Abstract: Scannable thumbnails for identifying data objects stored on a database. A method includes identifying a data collection comprising one or more data objects stored on a database and receiving a thumbnail selection to represent the data collection, wherein the thumbnail selection comprises one or more of an image from the data collection, a graphic, or text. The method includes generating a scannable code associated with the data collection merging the scannable code with the thumbnail selection to generate a scannable thumbnail.
Type:
Grant
Filed:
October 6, 2021
Date of Patent:
November 28, 2023
Assignee:
Relevant, Inc.
Inventors:
Mark W. Willis, Zachary M. Willis, Tyson S. Willis
Abstract: Scannable thumbnails for identifying data objects stored on a database. A method includes identifying a data collection comprising one or more data objects stored on a database and receiving a thumbnail selection to represent the data collection, wherein the thumbnail selection comprises one or more of an image from the data collection, a graphic, or text. The method includes generating a scannable code associated with the data collection merging the scannable code with the thumbnail selection to generate a scannable thumbnail.
Type:
Application
Filed:
October 6, 2021
Publication date:
April 6, 2023
Applicant:
Relevant, Inc.
Inventors:
Mark W. Willis, Zachary M. Willis, Tyson S. Willis
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a machine learning model. One of the methods includes receiving a complete set of training data; receiving instructions to train a predictive model having a plurality of parameters on an initial subset of the complete set of training data; training the predictive model on the initial subset; storing data representing a first state of the predictive model after training the predictive model on the initial subset; receiving updated parameter values and instructions to train the predictive model on a new subset of the complete set of training data; and training the predictive model on the new subset.
Type:
Grant
Filed:
August 15, 2013
Date of Patent:
September 27, 2016
Assignee:
Context Relevant, Inc.
Inventors:
Stephen Purpura, James E. Walsh, Dustin Lundring Rigg Hillard
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a machine learning model. One of the methods includes performing experiments to select a training strategy for use in training the model on a particular data set. The selected training strategy includes a binning strategy for binning the raw feature vectors before the raw feature vectors are provided to the predictive model.
Type:
Grant
Filed:
August 16, 2013
Date of Patent:
September 20, 2016
Assignee:
Context Relevant, Inc.
Inventors:
Stephen Purpura, James E. Walsh, Dustin Lundring Rigg Hillard
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training machine learning models. The models can include models for predicting a next transaction price or a next transaction price direction for one or more financial products, for classifying particular debit or credit card transactions as likely being anomalous or fraudulent or not, or for classifying particular financial claims processing transactions, e.g., insurance, health care, or employee expense claims transactions, as likely being anomalous or fraudulent or not.
Type:
Grant
Filed:
August 16, 2013
Date of Patent:
May 10, 2016
Assignee:
Context Relevant, Inc.
Inventors:
Stephen Purpura, James E. Walsh, Dustin Lundring Rigg Hillard