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This invention describes an automated assistant that understands what a user wants by breaking down their request into a specific topic (domain), what they want to do (task), and any details (parameters). For example, it could understand a request like "find a hotel in Paris for tonight." It then searches specialized databases, such as those for hotels, restaurants, or flights, to find a matching response. The assistant can also be triggered by events like calendar alerts or price changes, and it can understand time-related phrases like "in an hour" from the user's request.
Why it matters: Filed before the widespread adoption of Large Language Models (LLMs) and advanced deep learning for Natural Language Processing. The core challenge of accurately determining semantics and dynamically searching relevant information, which was complex in 2013, is now significantly more feasible and robust with modern AI capabilities.
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