# How AI Understands Answers Across Multiple Documents > Microsoft's 2024 patent on using AI to find answers by understanding how information in one document relates to information in another. - **Patent:** US 20240338414 - **Original title:** Inter-document attention mechanism - **Owner:** Microsoft Technology Licensing - **Status:** Active - **Times cited:** 1 - **Field:** software, ai_ml, telecommunications ## What it does This patent describes a method for a computer to find answers to a query. It starts by getting a query and then finding several documents that might contain the answer. These documents are broken down into 'tokens' (like words or parts of words). The system then uses a special AI model, called a transformer, to process these tokens. The key part is that the AI learns to pay 'attention' to how tokens in one document relate to tokens in another document. For example, it can see how a word in a first document helps explain a word in a second document. This helps the AI create a better understanding of the information and then pick out the specific tokens that form the answer to the original query, which is then presented to the user. Claim 21 specifically mentions propagating attention from first tokens of a first result document to second tokens of a second result document. ## What it does NOT cover - Methods that do not use a transformer-based machine learning model. - Systems that only process a single document to find an answer. - Methods that do not involve propagating attention between tokens from different documents. - Systems that do not output an answer in response to a query. - AI models that do not generate contextualized semantic representations of words. ## The clever bit The innovation lies in teaching an AI to understand how information in one document 'attends' to or relates to information in another document. This allows the AI to build a richer, interconnected understanding of the data, leading to more precise answer extraction. ## Real-world examples 1. AI-powered search engines 2. Virtual assistants like Cortana 3. Document analysis tools 4. Information retrieval systems ## Why it matters This technology is crucial for modern search engines and AI assistants that need to synthesize information from multiple sources. It enables systems to go beyond simply finding documents and instead understand the relationships between pieces of information across those documents to provide more direct and accurate answers. ## Frequently asked questions ### What does How AI Understands Answers Across Multiple Documents cover? Microsoft's 2024 patent on using AI to find answers by understanding how information in one document relates to information in another. ### Who owns patent US 20240338414? This patent is owned by Microsoft Technology Licensing. ### When does this patent expire? This patent is expected to expire on May 10, 2044, when the invention enters the public domain. ### What is patent US 20240338414 cited by? This patent has been cited by 1 later patents that build on its ideas. ### What problem does this patent solve? This technology is crucial for modern search engines and AI assistants that need to synthesize information from multiple sources. It enables systems to go beyond simply finding documents and instead understand the relationships between pieces of information across those documents to provide more direct and accurate answers. ### What does this patent NOT cover? Methods that do not use a transformer-based machine learning model. **Full plain-English explainer:** https://patentbrief.org/patent/us/20240338414/inter-document-attention-mechanism **Original patent:** https://patents.google.com/patent/US20240338414 --- _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 Computers Use Memory Networks to Answer Questions](https://patentbrief.org/patent/us/10664744/watson-question-answering-system-deepqa) — 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. - [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. - [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. - [How AI Learns New Tasks Using Old Data Labels](https://patentbrief.org/patent/us/11062228/gpt-3-few-shot-learning) — A method for helping AI models understand new topics by grouping similar labels from different datasets into a shared, broader category. - [How Assistant Systems Combine Information About One Thing from Many Places](https://patentbrief.org/patent/us/11704899/resolving-entities-from-multiple-data-sources-for-assistant-systems) — This patent describes a system that gathers all known information about a single person, place, or thing from various sources and combines it into one complete profile for an assistant system.