How AI Learns New Tasks Using Old Data Labels

A method for helping AI models understand new topics by grouping similar labels from different datasets into a shared, broader category.

What it covers

This patent describes a way to teach an AI model about a new subject by using knowledge from a subject it already knows. It works by turning labels (like 'weather' or 'flight status') into mathematical vectors, which are lists of numbers that represent their meaning. The system then uses clustering algorithms to find common ground between these different labels, creating a 'coarse label' that acts as a bridge. For example, if the AI knows 'book flight' and 'reserve seat,' it can use this shared 'coarse' category to better understand a new, related task like 'check-in for flight' even if it has very little training data for that specific task.

What it doesn't cover

• —Does not cover systems that rely solely on manually defined rules rather than vector-based clustering.
• —Does not cover models that perform transfer learning without first creating a coarse label set as an abstraction layer.
• —Does not cover the specific hardware architecture, only the software-based method of label mapping.

The clever bit

Instead of trying to map labels directly from one domain to another, it creates an intermediate 'coarse' layer. This abstraction acts as a translator, allowing the model to see the semantic relationship between labels that might look completely different on the surface.

Why it matters

Training AI models from scratch is expensive and requires massive amounts of data. This technique allows companies to build smarter virtual assistants and chatbots by recycling knowledge across different domains, making AI more efficient and capable of handling new user requests without needing millions of new examples.

Real-world examples

• 1.Virtual assistants like Microsoft Cortana or Alexa
• 2.Customer service chatbots
• 3.Natural language understanding modules in search engines