Dental Impression Model Merging via Point Cloud Alignment

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Solution Overview

Problem

Traditional dental impression methods are time-consuming and prone to errors, especially when made at home, leading to incomplete or poor-quality impressions that may require multiple retakes, increasing patient inconvenience and dental office workload.

Innovation Solution

A computing device and method for merging three-dimensional models of dental impressions by generating and aligning point clouds from multiple scans, allowing for the creation of a complete and accurate merged model, even from incomplete impressions, using geometric face alignment and selection strategies.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Manufacturing precision

If multiple dental impressions are taken to ensure completeness, then the quality and completeness of the dental model improves, but the time required and patient inconvenience increases

Engineering Contradiction:

Improvecompleteness of dental modelVSAvoidtime for multiple impressions

Core Design Contradiction:

Manufacturing precisionVSLoss of time

Solution Approach 1:

The patent merges multiple incomplete dental impressions into a single complete digital model by aligning and combining the point cloud data from each impression, eliminating the need for multiple physical retakes

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The patent creates digital copies of dental impressions through scanning, allowing multiple impressions to be captured and merged virtually without requiring multiple physical impression retakes

Inventive Principle:

Principle #26Copying

2Ease of operation

If at-home dental impression kits are used to reduce office workload, then patient convenience improves, but the quality and accuracy of impressions deteriorates

Engineering Contradiction:

Improvepatient convenienceVSAvoidquality of dental impression

Core Design Contradiction:

Ease of operationVSManufacturing precision

Solution Approach 1:

The patent introduces automated alignment algorithms and point cloud merging technology as intermediaries to process at-home impressions, compensating for potential quality issues and achieving complete accurate models without requiring professional operator skill

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent replaces manual mechanical alignment and model merging processes with automated computational algorithms, enabling accurate merging of multiple impressions even when taken by non-professionals at home

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Device complexity

If traditional manual alignment methods are used for merging models, then the process is simple to understand, but the alignment precision and merging accuracy deteriorates

Engineering Contradiction:

Improvesimplicity of alignment processVSAvoidalignment accuracy

Core Design Contradiction:

Device complexityVSMeasurement precision

Solution Approach 1:

The patent replaces manual mechanical alignment with automated point cloud alignment algorithms that compute optimal transformations, achieving high precision without requiring operator skill or understanding of complex alignment procedures

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS10861250B2Technologies for merging three-dimensional models of dental impressions

Publication Date: 2020.12.08 OTIP HLDG LLC

AI summary

A computing device for dental impression scan merging includes a processor configured to generate a first model and a second model including a first and second plurality of geometric faces indicative of a first and second dental arch of a user. The processor generates a first point cloud of the first model and a second point cloud of the second model. The processor aligns the first point cloud and the second point cloud. The processor merges the first and second model to generate a merged model where merging the first and second model is based on the alignment of the first point cloud and the second point cloud.