MACHINE LEARNING STRUCTURED RESULT GENERATION

Organization Name

microsoft technology licensing, llc

Inventor(s)

Shawn Cantin Callegari of Bellevue WA (US)

Abby Harrison of Woodinville WA (US)

Umesh Madan of Bellevue WA (US)

LeRoy F. Miller of Tacoma WA (US)

Brian Krabach of Snohomish WA (US)

Devis Lucato of Kirkland WA (US)

Alexander Chao of Irvine CA (US)

Mark Karle of Seattle WA (US)

Gina Elizabeth Triolo of Redmond WA (US)

Tara Eve Walker of Atlanta GA (US)

Nicholas Becker of Boulder CO (US)

MACHINE LEARNING STRUCTURED RESULT GENERATION - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240201959 titled 'MACHINE LEARNING STRUCTURED RESULT GENERATION

Simplified Explanation: The patent application relates to using machine learning to generate structured results. By providing a data format description as input to the ML model, structured model output is generated, which can be further processed to create an instance of the result interface.

**Key Features and Innovation:**

   - Utilizes machine learning for structured result generation
   - Provides a data format description as input to the ML model
   - Generates structured model output corresponding to the result interface
   - Processes the model output to create an instance of the result interface

**Potential Applications:**

   - Data analysis and interpretation
   - Natural language processing
   - Image recognition and classification

**Problems Solved:**

   - Enhances the reliability of processing based on ML model output
   - Facilitates the creation of structured data formats
   - Improves the efficiency of result generation

**Benefits:**

   - Streamlines data processing tasks
   - Increases accuracy in result interpretation
   - Enhances the usability of ML models

**Commercial Applications:**

   - "Machine Learning Structured Result Generation in Programmatic Code" can be utilized in various industries such as healthcare, finance, and e-commerce for data analysis, pattern recognition, and predictive modeling.

**Prior Art:**

   - Prior art related to this technology may include research papers, patents, and academic studies on machine learning model output generation and data format descriptions.

**Frequently Updated Research:**

   - Stay updated on advancements in machine learning algorithms for structured result generation and data format processing.

Questions about Machine Learning Structured Result Generation in Programmatic Code:

1. *How does providing a data format description as input to the ML model improve the reliability of result generation?*

  - By providing a data format description, the ML model is induced to generate structured output that corresponds to the result interface, enhancing the reliability of the generated results.

2. *What are the potential commercial applications of using machine learning for structured result generation?*

  - The technology can be applied in various industries such as healthcare, finance, and e-commerce for tasks like data analysis, pattern recognition, and predictive modeling.

Original Abstract Submitted

aspects of the present application relate to machine learning (ml) structured result generation. in examples, an instruction of programmatic code that invokes an ml model indicates a result interface in which model output is to be stored. the result interface is processed to generate a data format description for the result interface, such that the input to the ml model further includes the data format description. as a result of providing the data format description as input to the ml model, the ml model is induced to generate structured model output that corresponds to the result interface. the resulting model output is processed to generate an instance of the result interface, for example having one or more corresponding properties from the structured model output. accordingly, the programmatic code is able to reliably perform subsequent processing based on the generated instance of the result interface.