Patents by Inventor Valerie R. COFFMAN
Valerie R. COFFMAN has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
-
Publication number: 20230350380Abstract: The subject technology is related to methods and apparatus for discretization and manufacturability analysis of computer assisted design models. In one embodiment, the subject technology implements a computer-based method for the reception of an electronic file with a digital model representative of a physical object. The computer-based method determines geometric and physical attributes from a discretized version of the digital model, a cloud point version of the digital model, and symbolic functions generated through evolutionary algorithms. A set of predictive machine learning models is utilized to infer predictions related to the manufacture process of the physical object.Type: ApplicationFiled: July 10, 2023Publication date: November 2, 2023Applicant: Xometry, Inc.Inventors: Valerie R. Coffman, Yuan Chen, Luke S. Hendrix, William J. Sankey, Joshua Ryan Smith, Daniel Wheeler
-
Publication number: 20230288907Abstract: The subject technology is related to methods and apparatus for training a set of regression machine learning models with a training set to produce a set of predictive values for a pending manufacturing request, the training set including data extracted from a set of manufacturing transactions submitted by a set of entities of a supply chain. A multi-objective optimization model is implemented to (1) receive an input including the set of predictive values and a set of features of a physical object, and (2) generate an output with a set of attributes associated with a manufacture of the physical object in response to receiving the input, the output complying with a multi-objective condition satisfied in the multi-objective optimization model.Type: ApplicationFiled: May 17, 2023Publication date: September 14, 2023Applicant: Xometry, Inc.Inventors: Valerie R. COFFMAN, Mark WICKS, Daniel WHEELER
-
Patent number: 11698623Abstract: The subject technology is related to methods and apparatus for discretization and manufacturability analysis of computer assisted design models. In one embodiment, the subject technology implements a computer-based method for the reception of an electronic file with a digital model representative of a physical object. The computer-based method determines geometric and physical attributes from a discretized version of the digital model, a cloud point version of the digital model, and symbolic functions generated through evolutionary algorithms. A set of predictive machine learning models is utilized to infer predictions related to the manufacture process of the physical object.Type: GrantFiled: May 23, 2022Date of Patent: July 11, 2023Assignee: Xometry, Inc.Inventors: Valerie R. Coffman, Yuan Chen, Luke S. Hendrix, William J. Sankey, Joshua Ryan Smith, Daniel Wheeler
-
Patent number: 11693388Abstract: The subject technology is related to methods and apparatus for training a set of regression machine learning models with a training set to produce a set of predictive values for a pending manufacturing request, the training set including data extracted from a set of manufacturing transactions submitted by a set of entities of a supply chain. A multi-objective optimization model is implemented to (1) receive an input including the set of predictive values and a set of features of a physical object, and (2) generate an output with a set of attributes associated with a manufacture of the physical object in response to receiving the input, the output complying with a multi-objective condition satisfied in the multi-objective optimization model.Type: GrantFiled: August 10, 2021Date of Patent: July 4, 2023Assignee: Xometry, Inc.Inventors: Valerie R. Coffman, Mark Wicks, Daniel Wheeler
-
Publication number: 20220365509Abstract: The subject technology is related to methods and apparatus for discretization and manufacturability analysis of computer assisted design models. In one embodiment, the subject technology implements a computer-based method for the reception of an electronic file with a digital model representative of a physical object. The computer-based method determines geometric and physical attributes from a discretized version of the digital model, a cloud point version of the digital model, and symbolic functions generated through evolutionary algorithms. A set of predictive machine learning models is utilized to infer predictions related to the manufacture process of the physical object.Type: ApplicationFiled: May 23, 2022Publication date: November 17, 2022Applicant: Xometry, Inc.Inventors: Valerie R. Coffman, Yuan Chen, Luke S. Hendrix, William J. Sankey, Joshua Ryan Smith, Daniel Wheeler
-
Patent number: 11347201Abstract: The subject technology is related to methods and apparatus for discretization and manufacturability analysis of computer assisted design models. In one embodiment, the subject technology implements a computer-based method for the reception of an electronic file with a digital model representative of a physical object. The computer-based method determines geometric and physical attributes from a discretized version of the digital model, a cloud point version of the digital model, and symbolic functions generated through evolutionary algorithms. A set of predictive machine learning models is utilized to infer predictions related to the manufacture process of the physical object.Type: GrantFiled: July 14, 2020Date of Patent: May 31, 2022Assignee: XOMETRY, INC.Inventors: Valerie R. Coffman, Yuan Chen, Luke S. Hendrix, William J. Sankey, Joshua Ryan Smith, Daniel Wheeler
-
Publication number: 20210365003Abstract: The subject technology is related to methods and apparatus for training a set of regression machine learning models with a training set to produce a set of predictive values for a pending manufacturing request, the training set including data extracted from a set of manufacturing transactions submitted by a set of entities of a supply chain. A multi-objective optimization model is implemented to (1) receive an input including the set of predictive values and a set of features of a physical object, and (2) generate an output with a set of attributes associated with a manufacture of the physical object in response to receiving the input, the output complying with a multi-objective condition satisfied in the multi-objective optimization model.Type: ApplicationFiled: August 10, 2021Publication date: November 25, 2021Applicant: Xometry, Inc.Inventors: Valerie R. COFFMAN, Mark WICKS, Daniel WHEELER
-
Patent number: 11086292Abstract: The subject technology is related to methods and apparatus for training a set of regression machine learning models with a training set to produce a set of predictive values for a pending manufacturing request, the training set including data extracted from a set of manufacturing transactions submitted by a set of entities of a supply chain. A multi-objective optimization model is implemented to (1) receive an input including the set of predictive values and a set of features of a physical object, and (2) generate an output with a set of attributes associated with a manufacture of the physical object in response to receiving the input, the output complying with a multi-objective condition satisfied in the multi-objective optimization model.Type: GrantFiled: June 27, 2019Date of Patent: August 10, 2021Assignee: Xometry, Inc.Inventors: Valerie R. Coffman, Mark Wicks, Daniel Wheeler
-
Publication number: 20200348646Abstract: The subject technology is related to methods and apparatus for discretization and manufacturability analysis of computer assisted design models. In one embodiment, the subject technology implements a computer-based method for the reception of an electronic file with a digital model representative of a physical object. The computer-based method determines geometric and physical attributes from a discretized version of the digital model, a cloud point version of the digital model, and symbolic functions generated through evolutionary algorithms. A set of predictive machine learning models is utilized to infer predictions related to the manufacture process of the physical object.Type: ApplicationFiled: July 14, 2020Publication date: November 5, 2020Inventors: Valerie R. Coffman, Yuan Chen, Luke S. Hendrix, William J. Sankey, Joshua Ryan Smith, Daniel Wheeler
-
Patent number: 10712727Abstract: The subject technology is related to methods and apparatus for discretization and manufacturability analysis of computer assisted design models. In one embodiment, the subject technology implements a computer-based method for the reception of an electronic file with a digital model representative of a physical object. The computer-based method determines geometric and physical attributes from a discretized version of the digital model, a cloud point version of the digital model, and symbolic functions generated through evolutionary algorithms. A set of predictive machine learning models is utilized to infer predictions related to the manufacture process of the physical object.Type: GrantFiled: February 10, 2020Date of Patent: July 14, 2020Assignee: XOMETRY, INC.Inventors: Valerie R. Coffman, Yuan Chen, Luke S. Hendrix, William J. Sankey, Joshua Ryan Smith, Daniel Wheeler
-
Publication number: 20200183355Abstract: The subject technology is related to methods and apparatus for discretization and manufacturability analysis of computer assisted design models. In one embodiment, the subject technology implements a computer-based method for the reception of an electronic file with a digital model representative of a physical object. The computer-based method determines geometric and physical attributes from a discretized version of the digital model, a cloud point version of the digital model, and symbolic functions generated through evolutionary algorithms. A set of predictive machine learning models is utilized to infer predictions related to the manufacture process of the physical object.Type: ApplicationFiled: February 10, 2020Publication date: June 11, 2020Inventors: Valerie R. Coffman, Yuan Chen, Luke S. Hendrix, William J. Sankey, Joshua Ryan Smith, Daniel Wheeler
-
Patent number: 10558195Abstract: The subject technology is related to methods and apparatus for discretization and manufacturability analysis of computer assisted design models. In one embodiment, the subject technology implements a computer-based method for the reception of an electronic file with a digital model representative of a physical object. The computer-based method determines geometric and physical attributes from a discretized version of the digital model, a cloud point version of the digital model, and symbolic functions generated through evolutionary algorithms. A set of predictive machine learning models is utilized to infer predictions related to the manufacture process of the physical object.Type: GrantFiled: April 26, 2019Date of Patent: February 11, 2020Assignee: XOMETRY, INC.Inventors: Valerie R. Coffman, Yuan Chen, Luke S. Hendrix, William J. Sankey, Joshua Ryan Smith, Daniel Wheeler
-
Publication number: 20190339669Abstract: The subject technology is related to methods and apparatus for training a set of regression machine learning models with a training set to produce a set of predictive values for a pending manufacturing request, the training set including data extracted from a set of manufacturing transactions submitted by a set of entities of a supply chain. A multi-objective optimization model is implemented to (1) receive an input including the set of predictive values and a set of features of a physical object, and (2) generate an output with a set of attributes associated with a manufacture of the physical object in response to receiving the input, the output complying with a multi-objective condition satisfied in the multi-objective optimization model.Type: ApplicationFiled: June 27, 2019Publication date: November 7, 2019Applicant: Xometry, Inc.Inventors: Valerie R. COFFMAN, Mark WICKS, Daniel WHEELER
-
Publication number: 20190271966Abstract: The subject technology is related to methods and apparatus for discretization and manufacturability analysis of computer assisted design models. In one embodiment, the subject technology implements a computer-based method for the reception of an electronic file with a digital model representative of a physical object. The computer-based method determines geometric and physical attributes from a discretized version of the digital model, a cloud point version of the digital model, and symbolic functions generated through evolutionary algorithms. A set of predictive machine learning models is utilized to infer predictions related to the manufacture process of the physical object.Type: ApplicationFiled: April 26, 2019Publication date: September 5, 2019Inventors: Valerie R. Coffman, Yuan Chen, Luke S. Hendrix, William J. Sankey, Joshua Ryan Smith, Daniel Wheeler
-
Patent number: 10338565Abstract: The subject technology is related to methods and apparatus for training a set of regression machine learning models with a training set to produce a set of predictive values for a pending manufacturing request, the training set including data extracted from a set of manufacturing transactions submitted by a set of entities of a supply chain. A multi-objective optimization model is implemented to (1) receive an input including the set of predictive values and a set of features of a physical object, and (2) generate an output with a set of attributes associated with a manufacture of the physical object in response to receiving the input, the output complying with a multi-objective condition satisfied in the multi-objective optimization model.Type: GrantFiled: August 27, 2018Date of Patent: July 2, 2019Assignee: Xometry, Inc.Inventors: Valerie R. Coffman, Mark Wicks, Daniel Wheeler
-
Patent number: 10281902Abstract: The subject technology is related to methods and apparatus for discretization and manufacturability analysis of computer assisted design models. In one embodiment, the subject technology implements a computer-based method for the reception of an electronic file with a digital model representative of a physical object. The computer-based method determines geometric and physical attributes from a discretized version of the digital model, a cloud point version of the digital model, and symbolic functions generated through evolutionary algorithms. A set of predictive machine learning models is utilized to infer predictions related to the manufacture process of the physical object.Type: GrantFiled: November 1, 2016Date of Patent: May 7, 2019Assignee: Xometry, Inc.Inventors: Valerie R. Coffman, Yuan Chen, Luke S. Hendrix, William J. Sankey, Joshua Ryan Smith, Daniel Wheeler
-
Patent number: 10274933Abstract: The subject technology is related to methods and apparatus for discretization and manufacturability analysis of computer assisted design models. In one embodiment, the subject technology implements a computer-based method for the reception of an electronic file with a digital model representative of a physical object. The computer-based method determines geometric and physical attributes from a discretized version of the digital model, a cloud point version of the digital model, and symbolic functions generated through evolutionary algorithms. A set of predictive machine learning models is utilized to infer predictions related to the manufacture process of the physical object.Type: GrantFiled: July 26, 2018Date of Patent: April 30, 2019Assignee: Xometry, Inc.Inventors: Valerie R. Coffman, Yuan Chen, Luke S. Hendrix, William J. Sankey, Joshua Ryan Smith, Daniel Wheeler
-
Publication number: 20180341246Abstract: The subject technology is related to methods and apparatus for discretization and manufacturability analysis of computer assisted design models. In one embodiment, the subject technology implements a computer-based method for the reception of an electronic file with a digital model representative of a physical object. The computer-based method determines geometric and physical attributes from a discretized version of the digital model, a cloud point version of the digital model, and symbolic functions generated through evolutionary algorithms. A set of predictive machine learning models is utilized to infer predictions related to the manufacture process of the physical object.Type: ApplicationFiled: July 26, 2018Publication date: November 29, 2018Applicant: Xometry, Inc.Inventors: Valerie R. Coffman, Yuan Chen, Luke S. Hendrix, William J. Sankey, Joshua Ryan Smith, Daniel Wheeler
-
Patent number: 10061300Abstract: The subject technology is related to methods and apparatus for discretization, manufacturability analysis, and optimization of manufacturing process based on computer assisted design models and machine learning. An apparatus determines from the digital model features of a physical object. Thereafter, the apparatus produces predictive values for manufacturing processes based on regression machine learning models. The apparatus generates a non-deterministic response including a non-empty set of attributes of manufacture processes of the physical object based on a multi-objective optimization model. The non-deterministic response complies or satisfies a selected multi-objective condition included in the multi-objective optimization model.Type: GrantFiled: September 29, 2017Date of Patent: August 28, 2018Assignee: Xometry, Inc.Inventors: Valerie R. Coffman, Mark Wicks, Daniel Wheeler
-
Publication number: 20180120813Abstract: The subject technology is related to methods and apparatus for discretization and manufacturability analysis of computer assisted design models. In one embodiment, the subject technology implements a computer-based method for the reception of an electronic file with a digital model representative of a physical object. The computer-based method determines geometric and physical attributes from a discretized version of the digital model, a cloud point version of the digital model, and symbolic functions generated through evolutionary algorithms. A set of predictive machine learning models is utilized to infer predictions related to the manufacture process of the physical object.Type: ApplicationFiled: November 1, 2016Publication date: May 3, 2018Applicant: Xometry, Inc.Inventors: Valerie R. COFFMAN, Yuan CHEN, Luke S. HENDRIX, William J. SANKEY, Joshua Ryan SMITH, Daniel WHEELER