AI Patent Watch – Détails, épisodes et analyse

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AI Patent Watch

Bastian Best

Technologie

Fréquence : 1 épisode/0j. Total Éps: 5

Your go-to podcast for exploring the most intriguing patents shaping the future of artificial intelligence. Each episode breaks down a fascinating AI patent—what it covers, why it matters, and how it fits into the bigger picture of innovation and intellectual property. Whether you're a patent professional, an AI enthusiast, or an inventor looking for inspiration, AI Patent Watch keeps you on the cutting edge of AI patents. Curated with ❤ by European Patent Attorney Bastian Best, narrated by AI. To learn more about the exciting intersection of patents and AI, sign up for Bastian’s mailing list for exclusive insights: https://bestpatent.eu/list/

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AI-Powered Calendar Event Conflict Resolution

vendredi 14 mars 2025 • Durée 09:40

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

vendredi 14 mars 2025 • Durée 16:09

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

vendredi 14 mars 2025 • Durée 14:40

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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AI-Powered Electronic Device Configuration via Telemetry

vendredi 14 mars 2025 • Durée 12:05

This patent describes an innovative system and method for automatically configuring electronic devices using artificial intelligence (AI). The core idea involves leveraging device usage data (telemetry data) as input for machine learning models to predict future events and proactively configure the device's operating system, applications, and hardware accordingly. This approach aims to personalize the user experience, optimize device performance (speed, memory, battery), improve resource allocation, and even enable preemptive actions and automatic remediation. A key aspect is the use of cloud-based machine learning models tailored to device metadata, with the potential for local augmentation using device-specific, non-shared information.

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Cyber-Threat Score Generation Using Machine Learning

vendredi 14 mars 2025 • Durée 16:56

This patent describes a system and method for generating cyber-threat scores by leveraging machine learning (ML) and explicitly considering the quality of the sources providing the threat intelligence. The core innovation lies in training an ML model not only on the presence or absence of threat indicators and their classifications but also on "quality metrics" associated with the sources. During inference, the system identifies votes from various sources on a new indicator, assesses the quality of those sources, and generates a threat score based on the trained ML model, which has learned to weight source reliability. This approach aims to improve the accuracy and reliability of threat intelligence by mitigating the impact of low-quality or unreliable sources.

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AI Patent Watch – Détails, épisodes et analyse

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