Author: Olaf Kopp
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Interesting Google patents & research papers for search and SEO in 2024

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In this article I would like to contribute to archiving well-founded knowledge from Google patents of 2024.

Disclaimer: Are the systems and methods in the patents used by Google?

A patent application does not mean that the methods described there will find its way into practice in Google search. An indication of whether a methodology/technology is so interesting for Google that it could find its way into practice can be obtained by checking whether the patent is pending only in the US or other countries. The claim for a patent priority for other countries must be made 12 months after the first filing.

Regardless of whether a patent finds its way into practice, it makes sense to deal with Google patents, as you get an indication of the topics and challenges that product developers at Google are dealing with.

Below are summaries of the most interesting Google patents from 2024.

More about Google patents in my arcticles:

SEMQA: Semi-Extractive Multi-Source Question Answering

The paper “SEMQA: Semi-Extractive Multi-Source Question Answering” introduces a new QA task where models generate comprehensive answers by combining verbatim quotes from multiple sources with free-text connectors, enhancing verifiability and evaluation ease. The study presents QuoteSum, a dataset of human-written semi-extractive answers, and demonstrates the effectiveness and challenges of this approach through various experiments and evaluations with large language models. Here you can find detailed Summary and Implications for SEO.

Predicting Latent Structured Intents from Shopping Queries

The paper titled “Predicting Latent Structured Intents from Shopping Queries” presents a novel framework designed to infer and map user intents from unstructured shopping queries to structured attributes using a hybrid model. This model combines Long Short-Term Memory (LSTM) networks with autoencoders to jointly learn from user behaviors and product metadata. The approach aims to improve the quality of search results on Google Shopping by understanding and predicting user intents accurately.

The paper reminds me of my approaches I introduce in the article about Shopping Graph Optimization at Search Engine Land. Here some citations from this article:

“Large language models (LLMs) learn based on the frequency of co-occurrences that occur or, in the context of ecommerce, from co-mentions of attributes with the respective product.”

“The frequency of the attributes requested in prompts and search queries determines which attributes are important for a product entity.”

“When optimizing for the shopping graph, you should mention the relevant attributes in the data sources mentioned above, if possible.”

“The more the attributes associated with the respective product resemble the context specified in the prompt and the attributes derived from the LLM, the more likely the products will be mentioned in a response from the generative AI.”

  • Identify and understand user and product-relevant attributes: Long-tail analysis of search queries and prompts is becoming increasingly important.
  • Think beyond keywords: SEOs must think in terms of concepts, entities, attributes and relationships. The time of keywords as a central focus is coming to an end.

Here you can find detailed Summary and Implications for SEO.

Contextual search on multimedia content

The Google patent discusses techniques for contextual search on multimedia content. It involves extracting entities from multimedia content, generating query rewrite candidates based on those entities, and providing rewritten queries to enhance search results without interrupting the user’s content consumption.

Background

The background discusses the increasing consumption of multimedia content online, such as streaming videos, and the challenges users face when searching for information related to the multimedia content they are viewing. Traditional methods require users to manually input search queries, which can be time-consuming and distracting. The document focuses on enhancing the search experience for users by providing relevant results without interrupting the content consumption

Here you can find detailed Summary and Implications for SEO.

Parsing natural language queries without retraining

The patent related to the parsing of natural language queries, enhancing the parsing quality without retraining. This technology is significant for improving the interaction between users and knowledge databases by parsing natural language queries into structured operations that can be executed on APIs of a knowledge database.

Background

The background of the patent discusses the challenge of bridging the gap between the output quality provided by conventional natural language parsers and the high-quality input required by applications that have a low tolerance for misinterpretations. It acknowledges that the quality of parsing can typically be enhanced by either retraining parsers with improved methods or by supplying high-quality training data. However, both approaches are resource-intensive and time-consuming. This sets the stage for the need for innovative solutions that can improve parsing quality without the extensive resource investment typically associated with retraining parsers and collecting high-quality training data.

Here you can find detailed Summary and Implications for SEO.

Media consumption history

The Google patent introduces a sophisticated system designed to enhance user experiences by personalizing content recommendations and search responses based on users’ historical content consumption. This system leverages a detailed analysis of media consumption histories, utilizing various components to process user queries and deliver highly relevant information.

Using user input to adapt search results provided for presentation to the user

The background of the patent US11875086B2 addresses the interaction between users and automated assistants, which are also known as personal assistant modules, mobile assistants, or chatbots. These assistants can be accessed through various computing devices such as smartphones, tablets, wearable devices, automobile systems, standalone personal assistant devices, and more. They are designed to receive textual input from users—either typed or spoken—and respond with appropriate output, which can be visual and/or audible. Here you can find detailed Summary and Implications for SEO.

Identification and Issuance of Repeatable Queries

The Google patent describes methods, systems, and apparatuses, including computer programs encoded on a computer storage medium, for identifying and issuing search queries expected to be issued in the future. It involves obtaining a set of search queries that have been issued by multiple user devices and determining contextual data for each query instance. A model is used to calculate the likelihood of a query being issued in the future based on the contextual data.

The background of the patent highlights the inefficiencies and user experience challenges associated with the traditional handling of search queries by search engines. It sets the stage for the invention by outlining the need for a more predictive, efficient approach to managing repeat search queries, ultimately aiming to enhance the search experience while reducing computational waste. Here you can find detailed Summary and Implications for SEO.

About Olaf Kopp

Olaf Kopp is Co-Founder, Chief Business Development Officer (CBDO) and Head of SEO & Content at Aufgesang GmbH. He is an internationally recognized industry expert in semantic SEO, E-E-A-T, modern search engine technology, content marketing and customer journey management. As an author, Olaf Kopp writes for national and international magazines such as Search Engine Land, t3n, Website Boosting, Hubspot, Sistrix, Oncrawl, Searchmetrics, Upload … . In 2022 he was Top contributor for Search Engine Land. His blog is one of the most famous online marketing blogs in Germany. In addition, Olaf Kopp is a speaker for SEO and content marketing SMX, CMCx, OMT, OMX, Campixx...

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