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Related Experiment Video

Updated: Dec 15, 2025

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

Measuring the drafting alignment of patent documents using text mining.

Davit Khachatryan¹, Brigitte Muehlmann²

¹Division of Mathematics and Science, Babson College, Babson Park, Massachusetts, United States of America.

|July 11, 2020

Summary

Inventors can now quantify patent drafting alignment using Latent Dirichlet Allocation (LDA). This novel measure assesses claim-specification consistency, crucial for patentability and validity.

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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

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Published on: August 30, 2013

Area of Science:

Intellectual Property Law
Computer Science
Data Science

Background:

Assessing the alignment between patent claims and specifications is critical for determining patentability and validity.
Poor alignment can lead to unpatentable inventions or invalidation of issued patents.
Quantifying this alignment has been a significant challenge for inventors, patent examiners, and legal professionals.

Purpose of the Study:

To introduce a novel quantitative measure for assessing the drafting alignment between patent claims and specifications.
To apply this measure to patent documents in the field of data processing and cybersecurity.
To explore the properties and implications of this alignment measure.

Main Methods:

Latent Dirichlet Allocation (LDA) was employed to identify latent topics within patent claims and specifications.
The Hellinger distance was used to quantify the proximity between the topical distributions of claims and specifications.
The developed measure was applied to a dataset of cybersecurity-related data processing patent documents.

Main Results:

A novel measure of drafting alignment based on topic modeling and Hellinger distance was successfully defined and applied.
Exploratory data analysis revealed that higher alignment generally correlates with increased prior patenting activity.
The tendency to include figures in patent documents was also found to be positively associated with drafting alignment.

Conclusions:

The proposed LDA-based measure offers a quantitative approach to assess claim-specification alignment in patent documents.
This measure can aid inventors, entrepreneurs, investors, and patent examiners in evaluating patent quality and potential risks.
The findings suggest that drafting practices, such as incorporating figures and prior patenting experience, may influence alignment.