Patents by Inventor Chundi Liu
Chundi Liu 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: 20230401252Abstract: An image retrieval system receives an image for which to identify relevant images from an image repository. Relevant images may be of the same environment or object and features and other characteristics. Images in the repository are represented in an image retrieval graph by a set of image nodes connected by edges to other related image nodes with edge weights representing the similarity of the nodes to each other. Based on the received image, the image traversal system identifies an image in the image retrieval graph and alternatively explores and traverses (also termed “exploits”) the image nodes with the edge weights. In the exploration step, image nodes in an exploration set are evaluated to identify connected nodes that are added to a traversal set of image nodes. In the traversal step, the relevant nodes in the traversal set are added to the exploration set and a query result set.Type: ApplicationFiled: June 29, 2023Publication date: December 14, 2023Inventors: Maksims Volkovs, Cheng Chang, Guangwei Yu, Chundi Liu
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Patent number: 11809486Abstract: A content retrieval system uses a graph neural network architecture to determine images relevant to an image designated in a query. The graph neural network learns a new descriptor space that can be used to map images in the repository to image descriptors and the query image to a query descriptor. The image descriptors characterize the images in the repository as vectors in the descriptor space, and the query descriptor characterizes the query image as a vector in the descriptor space. The content retrieval system obtains the query result by identifying a set of relevant images associated with image descriptors having above a similarity threshold with the query descriptor.Type: GrantFiled: August 31, 2022Date of Patent: November 7, 2023Assignee: The Toronto-Dominion BankInventors: Chundi Liu, Guangwei Yu, Maksims Volkovs
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Patent number: 11748400Abstract: An image retrieval system receives an image for which to identify relevant images from an image repository. Relevant images may be of the same environment or object and features and other characteristics. Images in the repository are represented in an image retrieval graph by a set of image nodes connected by edges to other related image nodes with edge weights representing the similarity of the nodes to each other. Based on the received image, the image traversal system identifies an image in the image retrieval graph and alternatively explores and traverses (also termed “exploits”) the image nodes with the edge weights. In the exploration step, image nodes in an exploration set are evaluated to identify connected nodes that are added to a traversal set of image nodes. In the traversal step, the relevant nodes in the traversal set are added to the exploration set and a query result set.Type: GrantFiled: June 23, 2022Date of Patent: September 5, 2023Assignee: The Toronto-Dominion BankInventors: Maksims Volkovs, Cheng Chang, Guangwei Yu, Chundi Liu
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Publication number: 20220414145Abstract: A content retrieval system uses a graph neural network architecture to determine images relevant to an image designated in a query. The graph neural network learns a new descriptor space that can be used to map images in the repository to image descriptors and the query image to a query descriptor. The image descriptors characterize the images in the repository as vectors in the descriptor space, and the query descriptor characterizes the query image as a vector in the descriptor space. The content retrieval system obtains the query result by identifying a set of relevant images associated with image descriptors having above a similarity threshold with the query descriptor.Type: ApplicationFiled: August 31, 2022Publication date: December 29, 2022Inventors: Chundi Liu, Guangwei Yu, Maksims Volkovs
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Patent number: 11475059Abstract: A content retrieval system uses a graph neural network architecture to determine images relevant to an image designated in a query. The graph neural network learns a new descriptor space that can be used to map images in the repository to image descriptors and the query image to a query descriptor. The image descriptors characterize the images in the repository as vectors in the descriptor space, and the query descriptor characterizes the query image as a vector in the descriptor space. The content retrieval system obtains the query result by identifying a set of relevant images associated with image descriptors having above a similarity threshold with the query descriptor.Type: GrantFiled: June 30, 2020Date of Patent: October 18, 2022Assignee: The Toronto-Dominion BankInventors: Chundi Liu, Guangwei Yu, Maksims Volkovs
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Publication number: 20220318298Abstract: An image retrieval system receives an image for which to identify relevant images from an image repository. Relevant images may be of the same environment or object and features and other characteristics. Images in the repository are represented in an image retrieval graph by a set of image nodes connected by edges to other related image nodes with edge weights representing the similarity of the nodes to each other. Based on the received image, the image traversal system identifies an image in the image retrieval graph and alternatively explores and traverses (also termed “exploits”) the image nodes with the edge weights. In the exploration step, image nodes in an exploration set are evaluated to identify connected nodes that are added to a traversal set of image nodes. In the traversal step, the relevant nodes in the traversal set are added to the exploration set and a query result set.Type: ApplicationFiled: June 23, 2022Publication date: October 6, 2022Inventors: Maksims Volkovs, Cheng Chang, Guangwei Yu, Chundi Liu
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PREDICTING OCCURRENCES OF TARGETED CLASSES OF EVENTS USING TRAINED ARTIFICIAL-INTELLIGENCE PROCESSES
Publication number: 20220277227Abstract: The disclosed embodiments include computer-implemented apparatuses and processes that dynamically predict future occurrences of targeted classes of events using adaptively trained machine-learning or artificial-intelligence processes. For example, an apparatus may generate an input dataset based on interaction data associated with a prior temporal interval, and may apply a trained, gradient-boosted, decision-tree process to the input dataset. Based on the application of the trained, gradient-boosted, decision-tree process to the input dataset, the apparatus may generate output data representative of an expected occurrence of a corresponding one of a plurality of targeted events during a future temporal interval, which may be separated from the prior temporal interval by a corresponding buffer interval.Type: ApplicationFiled: February 25, 2022Publication date: September 1, 2022Inventors: Guangwei YU, Chundi LIU, Cheng CHANG, Saba ZUBERI, Maksims VOLKOVS, Tomi Johan POUTANEN -
Patent number: 11397765Abstract: An image retrieval system receives an image for which to identify relevant images from an image repository. Relevant images may be of the same environment or object and features and other characteristics. Images in the repository are represented in an image retrieval graph by a set of image nodes connected by edges to other related image nodes with edge weights representing the similarity of the nodes to each other. Based on the received image, the image traversal system identifies an image in the image retrieval graph and alternatively explores and traverses (also termed “exploits”) the image nodes with the edge weights. In the exploration step, image nodes in an exploration set are evaluated to identify connected nodes that are added to a traversal set of image nodes. In the traversal step, the relevant nodes in the traversal set are added to the exploration set and a query result set.Type: GrantFiled: October 3, 2019Date of Patent: July 26, 2022Assignee: The Toronto-Dominion BankInventors: Maksims Volkovs, Cheng Chang, Guangwei Yu, Chundi Liu
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Publication number: 20210049202Abstract: A content retrieval system uses a graph neural network architecture to determine images relevant to an image designated in a query. The graph neural network learns a new descriptor space that can be used to map images in the repository to image descriptors and the query image to a query descriptor. The image descriptors characterize the images in the repository as vectors in the descriptor space, and the query descriptor characterizes the query image as a vector in the descriptor space. The content retrieval system obtains the query result by identifying a set of relevant images associated with image descriptors having above a similarity threshold with the query descriptor.Type: ApplicationFiled: June 30, 2020Publication date: February 18, 2021Inventors: Chundi Liu, Guangwei Yu, Maksims Volkovs
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Publication number: 20200159766Abstract: An image retrieval system receives an image for which to identify relevant images from an image repository. Relevant images may be of the same environment or object and features and other characteristics. Images in the repository are represented in an image retrieval graph by a set of image nodes connected by edges to other related image nodes with edge weights representing the similarity of the nodes to each other. Based on the received image, the image traversal system identifies an image in the image retrieval graph and alternatively explores and traverses (also termed “exploits”) the image nodes with the edge weights. In the exploration step, image nodes in an exploration set are evaluated to identify connected nodes that are added to a traversal set of image nodes. In the traversal step, the relevant nodes in the traversal set are added to the exploration set and a query result set.Type: ApplicationFiled: October 3, 2019Publication date: May 21, 2020Inventors: Maksims Volkovs, Cheng Chang, Guangwei Yu, Chundi Liu
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Patent number: D1008971Type: GrantFiled: May 22, 2023Date of Patent: December 26, 2023Inventor: Chundi Liu