The SEO Patent Podcast

A weekly short discussion for SEOs that examines specific Google patents with discussions led by NotebookLM to make search-relevant patents easier to understand.

Predicting Latent Structured Intents from Shopping Queries

This week we are checking out Predicting Latent Structured Intents from Shopping Queries which is how Google uses an AI framework to extract from ambiguous shopping queries

01-10
08:54

Systems and Methods for Improving the Ranking of News Articles

Today we are looking at the 'Systems and Methods for Improving the Ranking of News Articles' patent which outlines a method to rank news articles based on the quality of their sources, improving the relevance and reliability of search results.

01-03
26:10

Systems and Methods for Using Document Activity Logs to Train Machine-Learned Models for Determining Document Relevance

We discuss the patent that outlines a cutting-edge approach to leveraging document activity logs for training machine-learned models. It highlights how this innovation enhances the ability to determine document relevance, streamlining information retrieval and improving user experiences.

12-27
12:52

Providing Search Results Based on a Compositional Query

In this episode, we take a long look at a system for generating search results from compositional queries. It highlights how this method combines multiple query elements to refine and contextualize searches, enabling users to achieve more precise and relevant results efficiently.

12-20
12:04

Evaluating an Interpretation for a Search Query

This episode looks at a system that evaluates multiple interpretations of a search query. It discusses how this innovative approach improves search accuracy by ranking interpretations based on relevance, providing users with results that align closely with their intended queries.

12-13
13:54

Search Result Filters From Resource Content

This episode examines an innovative system designed to enhance search result filtering by analyzing the content of resources. It highlights how the patented approach allows users to more precisely navigate search results, leveraging contextual filters generated from the resources themselves to refine and target their queries effectively.

12-06
11:32

Contextual Estimation of Link Information Gain

The podcast episode explores Google's innovative approach to Contextual Estimation of Link Information Gain. It explores how machine learning models rank documents by assessing the novelty of information they provide to users, enhancing search efficiency and user satisfaction.

11-29
11:37

Predicting Site Quality Score

This episode explores Google's patent on 'Predicting Site Quality,' which outlines methods to estimate a website's quality using phrase-based models and frequency measures, providing insights into SEO and search rankings.

11-22
12:35

Site Quality Score

This episode dives into Google's 'Site Quality Score' patent, which details a system for evaluating the quality of websites based on user queries, interactions, and selections, and its implications for search rankings.

11-15
10:28

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