Interesting Google patents for search and SEO in 2024
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:
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- Published for: United States, Europe, Japan, China, WIPO
- Last Publication Date: January 9, 2024
- Expected Expiration Date: November 6, 2039
- Inventors: Yew Jin Lim, David Adam Faden, Mario Tanev, Lauren Ashley Koepnick, Sagar Gandhi, William Ming Zhang
The 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.
- Obtaining a Set of Search Queries: That have been issued by multiple user devices.
- Determining Contextual Data: For each query instance that represents the context in which the query was issued.
- Inputting into a Learning Model: The model calculates the likelihood that a query will be issued in the future.
- Identification of Repeatable Queries: Queries that meet a certain repeatability threshold are stored as repeatable.
- Issuance of Repeatable Queries: Upon selection of a user-selectable interface component, stored repeatable queries are issued, and the search engine provides search results for the query.
- Saving Resources: Reduces the computing resources needed to provide search results in response to issued search queries.
- Saving Time: Saves time and resources typically required for manually issuing a search query.
- Improving Usability: Allows users to issue certain repeatable queries without needing to input any part of the query.
- Identification of Repeatable Queries: The system obtains a set of search queries issued by multiple user devices and determines contextual data for each query instance. It then uses a learning model to predict the likelihood of each query being issued in the future. Queries that meet a certain likelihood threshold are classified as repeatable.
- Storage and Issuance: Repeatable queries, once identified, are stored. A user interface component allows users to select and issue these stored repeatable queries without needing to manually input them. This process is designed to streamline the search experience by preemptively recognizing and facilitating the issuance of queries that are likely to be repeated.
- Contextual Data Utilization: The system uses a variety of contextual data in its process, including the language of the query, geographic location from which the query was issued, the frequency of the query’s issuance, and a semantic embedding of the query that captures its relationship to other queries.
- User Interface Components: The patent describes different types of user-selectable interface components that can trigger the issuance of repeatable queries. These components could include shortcut links, drop-down menus listing repeatable queries, or other interface elements that allow for easy selection and issuance of queries.
- Dynamic Response to User Behavior: The system can dynamically update the set of repeatable queries based on ongoing user behavior and interactions. This includes adjusting which queries are considered repeatable based on new data about their frequency and context of use.
- Efficiency and Resource Saving: By identifying and storing responses to repeatable queries, the system aims to reduce the computational resources required to process these queries. This can lead to faster response times for users and reduced load on the search engine’s infrastructure.
- Enhanced User Experience: The patent claims that this system can improve the user experience by making it easier to reissue frequent queries and by ensuring that the search results for these queries are readily available, potentially even in a predictive manner before the user explicitly requests them.
These claims outline a comprehensive system for enhancing search engine functionality by leveraging predictive modeling to identify and facilitate the reuse of queries that are likely to be repeated by users. The focus is on improving efficiency, reducing resource consumption, and enhancing the overall user experience by making frequent searches more accessible and less burdensome to issue.
Implications for SEO
1. Emphasis on User Intent and Query Predictability
- The patent underscores the importance of understanding user intent and the predictability of search queries. SEO strategies should increasingly focus on identifying patterns in user search behavior and optimizing content to meet these recurring needs. This involves creating content that addresses not just immediate informational needs but also anticipates repeat queries.
2. Content Optimization for Repeat Queries
- Since the system identifies and facilitates the reuse of queries likely to be repeated, optimizing content for these repeat queries becomes crucial. This means analyzing search query data to identify which queries are repeated over time and ensuring that content is optimized to rank well for these queries.
3. Semantic Search and Query Context
- With the patent’s focus on using contextual data (like language, location, and semantic relationships between queries) to identify repeatable queries, there’s a clear implication for SEO to leverage semantic search optimization. This involves optimizing content to ensure it is contextually relevant, uses natural language, and aligns with the semantic relationships that search engines use to understand and categorize content.
4. Predictive SEO
- The concept of predictive SEO, where strategies are not just reactive but also anticipatory, gains more ground with this patent. SEO professionals might need to leverage data analytics more extensively to predict future search trends and prepare content that addresses these trends before they become apparent in search data.
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