Google's patents provide valuable insight into the search engine giant's latest innovations and priorities for improving search technology.
This article delves into nine interesting Google patents from 2023 and analyzes their potential impact on the future of SEO.
Just because Google filed and published a patent application does not guarantee that the method outlined will be implemented in Google Search.
To gauge whether Google has found a method or technology that is sufficient for use in practice, you can check if the patent is pending in the United States and other countries.
Patent priority claims in other countries must be filed within 12 months of the first filing.
Even if patents can't be directly translated into practice, there's value in examining Google's patents. It provides insights into the topics and challenges that Google product developers are concerned about.
Note: This article only covers Google patents that are also published outside of the United States. Patents are ranked in no particular order. ）
Identifiers：us11797626b2
countries: United States, China, Europe, Russia.
Publication date: October 2023.
Google Search has gotten smarter, with more filters to refine searches. This new patent can be the basis for a filtration method.
The patent outlines a system designed to enhance the search experience by dynamically generating search query filters tailored to the content of a resource, such as a web page, that is relevant to the user's query. This approach is designed to increase the relevance and variety of search options.
Conceptual overview. Data Processing: The system first analyzes the user's search query to pinpoint a relevant resource, such as a web page or document.
Keyword extraction: When a related resource is identified, it extracts keywords from its content. These keywords reflect the main topic or topic in the resource. For example, a search for "best smartphones in 2023" may yield keywords like "battery life," "camera quality," and "5G support."
Filter selection: The system refines these extracted keywords into a set of query filters. It employs criteria such as diversity and discrepancy thresholds to ensure that each filter provides a unique perspective on search results. As a result, filters such as "Best Camera Phone", "5G Smartphone", and "Long Battery Life Phone" are formed, each pointing to a different subset of search results.
User interaction: These query filters will then be displayed with their search results. This feature allows users to further refine their search based on specific interests. Filters are dynamic and change based on the search query and the content of the currently available resources. For example, in a smartphone search scenario, selecting the "Best camera phone" filter will narrow down the results to focus on phones with excellent photo quality.
The system provides a refined, user-centric search experience by:
Process search queries.
Extract relevant keywords.
Create diverse filters.
Allows users to interact dynamically with these filters.
The impact of search engine optimization.
Understanding the nuances of these dynamic search filtering systems is essential for SEO. It highlights the need to produce diverse, rich, and relevant content that fits well with potential search filters.
This consistency allows you to effectively position yourself in search results to meet the diverse interests and queries of your users.
Emphasize diverse and relevant content: An SEO strategy must focus on developing content that encompasses a wide range of relevant topics within a given area. This approach may affect the dynamic filters that search engines may generate, thus increasing the visibility of **.
Keyword optimization: It's more important than ever to gain insight into the most relevant and diverse keywords in a particular field. These keywords may shape the search filter, making it a key factor in how Google discovers and ranks content.
Align with user intent: SEO efforts should shift towards a keen understanding and realization of the user's intent. As search engines increasingly focus on dynamically satisfying user intent through filters, aligning with those intent becomes a strategic necessity.
Stay on top of emerging trends: It's crucial to stay up-to-date on emerging keywords and trends in a particular niche. These emerging elements can be quickly incorporated into dynamic filters, influencing the relevance of search results.
Increase User Engagement:**Efforts should be made to provide comprehensive and diverse information. This engages users more effectively and can affect how they appear in filtered search results, affecting their overall search visibility.
Identifiers：us20230334045a1
countries: United States, China, South Korea, Europe.
Publication date: October 2023.
Identifying the meaning and intent of a query is essential for search engines. The patent may be part of the process.
The patent specifically mentions BERT (bidirectional encoder representation from transformers), suggesting that the method may be relevant to the application of BERT in search algorithms.
The patent outlines a system and method for assessing the accuracy of human interpretation of search queries, with two different models:
The first model: This is trained on a dataset that includes historical search queries, human interpretations, and human-assigned labels that indicate the accuracy of those interpretations. Its primary function is to determine the accuracy of human interpretation of search queries.
The second model: Based on the initial evaluation carried out by the first model, the model integrates other factors, such as the temporal correlation of search queries and the characteristics of clusters. Its role is to provide the final judgment on the accuracy of a human interpretation search query.
Google's patent delves into the concept of search query grouping, or clustering, as a key aspect of its approach to evaluating search query interpretation.
The patent incorporates the concept of search intent, although it may not explicitly mention the term "search intent".
This patent focuses on the accuracy of human interpretation of search queries, which essentially involves discerning the intended purpose or goal behind a user's query, which is the essence of search intent.
Conceptual overview. The following outlines how this patent implicitly addresses search intent.
Human interpretation of search queries
The system's assessment of the accuracy of a human interpretation of a search query essentially requires an understanding of the user's intended meaning or goal.
This understanding is at the heart of the concept of search intent.
Search query refinement
This patent discusses the identification of subsequent search queries as an improvement over previous search queries.
This process is intrinsically related to search intent, as users often optimize their search when the initial results don't fully meet their intent, causing them to adjust their queries for more precise results.
Temporal and clustering characteristics
By considering time and clustering characteristics during the evaluation process, the system indirectly processes the context and nuances of search intent.
For example, the timing of a query or its grouping within a particular topic cluster can provide insight into what users are expecting.
Train the dataset using human evaluation labels
The inclusion of human interpretation and evaluation labels for past search queries in the training dataset indicates that the system learns from previous instances of using human judgment to understand the intent behind the query, Xi.
Vector sentence representation and distance algorithms
The use of vector sentence representations and distance algorithms in parsing and grouping queries involves understanding search intent.
These techniques help to understand the semantics and subtleties of queries, which are critical to discerning user intent.
The impact of search engine optimization.
Emphasis is placed on accurate query interpretation: SEO strategies should prioritize aligning content with the user's possible interpretation of the search query. Understanding and matching the intended interpretation of user queries is essential for effective SEO.
The importance of context and temporality: Content must be optimized by considering time context and potential clustering of topics or keywords. This approach ensures that content remains relevant and indexed accurately based on emerging trends and time-sensitive queries.
Adapt to search refinement: It's important to optimize for refined searches, as these refinements may indicate an initial misunderstanding or misunderstanding by search engines. Focusing on satisfying precise search queries can improve relevance and accuracy in search results.
Leverage natural language processing (NLP).: With the integration of methods such as BERT, it is becoming increasingly important to incorporate an NLP strategy into content creation. This combination with search engines' approach to query interpretation can improve visibility and relevance in search results.
Identifiers：us11762933b2
countries: United States, Europe, China.
Publication date: September 2023.
Google is constantly evolving search into an entity-based search engine. Therefore, it is crucial to provide relevant results based on the subject.
This patent may be part of a better understanding of entities and their relationships.
The patent details a technology that provides search results based on a combined query. The method includes:
Identify the types of entities in the query and their relationships.
Pinpoint nodes in the knowledge graph.
Evaluate the attribute values to determine the resulting entity references.
The system excels at managing queries that involve the relative relationships between various entity types, providing more relevant and contextual search results.
A composite query involves a query that contains multiple entity types and their interrelationships.
Unlike queries that focus on a single keyword or entity, a composite query focuses on interpreting and generating results based on the interrelationships between different entities in the query.
Conceptual overview. Multiple entity types: A combined query includes references to at least two different types of entities. An entity here refers to anything that is unique, unique, and well-defined, such as a person, place, object, concept, etc.
Relativity: The entities in these queries are linked by some kind of relative. These relationships may be spatial, temporal, or other types of connections that meaningfully connect entities together.
The impact of search engine optimization.
Complex query processingSEO experts should note that search engines may be moving in the direction of more complex query processing, which involves the interaction between different entities. This evolution requires a deeper understanding of how to optimize the content of these complex query structures.
Knowledge graph optimization: Given that the patent focuses on the use of knowledge graphs, it is imperative to optimize the content to accurately identify and classify in these graphs. Effective integration into a knowledge graph can significantly enhance the visibility and relevance of content.
Entity Recognition: It's critical to structure content in a way that makes it easy for search engines to identify and classify different entities and their relationships. Clear and logical organization of information can improve the discoverability and relevance of content in search queries involving multiple entities.
Contextual relevance: SEO strategies should prioritize ensuring that content is contextually relevant. This involves considering the search engine's ability to understand and compare the attributes of different entities, so that the content strategy is aligned with the engine's advanced interpretive capabilities.
Identifiers：us11720577b2
countries: United States, Japan, South Korea, China, Germany, Europe.
Last published date: August 2023.
The knowledge panel is a window into the Google Knowledge Graph and the entities it stores.
It is crucial to provide relevant and correct information based on the entity. These panels integrate with standard search results to provide a comprehensive source of information. The patent discusses the method of dealing with the task.
The patent focuses on methods, systems, and apparatus for enhancing search engine results by incorporating knowledge panels that provide contextual information relevant to search queries.
These knowledge panels are generated based on contextual terms in identified entities (such as singers, actors, and homes) and user search queries.
Entity Recognition: The system identifies the entity referenced in the search query.
Contextual terms: It also identifies the contextual terms associated with these entities.
Knowledge panel: Based on these identities, build a knowledge panel that provides relevant facts and information about the entity in the context of the search query.
Ranking and selection: The system assigns ranking scores to various knowledge elements based on their relevance to contextual terms, and selects the most relevant knowledge elements for display.
Knowledge panels are designed to enhance the user experience by providing more relevant, contextual information directly in the search results.
The content of the knowledge panel changes dynamically based on the contextual terms included in the search query. The system uses a sophisticated ranking mechanism to determine the most relevant elements of knowledge to display.
The patent highlights the evolving nature of search engines towards more context-aware and user-centered information retrieval, which is essential for SEO practitioners to understand and adapt.
The impact of search engine optimization.
Focus on entity-based optimization: SEO strategies should consider the importance of entities and their context in content creation.
Rich content creation: Creating content that thoroughly covers entities and their related aspects can increase the chances of appearing in the knowledge panel.
Keyword strategy: Combining relevant contextual terms with primary keywords can improve the visibility of your content.
Understand user intent: SEO efforts should be consistent with understanding user intent and contextual use of search terms.
Identifiers：us20230267277a1
countries: United States, World Intellectual Property Organization (WIPO).
Last published date: August 24, 2023.
Note: This patent is pending. This means that it is not currently in use, but it may be used in the future. ）
User engagement and user logs are important for adjusting Google's machine-Xi algorithm responsible for ranking results**. The patent describes the technology used to handle the task.
The patent describes a system and method for using document activity logs to train machine learning Xi semantic matching models to determine document relevance.
This approach is especially useful for environments such as the cloud or private document storage, where access to content or user interaction data is restricted.
This approach is useful when traditional data sources such as user interaction data or full document content are not available.
Process. Data Collection: Gets two documents and their respective activity logs.
The relationship label is determined: Based on the activity log, determines the relationship label that indicates whether the document is relevant.
Semantic similarity assessment: Documents are input into the model to obtain a semantic similarity value, which represents the estimated semantic similarity between documents.
Model training: Evaluates the loss function, evaluating the difference between the relationship label and the semantic similarity value. Modify the model parameters based on the loss function.
Factor. Document activity log: Includes access timestamps and interaction types (e.g., edit, share).
Relationship labels: Generated based on the time difference between document visits.
Semantic similarity value: Estimate the output of the model to how similar the two documents are.
Loss function: Used to refine the model by comparing relationship labels with semantic similarity values.
The impact of search engine optimization.
Emphasize user interaction: SEO strategies may need to focus more on user interactions with documents, as this data can affect the relevance of documents.
Go beyond keywords: Content relevance can be determined by user behavior and document interactions, not just keywords.
Private and cloud documents: SEO for private or cloud-stored documents may rely more on how those documents are accessed and used than traditional page factors.
ModelingUnderstanding and user behavior can be key to an SEO strategy.
Identifiers：us20230244657a1
countries: United States, China, WIPO, Russia.
Last published date: August 3, 2023.
Note: This patent is pending. This means that it is not currently in use, but it may be used in the future. ）
Search results are becoming more and more contextual. Identifying the context of a query and the user can lead to better search results and user experience. This patent provides a solution to this challenge.
The patent focuses on methods, systems, and apparatus used to generate data that describes contextual clustering and contextual clustering probabilities. These clusters are formed based on query inputs and the context associated with each query input.
The patent describes a system that uses context clustering to simplify the search query process. These clusters are formed based on the context and content of previous queries.
When a user initiates a search, the system renders a cluster of relevant contexts, allowing the user to select a query without having to type.
The system is designed to enhance the user experience by providing contextually relevant query suggestions without requiring the user to enter any characters for the search query.
Process. Data processing and grouping: The system accesses query data from multiple users and groups those queries into context clusters based on input context and content.
Contextual clustering probability determination: For each context cluster, the probability is calculated, indicating the likelihood that the user will select the query input that belongs to that cluster.
User incident response: When indicating a user event, such as a search engine visit, the system selects a context cluster based on the context of the user's device and the calculated probability.
Display and selection: The selected context cluster is then displayed to the user for selection, followed by a list of queries within that cluster for further input.
Factor. Enter context: including factors such as location, date and time, and user preferences.
Query what you entered: The actual content of each query input description.
Contextual clustering probability: A measure that indicates how likely a user is to select query input from a cluster.
The impact of search engine optimization.
Focus on contextual relevance: SEO strategies should prioritize content that aligns with the user's context, such as location and time.
Enhance your understanding of user intent: Understanding possible contextual clusters can help tailor content to more accurately match user intent.
Adapt to query-free search:seo must adapt to scenarios where users get suggestions before entering any query, emphasizing the importance of inclusion in a cluster of relevant contexts.
Identifiers：us11762848b2
countries: United States, China.
Last published date: September 19, 2023.
The patent once again shows the importance of a user's personal context to Google, with a focus on enhanced search query processing.
It introduces a way to generate combined search queries based on the parameters of the current search query and one or more previous queries from the same user, provided that they share a single row of queries.
The patent describes a method for simplifying the search experience by intelligently combining multiple related search queries into one more efficient query.
This approach leverages semantic analysis and user interaction, potentially reducing redundancy in search results and enhancing the relevance of the retrieved information.
The patent proposes a significant shift to a more nuanced, context-aware search process, which could reinvent SEO strategies that focus on semantic relevance and user intent.
Process. Identification of shared query rows: The system identifies when a user's two or more search queries are semantically related, thus sharing a query row.
Combined search queries: Once a shared query row is established, a combined search query is formulated that contains the parameters of the current query and the previous query.
User interaction and feedback: Users can interact with search parameters or results to refine the combined search query.
Factor. Semantic similarity: The system uses semantic similarity (measured by embedding queries in latent space and calculating the distance between those embeddings) to determine whether a query is relevant.
Link syntax and heuristics: The system can also use link syntax or heuristics to identify related queries, especially in voice search scenarios.
Stateful study mode: The user may be prompted to enter stateful study mode, allowing the system to formulate composite queries using past queries.
The impact of search engine optimization.
Increased focus on semantic relevance: SEO strategies may need to place more emphasis on semantic relevance and context, as the patent shows that Google is increasingly focused on understanding and linking semantically related queries.
Long-tail keyword optimization: The ability to combine queries indicates a possible shift to long-tail keywords and a more conversational query format.
Content structure: Content may need to be structured to align seamlessly with a series of related queries, improving the chances of being picked up in a composite search scenario.
Voice search optimization: With the use of link syntax, the optimization of voice search becomes more important because the system can link voice queries over time.
Identifiers：us20230244657a1
countries: United States, China, WIPO, Russia.
Last published date: October 3, 2023.
At first glance, the patent may seem a bit confusing as it discusses the use of content, marking, and annotations from the user's device. But most importantly, it shows that search engines like Google can deliver highly personalized search results in the future.
The patent focuses on a method for presenting computer-generated search results. It involves:
Receive a search request.
Identify multiple search results.
These results are ranked using content from one or more web notebooks.
These ranking results are provided for presentation.
The patent describes a method to improve the accuracy and relevance of search results by merging web notebook content.
This approach can provide a more personalized and contextually relevant search experience, as the ranking of search results is influenced by user-generated content and annotations in the web notebook.
As mentioned in Google's patent, a web notebook is a digital collection of content created and compiled by users from various web resources. These notebooks can include a variety of content types, such as text excerpts, images, and possibly user notes or metadata.
Key features and uses of a web notebook include:
Content aggregation: Users clip or select content from different web pages and aggregate that information into one place. This can be used for personal reference, research, or to share with others.
User notes and metadata: In addition to the content of the clip, users can add their own notes, comments, or metadata to the content in these notebooks. This can provide context or personal insights into the information being gathered.
Theme-centric collections: Web notebooks are often centered around a specific topic or topic. For example, a user might write a web notebook about "sustainable gardening practices" or "web development resources."
Shareability and accessibility: These notebooks can be private, shared with selected groups of users, or even made public. This allows for the sharing of curated information and insights.
Dynamics: Unlike static bookmarks, web notebooks can be constantly updated and edited, making them a dynamic resource for collecting and organizing web content.
Search engine integration: As the patent indicates, the content in a web notebook can affect search engine results. Search engines may consider the relevance of the content in these notebooks to a particular search query and may use them to optimize and personalize search results.
Process. Receive a search request. : This method first receives a search request from the client computer.
Identify search results: Multiple search results are then identified in response to the request.
Use web notebooks to rank: Rank search results using content from web notebooks. This includes checking whether titles, titles, clip content, metadata, or user comments in the web notebook are relevant to the search request. If so, improve the ranking of the cited search results.
Ranking results are available: Finally, provide ranked search results to render on the client computer.
Factor. Web notebook content: The content of the web notebook plays a crucial role in the ranking. It includes titles, titles, clip content, metadata, and user notes.
Backlink analysis: The process may also involve analyzing the backlinks that correspond to the search results.
User Identity: Network notebooks can be selected to rank based on the identity of the user who initiated the search request.
Fragment information: Generating snippet information by identifying the part of the document in the web notebook that is associated with the search results is part of the process.
The impact of search engine optimization.
The importance of user-generated content: SEO strategies may need to emphasize user-generated content, as web notebooks can affect search rankings.
Personalization and context: People are moving to more personalized and context-aware search results, so it's important for SEO to focus on these aspects.
Diverse content types: Incorporating various content types, such as metadata, annotations, and clip content, can become even more important for SEO.
Identifiers：us20230342411a1
countries: United States, Europe, WIPO, Republic of Korea.
Last published date: October 26, 2023.
There is an increasing number of direct answers in the SERPs. An example is information output directly from the knowledge graph, featured snippets, and answers in the SGE snapshot AI box. The patent demonstrates the method of generating and selecting such direct answers.
The focus of this patent is to improve the quality of short answers provided by search engines. It introduces a way to generate and score these short answers based on multiple **s, rather than relying on a single top-ranking search result.
The patent describes a method to improve the reliability and accuracy of short answers in search engine results. It evaluates candidate paragraphs based on other contextual paragraphs in different search results, ensuring greater accuracy and relevance.
Process. Receive query data: The process begins with the search engine receiving the user's search query.
Generate search results: Generates multiple search results, each containing a paragraph related to the query.
Select the paragraph: Selects a set of paragraphs, including candidate paragraphs from top-ranking search results and additional contextual paragraphs from other results.
Score candidate paragraphs: Candidate passages are scored using contextual paragraphs to produce an accuracy score.
Displays the decision: Based on the accuracy score, candidate passages appear as short answers in search results.
Factor. Accuracy score: The decision to display a short answer is based on its accuracy score, which is compared to a predetermined threshold.
Consistency with contextual paragraphs: The accuracy score is derived from the degree of consistency between candidate and contextual paragraphs.
The quality of the short-answer questions: This multi-source approach improves the quality and reliability of short-answer questions.
The impact of search engine optimization.
The patent proposal moves to relevant, contextually accurate, and consensus-driven content.
SEO strategies may need to focus more on providing comprehensive, well-rounded content that aligns with the broader context of the topic, rather than just targeting top-ranking keywords or phrases.
This may lead to a greater emphasis on thorough research, diverse content perspectives, and the accuracy of the information presented on web pages.
Many people start making changes to improve their** based on the "hacks" they find in blogging, social**, youtube, etc., without really understanding the basic principles behind SEO.
That's why I recommend anyone interested in SEO to learn the basics of crawling, indexing, and information retrieval Xi.
The next step is to understand the basics below:
Modern search engine technology.
Semantic Search entity.
Natural language processing.
Embed. Without understanding scientific principles and technology, simply looking for practical experience often makes us look at things subjectively.
Understanding the technical and scientific foundations is like a rational layer that can refute our subjective theories. This way, you'll be better protected from all kinds of hype.
Google representatives only revealed some information when talking about search, especially about how the search results ranked. The details they provide are often vague. This is intentional because Google's purpose is to prevent people from manipulating search results.
Check out other sources of information for deeper insights. Patent research is a more advanced approach. If you're just starting out, it's best to stick to the previous steps.
Regardless of whether a patent is put into practice or not, it makes sense to study Google patents because you can learn about the issues and challenges faced by developers of Google products.