17851309. GENERATING PREDICTED INK STROKE INFORMATION USING TEXT-BASED SEMANTICS simplified abstract (Microsoft Technology Licensing, LLC)

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GENERATING PREDICTED INK STROKE INFORMATION USING TEXT-BASED SEMANTICS

Organization Name

Microsoft Technology Licensing, LLC

Inventor(s)

Steven N. Bathiche of Bellevue WA (US)

Moshe R. Lutz of Bellevue WA (US)

GENERATING PREDICTED INK STROKE INFORMATION USING TEXT-BASED SEMANTICS - A simplified explanation of the abstract

This abstract first appeared for US patent application 17851309 titled 'GENERATING PREDICTED INK STROKE INFORMATION USING TEXT-BASED SEMANTICS

Simplified Explanation

The patent application describes systems and methods for generating predicted ink strokes based on text-based semantics. Here is a simplified explanation of the abstract:

  • Ink stroke data is received and input into a first model.
  • Text data corresponding to the ink stroke data is received from the first model.
  • The text data and a semantic context are input into a second model.
  • The second model determines a predicted ink stroke.
  • An indication of the predicted ink stroke is generated.

Potential applications of this technology:

  • Digital handwriting recognition and prediction systems.
  • Virtual reality or augmented reality applications that require real-time ink stroke generation.
  • Collaborative writing tools that can predict and generate ink strokes based on text input.

Problems solved by this technology:

  • Improves the accuracy and efficiency of handwriting recognition systems.
  • Enables real-time generation of ink strokes based on text input, reducing the need for manual drawing or writing.
  • Enhances the user experience in digital writing and drawing applications.

Benefits of this technology:

  • Provides more accurate and intuitive digital handwriting recognition.
  • Saves time and effort by automatically generating ink strokes based on text input.
  • Enables seamless integration of text and ink-based input in various applications.


Original Abstract Submitted

In some examples, systems and methods for generating predicted ink strokes, using text-based semantics, are provided. Ink stroke data may be received, the ink stroke data may be input into a first model, and text data may be received from the first model. The text data may correspond to the ink stroke data. The text data and a semantic context may be input into a second model. A predicted ink stroke may be determined, from the second model. Further, an indication of the predicted ink stroke may be generated.

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