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Ravi R Soni

from Sunnyvale, CA
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Also known as:
Raul R Soni
Related to:
Vaibhav V Soni, 38
Connected to:
Aakanksha Soni, 37Abu S Soni, 42Ajay K Soni, 71Ameet Soni, 43Amitkumar J Soni, 62Amrita Soni, 44Anand Soni, 51Ankoor B Soni, 43Anupam Soni, 48Apoorva Soni, 36

Ravi Soni Phones & Addresses

  • Sunnyvale, CA
  • Mountain View, CA
  • Foster City, CA
  • Daly City, CA
  • San Francisco, CA
  • Fremont, CA

Resumes

Resumes

Ravi Soni Photo 1

Buffalo Grove High School

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Location:
451 Shoreline Blvd north, Mountain View, CA 94041
Industry:
Legal Services
Work:
Casetext May 2019 - Jan 2020
Product Manager
P.i.n.g. Inc. Feb 2017 - May 2017
Data Science Intern
Hp May 2016 - May 2017
Product Management Intern
Legalforce Rapc Jan 2013 - Jan 2016
Senior Trademark Prosecution Specialist
Legalforce Rapc Feb 2013 - Aug 2015
Business Development
Education:
University of California, Berkeley 2015 - 2017
Bachelors, Bachelor of Arts, Economics, Computer Science, Applied Mathematics De Anza College 2013 - 2015
Buffalo Grove High School
Skills:
Microsoft Office
Microsoft Excel
Microsoft Word
Customer Service
Powerpoint
English
Research
Windows
Teaching
Public Speaking
Budgets
Strategic Planning
Trademarks
Trademark Infringement
Copyright Law
Intellectual Property
Languages:
English
Gujarati
Hindi
Ravi Soni Photo 2

Ravi Soni

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Ravi Soni Photo 3

Ravi Soni

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Ravi Soni Photo 4

Ravi Soni

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Location:
United States
Ravi Soni Photo 5

Ravi Soni

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Location:
United States

Publications

Us Patents

System And Methods For Inferring Thickness Of Anatomical Classes Of Interest In Two-Dimensional Medical Images Using Deep Neural Networks

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US Patent:
20220284570, Sep 8, 2022
Filed:
Mar 4, 2021
Appl. No.:
17/192804
Inventors:
- Wauwatosa WI, US
Máté Fejes - Budapest, HU
Gopal Avinash - San Ramon CA, US
Ravi Soni - San Ramon CA, US
Bipul Das - Chennai, IN
Rakesh Mullick - Bangalore, IN
Pál Tegzes - Budapest, HU
Lehel Ferenczi - Budapest, HU
Vikram Melapudi - Bangalore, IN
Krishna Seetharam Shriram - Bangalore, IN
International Classification:
G06T 7/00
G06N 3/08
G06T 15/08
Abstract:
Methods and systems are provided for inferring thickness and volume of one or more object classes of interest in two-dimensional (2D) medical images, using deep neural networks. In an exemplary embodiment, a thickness of an object class of interest may be inferred by acquiring a 2D medical image, extracting features from the 2D medical image, mapping the features to a segmentation mask for an object class of interest using a first convolutional neural network (CNN), mapping the features to a thickness mask for the object class of interest using a second CNN, wherein the thickness mask indicates a thickness of the object class of interest at each pixel of a plurality of pixels of the 2D medical image; and determining a volume of the object class of interest based on the thickness mask and the segmentation mask.

System And Methods For Visualizing Variations In Labeled Image Sequences For Development Of Machine Learning Models

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US Patent:
20230016464, Jan 19, 2023
Filed:
Jul 14, 2021
Appl. No.:
17/375982
Inventors:
- Milwaukee WI, US
Justin Tyler Wright - San Ramon CA, US
Ravi Soni - San Ramon CA, US
James Gualtieri - Pittsburgh PA, US
Kristin Anderson - Alameda CA, US
International Classification:
G06T 7/38
G06N 20/00
G06K 9/62
G06K 9/32
G06T 7/00
G06T 7/11
G06T 7/136
G06F 3/0484
Abstract:
The current disclosure provides methods and systems for visualizing, comparing, and navigating through, labeled image sequences. In one example, a degree of variation between a plurality of labels for an image in a sequence of images may be encoded as a comparison metric, and the comparison metric for each image may be graphed as a function of image position in the sequence of images, thereby providing a contextually rich view of label variation as a function of progression through the sequence of images. Further, the encoded variation of image labels may be used to automatically flag inconsistently labeled images, wherein the flagged images may be highlighted in a graphical user interface presented to a user, pruned from a training dataset, or a loss associated with the flagged image may be scaled based on the encoded variation during training of a machine learning model.

Systems And Methods For Detecting Laterality Of A Medical Image

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US Patent:
20210350186, Nov 11, 2021
Filed:
Jul 26, 2021
Appl. No.:
17/385762
Inventors:
- Wauwatosa WI, US
Ravi Soni - San Ramon CA, US
Katelyn Rose Nye - Glendale WI, US
Gireesha Chinthamani Rao - Pewaukee WI, US
John Michael Sabol - Sussex WI, US
Yash N. Shah - Sunderland MA, US
International Classification:
G06K 9/62
G06T 7/00
G06N 3/08
G16H 30/40
G16H 30/20
Abstract:
An x-ray image laterality detection system is provided. The x-ray image laterality detection system includes a detection computing device. The processor of the computing device is programmed to execute a neural network model for analyzing x-ray images, wherein the neural network model is trained with training x-ray images as inputs and observed laterality classes associated with the training x-ray images as outputs. The process is also programmed to receive an unclassified x-ray image, analyze the unclassified x-ray image using the neural network model, and assign a laterality class to the unclassified x-ray image. If the assigned laterality class is not target laterality, the processor is programmed to adjust the unclassified x-ray image to derive a corrected x-ray image having the target laterality and output the corrected x-ray image. If the assigned laterality class is the target laterality, the processor is programmed to output the unclassified x-ray image.

Systems And Methods For Detecting Laterality Of A Medical Image

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US Patent:
20210271931, Sep 2, 2021
Filed:
Feb 27, 2020
Appl. No.:
16/803209
Inventors:
- Milwaukee WI, US
Ravi Soni - San Ramon CA, US
Katelyn Rose Nye - Glendale WI, US
Gireesha Chinthamani Rao - Pewaukee WI, US
John Michael Sabol - Sussex WI, US
Yash N. Shah - Sunderland MA, US
International Classification:
G06K 9/62
G06T 7/00
G16H 30/20
G16H 30/40
G06N 3/08
Abstract:
An x-ray image laterality detection system is provided. The x-ray image laterality detection system includes a detection computing device. The processor of the computing device is programmed to execute a neural network model for analyzing x-ray images, wherein the neural network model is trained with training x-ray images as inputs and observed laterality classes associated with the training x-ray images as outputs. The process is also programmed to receive an unclassified x-ray image, analyze the unclassified x-ray image using the neural network model, and assign a laterality class to the unclassified x-ray image. If the assigned laterality class is not target laterality, the processor is programmed to adjust the unclassified x-ray image to derive a corrected x-ray image having the target laterality and output the corrected x-ray image. If the assigned laterality class is the target laterality, the processor is programmed to output the unclassified x-ray image.

Medical Machine Time-Series Event Data Processor

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US Patent:
20200337648, Oct 29, 2020
Filed:
Nov 27, 2019
Appl. No.:
16/697736
Inventors:
- Wauwatosa WI, US
Gopal Avinash - San Ramon CA, US
Min Zhang - San Ramon CA, US
Ravi Soni - San Ramon CA, US
Jiahui Guan - San Ramon CA, US
Dibyajyoti Pati - San Ramon CA, US
Zili Ma - San Ramon CA, US
International Classification:
A61B 5/00
G16H 50/30
G16H 40/67
G06N 3/08
Abstract:
Systems, apparatus, instructions, and methods for medical machine time-series event data processing are disclosed. An example time series event data processing apparatus includes memory storing instructions and one-dimensional time series healthcare-related data; and at least one processor. The example at least one processor is to: execute artificial intelligence model(s) trained on aggregated time series data to at least one of a) predict a future medical machine event, b) detect a medical machine event, or c) classify the medical machine event using the one-dimensional time series healthcare-related data; when the artificial intelligence model(s) are executed to predict the future medical machine event, output an alert related to the predicted future medical machine event to trigger a next action; and when the artificial intelligence model(s) are executed to detect and/or classify the medical machine event, label the medical machine event and output the labeled event to trigger the next action.

Visualization Of Medical Device Event Processing

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US Patent:
20200342968, Oct 29, 2020
Filed:
Oct 17, 2019
Appl. No.:
16/656034
Inventors:
- Wauwatosa WI, US
Qian Zhao - San Ramon CA, US
Zili Ma - San Ramon CA, US
Dibyajyoti Pati - San Ramon CA, US
Venkata Ratnam Saripalli - San Ramon CA, US
Ravi Soni - San Ramon CA, US
Jiahui Guan - San Ramon CA, US
Min Zhang - San Ramon CA, US
International Classification:
G16H 15/00
G16H 40/67
G06F 9/451
G06N 20/00
G16H 10/60
Abstract:
Systems, apparatus, instructions, and methods for medical machine time-series event data processing are disclosed. An example apparatus includes a data processor to process one-dimensional data captured over time with respect to patient(s). The example apparatus includes a visualization processor to transform the processed data into graphical representations and to cluster the graphical representations including the first graphical representation into at least first and second blocks arranged with respect to an indicator of a criterion to provide a visual comparison of the first block and the second block with respect to the criterion. The example apparatus includes an interaction processor to facilitate interaction, via the graphical user interface, with the first and second blocks of graphical representations to extract a data set for processing from at least a subset of the first and second blocks.

Artificial Neural Network Compression Via Iterative Hybrid Reinforcement Learning Approach

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US Patent:
20200272905, Aug 27, 2020
Filed:
Jun 24, 2019
Appl. No.:
16/450474
Inventors:
- Milwaukee WI, US
Ravi Soni - San Ramon CA, US
Jiahui Guan - San Ramon CA, US
Gopal B. Avinash - San Ramon CA, US
International Classification:
G06N 3/08
H03M 7/30
Abstract:
Systems and computer-implemented methods for facilitating automated compression of artificial neural networks using an iterative hybrid reinforcement learning approach are provided. In various embodiments, a compression architecture can receive as input an original neural network to be compressed. The architecture can perform one or more compression actions to compress the original neural network into a compressed neural network. The architecture can then generate a reward signal quantifying how well the original neural network was compressed. In (α)-proportion of compression iterations/episodes, where α∈[0,1], the reward signal can be computed in model-free fashion based on a compression ratio and accuracy ratio of the compressed neural network. In (1−α)-proportion of compression iterations/episodes, the reward signal can be predicted in model-based fashion using a compression model learned/trained on the reward signals computed in model-free fashion. This hybrid model-free-and-model-based architecture can greatly reduce convergence time without sacrificing substantial accuracy.
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Ravi R Soni from Sunnyvale, CA Get Report