# 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 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. - [Automated AI for Adapting to New Data Without Retraining](https://patentbrief.org/patent/us/20230162023/system-and-method-for-automated-transfer-learning-with-domain-disentanglement) — This patent describes an automated system that builds artificial intelligence models capable of adapting to new, different data without needing full retraining, by learning to ignore irrelevant changes. - [How AI Automatically Labels Images and Checks Its Own Work](https://patentbrief.org/patent/us/11023780/siri-on-device-speech-recognition) — A method for training AI to label images by calculating how uncertain it is about its own predictions, allowing it to verify its accuracy without human help. - [How to Update AI on Small Devices with Slow Internet](https://patentbrief.org/patent/us/20250363357/systems-and-methods-for-deploying-and-updating-neural-networks-at-the-edge-of-a-) — This patent describes a method for efficiently updating artificial intelligence models on small, internet-connected devices, like smart cameras, by sending only the changes, or 'patches,' instead of the entire updated model, which saves bandwidth. - [How Projection Neural Networks Speed Up AI Predictions](https://patentbrief.org/patent/us/11544573/llama-large-language-model-architecture) — A method for making artificial intelligence models faster and more efficient by using fixed, non-trainable projections to simplify complex data before processing.