Module 11: Neural Networks & AI Search Patents

120+ Patents

This module contains Google's comprehensive patent portfolio for neural networks, machine learning, and AI applied to search. These patents represent the cutting edge of how Google uses AI to understand and rank content.

Overview

Google has invested heavily in neural network-based search technologies. This module covers:

• Neural Ranking Models - Deep learning for document ranking
• BERT & Transformer Models - Language understanding in search
• Embeddings & Vector Search - Semantic similarity matching
• Large Language Models (LLMs) - Generative AI for search
• Attention Mechanisms - Core transformer innovations
• Query Understanding NLP - Natural language processing for queries

---

Part 1: Neural Ranking Models (13 Patents)

The foundation of modern Google ranking uses neural networks to score relevance.

Patent	Title	Key Innovation
US7840569	Enterprise relevancy ranking using a neural network	Neural network for ranking features
US20090106223A1	Enterprise relevancy ranking using a neural network	Neural ranking feature processing
US8117209B1	Ranking documents based on user behavior and/or feature	User behavior + feature model
US20210125108A1	Training a ranking model	Position bias correction in L2R
US20210319033A1	Learning to rank with alpha divergence and entropy	Divergence-based ranking training
US20250124067A1	Method for Text Ranking with Pairwise Ranking Prompting	LLM pairwise ranking
US10452978B2	Attention-based sequence transduction neural networks	Attention mechanisms for ranking
US12260303B2	Machine learning ranking system	Entity ranking with ML
US8543521	Supervised re-ranking for visual search	Visual search re-ranking
US6871202B2	Method and apparatus for ranking web page search results	Linear combination ranking
US8938463B1	Modifying search result ranking based on implicit user	Click weighting
US9477714B1	Methods and apparatus for ranking documents	Advanced document ranking
US20250238474A1	Search ranker with cross attention encoder	CROSS-JEM BERT encoder

Neural Ranking Architecture

---

Part 2: BERT & Transformer Models (18 Patents)

BERT (Bidirectional Encoder Representations from Transformers) revolutionized Google's query understanding.

BERT-Specific Patents

Patent	Title	Key Innovation
US20210232773A1	Unified Vision and Dialogue Transformer with BERT	Vision-language BERT
US12141186B1	Text embedding-based search taxonomy	Fine-tuned search embeddings
US12111859B2	Enterprise generative AI architecture	ColBERT implementation
US20240289407A1	Search with stateful chat	Generative companion search
US20240370479A1	Semantic search and summarization	BERT contextualized embeddings
US20230334045A1	Evaluating an Interpretation for a Search Query	BERT query interpretation
US20210374168A1	Semantic cluster formation in deep learning	BERT semantic clustering

Transformer Architecture Patents

Patent	Title	Key Innovation
US20230214629A1	Transformer-based autoregressive language model selection	Model selection
US10740433B2	Universal transformers	Universal transformer arch
US20230080247A1	Pruning a vision transformer	Vision transformer optimization
US20220405484A1	Methods for Reinforcement Document Transformer	Cross-encoder transformers
US20220067533A1	Transformer-Based Neural Network with Mask Attention	Mask attention network
US11790885B2	Semi-structured content aware bi-directional transformer	Bi-directional processing
US20230104491A1	Small and fast transformer with shared dictionary	Compact transformers
US12335379B1	Privacy-preserving transformer model	Encrypted transformers
US20250036582A1	AI accelerator apparatus using in-memory compute	Hardware acceleration
US12079106B1	Transformer-based bug fixing	Code analysis transformers
US11887270B2	Multi-scale transformer for image analysis	Multi-scale processing

BERT Integration in Search

---

Part 3: Deep Learning for Information Retrieval (40+ Patents)

Core Deep Learning IR Patents

Patent	Title	Key Innovation
US20180341871A1	Utilizing deep learning with information retrieval	Query expansion with DL
US11003865B1	Retrieval-augmented language model pre-training	RAG pre-training
US20240346256A1	Response generation using retrieval augmented AI	RAG response generation
US20200279105A1	Deep learning for content and context	Content classification
US20250147973A1	In-context and semantic-aware ensemble model	Ensemble document retrieval
US11269898B1	Machine learning based database query retrieval	ML query retrieval
US11651014B2	Source code retrieval	Code retrieval DNN
US20230394387A1	Content analysis and retrieval using ML	ML content analysis
US11120073B2	Generating metadata for image-based querying	Visual DL for image search
US20190005357A1	Classification, search and retrieval of images	CNN image retrieval
US11645277B2	Generating ML models for resource ranking	ML model deployment
US11960550B1	Embedding-based retrieval techniques for feeds	Feed retrieval embeddings
US9659248B1	ML and training neural networks	Semantic question retrieval
US20180330238A1	Continual, memory-bounded learning	Continual learning
US20230057512A1	Retrieval augmented reinforcement learning	RL + retrieval
US11860928B2	Dialog-based image retrieval	Interactive image retrieval
US7536408B2	Phrase-based indexing in IR system	Phrase-based indexing
US10769138B2	Processing context-based inquiries	Context-aware retrieval
US4839853A	Latent semantic structure	LSA foundation
US10635979B2	Category learning neural networks	Category DL
US10089576B2	Representation learning multi-task DNN	Multi-task learning
US10438113B2	Hierarchical device placement with RL	Device optimization
US10936863B2	Neuronal visual-linguistic data	Visual-linguistic retrieval
US11687730B1	Automated conversation goal discovery	Goal discovery NN

---

Part 4: Embeddings & Vector Search (41 Patents)

Vector embeddings are fundamental to modern semantic search.

Core Embedding Patents

Patent	Title	Key Innovation
US12099533B2	Searching using embeddings of vector space	Vector space search
US20200004886A1	Generating supervised embedding representations	Entity embedding search
US11782998B2	Embedding based retrieval for image search	Image-text embeddings
US10896183	Information processing with embedding vectors	ANN search
US11960550B1	Embedding-based retrieval for feeds	Feed embeddings
US20240152515A1	Query graph embedding	Workload embeddings
US20190114362A1	Searching social networks using entity embeddings	Entity embedding search
US20220253435A1	Retrieval aware embedding	Multi-dimensional embedding
US12067021B2	Caching historical embeddings	Conversational embeddings
US12141186B1	Text embedding-based search taxonomy	Latent space embeddings
US11163761	Vector embedding for relational tables	Relational embeddings
US20240330193A1	System for embeddings retrieval	Document embedding storage
US12430344B1	Secure distributed document discovery	Private vector similarity
US20250069128A1	Predicting relevant search query	Query embedding prediction
US20230267126A1	Caching Historical Embeddings	Search efficiency
US11971914B1	AI systems with vector database	Pinecone-style similarity
US20240211759A1	Using embedding functions with deep network	Parallel embedding
US12222898B1	AI platform for processing objects	Vector retrieval
US11403663B2	Ad preference embedding model	Vector ad preferences
US9659560B2	Semi-supervised learning of word embeddings	Word embedding training
US11481417B2	Generation and utilization of vector indexes	Sentence embedding search
US11188828B2	Set-centric semantic embedding	KG semantic embedding
US20230273940A1	Distributed approximate nearest neighbor	ANN at scale
US20200193511A1	Utilizing embeddings for entity matching	High-dimensional matching
US10650311B2	Suggesting resources using context hashing	Context embeddings
US8312021B2	Generalized latent semantic analysis	Dimensionality reduction
US10685184B1	Consumer insights using word vectors	Word relationships
US11868724B2	Generating author vectors	LSTM author embeddings
US12164664B1	Semantic search over encrypted vector space	Encrypted vectors

Vector Search Architecture

---

Part 5: Large Language Models for Search (29 Patents)

The latest frontier in Google Search - LLM integration.

Patent	Title	Key Innovation
US20250053430A1	Large language model-based virtual assistant	LLM goal achievement
US12051205B1	Systems for interacting with multimodal LLMs	Multimodal LLM
US20250117381A1	Utilizing LLM in NLP content processing	LLM content generation
US20240403564A1	Privacy-preserving personalized response generation	Private LLM responses
US20230259705A1	Computer methods for large language models	Statistical LLM
US20240354322A1	LLM-based data object extraction	LLM extraction
US11769017B1	Generative summaries for search results	PaLM/LaMDA for search
US20230351118A1	Controlling generative language model behavior	LLM behavior control
US12019663B1	Utilizing LLM to perform a query	LLM subtopic generation
US20240020096A1	Generating code using language models	LLM code generation
US20240338232A1	AI system user interfaces	LLM NLP UI
US20250045534A1	Efficient training of large language models	LLM training efficiency
US20250124235A1	Using generative AI to evaluate fine-tuning	LLM fine-tuning eval
US12353469B1	Verification and citation for LLM outputs	LLM verification
US20240362696A1	Generating item replacements using ML language models	LLM replacements
US20250068838A1	Power optimized architecture for LLM deployment	LLM hardware
US20240370718A1	Multi-modal language models	Multimodal encoder LLM
US20240311405A1	Dynamic selection from multiple candidate LLMs	PaLM/LaMDA selection
US20240303711A1	Conversational and interactive search using ML	LLM conversational search
US20240354711A1	Enhanced scheduling operations using LLMs	LLM scheduling
US20240362093A1	Query response using custom corpus	PaLM/LaMDA custom corpus
US20250225400A1	Improving chat and search	GPT-4/PaLM/LaMDA
US20240370662A1	Optimizing NLP with LLM clustering	LLM prompt generation
US12039431B1	Interacting with multimodal LLM	Multimodal processing
US11968088B1	AI for Intent-Based Networking	Intent LLM
US12032919B1	Post-calibration of LLM confidence scores	LLM calibration
US12008341B2	Generating natural language from code	Code-to-NL LLM
US12288570B1	Conversational AI-encoded language for video	Video LLM
US20250103300A1	Iterative code generation with LLMs	Code LLM generation

Google's LLM Search Integration

---

Part 6: Attention Mechanisms for Ranking (26 Patents)

Attention mechanisms are the core innovation enabling transformer-based search.

Patent	Title	Key Innovation
US10452978B2	Attention-based sequence transduction	Foundational attention
US11556786B2	Attention-based decoder-only sequence NNs	Decoder attention
US20220318601A1	Resource-Efficient Attention in NN	Efficient attention
US11565715B2	Neural networks with attention bottlenecks	Attention bottlenecks
US20210248450A1	Sorting attention neural networks	Sorting attention
US20210081503A1	Gated self-attention memory network	Gated attention
US11790181B2	Extractive structured prediction	Query-context attention
US20190370587A1	Attention-based explanations for AI	Explainable attention
US20240104353A1	Sequence neural networks with look ahead	Attention + self-attention
US11210559B1	ANNs with attention-based selective filtering	Selective attention
US20200089755A1	Multi-task multi-modal ML system	Multi-task attention
US20230112862A1	Leveraging redundancy in attention mechanisms	Attention efficiency
US11244111B2	Adaptive attention for image captioning	CNN + attention
US11687770B2	Recurrent multimodal attention system	Multimodal attention
US11625573B2	Relation extraction with ML	Sentence attention
US9715642B2	Processing images with DNNs	Soft attention
US20200285892A1	Structured Weight Based Sparsity in ANN	Sparse attention
US11636570B2	Generating images with high-res networks	Semantic attention
US11875699B2	Online language learning with ANNs	Transformer attention
US20250173564A1	Training NN to forecast multivariate data	Forecasting attention
US10796686B2	Neural text-to-speech using attention	TTS attention
US20180349359A1	NLP using neural network	NLP attention
US20200210832A1	Adapting neural network model	Hard attention
US20240256879A1	Training NN for algorithmic tasks	Attention transformers
US20200279151A1	Graph neural networks for structured data	GNN architectures

---

Part 7: Query Understanding with NLP (44 Patents)

Machine learning for understanding user queries.

Patent	Title	Key Innovation
US7403938B2	Natural language query processing	NLP query automation
US10430407B2	Generating structured queries from NL	NL to SQL
US11907226	Refining NL database queries	NL database interface
US7702508B2	Natural language query analysis	Answer analysis
US9152623B2	Natural language processing system	ML word taggers
US10614109B2	NLP keyword analysis	Query keyword NLP
US11947536B2	Processing poly-process NL queries	Complex query processing
US20250036616A1	Universal reporting using NL queries	ML NL translation
US8639497B2	Natural language processing core	Core NLP methods
US12141181B2	Database query generation from NL	NLP database ops
US6766320B1	Search engine with NL-based robust parsing	Robust NL parsing
US12147754B2	Offline interactive NLP results	Offline NLP
US9619459B2	Situation aware NLU/NLP	Web search + NLU
US12019627	Incrementally specifying queries through conversation	Conversational query
US10282419B2	Multi-domain NLP architecture	Parallel domain NLP
US9613004B2	NLP-based entity recognition and disambiguation	Entity NER
US20200293874A1	Matching based intent understanding	Intent transfer learning
US20160147878A1	Semantic search engine	Semantic matching
US11954138B2	Summary generation guided by queries	NLP dialogue summary
US20240256582A1	Search with Generative AI	Permissions-aware NLP
US12013884B2	KG question answering with neural MT	NL KG Q&A
US9449105B1	User-context-based search engine	Context-aware understanding

---

Summary Statistics

Category	Patent Count
Neural Ranking Models	13
BERT & Transformer Models	18
Deep Learning for IR	40+
Embeddings & Vector Search	41
Large Language Models	29
Attention Mechanisms	26
Query Understanding NLP	44+
TOTAL	120+

---

Key Insights for SEO

1. Neural Ranking Signals

• Traditional signals (links, content) are now processed through neural networks
• Position in rankings is influenced by learned feature interactions
• Click-based signals are weighted by neural models

2. Semantic Understanding

• BERT enables understanding of query intent beyond keywords
• Context and relationships matter more than exact matches
• Entity recognition improves with neural models

3. Vector Search Impact

• Content should be semantically similar to query intent
• Related entities and topics strengthen relevance
• Embedding quality affects discoverability

4. LLM Integration

• AI Overviews use LLMs for response generation
• Source quality matters for LLM citation
• Structured, authoritative content gets preferential treatment

5. Future Direction

• More multimodal search (text + image + video)
• Personalized neural ranking
• Real-time embedding updates

---

Related Modules

• Module 10: Comprehensive Expansion - More patents and research papers
• Module 4: Query Understanding - Query processing fundamentals
• Module 3: Entity & Knowledge Graph - Entity-based search

---

Pro Tip

To optimize for neural ranking systems:

1. Focus on semantic relevance, not keyword density
2. Build topical authority through comprehensive coverage
3. Ensure content answers the user's underlying intent
4. Use structured data to help entity recognition