US 12,632,614 B2Granted

Computer aided drawing drafting automation from markups using machine learning

AssigneeAutodesk, Inc.

InventorsKin Ming Kevin Cheung, Ravinder P. Krishnaswamy, Damian Paul Stephen Willcox

Life of this patent

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Abstract

A method and system provide for computer aided drawing (CAD) automation. A CAD drawing in a first format that is native to a CAD application is obtained. Markup for the CAD drawing that is in a second non-native format is acquired. A machine learning engine processes the markup using a machine learning model, to identify a markup type, identify markup properties, correlate, the markup to a CAD object needed to update the CAD drawing, and determine, CAD object properties and CAD application properties. The CAD object with the CAD object properties is provided in the CAD application using the CAD application properties. User input is accepted and the CAD drawing is updated based thereon. The machine learning model is updated based on the user input and utilized to process future markups.

Claims16

Dependent claims

A computer-implemented method for computer aided drawing (CAD) automation, comprising:

(a) obtaining the CAD drawing, wherein the CAD drawing is in a first format that is native to a CAD application;
(b) acquiring a first markup and a second markup on an image of the CAD drawing, wherein:
(i) the first markup and the second markup are both in a second format that is not native to the CAD application;
(ii) the first markup and the second markup each have a markup type;
(iii) the markup type is either a literal drafting object to be added to the CAD drawing or a drafting instruction to modify the CAD drawing;
(c) processing, in a machine learning engine, the first markup and the second markup, using a machine learning model, to:
(i) identify the markup type of the first markup, wherein the markup type comprises the drafting instruction to modify the CAD drawing;
(ii) identify the markup type of the second markup as the literal drafting object;
(iii) identify markup properties of the first markup and the second markup;
(iv) correlate, based on the markup types, the first markup to a first CAD object needed to update the CAD drawing, and the second markup to a second CAD object needed to update the CAD drawing;
(v) determine, based on the markup types and the markup properties, CAD object properties for the first CAD object and the second CAD object and CAD application properties for the CAD application;
and
(d) providing, in the CAD application using the CAD application properties, the first CAD object and the second CAD object with the CAD object properties, wherein the providing comprises:
(i) identifying one or more CAD application commands necessary to perform the drafting instruction;
(ii) identifying the first CAD object necessary to perform the drafting instruction;
and
(iii) identifying the second CAD object to be added to the CAD drawing;
(e) accepting user input in response to the providing;
(f) updating, in the CAD application, the CAD drawing based on the user input, wherein the updating the CAD drawing comprises:
(i) executing the identified one or more CAD application commands using the first identified CAD object;
and
(ii) creating the second CAD object in the CAD drawing;
and
(g) updating the machine learning model based on the user input, wherein the machine learning model is utilized to process future markups.

21 more elements

A computer-implemented system for computer aided drawing (CAD) automation, comprising:

(a) a computer having a memory;
(b) a processor executing on the computer;
(c) the memory storing a set of instructions, wherein the set of instructions, when executed by the processor cause the processor to perform operations comprising:
(i) obtaining the CAD drawing, wherein the CAD drawing is in a first format that is native to a CAD application;
(ii) acquiring first markup and a second markup on an image of the CAD drawing, wherein:
(1) the first markup and the second markup are both in a second format that is not native to the CAD application;
(2) the first markup and the second markup each have a markup type;
(3) the markup type is either a literal drafting object to be added to the CAD drawing or a drafting instruction to modify the CAD drawing;
(iii) processing, in a machine learning engine, the first markup and the second markup, using a machine learning model, to:
(1) identify the markup type of the first markup, wherein the markup type comprises the drafting instruction to modify the CAD drawing;
(2) identify the markup type of the second markup as the literal drafting object;
(3) identify markup properties of the first markup and the second markup;
(4) correlate, based on the markup types, the first markup to a first CAD object needed to update the CAD drawing, and the second markup to a second CAD object needed to update the CAD drawing;
(5) determine, based on the markup types and the markup properties, CAD object properties for the first CAD object and the second CAD object and CAD application properties for the CAD application;
and
(iv) providing, in the CAD application using the CAD application properties, the first CAD object and the second CAD object with the CAD object properties, wherein the providing comprises:
(1) identifying one or more CAD application commands necessary to perform the drafting instruction;
(2) identifying the first CAD object necessary to perform the drafting instruction;
and
(3) identifying the second CAD object to be added to the CAD drawing;
(v) accepting user input in response to the providing;
(vi) updating, in the CAD application, the CAD drawing based on the user input, wherein the updating the CAD drawing comprises:
(1) executing the identified one or more CAD application commands using the first identified CAD object;
and
(2) creating the second CAD object in the CAD drawing;
and
(vii) updating the machine learning model based on the user input, wherein the machine learning model is utilized to process future markups.

24 more elements

Dependencies are inferred from the claim text.

Facts

Application: 17/514,740Filed 29 Oct 2021 · us provisional application
Priority: 17 Nov 2020
Published: US 2022/0156419 A119 May 2022
Granted: 19 May 2026
Term ends: Not estimatedThe source records don’t support an estimate.

Classification8 codes

G06F 30/12
Geometric CAD: characterised by design entry means specially adapted for CAD, e.g. graphical user interfaces [GUI] specially adapted for CAD
G06F 30/27
Design optimisation, verification or simulation: using machine learning, e.g. artificial intelligence, neural networks, support vector machines…
G06V 30/153
Segmentation of character regions: using recognition of characters or words
G06F 3/0488
Using specific features provided by the input device, e.g. functions controlled by the rotation of a mouse with dual sensing arrangements, or of…:…