Example-driven machine learning scheme for dialog system engines

This invention helps developers build conversational AI systems more easily. When a developer creates a new rule for how the AI should respond, the system prompts them to provide several example phrases that a user might say to trigger that rule. These examples help the AI understand the user's intent for that specific rule, making the AI's responses more accurate. The claims specifically narrow this to a system that guides a developer through providing these examples during the rule creation process.

Why it matters: Filed before large language models (LLMs) became widely capable of generating diverse examples and inferring intent from minimal input. The manual provision of exemplary phrases by a developer, as described, could now be significantly automated or enhanced by integrating LLMs, potentially reducing the original manual burden or making the system more powerful.

Status: Not in forceListed as no longer active. The specific reason is not in the record we hold.
How hard to build: SpecializedSoftware logic, developer interface, dialog system engine.

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