Using a Trained Generative Model to Generate Query Variants for Google’s Search Features: Exploring the Possibilities of PASF, PAA, and More [Patent]

In today’s digital age, search engines have become an integral part of our lives. Google, being the most popular search engine, has been constantly improving its search features to provide users with the most relevant and accurate results. One of the ways Google achieves this is by using query variants, which are variations of a user’s search query that can help improve the accuracy of search results.

Query variants can be generated in various ways, such as using synonyms, stemming, or by analyzing user behavior. However, these methods have their limitations and may not always produce the best results. This is where a trained generative model comes in.

A trained generative model is a machine learning algorithm that can generate new data based on patterns it has learned from existing data. In the context of search engines, a trained generative model can be used to generate query variants that are more accurate and relevant to a user’s search query.

Google has recently filed a patent for a method of using a trained generative model to generate query variants for its search features. The patent, titled “Generating Query Variants Using Trained Generative Models,” describes a system that uses a trained generative model to generate query variants for various Google search features, such as People Also Ask (PAA) and People Also Search For (PASF).

PAA and PASF are two of Google’s search features that provide users with additional information related to their search query. PAA displays a list of questions related to the user’s search query, while PASF displays a list of related searches. By using a trained generative model to generate query variants for these features, Google can provide users with more accurate and relevant information.

The patent describes how the system would work by first training the generative model on a large dataset of search queries and their corresponding results. The model would then be used to generate query variants based on the user’s search query. These query variants would be evaluated based on their relevance and accuracy, and the most relevant ones would be displayed in the PAA or PASF features.

The use of a trained generative model to generate query variants has several advantages over traditional methods. Firstly, it can generate more accurate and relevant query variants by analyzing patterns in the data. Secondly, it can generate a larger number of query variants, which can improve the accuracy of search results. Finally, it can adapt to changes in user behavior and search trends, ensuring that the query variants generated are always up-to-date and relevant.

In conclusion, the use of a trained generative model to generate query variants for Google’s search features has the potential to improve the accuracy and relevance of search results. By analyzing patterns in the data, the model can generate more accurate and relevant query variants, which can improve the user experience. While the patent is still pending, it is clear that Google is exploring new ways to improve its search features and provide users with the best possible experience.

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• Source: Plato Data Intelligence.