AI That Links Information Across Documents for Better Answers

This patent describes an AI system that improves answers to questions by linking and understanding information across several related documents, rather than just one at a time.

What it covers

This technology improves how AI systems answer questions by understanding connections between different documents. When you ask a question, the system first finds several related documents. It then feeds these documents into a special AI model called a transformer, which is designed to 'pay attention' to important words and phrases. Crucially, this model propagates attention from words in a first document to words in a second document (claim 1), building a richer understanding. For example, if you ask about a person, and one document mentions their birth city while another mentions their current job, the AI can connect these details by letting the information flow between the documents. This process creates 'contextualized embeddings' for each word, allowing the AI to select the most relevant words to form an accurate answer to your query (claim 1).

What it doesn't cover

• —Does not cover systems that do not use a transformer-based machine learning model for propagating attention between documents.
• —Does not cover methods that only propagate attention within a single document, without explicitly linking information between separate documents.
• —Does not cover systems where the identification of related documents is not based on similarity to the query or explicit links between documents (claim 2).
• —Does not cover AI models that retrieve answers without first processing tokens from multiple result documents through stacked network layers (claim 1).
• —Does not cover systems that fail to propagate attention between the query's own words and the words within the retrieved documents (claim 5, 6).

The clever bit

The clever part is explicitly propagating 'attention' from words in one document to words in another document, rather than treating each document as a separate island. This allows the AI to build a more complete understanding of a topic by connecting related pieces of information spread across different sources.

Why it matters

This patent addresses a core challenge in natural language processing: understanding context across multiple sources. By enabling AI models to propagate attention between documents, it allows for more comprehensive and accurate answers to complex questions. This is crucial for search engines, virtual assistants, and knowledge management systems that need to synthesize information from vast amounts of text. It helps these systems move beyond simple keyword matching to provide more intelligent, nuanced responses.

Real-world examples

• 1.Advanced search engines like Bing or Google Search
• 2.Question-answering systems in customer service bots
• 3.AI assistants that summarize research papers
• 4.Enterprise search tools for large document repositories