AI Patent Watch

• Natural Conversations with AI Assistants Based on Speech Pauses

  This patent describes a method for enabling more natural conversations with automated assistants. The core problem addressed is the unnatural, turn-based nature of current automated assistant interactions, which doesn’t reflect how humans converse. The patented invention introduces a “soft endpointing” mechanism that allows the assistant to better understand when a user has paused versus completed their utterance, enabling more fluid and natural conversations. The system can provide natural conversational outputs like “Mmhmm” or proactively ask for clarification, rather than immediately fulfilling potentially incomplete requests, thus improving the user experience.

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• Example-Guided Image Inpainting Using Machine Learning Models

  This patent describes a novel system and method for image inpainting, where missing or undesirable regions of an input image are filled in using guidance from a separate "guide image." The core innovation lies in the use of machine learning models, particularly a Style Generative Adversarial Network (StyleGAN), to combine visual features from both the input image and the guide image in a deep latent space. This approach aims to generate inpainted content that is not only consistent with the remaining parts of the input image but also incorporates desirable visual characteristics from the guide image, offering greater control and improved quality, especially for large or complex missing regions.

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• AI-Powered Calendar Event Conflict Resolution

  This patent discloses systems, methods and devices for prioritizing calendar events with artificial intelligence to resolve scheduling conflicts. When a request to schedule a new event clashes with an existing one, the system compares their "event priority scores," generated by a statistical machine learning model considering various factors. If the new event's score is higher, a selectable option to replace the conflicting calendar event with the new calendar event may be presented.

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• Generating and Editing Text with Language Models

  This patent discloses systems and methods developed by OpenAI for automatically generating and editing text using language models (LMs). The core innovation lies in a flexible approach that takes an input text prompt and user instructions to access a language model, generate output text, and then edit the original prompt by replacing portions with the LM's output. The patent also covers systems for automatically generating and inserting text based on prefix and suffix prompts. A key emphasis is placed on iterative refinement of the LM through training and optimization based on user interactions and labeled data.

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• Training Data Migration for Machine Learning Models

  This patent discloses techniques for adapting previously-annotated training examples into updated training examples for training machine learning models. The core idea involves identifying a specific part (the "find expression") within a targeted subset of training examples (defined by a "filtering constraint") and replacing it with a new part (the "replacement expression"). This process allows for efficient modification of existing training data to reflect changes in system capabilities, user expectations, or to correct inaccuracies.

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