Federated Machine Learning Training

A Software Patent on Seeing Through Walls?

This European patent introduces a clever way for cameras and tracking systems to better handle moments when an object temporarily disappears from view—like when a person walks behind a tree or a car passes behind a building. Instead of losing track completely, the system learns from past tracking failures. It records where objects typically vanish and where they tend to reappear, building a kind of “map of blind spots.” Over time, this map helps the system make smarter guesses about where a hidden object is likely to go next. It can even adapt how long it waits before giving up on a lost object or decide where to focus its efforts to re-identify something. The result: smoother, more reliable object tracking, even when visibility isn’t perfect.

Federated Machine Learning Training

This European patent describes a method for training a machine learning model, particularly in the context of communication networks like 5G. The primary focus is to improve the accuracy of machine learning models trained using federated learning (FL) by addressing the issue of data heterogeneity among the FL clients. The proposed method involves determining the similarity between the federated learning server's test data and the clients' training data and using this determination to decide whether or not to incorporate a client's model update into the global model.

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.

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.

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.