Machine Learning Model for Patent Docketing Data Anomaly Detection

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Solution Overview

Problem

Manual entry of docketing information in patent management systems is prone to errors, leading to inaccuracies and reduced reliability in tracking important due dates and document-related activities.

Innovation Solution

A machine learning model is trained to classify documents and generate expected activities, alerting operators to anomalies and automatically populating activity lists, thereby ensuring accurate data entry and enhancing system reliability.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If manual entry of docketing information is used, then operators can input data flexibly, but errors and inaccuracies occur leading to reduced reliability

Engineering Contradiction:

Improvemanual data entry flexibilityVSAvoiddocketing information accuracy

Core Design Contradiction:

Ease of operationVSReliability

Solution Approach 1:

The system automatically generates activity lists by extracting information from uploaded documents and processing them through machine learning models. The docketing system serves itself by automatically populating activity fields without requiring manual operator intervention, thereby eliminating human entry errors while maintaining operational flexibility

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the manual mechanical process of data entry with an automated information processing system using machine learning models. The system automatically classifies documents, extracts relevant information, and generates activity lists, substituting human operators with intelligent algorithms that eliminate errors while maintaining ease of use through automated workflows

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Adaptability or versatility

If manual entry of docketing information is used, then operators can input data freely, but the system reliability is reduced due to errors

Engineering Contradiction:

Improvemanual data input freedomVSAvoiddocketing data accuracy

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The machine learning model provides feedback by comparing generated activity lists against expected activities based on document classification. This feedback mechanism identifies anomalies and errors automatically, allowing the system to self-correct or flag issues for review, thereby maintaining both adaptability and reliability

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS10909188B2Machine learning techniques for detecting docketing data anomalies

Publication Date: 2021.02.02 BLACK HILLS IP HLDG LLC

AI summary

Methods and systems for automatically detecting docketing data anomalies are provided. The method includes storing in a docketing system docketing information for a plurality of matters, each of the plurality of matters including a plurality of activities and a plurality of documents. Retrieving a first document from the plurality of documents associated with a first matter of the plurality of matters. Determining a document type of the first document. Extracting one or more features from the first document and the plurality of activities associated with the first matter. Training a machine learning model, based on the extracted features and the document type of the first document, to determine one or more expected docketing activities for a new document determined to match the document type.