YAHOO ASSETS LLC

Features within messaging platforms that enhance user interaction and content consumption through intelligent suggestions, content persistence mechanisms, engagement analytics, and adaptive presentation of conversational media.

Systems that use user data, preferences, and machine learning to generate tailored advice, product recommendations, goal-setting plans, or contextual information for individuals across different domains.

Methods and systems for enhancing the security and privacy of electronic messages, often by integrating contextual data such as location, social network graphs, or user authentication levels to control access, filter content, or enable specific group interactions.

Systems that process data to provide personalized recommendations, predict events, or automate decision-making processes based on learned patterns, user behavior, or environmental factors.

User interface designs and systems that enable multiple users to interact with shared content, provide feedback, or coordinate activities, often across different devices or locations.

Platforms and methods for aggregating data from diverse sources, generating dynamic content, and delivering it efficiently to users, often involving social media, programmatic advertising, or interactive experiences within cloud environments.

Methods and apparatus for improving the visual fidelity, resolution, or compression efficiency of video signals, often through advanced processing, up-scaling, or neural network-based filters.

Systems leveraging artificial intelligence and machine learning to dynamically adjust educational content, learning paths, goals, or feedback based on individual user performance, progress, or physiological data. This includes generating personalized exercises, recommendations, and adaptive sequencing of knowledge points.

Systems and methods utilizing artificial intelligence, particularly large language models and neural networks, to extract, summarize, generate, or categorize information from unstructured or semi-structured data sources.

Methods and systems for identifying, extracting, and structuring specific entities, relationships, or insights from text-based documents, often involving techniques like named entity recognition, relation extraction, or summarization.

Methods and systems for improving the quality of video streams, generating intermediate frames, or continuously locating and following objects within a sequence of images, even under occlusion.

Techniques for generating human-like text or other content using large pre-trained models, often involving prompt engineering, speculative decoding, or multi-modal inputs for content creation.

Methods and systems for integrating, transforming, and managing complex or domain-specific data from disparate sources into a unified structure, often for specific applications like social networks, genomics, or business forms.

Algorithms and hardware optimizations for rapidly identifying and characterizing relevant visual features (e.g., objects, motion, gradients) from images or video streams, often integrating machine learning for feature representation and recognition, with a focus on real-time performance and reduced computational cost.

Application of machine learning models to process complex data and generate actionable insights, predictions, or classifications that inform or automate decision-making processes in various domains like healthcare, business, or industrial control.

Techniques for distributing computational tasks, data storage, and service logic across cloud data centers, edge devices, and user equipment to improve performance, resilience, or resource utilization. This includes architectures for split rendering, decentralized ledgers, and microservices.

Methods and systems that identify unusual or suspicious patterns in data streams, often leveraging machine learning models trained on normal behavior, to detect threats, faults, or significant events as they occur.

Core infrastructure and operational techniques for efficient and reliable message handling, including server-side logic for managing subscriptions, aggregating messages, optimizing network connections, and ensuring data consistency across distributed messaging services.

Techniques utilizing deep learning models like Generative Adversarial Networks (GANs) or diffusion models to create new images, modify existing ones, or generate synthetic data based on various inputs or conditions.

Systems designed to streamline and automate various commercial transactions, including mobile-enhanced processes, secure online checkouts, customer service interactions, and privilege issuance, often leveraging digital authentication.

Novel hardware designs and processing pipelines tailored for specific computational tasks, such as graphics rendering, neural network operations, or matrix transformations, often involving custom circuits, memory arrays, or data flow mechanisms.

Designing user interfaces and interaction methods specifically for mobile or wearable devices, enabling control of external systems, monitoring user states, or facilitating real-world transactions.

Systems and methods for authenticating users, devices, or applications, authorizing their access to resources based on policies, and managing digital identities across various platforms.

Systems and methods for identifying and blocking unauthorized access, malicious activities, or abnormal behavior within a network by analyzing traffic, system logs, or behavioral patterns.

AI systems designed to engage in natural language dialogue, maintain conversation state, understand user intent, and generate relevant responses, often across multiple communication channels or modalities.

Systems and methods for automating the lifecycle of machine learning models, including pipeline deployment, model management, versioning, and configuring for different inference environments.

Methods and apparatus for detecting objects and determining their three-dimensional position and orientation (pose) using imagery or point cloud data, often for navigation, surveying, or environmental understanding.

Techniques for combining and analyzing information from multiple distinct data modalities (e.g., text, image, video, audio, sensor data) to derive richer insights or improve system performance and decision-making.

Methods for training machine learning models across multiple decentralized devices or servers while keeping data localized, often involving aggregation of model parameters and secure communication.

Utilizing machine learning, particularly deep learning, to analyze medical data such as images, sensor readings, or physiological signals for disease prediction, diagnosis, or treatment assessment.