# How AI Uses Question-Guided Attention to Answer Questions About Images > A method for AI to answer questions about images by dynamically focusing on relevant parts of the picture based on the specific question asked. - **Patent:** US 9965705 - **Original title:** Systems and methods for attention-based configurable convolutional neural networks (ABC-CNN) for visual question answering - **Owner:** Baidu USA LLC - **Granted:** 2018 - **Status:** Active - **Times cited:** 28 - **Field:** ai_ml, consumer_electronics, software ## What it does This patent describes a way to make AI better at answering questions about photos or videos. It uses a system called ABC-CNN that takes both an image and a text question as input. The system extracts features from the image and turns the question into a mathematical representation. It then uses the question to create special 'kernels'—small filters that act like a spotlight—to highlight only the parts of the image relevant to the question. Finally, it combines this focused information with the original image data to generate an accurate answer, effectively ignoring irrelevant background noise. ## What it does NOT cover - Does not cover general-purpose image classification that does not involve a natural language question input. - Does not cover attention mechanisms that are not specifically implemented via configurable convolutional kernels. - Does not cover non-neural network methods for image analysis or traditional rule-based computer vision. - Does not cover the specific hardware used to run the neural network, only the algorithmic method. ## The clever bit The innovation is using the question itself to dynamically generate the convolutional kernels, effectively letting the text input 'program' the AI's visual focus mechanism on the fly. ## Real-world examples 1. Visual Question Answering (VQA) systems 2. Smart camera search features 3. Automated accessibility tools for the visually impaired 4. Advanced content moderation systems ## Why it matters This technology is a building block for multimodal AI, which allows computers to 'see' and 'understand' the world in a human-like way. By enabling AI to selectively focus on specific regions of an image, it significantly improves the accuracy of tasks like automated image captioning and visual search. It represents a shift from static image processing to dynamic, context-aware analysis. ## Frequently asked questions ### What does How AI Uses Question-Guided Attention to Answer Questions About Images cover? A method for AI to answer questions about images by dynamically focusing on relevant parts of the picture based on the specific question asked. ### Who owns patent US 9965705? Baidu USA LLC owns this patent, granted in 2018. ### When does this patent expire? This patent is expected to expire on June 16, 2036, when the invention enters the public domain. ### What is patent US 9965705 cited by? This patent has been cited by 28 later patents that build on its ideas. ### What problem does this patent solve? This technology is a building block for multimodal AI, which allows computers to 'see' and 'understand' the world in a human-like way. By enabling AI to selectively focus on specific regions of an image, it significantly improves the accuracy of tasks like automated image captioning and visual search. It represents a shift from static image processing to dynamic, context-aware analysis. ### What does this patent NOT cover? Does not cover general-purpose image classification that does not involve a natural language question input. **Full plain-English explainer:** https://patentbrief.org/patent/us/9965705/systems-and-methods-for-attention-based-configurable-convolutional-neural-networks-abc-cnn-for-visual-question-answering **Original patent:** https://patents.google.com/patent/US9965705 --- _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 Voice Commands Help Computers Find Objects in Pictures and Videos](https://patentbrief.org/patent/us/9098533/amazon-kinesis) — A method for using voice commands to tell a computer which object in a photo or video you want to search for, allowing it to automatically isolate that object and perform a visual search. - [How Computers Use Neural Networks to Read Messy Handwriting](https://patentbrief.org/patent/us/11715014/gemini-multimodal-model) — A system that uses artificial intelligence to identify text in images by combining dictionary lookups with neural networks that analyze visual traits like handwriting style or blur. - [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 AI Understands Answers Across Multiple Documents](https://patentbrief.org/patent/us/20240338414/inter-document-attention-mechanism) — Microsoft's 2024 patent on using AI to find answers by understanding how information in one document relates to information in another.