Google Patent US9940367B1: In-depth Analysis on Answer Passage Scoring and Its SEO Implications

Introduction to Google's Answer Passage Patent

Google’s patent, US9940367B1, introduces a sophisticated system for generating and scoring “answer passages” to improve search engine results. This system is particularly relevant in the age of AI-driven search, where user expectations have evolved to include precise, conversational answers rather than simply a list of links. Let’s explore the major components and implications of this patent, including the concepts of answer passages, candidate answer passages, scoring mechanisms, and the implications for SEO.

Answer Paragraphs vs. Short Answers

In Google’s SERPs, both “answer paragraphs” and “short answers” serve to deliver quick and useful information, but each fulfills a unique role depending on the type of query.

Answer Paragraphs: Detailed Responses for Complex Queries

Answer paragraphs appear most often in Featured Snippets, where Google extracts a paragraph from a webpage that provides a well-rounded explanation. These snippets are typically reserved for queries that demand more context or elaboration, such as "How does SEO scoring work?" or "What are the benefits of structured content in SEO?" Google selects these answer paragraphs to provide a balanced mix of depth and relevance, allowing users to understand a topic more thoroughly.

Short Answers: Direct Responses for Simple, Fact-Based Queries

Short answers are usually displayed in Direct Answer Boxes and are designed for straightforward, factual queries that require minimal explanation. Examples include questions like "Who invented SEO?" or "What year was Google founded?" In these cases, Google pulls only a brief, one- to two-line response that directly answers the question without additional detail. These short answers are optimal for clear-cut queries where users need a quick fact or figure.

For SEO professionals, understanding the differences between these two answer types can guide content creation. By structuring content to include both concise answers for factual questions and comprehensive paragraphs for more complex inquiries, SEO practitioners can increase the likelihood of appearing in both answer paragraphs and short answer snippets, enhancing visibility in the SERPs.

See some sample Answer Passages

Key Terms and Concepts

1. Answer Passage Generator

• The answer passage generator is a system that identifies specific segments, or “passage units,” in top-ranked resources that might be relevant to the query. These passage units can be entire sentences or discrete data values from structured elements, such as table cells.
• The generator assesses multiple passage units from each resource, ensuring the response includes a balanced mix of detailed prose and factual information.

2. Candidate Answer Passage

• Once passage units are identified, they are compiled into what the patent terms a “candidate answer passage.” This candidate passage is a potential answer segment that could be presented to the user.
• Google evaluates these passages for relevance and quality, aiming to deliver the passage that best satisfies the user’s search intent, considering a mix of prose and factual data when necessary.

3. Answer Passage Scorer

• The answer passage scorer is responsible for assessing candidate passages for both relevance and quality.
• To optimize user experience, it scores passages from both structured (e.g., tables) and unstructured content (e.g., paragraphs), based on selection criteria tailored to the specific informational need.
• This approach allows users to receive well-rounded answers containing both narrative explanations and factual data.

The Scoring Mechanisms

4. Query-Dependent Scorer

• The query-dependent scorer evaluates how closely the candidate answer aligns with the user query. This evaluation considers two main scores:Answer Term Match Score: Measures how closely terms in the candidate answer align with anticipated answers.Query Term Match Score: Measures how well terms in the candidate answer match the terms in the user’s search query.

5. Query-Independent Scorer

• The query-independent scorer evaluates each candidate answer based on interrogative terms, independent of the specific query terms. This helps gauge the quality of a candidate answer passage even when a precise query match is absent.
• By identifying common interrogative terms and linguistic patterns, this scorer ensures that each answer maintains high standards in coherence and relevance.

6. Historical Answer Passages

• To improve the quality of candidate passages, the system uses a model of “historical answer passages” — past answers that performed well.
• This model includes an n-gram language model (such as tri-grams) to compare candidate passages to historical high-quality answers. A candidate with high tri-gram similarity to effective past answers is likely to receive a higher quality score.

7. Score Combiner

• A final score combiner integrates the results of the query-dependent and query-independent scorers to assign an overall relevance and quality score to each candidate answer.
• The highest-scoring passage is then selected for display, presenting the user with a well-rounded, precise, and informative answer.

How Google Uses Answer Passages in Search Results

This patent system is most commonly applied in:

• Featured Snippets: These are rich answer boxes that provide a concise answer to the user’s query, often with a mix of prose and data.
• People Also Ask: These sections provide brief, question-based answers, closely matching user queries with concise passages.
• Direct Answer Boxes: These boxes directly respond to straightforward factual queries with structured answers, often containing numerical or table data.

Implications for SEO and Best Practices

This answer passage scoring mechanism has several critical implications for SEO:

1. Content Relevance and Structure:
2. Combining Narrative and Factual Data:
3. Use of N-grams for Consistency:
4. Optimize for Interrogative Terms:
5. Historical Answer Matching:

Does ChatGPT Use Similar Mechanisms?

AI models like ChatGPT leverage natural language processing (NLP) but follow a different approach than Google’s answer passage scoring. ChatGPT is trained on diverse language models, generating responses based on language structure rather than passage scoring. However, ChatGPT does mirror some features of Google’s system:

• Language Models: ChatGPT uses extensive n-gram and transformer models to predict and generate coherent, contextually relevant answers.
• Answer Structuring: Like Google’s system, ChatGPT breaks down large content into manageable “answer passages” to improve the conversational flow, similar to how Google selects and refines snippets.

However, unlike Google’s system, ChatGPT doesn’t rely on a query-dependent and query-independent scoring system to determine which passages to display, as it generates responses in real time rather than selecting from a pre-indexed content base.

Conclusion

Google’s patent for answer passage generation and scoring represents a refined approach to extracting, ranking, and delivering high-quality answers tailored to user queries. The structured methodology of answer passage selection is reshaping SEO, encouraging content creators to produce not only relevant but also well-structured and mixed-content answers. By optimizing content for relevance, structure, factuality, and consistent phrasing, SEO professionals can better position their content to rank in featured snippets and answer boxes, ultimately enhancing visibility and user engagement.

Sources:

Patent: Scoring candidate answer passages

Patent: Context scoring adjustments for answer passages