Here you can find a database of hundreds of search related active patents and papers. The patents and papers are tagged by SEO and GEO related topics, steps of the information retrieval process and the probabilty they could be used nowadays or in the future.
You can navigate and filter the analysis by the internal search or by the tags. It is possible to combine the tags.
Subscribe to my newsletter or follow my RSS feed to make sure you don’t miss any new patents and papers.
To read the full patent and paper analysis and full usage of the GEO Research Suite including AI research assistants you have to sign up for a monthly or yearly membership.
I am very grateful if you support and motivate me and this project with a paid membership, recommendation, reference …
Your advantages as a GEO Research Suite member:
A patent application does not mean that the methods described there will find its way into practice in the search engines. 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 and other search engines are dealing with.
The provided patent outlines a series of methods, systems, and non-transitory computer-readable media implementations focused on enhancing search functionalities through natural language processing (NLP) and the utilization of knowledge graphs.
The paper proposes a hybrid document retrieval approach that combines deep neural network models with traditional lexical models. This approach aims to enhance the initial retrieval stage, which is usually read more
The document introduces a new approach called Regression Compatible Ranking (RCR) that aims to optimize listwise ranking while maintaining scale-calibrated scores. This method is designed to improve the alignment of read more
The document discusses a new model called Term Weighting BERT (TW-BERT) aimed at improving the effectiveness of lexical retrieval systems by predicting weights for query terms such as unigrams and read more
The paper is titled “Towards Disentangling Relevance and Bias in Unbiased Learning to Rank” by authors affiliated with various institutions including the University of Illinois and Google Research. The study read more
This patent application describes a method and system for training a ranking machine learning model, particularly for use in search engines to enhance the relevance and ranking of search results read more
The patent describes methods and apparatus for using document and query features to determine a presentation characteristic for displaying a search result. It focuses on improving the relevance and presentation read more
The paper introduces a machine learning algorithm for document ranking, employing BERT for query and document encoding and TF-Ranking (TFR) for further optimization. TF-Ranking, a TensorFlow-based library, specializes in ranking read more
This research paper by Rama Kumar Pasumarthi, Honglei Zhuang, Xuanhui Wang, Michael Bendersky, and Marc Najork focuses on improving ranking performance in information retrieval using deep learning.
The patent revolves around methods, systems, and apparatuses for generating “author vectors,” which are essentially digital representations of authors based on their writing style and content. This invention uses neural read more
