Microsoft technology licensing, llc (20240184979). EXAMPLE-BASED AUTOGENERATED DATA PROCESSING RULES simplified abstract

EXAMPLE-BASED AUTOGENERATED DATA PROCESSING RULES

Organization Name

microsoft technology licensing, llc

Inventor(s)

Mukul Singh of Delhi (IN)

[[:Category:José Pablo Cambronero S�nchez of New Haven CT (US)|José Pablo Cambronero S�nchez of New Haven CT (US)]][[Category:José Pablo Cambronero S�nchez of New Haven CT (US)]]

Sumit Gulwani of Sammamish WA (US)

Vu Minh Le of Redmond WA (US)

Carina Suzana Negreanu of Cambridge (GB)

Mohammad Raza of Sammamish WA (US)

Daniel Galen Simmons of Carnation WA (US)

Gust Ben Anneloes Verbruggen of Keerbergen (BE)

EXAMPLE-BASED AUTOGENERATED DATA PROCESSING RULES - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240184979 titled 'EXAMPLE-BASED AUTOGENERATED DATA PROCESSING RULES

• • Simplified Explanation:**

Some embodiments of this technology automatically generate data processing rules based on examples of processed data, such as formatting, filtering, and validating rules. It also utilizes negative examples to enhance rule generation. The architecture includes various components like a predicate generator, a cell cluster creator, a rule enumerator, and a rule ranker to streamline the rule generation process.

• • Key Features and Innovation:**
• Automatic generation of data processing rules based on positive and negative examples of processed data.
• Components like predicate generator, cell cluster creator, rule enumerator, and rule ranker to facilitate rule generation.
• Enhancement of spreadsheet formatting rule functionality.
• Replacement of user-written formatting rules with autogenerated rules.

• • Potential Applications:**

This technology can be applied in various industries where data processing and formatting are essential, such as finance, healthcare, and marketing.

• • Problems Solved:**
• Streamlines the process of generating data processing rules.
• Enhances spreadsheet formatting rule functionality.
• Reduces the complexity of formatting rules by replacing user-written rules with autogenerated ones.

• • Benefits:**
• Improved efficiency in data processing.
• Enhanced user experience with simplified rule generation.
• Increased accuracy in data formatting and validation.

• • Commercial Applications:**
• "Automated Data Processing Rule Generation Technology for Enhanced Spreadsheet Functionality"
• This technology can be utilized in software applications for data processing, spreadsheet management tools, and data analytics platforms.

• • Prior Art:**

There is prior art related to machine learning algorithms for rule generation based on examples of processed data, but the specific approach of utilizing both positive and negative examples for rule generation may be novel.

• • Frequently Updated Research:**

There may be ongoing research in the field of automated rule generation for data processing and formatting, focusing on improving the accuracy and efficiency of rule generation algorithms.

• • Questions about Automated Data Processing Rule Generation Technology:**

1. How does this technology compare to traditional manual rule generation methods? 2. What are the potential limitations of using negative examples in rule generation algorithms?

Original Abstract Submitted

some embodiments automatically generate data processing rules based on positive examples of processed data, e.g., formatting rules based on formatted data, filtering rules based on filtered data, or validating rules based on valid data. some embodiments also use negative examples, e.g., unformatted data. a machine learning rule generation architecture includes a predicate generator, a cell cluster creator, a rule enumerator, and in some versions a rule ranker. formatting rules written by a user are replaced by simpler autogenerated rules. spreadsheet formatting rule functionality is enhanced, and surfaced in a user interface.