Joseph W Hanna

US Patent:

20220385546, Dec 1, 2022

Filed:

Jun 1, 2022

Appl. No.:

17/830201

Inventors:

- Charleston SC, US
Christina R. Petrosso - Folly Beach SC, US
Joseph W. Hanna - Ft. Lauderdale FL, US
David Trachtenberg - Charleston SC, US

International Classification:

H04L 41/16
H04L 43/04
H04L 41/14

Abstract:

Systems and processes for iteratively training a network training module are described herein. In various embodiments, the process includes: (1) retrieving bulk data comprising a plurality of a data types, (2) transforming the bulk data according to preconfigured classification values to generate network information data sets; (3) training a raw training module by iteratively processing each of the network information data sets through a raw training module to generate respective output classification values; (4) updating one or more classification values based on a comparison of the respective output classification values; (5) processing an input network information data set with a trained training module to generate a specific network constituent; and (6) modifying a display based on the plurality of classification values.

US Patent:

20220343249, Oct 27, 2022

Filed:

Apr 26, 2022

Appl. No.:

17/729898

Inventors:

- Charleston SC, US
Joseph W. Hanna - Charleston SC, US
Diego Valdes - Gainesville FL, US
David Trachtenberg - Charleston SC, US

Assignee:

Job Market Maker, LLC - Charleston SC

International Classification:

G06Q 10/06
G06Q 10/10
G06K 9/62
G06F 16/23

Abstract:

Systems and processes for iteratively training a training module are described herein. In various embodiments, the process includes: (1) retrieving bulk data comprising a plurality of raw position data elements from a plurality of data sources, (2) transforming the raw position data elements according to preconfigured classification guidelines to generate standardized position data element groups; (3) training a raw training module by iteratively processing each of the standardized position data element groups through a raw training module to generate respective output renumeration values; (4) updating one or more emphasis guidelines based on a comparison of the respective output renumeration values; (5) processing an input position data element set with a trained training module to generate a display renumeration value; and (6) modifying a display based on the display renumeration value.

US Patent:

20210103876, Apr 8, 2021

Filed:

Oct 5, 2020

Appl. No.:

17/063263

Inventors:

- Charleston SC, US
Joseph W. Hanna - Charleston SC, US
Nicholas Castro - North Charleston SC, US
David Trachtenberg - Charleston SC, US

International Classification:

G06Q 10/06
G06Q 10/10
G06N 20/20
G06F 40/56
G06K 9/62

Abstract:

A machine learning (ML) process can include teaching, with a teaching set, a first ML algorithm to generate one or more machine-predicted results. One or more weights can be generated based on the one or more machine-predicted results and the teaching set. A second ML algorithm can be generated based on the one or more weights. Via the second ML algorithm, one or more machine-learned results can be generated. A description of one or more candidates can be received. Based on the one or more machine-learned results, a respective likelihood of interest in a CCG class of positions for each of the one or more candidates can be generated. A respective communication can be transmitted to each of a subset of the one or more candidates open to the respective likelihood of interest in the CCG class of positions for the subset above a threshold.

US Patent:

20200065772, Feb 27, 2020

Filed:

Aug 21, 2019

Appl. No.:

16/546849

Inventors:

- Charleston SC, US
Joseph W. Hanna - Charleston SC, US

International Classification:

G06Q 10/10
G06N 20/00
G06N 5/04

Abstract:

Machine learning systems for predictive targeting and optimizing engagement are described herein. In various embodiments, the system includes 1) training a first machine learning computer model to generate machine predicted outcomes; (2) determining weights based on the machine predicted outcomes; (3) generating a second machine learning computer model based on the weights; and (4) generating machine learned predictions for candidates.