# 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._