# How Google Distributed Machine Learning Across Many Computers > A 2003 Google patent describing a way to build machine learning models by splitting the work across a large network of computers rather than a single machine. - **Patent:** US 7222127 - **Original title:** Large scale machine learning systems and methods - **Owner:** Google LLC - **Granted:** 2007 - **Status:** Public domain (expired) - **Times cited:** 72 - **Field:** software, ai_ml, consumer_electronics ## What it does This patent describes a distributed system where a large machine learning model is built by multiple computer nodes working together. Instead of one computer processing all data, one node selects a 'candidate condition'—a potential rule for the model—and asks other nodes to provide statistics about how often that condition occurs in their specific slice of data. These nodes calculate derivatives of log-likelihood or histograms to help determine if the rule is useful. Finally, the system aggregates this information to decide whether to add the rule to the final model, effectively allowing the model to grow in complexity by leveraging the combined power of the entire network. ## What it does NOT cover - Does not cover machine learning models that run entirely on a single processor or single computer node. - Does not cover specific neural network architectures like Transformers or CNNs, as the claims focus on rule-based model generation. - Does not cover real-time inference or prediction methods, only the process of generating the model itself. ## The clever bit The system uses a 'feature-to-instance index' to quickly identify which data points satisfy a condition, then offloads the heavy mathematical lifting (calculating derivatives) to the nodes that actually hold the data, minimizing the need to move large datasets across the network. ## Real-world examples 1. Google Search ranking algorithms 2. Large-scale ad-click prediction systems 3. Distributed training clusters in data centers ## Why it matters This patent represents an early architectural blueprint for the massive-scale computing that defines modern Google. By enabling models to be trained across distributed clusters, it allowed for the processing of datasets far too large for the hardware of the early 2000s, laying the groundwork for the company's dominance in search ranking and ad-targeting algorithms. ## Frequently asked questions ### What does How Google Distributed Machine Learning Across Many Computers cover? A 2003 Google patent describing a way to build machine learning models by splitting the work across a large network of computers rather than a single machine. ### Who owns patent US 7222127? Google LLC owns this patent, granted in 2007. ### When does this patent expire? This patent is expected to expire on May 22, 2027, when the invention enters the public domain. ### What is patent US 7222127 cited by? This patent has been cited by 72 later patents that build on its ideas. ### What problem does this patent solve? This patent represents an early architectural blueprint for the massive-scale computing that defines modern Google. By enabling models to be trained across distributed clusters, it allowed for the processing of datasets far too large for the hardware of the early 2000s, laying the groundwork for the company's dominance in search ranking and ad-targeting algorithms. ### What does this patent NOT cover? Does not cover machine learning models that run entirely on a single processor or single computer node. **Full plain-English explainer:** https://patentbrief.org/patent/us/7222127/google-adsense **Original patent:** https://patents.google.com/patent/US7222127 --- _Source: PatentBrief — https://patentbrief.org. Patent facts are from public records; the plain-English explanation is PatentBrief's._ ## Related patents Semantically similar inventions in the PatentBrief corpus: - [Training AI on Private Data Without Seeing It](https://patentbrief.org/patent/us/12518214/distributed-machine-learning-systems-including-generation-of-synthetic-data) — This patent describes a way to train artificial intelligence models using private data stored on many separate computers, by generating fake data that mimics the real data's patterns, so the private data itself never leaves its original location. - [Training AI Models Across Different Computers](https://patentbrief.org/patent/us/12574477/distributed-deep-learning-using-a-distributed-deep-neural-network) — This 2026 patent describes a way to train AI models on one computer, send a version to another computer for further training with private data, and then update the original model with the improvements. - [How to Shrink Large AI Models Using Knowledge Distillation](https://patentbrief.org/patent/us/10289962/deep-q-networks-dqn) — A method for teaching small, efficient AI models to mimic the complex decision-making patterns of much larger, more powerful neural networks. - [How Computers Automatically Adjust Tasks to Run Faster in Data Centers](https://patentbrief.org/patent/us/9405582/aws-elastic-beanstalk) — A method for cloud computers to monitor their own performance while processing massive data tasks and automatically changing their settings or resource levels to stay efficient. - [How Devices Train Shared AI Models While Keeping Your Data Private](https://patentbrief.org/patent/us/12443890/partially-local-federated-learning) — This patent describes a method for training a machine learning model across many devices, where each device keeps some parts of the model and its data private, only sharing updates for the common, global parts of the model.