achieved, but the process becomes time-consuming and reduces productivity
Solution Approach 1:
The system performs preliminary automatic identification and tagging of product references before final review. By pre-processing the content with automated NLP algorithms that identify potential product references and suggest appropriate product links, the system reduces the manual workload to verification and correction only, thereby maintaining high accuracy while improving productivity.
Solution Approach 2:
The patent implements a self-service mechanism where the system automatically generates product reference tags and links without requiring manual intervention for every instance. The automated system serves itself by using machine learning models trained on product data to independently identify and link product references, with manual review only needed for edge cases or corrections.
3Productivity
If automated product reference identification is used, then productivity is improved, but the ability to handle syntactically unconnected and variant product references deteriorates
Solution Approach 1:
The patent implements a dynamic hybrid system that automatically adjusts the level of automation based on the complexity and confidence of product reference identification. For straightforward cases, the system operates fully automatically to maximize productivity. For complex variant references or low-confidence matches, it dynamically switches to or requests manual review, thereby maintaining both high productivity and accurate identification of variant references.
Solution Approach 2:
The system incorporates feedback loops where automated identification results are continuously evaluated and used to improve the model. User corrections and verification data feed back into the training set, allowing the system to learn from mistakes and improve its ability to handle variant product references over time, thus maintaining both productivity and accuracy.
Data Source
AI summary
Various embodiments enable a user editing a document to tag product references in the document. These product references can then be recognizable when the document is published online, thereby enabling ad units (or other personalized units associated with the content) to be inserted either at a location associated with a particular product reference or within the document in a designated location. For example, when editing or drafting a document, a client-side tool could enable a user to tag particular words within the text to create a hyperlink to product pages of an electronic marketplace. In another example, a client-side tool could auto-insert a list of words associated with subject matter of the same. Accordingly, mentions of these words in content could be auto-converted into text-links or hyperlinks to a respective items page of the electronic marketplace.