# How Computers Use Memory Networks to Answer Questions > A method for AI to search through large amounts of stored information by repeatedly 'hopping' through memory to find the most relevant facts for answering a question. - **Patent:** US 10664744 - **Original title:** End-to-end memory networks - **Owner:** Facebook Inc - **Granted:** 2020 - **Status:** Active - **Times cited:** 1 - **Field:** ai_ml, software, telecommunications ## What it does This patent describes a way for an AI model to process information by treating a database of knowledge like a long-term memory. When the system receives a question, it converts both the question and the stored facts into mathematical vectors. It then performs a 'hop' operation, where it calculates which facts are most relevant to the question using a probability score. By repeating this process across multiple hops, the system refines its focus, effectively 'reading' through the memory to build a better answer. For example, if you provided a story about a character moving between rooms, the system would use these hops to track the character's location before answering 'Where is the character now?' ## What it does NOT cover - Does not cover traditional database lookups that rely on exact keyword matching. - Does not cover systems that use hard-coded rules or logic trees to find answers. - Does not cover non-vectorized storage methods for knowledge entries. - Does not cover architectures that do not utilize a multi-hop iterative refinement process. ## The clever bit The system uses a 'continuous weighting function' (like softmax) during the hop process, which allows the model to be trained end-to-end using back-propagation. This means the AI learns how to search its own memory without needing human-labeled instructions on which specific facts are important. ## Real-world examples 1. Question-answering chatbots 2. Automated document summarization tools 3. Context-aware AI assistants ## Why it matters This technology was a significant step in the evolution of Natural Language Processing (NLP) at Meta (formerly Facebook). It helped move AI beyond simple pattern matching toward models that can reason over long-term context, which is a foundational requirement for modern large language models. ## Frequently asked questions ### What does How Computers Use Memory Networks to Answer Questions cover? A method for AI to search through large amounts of stored information by repeatedly 'hopping' through memory to find the most relevant facts for answering a question. ### Who owns patent US 10664744? Facebook Inc owns this patent, granted in 2020. ### When does this patent expire? This patent is expected to expire on May 26, 2040, when the invention enters the public domain. ### What is patent US 10664744 cited by? This patent has been cited by 1 later patents that build on its ideas. ### What problem does this patent solve? This technology was a significant step in the evolution of Natural Language Processing (NLP) at Meta (formerly Facebook). It helped move AI beyond simple pattern matching toward models that can reason over long-term context, which is a foundational requirement for modern large language models. ### What does this patent NOT cover? Does not cover traditional database lookups that rely on exact keyword matching. **Full plain-English explainer:** https://patentbrief.org/patent/us/10664744/watson-question-answering-system-deepqa **Original patent:** https://patents.google.com/patent/US10664744 --- _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: - [How AI Uses Question-Guided Attention to Answer Questions About Images](https://patentbrief.org/patent/us/9965705/systems-and-methods-for-attention-based-configurable-convolutional-neural-networks-abc-cnn-for-visual-question-answering) — A method for AI to answer questions about images by dynamically focusing on relevant parts of the picture based on the specific question asked. - [How AI Models Understand Language Using 'Attention'](https://patentbrief.org/patent/us/10452978/transformer-attention-mechanism) — This patent describes a neural network architecture, known as a Transformer, that uses a "self-attention" mechanism to process sequences of information, like words in a sentence, by weighing the importance of different parts of the input. - [Teaching Computers to Understand Document Similarity Using AI](https://patentbrief.org/patent/us/10909459/federated-learning) — This patent describes a way to train a computer program (a neural network) to understand how similar documents are to each other, by showing it examples and teaching it to group similar ones together and separate dissimilar ones. - [How AI Predicts Who Will Speak Next in a Conversation](https://patentbrief.org/patent/us/11645473/palm-pathways-language-model) — IBM's patent describes a system that uses neural networks to analyze speech patterns and intentions to predict which person will talk next in a conversation. - [How Computers Find Hidden Connections Between Different Fields of Knowledge](https://patentbrief.org/patent/us/6523026/google-search-query-processing) — A method for finding related ideas in completely different subjects by using math to map how words appear together, even when the subjects use different vocabulary.