Google is constantly innovating to improve how users find information online. One of its latest advancements is the “query fan-out” technique, which is closely related to a patent filed in December 2024 called “Thematic Search” (Search Engine Journal). These technologies aim to make search results more comprehensive and user-friendly, especially for complex queries. This article explains these concepts in simple terms for beginners and discusses their impact on search engine optimization (SEO), with insights drawn from expert analyses.
What is Query Fan-Out?
Definition and Purpose
Query fan-out is an advanced information retrieval technique used in Google’s AI Mode. It takes a single user query and expands it into multiple sub-queries to capture different possible user intents. This approach retrieves a broader and more diverse set of results from various sources, including the live web, Google’s knowledge graph, and specialized data like Google Shopping. The technique is particularly helpful for complex queries that require synthesizing information from multiple perspectives, such as comparative analyses or multi-criteria decision-making (Aleyda Solis).
How Query Fan-Out Works
The process begins with Google’s advanced natural language processing (NLP) analyzing the user’s query to determine its complexity. Simple queries, like “capital of Spain,” may not trigger query fan-out, but complex ones, such as “how to optimize website performance,” do. When triggered, the system generates sub-queries that explore different facets of the original question. These sub-queries are processed in parallel, pulling information from multiple sources. The results are then evaluated using Google’s ranking and quality signals and combined into a coherent, easy-to-understand response (Aleyda Solis).
Example 1: Smartphone Search
Imagine you search for “best smartphones 2025.” Query fan-out might generate sub-queries like:
- “Top-rated smartphones 2025”
- “Smartphones with best camera 2025”
- “Smartphones with longest battery life 2025”
- “Affordable smartphones 2025”
Each sub-query fetches relevant information, such as reviews, specs, or prices. AI Mode then creates a response listing top smartphones, their features, and links to buy them, making it easy to compare options without visiting multiple sites Google AI Mode.
Example 2: Sleep Tracking Devices
Another example is the query “What’s the difference in sleep tracking features between a smart ring, smartwatch, and tracking mat?” AI Mode uses query fan-out to create sub-queries like:
- “Sleep tracking features of smart rings”
- “Sleep tracking features of smartwatches”
- “Sleep tracking features of tracking mats”
It gathers data from reviews, product pages, and articles, then presents a summary comparing accuracy, comfort, and features, with links to sources. This helps users quickly understand the differences Google AI Mode.
Example 3: Travel Planning
For a search like “best places to visit in Europe for history buffs,” query fan-out might create sub-queries such as:
- “Historical sites in Europe”
- “Museums in Europe”
- “Ancient ruins in Europe”
- “European cities with rich history”
The AI compiles a list of destinations like Rome, Athens, and London, with brief descriptions of their historical significance and links to travel guides, making trip planning easier Aleyda Solis.
These sub-queries retrieve diverse results, which AI Mode then organizes into a response that includes recommended products, specifications, reviews, and summaries of comfort and battery performance, often with links to source pages (Aleyda Solis).
Benefits for Users
Query fan-out provides users with a more comprehensive answer by addressing various aspects of their query. It’s especially useful for research-oriented searches, where users need in-depth information without navigating multiple websites. This technique enhances the breadth and depth of information compared to traditional Google Search, making it a powerful tool for complex queries (Aleyda Solis).
The Thematic Search Patent
Overview of the Patent
In December 2024, Google filed a patent for a “Thematic Search” system, which organizes search results into themed categories, each accompanied by an AI-generated summary. The system uses a thematic search engine, a large language model, and a summary generator to process queries and present results in a structured format. For instance, a query like “moving to Denver” might produce themes such as “neighborhoods,” “cost of living,” or “schools,” each with a concise summary to help users quickly grasp key information (Search Engine Journal).
The patent describes a system that interfaces with conventional search engines, using large language models to identify themes and sub-themes. It generates additional searches based on these sub-themes, combines the results, and presents them in formats like lists, UI cards, or carousels, often separate from traditional search results. The summaries are created using content from web pages, including passages, titles, metadata, and surrounding text, ensuring proper attribution with links to source pages (Search Engine Journal).
Connection to Query Fan-Out
The Thematic Search patent closely mirrors the query fan-out technique used in Google’s AI Mode. Both methods involve breaking down a query into smaller components—sub-queries in query fan-out and themes in Thematic Search—to provide a more organized and comprehensive response. While query fan-out focuses on generating diverse sub-queries, Thematic Search emphasizes categorizing results into themes with AI-generated summaries. This synergy suggests that the patent may describe a formalized version of the query fan-out process, though Google has not confirmed its use in their search algorithm (Search Engine Journal).
Technical Components
The Thematic Search system includes several key components:
| Component | Description |
|---|---|
| Thematic Search Engine | Receives the user query and organizes results into themed categories. |
| Large Language Model | Analyzes content to identify themes and sub-themes, generating related searches. |
| Summary Generator | Creates AI-generated summaries for each theme using web content and metadata. |
These components work together to deconstruct complex queries, retrieve relevant information, and present it in a user-friendly format (Search Engine Journal).
Example 1: Moving to Denver
The patent uses the query “moving to Denver” as an example. The system might organize results into themes like:
- Neighborhoods in Denver: Summarizes popular areas like LoDo (vibrant nightlife), Capitol Hill (historic charm), and Highlands (trendy restaurants), with average rent prices and links to local guides.
- Cost of Living in Denver: Highlights average costs for housing, groceries, and utilities, citing sources like city websites.
- Schools in Denver: Lists top-rated schools with summaries of their programs and ratings.
- Job Market in Denver: Describes key industries and job opportunities, linking to job boards.
Each theme includes an AI-generated summary and links to sources, helping users plan their move efficiently Search Engine Journal.
Example 2: Learning to Code
For a query like “learning to code,” the Thematic Search system might create themes such as:
- Programming Languages: Summarizes popular languages like Python (easy to learn), JavaScript (web development), and Java (versatile), with links to tutorials.
- Online Courses: Lists platforms like Codecademy, Coursera, and Udemy, summarizing their offerings and costs.
- Coding Bootcamps: Describes intensive programs with details on duration and outcomes.
- Career Opportunities: Outlines jobs like software developer or data analyst, with salary ranges and job sites.
These summaries make it easier for beginners to start their coding journey without digging through multiple websites Search Engine Journal.
Example 3: Climate Change Effects
For “climate change effects,” the system might generate themes like:
- Environmental Impacts: Summarizes rising sea levels, extreme weather, and biodiversity loss, citing reports like those from the IPCC.
- Economic Consequences: Discusses costs to industries like agriculture and insurance, with data from economic studies.
- Health Effects: Highlights issues like heat-related illnesses, linking to health organization reports.
- Policy and Mitigation: Outlines global efforts like the Paris Agreement, with links to policy documents.
This structure helps users quickly understand a complex topic Search Engine Journal.
How It Differs from Current Search
Unlike traditional Google Search, which lists websites in order of relevance, Thematic Search organizes results into themes with summaries. This is similar to features like “People Also Ask” but more structured, with AI-generated summaries for each category. It’s not confirmed if Google uses this patent yet, but it aligns with AI Mode’s approach Businesstechweekly.
Implications for SEO and Content Creators
Potential Impact on Website Traffic
The query fan-out technique and Thematic Search patent could significantly impact website traffic due to the rise of “zero-click” searches. With AI-generated summaries providing answers directly on the search results page, users may not need to visit individual websites. This shift could reduce traffic for content publishers, as users find the information they need without clicking through (Businesstechweekly).
As Roger Montti, an SEO expert with 25 years of experience, noted, “This patent suggests a fundamental shift in how search engines present information to users” (Search Engine Journal). This change challenges traditional SEO strategies, as visibility may depend less on ranking for primary keywords and more on being featured in thematic summaries or sub-query results.
Strategies for Adaptation
To stay visible, content creators and SEO professionals can use these strategies:
- Create Comprehensive Content: Write detailed articles that cover a topic fully to establish authority. For example, a site about smartphones should include specs, reviews, and comparisons to be picked up by AI summaries Businesstechweekly.
- Answer Related Questions: Anticipate follow-up questions users might ask. For the smartphone query, include sections on “best cameras” or “battery life comparisons.” Tools like AlsoAsked help find these questions Aleyda Solis.
- Use Structured Data: Add structured data (like schema markup) to help Google understand your content. For example, product pages with price and review markup are more likely to be included in summaries Businesstechweekly.
- Build Content Clusters: Create groups of related articles linked together, like a series on “moving to Denver” covering neighborhoods, costs, and schools, to align with thematic groupings Aleyda Solis.
- Focus on EEAT: Ensure content shows Experience, Expertise, Authoritativeness, and Trustworthiness (EEAT). High-quality, reliable content is more likely to be used in AI summaries Aleyda Solis.
- Optimize for Knowledge Graphs: Ensure products or businesses are listed in Google Shopping or other data sources, as AI Mode uses these for answers Collective Measures.
Example: Optimizing for Bluetooth Headphones
In a test of AI Mode, the query “Bluetooth headphones with comfortable over-ear design and long battery life” resulted in a response with recommended products, specs, reviews, and summaries, sourced from multiple pages. Content creators can optimize by:
- Writing articles like “Top 10 Over-Ear Bluetooth Headphones for Comfort” with clear headings and bullet points.
- Including structured data for product details and reviews.
- Answering related questions like “Which headphones have the best noise cancellation?” to capture sub-query traffic Aleyda Solis.
Tools for Optimization
Several SEO tools can assist in adapting to these changes:
| Tool | Purpose |
|---|---|
| AlsoAsked | Identifies related questions and user intents for content planning. |
| Keyword Insights | Helps discover sub-queries and build content clusters for topical authority. |
| Waikay | Assists in creating knowledge maps to organize content thematically. |
| InLinks | Enhances content structure and internal linking for better search visibility. |
These tools support the shift toward context-focused SEO, emphasizing comprehensive and structured content (Aleyda Solis).
Conclusion
Google’s query fan-out technique and Thematic Search patent represent a significant evolution in search technology, moving toward more structured and AI-driven results. By breaking down queries into sub-queries and organizing results into themes, these innovations provide users with comprehensive answers, particularly for complex searches. For content creators and SEO professionals, this shift necessitates a focus on in-depth content, structured data, and anticipating user needs to maintain visibility. As Roger Montti stated, this is a “fundamental shift” in search, and adapting to it will be crucial for success in the evolving digital landscape (Search Engine Journal).