Pritesh A Shah, Age 53New York, NY

2023009, Mar 23, 2023

Nov 28, 2022

17/994901

- St. Louis MO, US
Pritesh J. Shah - Paramus NJ, US
Christopher G. Lehmuth - St. Louis MO, US

G06F 9/451
G16H 40/67
G06F 17/11
G06Q 10/08
G16H 70/40
G06N 20/00

A method includes receiving a first set of identifiers selected based on commonality among descriptive data corresponding to the identifiers of the first set. Each identifier corresponds to a user who has been supplied a physical object. The method includes identifying event data for the first set within a specified epoch. The method includes training a machine learning model for the first set using the identified event data. The machine learning model is trained using parallel processing of records from a storage structure storing the event data. The parallel processing includes assigning analysis of the event data of each of a subset of the first set to respective processor threads for parallel execution on processing hardware. The trained machine learning model is configured to receive a selected identifier and generate an output representing an amount of resources expected to be used by the selected identifier for a subsequent epoch.

2023006, Mar 2, 2023

Sep 1, 2021

17/464213

- St. Louis MO, US
Christopher R. Markson - Hawthorne NJ, US
Pritesh J. Shah - Paramus NJ, US

G06K 9/62
G06F 40/295
G06F 16/383
G10L 15/26
G06K 9/00

A computer system includes processor hardware configured to execute instructions that include joining at least a portion of multiple call transcription data entries with at least a portion of multiple agent call log data entries according to timestamps associated with the entries to generate a set of joined call data entries, and validating the joined call data entry by determining whether a transcribed entity name matches with entity identifier information associated with the agent call log data entry. The instructions include preprocessing the joined call data entry according to word confidence score data entries associated with the call transcription data entry to generate preprocessed text, performing natural language processing vectorization on the preprocessed text to generate an input vector, and supplying the input vector to an unsupervised machine learning model to assign an output topic classification of the model to the joined call data entry associated with the input vector.

2023004, Feb 16, 2023

Aug 5, 2021

17/394647

- St.Louis MO, US
Christopher R. Markson - Hawthorne NJ, US
Pritesh J. Shah - Paramus NJ, US
Jiawei Kuang - St. Louis MO, US
Keith L. Widmer - Grapevine TX, US

G06F 3/0482
G06N 20/00
G06F 16/22
G16H 20/10
G06F 9/451

A computerized method for transforming an interactive graphical user interface according to machine learning includes selecting a persona, loading a data structure associated with the selected persona, and generating the interactive graphical user interface. The method includes, in response to a user selecting a first selectable element, inputting a first set of explanatory variables to a first trained machine learning model to generate a first metric, and transforming the user interface according to the selected persona and the first metric. The method includes, in response to the user selecting a second selectable element, inputting a second set of explanatory variables to a second trained machine learning model to generate a second metric, and transforming the user interface according to the selected persona and the second metric. In various implementations, first metric is a first probability of the persona being approved for a first prior authorization prescription.

2021025, Aug 19, 2021

May 4, 2021

17/307502

- St. Louis MO, US
Pritesh J. Shah - Paramus NJ, US
Christopher G. Lehmuth - St. Louis MO, US

G06F 9/451
G16H 40/67
G06F 17/11
G06Q 10/08
G16H 70/40
G06N 20/00

A computer system for transforming a user interface according to data store mining includes a data store configured to store a parameter related to a user and index event data of a set of events. A data processing circuit is configured to identify a first set of identifiers and train a machine learning model based on event data by the data store. An interface circuit is configured to receive an indication of a selected identifier of the plurality of identifiers, determine a first intake metric of the selected identifier using the machine learning model, and a second intake metric of the selected identifier and the parameter using the machine learning model. The interface circuit is configured to transform the user interface according to the first intake metric and the second intake metric.

2021015, May 27, 2021

Feb 5, 2021

17/168745

- St. Louis MO, US
Pritesh J. Shah - Paramus NJ, US
Amit K. Bothra - St. Louis MO, US
David A. Tomala - Seattle WA, US
Christopher R. Markson - Hawthorne NJ, US
Bose S. Daggubati - St. Peters MO, US
Christopher G. Lehmuth - St. Louis MO, US

G16H 10/60

A content analysis system includes a processor executing instructions from memory. The instructions include, in response to receiving a request signal from a user device, obtaining feedback items, each having a source indicator; identifying unique source indicators; and, for each source indicator, aggregating corresponding ones of the feedback items. A set of filtered feedback items is generated according to either first or second access levels associated with a user of the user device. A subset of filtered feedback items is selected according to a date range specified by the request signal, a set of automated rules is applied, and natural language processing is performed based on frequency of presence of salient terms to identify themes. A control signal is transmitted to a user interface of the user device instructing display of a representation that indicates a change in the frequency of the identified themes over the specified date range.

2021009, Apr 1, 2021

Dec 15, 2020

17/121943

- St. Louis MO, US
Pritesh J. Shah - Paramus NJ, US
Christopher G. Lehmuth - St. Louis MO, US

G06F 9/451
G16H 70/40
G06N 20/00
G16H 40/67
G06F 17/11
G06Q 10/08

A computer system for dynamic adaptation of a user interface according to data store mining includes a data store configured to index event data of a plurality of events. A data analyst device is configured to render the user interface to a data analyst and transmit a message that identifies a selected identifier of the plurality of identifiers. A data processing circuit is configured to train a machine learning model based on event data stored by the data store for a first set of identifiers from within a predetermined epoch. An interface circuit determines an interface metric for the selected identifier based on the determined output of the selected identifier and transmits the interface metric to the data analyst device. The data analyst device is configured to, in response to the interface metric from the interface circuit, selectively perform a modification or removal of a second user interface element.

2021000, Jan 7, 2021

Aug 20, 2020

16/998358

- St. Louis MO, US
Pritesh J. Shah - Paramus NJ, US
Christopher G. Lehmuth - St. Louis MO, US

G06T 7/00
G06T 7/70
G06T 7/13
G06T 11/00
G16H 20/13
G06T 7/12
G16H 30/40

A computer system includes an input configured to receive a first image of medication located in a receptacle, memory, and a processor configured to execute instructions including creating a second image based on the first image, dividing pixels of the second image into first and second subsets, and scanning the second image along a first axis to count, for each point along the first axis, a number of pixels in the first subset along a line perpendicular to the first axis that intersects the first axis at the point. The instructions also include estimating positions of first and second edges of the receptacle along the first axis based on the counts of the pixels, defining an opening of the receptacle based on the estimated positions of the first and second edges, and outputting a processed image that indicates areas of the image that are outside of the defined opening.

2019015, May 23, 2019

Nov 14, 2018

16/190548

- St. Louis MO, US
Pritesh J. Shah - Paramus NJ, US
Christopher G. Lehmuth - St. Louis MO, US

G06T 7/00
G06T 7/70
G06T 7/13
G06T 11/00
G06T 7/12
G16H 20/13

A method includes capturing a first image of medication held by a receptacle. The method includes creating a second image based on the first image. The method includes determining a first subset of pixels of the second image that are more likely to correspond to the receptacle. The method includes processing the second image along a first axis by, for each point: defining a line perpendicularly intersecting the first axis at the point and counting how many of the pixels located along the line are in the first subset. The method includes determining first and second local maxima of the counts. The method includes estimating positions of first and second edges of the receptacle based on positions of the local maxima. The method includes defining an ellipse based on the first and second edges and excluding areas of the first image outside the defined ellipse from further processing.