On-Page Signals & Text Analysis Patents Reference

150+ patents covering phrase-based indexing, entity salience, BERT query understanding, featured snippets, and content quality signals.

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Phrase-Based Indexing (Anna Patterson — 15 patents)

Patent	Description
US7536408	Phrase-based indexing — "Documents with MORE RELATED phrases rank higher"
US9990421B1	Phrase indexing continuation
US12013887B2	Contextual link information gain

Core principle: Google indexes by MEANINGFUL PHRASES, not isolated keywords.

Good phrase: "affordable roofing contractor"
  → Natural, strong signal (common co-occurrence)

Bad phrase: "roofing contractor affordable services"
  → Unnatural, weak signal (forced construction)

Phrase frequency is measured against the full corpus to detect over-optimization. Pages with unnaturally high phrase repetition are flagged.

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Phrase Architecture Distribution

Per Patterson patent + NLP principles — recommended distribution for a page:

Location	Usage
H1	1× exact primary phrase
First 100 words	1× exact phrase + 1× near-variant
H2 headings	Phrase variants (NOT exact primary phrase repeats)
Body text	1× per 200-300 words for primary phrase (natural distribution)
Image alt-text	1× descriptive phrase (not stuffed)

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Information Gain (WO2020081082)

Measures how much UNIQUE information a page provides compared to existing content on the same topic.

What scores high:

• Original research and proprietary data
• Unique expert perspectives not found elsewhere
• Specific numeric values and measurements
• First-hand experience accounts
• Information not yet covered in the top-ranking pages

What scores low:

• Restating information already in top-ranking pages
• Generic overviews without new insights
• AI-generated summaries of existing content
• Thin rewrites of competitor articles

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Entity Salience (US20150127617A1)

4 factors determine how important an entity is to a document:

Factor	Description	Signal Strength
Position	Earlier in document = higher salience	High
Frequency	More mentions = higher salience	Medium
Centrality	More connections to other entities = higher salience	High
Co-occurrence	Appears near related entities = stronger signal	Medium

Implication: The central entity of your content should appear in the first paragraph, be mentioned multiple times naturally, and be surrounded by related entities that create a semantic context cluster.

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BERT Query Understanding (US10452978B2, US20230334045A1)

BERT processes the FULL query context, not just individual keywords:

What BERT understands that keyword matching missed:

• Prepositions ("for", "to", "without", "near") — changes intent completely
• Pronoun references — "it", "they", "their" resolved in context
• Query intent from surrounding context words
• Negations — "shoes without heel" correctly parsed

Query-dependent ranking (US9218397B1): The weight of each ranking factor changes based on the query type. Information-heavy queries weight content depth. Navigational queries weight brand signals. Local queries weight proximity and prominence.

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Content Quality Signals

Patent	Description
US8898296B2	Boilerplate detection — DOM tree shape + text ratio analysis
US9959315B1	Passage quality — standalone answer passages in each section
US8707459B2	Content originality — original-to-copied ratio
US9767157B2	N-gram quality — writing quality via n-gram pattern analysis
US6424983B1	Grammar/spelling — lexicon finite state machine analysis
US20070067294A1	Readability — reading level match to target audience
US8458207B2	Heading structure — anchor/heading context analysis
WO2014209758A1	Above-the-fold — content visibility, scroll distance from top

Boilerplate detection (US8898296B2): Google compares the DOM tree shape (structural pattern) of a page against the ratio of meaningful content to repeated template elements. Sites with high boilerplate-to-content ratios score lower.

Passage quality (US9959315B1): Each section (under a heading) should contain a standalone, extractable answer. This enables passage indexing and featured snippet selection.

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TF-IDF and Relevance

Patent	Description
US20130346424A1	Term frequency, inverse document frequency

Modern application: Raw TF-IDF has been largely replaced by BERT-era semantic matching, but topic modeling across the document corpus still applies. Pages with the right DISTRIBUTION of topically relevant terms (not just the target phrase) score better for relevance.

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Featured Snippet Eligibility (EP3005168A1)

Pages eligible for featured snippets MUST have:

[ ] Direct, concise answer to the query (extractable paragraph)
[ ] Proper heading structure (H2/H3 over the answer)
[ ] Information in extractable format:
    - Paragraph: 40-60 words answering the question directly
    - List: Numbered or bulleted steps/items
    - Table: Data in rows and columns with headers
[ ] Query intent match (the page must be ABOUT this query, not just mention it)
[ ] Sufficient page authority to compete for the feature

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On-Page Optimization Checklist (Patent-Grounded)

Phrase Architecture:
[ ] H1: 1x exact primary phrase
[ ] First paragraph: entity + primary phrase + near-variant
[ ] H2s: phrase variants (not primary phrase repeats)
[ ] Body: natural phrase distribution (1x per 200-300 words)
[ ] Alt text: descriptive, 1x relevant phrase

Entity Coverage:
[ ] Central entity in first paragraph (position = high salience)
[ ] Related entities present (co-occurrence signals)
[ ] Entity mentioned multiple times naturally (frequency)
[ ] Entity connects to related entities in context (centrality)

Information Gain:
[ ] Unique data, research, or perspective not in top-10
[ ] Specific numeric values and measurements used
[ ] Original examples or case studies included

Passage Quality:
[ ] Each H2 section answers a standalone question
[ ] Featured snippet format for key questions (paragraph/list/table)
[ ] Above-fold content addresses primary query immediately

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Related Learning Modules

• Module 02: Phrase-Based Indexing — Full phrase architecture coverage
• Module 08: Modern Neural Search — Passage quality and featured snippets
• Module 14: Content Quality & Panda — Quality scoring