Patents by Inventor Ashutosh Jadhav
Ashutosh Jadhav 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: 20240111777Abstract: Mechanisms are provided to implement a visual analytics pipeline. The mechanisms generate, from an input database of records, a chronology-aware graph data structure of a plurality of records based features specified in an ontology data structure. The chronology-aware graph data structure has vertices representing one or more of events or records based features corresponding to events, and edges representing chronological relationships between events. The mechanisms execute a chronology-aware graph query on the chronology-aware graph data structure to generate a filtered set of vertices and corresponding features corresponding to criteria of the chronology-aware graph query.Type: ApplicationFiled: December 14, 2023Publication date: April 4, 2024Inventors: Andrea Giovannini, Joy Tzung-yu Wu, Tanveer Syeda-Mahmood, Ashutosh Jadhav
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Patent number: 11932939Abstract: Apparatus for processing a substrate are provided herein. In some embodiments, a lid for a substrate processing chamber includes: a lid plate comprising an upper surface and a contoured bottom surface, the upper surface having a central opening and the contoured bottom surface having a first portion that extends downwardly and outwardly from the central opening to a peripheral portion of the lid plate and a second portion that extends radially outward along the peripheral portion of the lid plate; an upper flange extending radially outward from the lid plate; and one or more channels formed through the lid plate from the upper surface of the lid plate to the second portion of the contoured bottom surface.Type: GrantFiled: April 28, 2021Date of Patent: March 19, 2024Assignee: APPLIED MATERIALS, INC.Inventors: Muhammad M. Rasheed, Srinivas Gandikota, Mario Dan Sanchez, Guoqiang Jian, Yixiong Yang, Deepak Jadhav, Ashutosh Agarwal
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Patent number: 11928121Abstract: Mechanisms are provided to implement a visual analytics pipeline. The mechanisms generate, from an input database of records, a chronology-aware graph data structure of a plurality of records based features specified in an ontology data structure. The chronology-aware graph data structure has vertices representing one or more of events or records based features corresponding to events, and edges representing chronological relationships between events. The mechanisms execute a chronology-aware graph query on the chronology-aware graph data structure to generate a filtered set of vertices and corresponding features corresponding to criteria of the chronology-aware graph query.Type: GrantFiled: September 13, 2021Date of Patent: March 12, 2024Assignee: International Business Machines CorporationInventors: Andrea Giovannini, Joy Tzung-Yu Wu, Tanveer Syeda-Mahmood, Ashutosh Jadhav
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Patent number: 11928186Abstract: Mechanisms are provided to improve an output of a trained machine learning (ML) computer model based on label co-occurrence statistics. For a corpus, label vector representations of the knowledge data structures are generated. Co-occurrence scores for each pairing of labels, across the label vector representations, are generated. A vector output of the ML computer model is received and a knowledge driven reasoning (KDR) computer model is configured with threshold(s) and delta value(s) specifying condition(s) of a co-occurrence of a first label in the output with a second label in the plurality of labels which, if present, causes the delta value(s) to be applied to modify a probability value associated with the second label in the output of the ML computer model. The KDR computer model is executed on the output of the ML computer model to modify probability value(s) in the output.Type: GrantFiled: November 1, 2021Date of Patent: March 12, 2024Assignee: International Business Machines CorporationInventors: Ashutosh Jadhav, Tanveer Syeda-Mahmood, Mehdi Moradi
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Publication number: 20230252631Abstract: A neural network apparatus receives, as input from a user device, digital imaging information and the clinical information for an aneurysm patient and generates, using a neural network trained for aneurysm outcome prediction, the digital imaging information, and the clinical information, an outcome prediction for at least one intrasaccular implant device for implant in an aneurysm sac identified in the digital imaging information and having a highest predicted likelihood of complete occlusion of the aneurysm sac from a set of potential treatment devices. The apparatus is further configured to output, for display on a device, an identification of the at least one intrasaccular implant device and the outcome prediction for each of the at least one intrasaccular implant device.Type: ApplicationFiled: February 7, 2023Publication date: August 10, 2023Inventors: Satyananda Kashyap, Hakan Bulu, Ashutosh Jadhav, Ronak Dholakia, Amon Y. Liu, Hussain S. Rangwala, William R. Patterson, Mehdi Moradi
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Patent number: 11694297Abstract: Mechanisms are provided to implement an automated medical image processing pipeline selection (MIPPS) system. The MIPPS system receives medical image data associated with a patient electronic medical record and analyzes the medical image data to extract evidence data comprising characteristics of one or more medical images in the medical image data indicative of a medical image processing pipeline to select for processing the one or more medical images. The evidence data is provided to a machine learning model of the MIPPS system which selects a medical image processing pipeline based on a machine learning based analysis of the evidence data. The selected medical image processing pipeline processes the medical image data to generate a results output.Type: GrantFiled: June 28, 2021Date of Patent: July 4, 2023Assignee: GuerbetInventors: Amin Katouzian, Anup Pillai, Chaitanya Shivade, Marina Bendersky, Ashutosh Jadhav, Vandana Mukherjee, Ehsan Dehghan Marvast, Tanveer F. Syeda-Mahmood
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Publication number: 20230135706Abstract: Mechanisms are provided to improve an output of a trained machine learning (ML) computer model based on label co-occurrence statistics. For a corpus, label vector representations of the knowledge data structures are generated. Co-occurrence scores for each pairing of labels, across the label vector representations, are generated. A vector output of the ML computer model is received and a knowledge driven reasoning (KDR) computer model is configured with threshold(s) and delta value(s) specifying condition(s) of a co-occurrence of a first label in the output with a second label in the plurality of labels which, if present, causes the delta value(s) to be applied to modify a probability value associated with the second label in the output of the ML computer model. The KDR computer model is executed on the output of the ML computer model to modify probability value(s) in the output.Type: ApplicationFiled: November 1, 2021Publication date: May 4, 2023Inventors: Ashutosh Jadhav, Tanveer Syeda-Mahmood, Mehdi Moradi
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Publication number: 20230083916Abstract: Mechanisms are provided to implement a visual analytics pipeline. The mechanisms generate, from an input database of records, a chronology-aware graph data structure of a plurality of records based features specified in an ontology data structure. The chronology-aware graph data structure has vertices representing one or more of events or records based features corresponding to events, and edges representing chronological relationships between events. The mechanisms execute a chronology-aware graph query on the chronology-aware graph data structure to generate a filtered set of vertices and corresponding features corresponding to criteria of the chronology-aware graph query.Type: ApplicationFiled: September 13, 2021Publication date: March 16, 2023Inventors: Andrea Giovannini, Joy Tzung-yu Wu, Tanveer Syeda-Mahmood, Ashutosh Jadhav
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Patent number: 11244755Abstract: Mechanisms are provided to implement an automated medical imaging report generator which receives an input medical image and inputs the input medical image into a machine learning (ML) computer model trained to predict finding labels based on patterns of image features extracted from the medical image. The ML computer model generates a prediction of a finding label applicable to the input medical image in terms of a finding label prediction output vector. Based on the finding label prediction output vector, a lookup operation is performed, in a medical report database of previously processed medical imaging report data structures, to find a matching medical imaging report data structure corresponding to the finding label. An output medical imaging report is generated for the input medical image based on natural language content of the matching medical imaging report data structure.Type: GrantFiled: October 2, 2020Date of Patent: February 8, 2022Assignee: International Business Machines CorporationInventors: Tanveer Syeda-Mahmood, Chun Lok Wong, Joy Tzung-yu Wu, Yaniv Gur, Anup Pillai, Ashutosh Jadhav, Satyananda Kashyap, Mehdi Moradi, Alexandros Karargyris, Hongzhi Wang
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Publication number: 20210327019Abstract: Mechanisms are provided to implement an automated medical image processing pipeline selection (MIPPS) system. The MIPPS system receives medical image data associated with a patient electronic medical record and analyzes the medical image data to extract evidence data comprising characteristics of one or more medical images in the medical image data indicative of a medical image processing pipeline to select for processing the one or more medical images. The evidence data is provided to a machine learning model of the MIPPS system which selects a medical image processing pipeline based on a machine learning based analysis of the evidence data. The selected medical image processing pipeline processes the medical image data to generate a results output.Type: ApplicationFiled: June 28, 2021Publication date: October 21, 2021Inventors: Amin Katouzian, Anup Pillai, Chaitanya Shivade, Marina Bendersky, Ashutosh Jadhav, Vandana Mukherjee, Ehsan Dehghan Marvast, Tanveer F. Syeda-Mahmood
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Patent number: 11094034Abstract: Mechanisms are provided to implement an automated medical image processing pipeline selection (MIPPS) system. The MIPPS system receives medical image data associated with a patient electronic medical record and analyzes the medical image data to extract evidence data comprising characteristics of one or more medical images in the medical image data indicative of a medical image processing pipeline to select for processing the one or more medical images. The evidence data is provided to a machine learning model of the MIPPS system which selects a medical image processing pipeline based on a machine learning based analysis of the evidence data. The selected medical image processing pipeline processes the medical image data to generate a results output.Type: GrantFiled: June 17, 2020Date of Patent: August 17, 2021Assignee: International Business Machines CorporationInventors: Amin Katouzian, Anup Pillai, Chaitanya Shivade, Marina Bendersky, Ashutosh Jadhav, Vandana Mukherjee, Ehsan Dehghan Marvast, Tanveer F. Syeda-Mahmood
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Publication number: 20200311861Abstract: Mechanisms are provided to implement an automated medical image processing pipeline selection (MIPPS) system. The MIPPS system receives medical image data associated with a patient electronic medical record and analyzes the medical image data to extract evidence data comprising characteristics of one or more medical images in the medical image data indicative of a medical image processing pipeline to select for processing the one or more medical images. The evidence data is provided to a machine learning model of the MIPPS system which selects a medical image processing pipeline based on a machine learning based analysis of the evidence data. The selected medical image processing pipeline processes the medical image data to generate a results output.Type: ApplicationFiled: June 17, 2020Publication date: October 1, 2020Inventors: Amin Katouzian, Anup Pillai, Chaitanya Shivade, Marina Bendersky, Ashutosh Jadhav, Vandana Mukherjee, Ehsan Dehghan Marvast, Tanveer F. Syeda-Mahmood
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Patent number: 10740866Abstract: Mechanisms are provided to implement an automated medical image processing pipeline selection (MIPPS) system. The MIPPS system receives medical image data associated with a patient electronic medical record and analyzes the medical image data to extract evidence data comprising characteristics of one or more medical images in the medical image data indicative of a medical image processing pipeline to select for processing the one or more medical images. The evidence data is provided to a machine learning model of the MIPPS system which selects a medical image processing pipeline based on a machine learning based analysis of the evidence data. The selected medical image processing pipeline processes the medical image data to generate a results output.Type: GrantFiled: June 26, 2018Date of Patent: August 11, 2020Assignee: International Business Machines CorporationInventors: Amin Katouzian, Anup Pillai, Chaitanya Shivade, Marina Bendersky, Ashutosh Jadhav, Vandana Mukherjee, Ehsan Dehghan Marvast, Tanveer F. Syeda-Mahmood
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Publication number: 20190392547Abstract: Mechanisms are provided to implement an automated medical image processing pipeline selection (MIPPS) system. The MIPPS system receives medical image data associated with a patient electronic medical record and analyzes the medical image data to extract evidence data comprising characteristics of one or more medical images in the medical image data indicative of a medical image processing pipeline to select for processing the one or more medical images. The evidence data is provided to a machine learning model of the MIPPS system which selects a medical image processing pipeline based on a machine learning based analysis of the evidence data. The selected medical image processing pipeline processes the medical image data to generate a results output.Type: ApplicationFiled: June 26, 2018Publication date: December 26, 2019Inventors: Amin Katouzian, Anup Pillai, Chaitanya Shivade, Marina Bendersky, Ashutosh Jadhav, Vandana Mukherjee, Ehsan Dehghan Marvast, Tanveer F. Syeda-Mahmood
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Publication number: 20190198137Abstract: Mechanisms are provided to implement a medical information summarization engine (MISE). The MISE receives input specifying a summarization template, wherein the summarization template specifies terms or concepts of interest to a medical professional when making a medical decision regarding a patient. The MISE maps the terms or concepts of interest to medical concepts in a medical knowledge base. The MISE processes electronic medical records (EMR) of the patient based on the mapping of the medical concepts in the medical knowledge base to the terms or concepts of interest in the summarization template to extract patient information from the patient EMR that matches at least one of the medical concepts from the mapping. The MIE generates and outputs a holistic summary of the patient's EMRs that summarizes the most salient portions of the patient EMR for use by the medical professional in making the medical decision regarding the patient.Type: ApplicationFiled: December 26, 2017Publication date: June 27, 2019Inventors: Tyler Baldwin, Marina Bendersky, Ashutosh Jadhav, Karina Kanjaria, Chaitanya Shivade, Tanveer F. Syeda-Mahmood, Joy Wu
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Publication number: 20190198138Abstract: Mechanisms are provided to implement a medical information summarization engine (MISE). The MISE receives input specifying a summarization template, wherein the summarization template specifies terms or concepts of interest to a medical professional when making a medical decision regarding a patient. The MISE expands the summarization template based on related concepts or related terms related to the terms or concepts of interest specified in the summarization template. The MISE processes an EMR of the patient based on the expanded summarization template to extract patient information corresponding to the terms or concepts of interest and the related concepts or related terms. The MISE generates and outputs a holistic summary of the EMR of the patient that summarizes the most salient portions of the patient EMR for use by the medical professional in making the medical decision regarding the patient, based on extracted patient information obtained from processing the patient EMR.Type: ApplicationFiled: December 26, 2017Publication date: June 27, 2019Inventors: Tyler Baldwin, Ashutosh Jadhav, Chaitanya Shivade, Tanveer F. Syeda-Mahmood, Joy Wu