Patents by Inventor Hayko Jochen Wilhelm Riemenschneider
Hayko Jochen Wilhelm Riemenschneider 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).
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Publication number: 20230377279Abstract: Techniques for automatically placing and manipulating virtual objects of augmented reality (AR) and mixed reality (MR) simulations are described. One technique includes obtaining an indication of at least one virtual object available for placing within a real-world environment during an AR simulation or a MR simulation. A first representation of the real-world environment is generated, based on a scan of the real-world environment. At least one second representation of the real-world environment is generated from the first representation. A match is determined between the at least one virtual object and at least one available space within the real-world environment, based at least in part on evaluating the at least one virtual object and the at least one second representation with a machine learning model(s). The at least one virtual object is rendered on a computing device, based on the match.Type: ApplicationFiled: May 19, 2022Publication date: November 23, 2023Inventors: Erika VARIS DOGGETT, Hayko Jochen Wilhelm RIEMENSCHNEIDER
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Publication number: 20230274138Abstract: A system includes a computing platform having a hardware processor and a memory storing a software code and a neural network (NN) having multiple layers including a last activation layer and a loss layer. The hardware processor executes the software code to identify different combinations of layers for testing the NN, each combination including candidate function(s) for the last activation layer and candidate function(s) for the loss layer. For each different combination, the software code configures the NN based on the combination, inputs, into the configured NN, a training dataset including multiple data objects, receives, from the configured NN, a classification of the data objects, and generates a performance assessment for the combination based on the classification. The software code determines a preferred combination of layers for the NN including selected candidate functions for the last activation layer and the loss layer, based on a comparison of the performance assessments.Type: ApplicationFiled: May 4, 2023Publication date: August 31, 2023Inventors: Hayko Jochen Wilhelm Riemenschneider, Leonhard Markus Helminger, Christopher Richard Schroers, Abdelaziz Djelouah
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Patent number: 11669723Abstract: A system includes a computing platform having a hardware processor and a memory storing a software code and a neural network (NN) having multiple layers including a last activation layer and a loss layer. The hardware processor executes the software code to identify different combinations of layers for testing the NN, each combination including candidate function(s) for the last activation layer and candidate function(s) for the loss layer. For each different combination, the software code configures the NN based on the combination, inputs, into the configured NN, a training dataset including multiple data objects, receives, from the configured NN, a classification of the data objects, and generates a performance assessment for the combination based on the classification. The software code determines a preferred combination of layers for the NN including selected candidate functions for the last activation layer and the loss layer, based on a comparison of the performance assessments.Type: GrantFiled: September 16, 2022Date of Patent: June 6, 2023Assignees: Disney Enterprises, Inc., ETH Zürich (EIDGENÖSSISCHE TECHNISCHE HOCHSCHULE ZÜRICH)Inventors: Hayko Jochen Wilhelm Riemenschneider, Leonhard Markus Helminger, Christopher Richard Schroers, Abdelaziz Djelouah
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Patent number: 11599804Abstract: A system includes a computing platform having a hardware processor, and a system memory storing a software code and a content labeling predictive model. The hardware processor is configured to execute the software code to scan a database to identify content assets stored in the database, parse metadata stored in the database to identify labels associated with the content assets, and generate a graph by creating multiple first links linking each of the content assets to its corresponding label or labels. The hardware processor is configured to further execute the software code to train, using the graph, the content labeling predictive model, to identify, using the trained content labeling predictive model, multiple second links among the content assets and the labels, and to annotate the content assets based on the second links.Type: GrantFiled: April 17, 2020Date of Patent: March 7, 2023Assignees: Disney Enterprises, Inc., ETH ZurichInventors: Hayko Jochen Wilhelm Riemenschneider, Leonhard Markus Helminger, Abdelaziz Djelouah, Christopher Richard Schroers
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Publication number: 20230009121Abstract: A system includes a computing platform having a hardware processor and a memory storing a software code and a neural network (NN) having multiple layers including a last activation layer and a loss layer. The hardware processor executes the software code to identify different combinations of layers for testing the NN, each combination including candidate function(s) for the last activation layer and candidate function(s) for the loss layer. For each different combination, the software code configures the NN based on the combination, inputs, into the configured NN, a training dataset including multiple data objects, receives, from the configured NN, a classification of the data objects, and generates a performance assessment for the combination based on the classification. The software code determines a preferred combination of layers for the NN including selected candidate functions for the last activation layer and the loss layer, based on a comparison of the performance assessments.Type: ApplicationFiled: September 16, 2022Publication date: January 12, 2023Inventors: Hayko Jochen Wilhelm Riemenschneider, Leonhard Markus Helminger, Christopher Richard Schroers, Abdelaziz Djelouah
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Patent number: 11475280Abstract: A system includes a computing platform having a hardware processor and a memory storing a software code and a neural network (NN) having multiple layers including a last activation layer and a loss layer. The hardware processor executes the software code to identify different combinations of layers for testing the NN, each combination including candidate function(s) for the last activation layer and candidate function(s) for the loss layer. For each different combination, the software code configures the NN based on the combination, inputs, into the configured NN, a training dataset including multiple data objects, receives, from the configured NN, a classification of the data objects, and generates a performance assessment for the combination based on the classification. The software code determines a preferred combination of layers for the NN including selected candidate functions for the last activation layer and the loss layer, based on a comparison of the performance assessments.Type: GrantFiled: March 3, 2020Date of Patent: October 18, 2022Assignees: Disney Enterprises, Inc., ETH ZurichInventors: Hayko Jochen Wilhelm Riemenschneider, Leonhard Markus Helminger, Christopher Richard Schroers, Abdelaziz Djelouah
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Publication number: 20210326720Abstract: A system includes a computing platform having a hardware processor, and a system memory storing a software code and a content labeling predictive model. The hardware processor is configured to execute the software code to scan a database to identify content assets stored in the database, parse metadata stored in the database to identify labels associated with the content assets, and generate a graph by creating multiple first links linking each of the content assets to its corresponding label or labels. The hardware processor is configured to further execute the software code to train, using the graph, the content labeling predictive model, to identify, using the trained content labeling predictive model, multiple second links among the content assets and the labels, and to annotate the content assets based on the second links.Type: ApplicationFiled: April 17, 2020Publication date: October 21, 2021Inventors: Hayko Jochen Wilhelm Riemenschneider, Leonhard Markus Helminger, Abdelaziz Djelouah, Christopher Richard Schroers
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Publication number: 20210150316Abstract: A system includes a computing platform having a hardware processor and a memory storing a software code and a neural network (NN) having multiple layers including a last activation layer and a loss layer. The hardware processor executes the software code to identify different combinations of layers for testing the NN, each combination including candidate function(s) for the last activation layer and candidate function(s) for the loss layer. For each different combination, the software code configures the NN based on the combination, inputs, into the configured NN, a training dataset including multiple data objects, receives, from the configured NN, a classification of the data objects, and generates a performance assessment for the combination based on the classification. The software code determines a preferred combination of layers for the NN including selected candidate functions for the last activation layer and the loss layer, based on a comparison of the performance assessments.Type: ApplicationFiled: March 3, 2020Publication date: May 20, 2021Inventors: Hayko Jochen Wilhelm Riemenschneider, Leonhard Markus Helminger, Christopher Richard Schroers, Abdelaziz Djelouah