IP Library Granted Patent US 11,995,613
Granted Patent B2
US 11,995,613 · App. 17/895,015 · Granted May 28, 2024

Search extraction matching, draw attention-fit modality, application morphing, and informed apply apparatuses, methods and systems

Inventor: Javid Muhammedali (Wellesley, MA)
G06Q10/1053G06F16/2456G06F16/24578G06F16/90344G06F40/284G06Q10/06G06Q10/063112G06Q10/10G06Q50/01H04L67/306
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Quick Facts
Patent No.
US 11,995,613
App. No.
17/895,015
Granted
May 28, 2024
Kind
B2
Abstract

The Search Extraction Matching, Draw Attention-Fit Modality, Application Morphing, and Informed Apply Apparatuses, Methods and Systems (“SEMATFM-AMIA”) transforms inputs including new job listing introduction inputs, via SEMATFM-AMIA components (e.g., the conductor component, the resume view controller component, the XY paths handler component, the title handler component, the resume librarian component, and the job listing librarian component), into outputs including relevant resume outputs and/or augmented new job listing record outputs. It is noted that the terms “component” and “object” may be used interchangeably hereinthroughout. In one embodiment, the SEMATFM-AMIA includes an apparatus, comprising: a memory, a component collection in the memory, and a processor disposed in communication with the memory, and configured to issue a plurality of processing instructions from the component collection stored in the memory. SEMATFM-AMIA may then receive, in connection with an application to a job, a resume adjustment request, where the request includes one or more raw terms of a resume, one or more normalized terms of the resume, one or more raw terms of a job listing corresponding to the job, and one or more normalized terms of the job listing. SEMATFM-AMIA may load said resume normalized terms and said job listing normalized terms into a joined normalized terms set, and add to a common normalized terms set normalized term members of the joined normalized terms set which meet a count criterion. SEMATFM-AMIA may visit each of one or more normalized term members of the common normalized terms set. After further receiving, adding, visiting, providing and otherwise processing data, SEMATFM-AMIA may receive, from the resume adjuster component, a request to formulate the adjusted resume record, wherein said record formulation request includes specification of the resume and substitution information, and formulate the adjusted resume record which substitutes each of user-selected resume raw terms with a corresponding user-selected job listing raw term, wherein the formulation includes accessing one or more stores.

Claims (32)

1. An apparatus, comprising:

a memory;

a component collection in the memory, including:

a resume adjuster component,

a resume raw norm correlator component,

a job listing raw norm correlator component, and

a resume librarian component;

a processor disposed in communication with the memory, and configured to issue a plurality of processing instructions from the component collection stored in the memory,

wherein the processor issues instructions from the resume adjuster component, stored in the memory, to:

receive, in connection with an application to a job, a resume adjustment request, where the request includes one or more raw terms of a resume, one or more normalized terms of the resume, one or more raw terms of a job listing corresponding to the job, and one or more normalized terms of the job listing;

load said resume normalized terms and said job listing normalized terms into a joined normalized terms set;

add to a common normalized terms set normalized term members of the joined normalized terms set which meet a count criterion;

visit each of one or more normalized term members of the common normalized terms set;

receive, from the resume raw norm correlator component, indication of which one or more of said job-application-request-provided resume raw terms potentially map to the visited common normalized term;

receive, from the job listing raw norm correlator component, indication of which one or more of said job-application-request-provided job listing raw terms potentially map to the visited common normalized term;

add to a crossover array an element setting forth the visited common normalized term, said indication of which one or more of said job-application-request-provided resume raw terms potentially map to the visited common normalized term, and said indication of which one or more of said job-application-request-provided job listing raw terms potentially map to the visited common normalized term;

visit each of one or more elements of the crossover array;

provide user notification of the normalized term of the element;

provide user notification of the one or more resume raw terms of the element, said subject-to-notification resume raw terms potentially mapping to the normalized term of the element;

provide user notification of the one or more job listing raw terms of the element, said subject-to-notification job listing raw terms potentially mapping to the normalized term of the element; and

receive user selection of one of the resume raw terms of the element;

receive user selection of one of the job listing raw terms of the element; and

request, from the resume librarian component, formulation of an adjusted resume record which substitutes each of user-selected resume raw terms with a corresponding user-selected job listing raw term;

wherein the processor issues instructions from the resume raw norm correlator component, stored in the memory, to:

receive, from the resume adjuster component, a correlations request, wherein said correlations request includes the visited common normalized term and the resume raw terms received in connection with the job application request; and

provide, to the resume adjuster component, said one or more of said job-application-request-provided resume raw terms which potentially map to the visited common normalized term;

wherein the processor issues instructions from the job listing raw norm correlator component, stored in the memory, to:

receive, from the resume adjuster component, a correlations request, wherein said correlations request includes the visited common normalized term and the job listing raw terms received in connection with the job application request; and

provide, to the resume adjuster component, said one or more of said job-application-request-provided job listing raw terms which potentially map to the visited common normalized term; and

wherein the processor issues instructions from the resume librarian component, stored in the memory, to:

receive, from the resume adjuster component, a request to formulate the adjusted resume record, wherein said record formulation request includes specification of the resume and substitution information; and

formulate the adjusted resume record which substitutes each of user-selected resume raw terms with a corresponding user-selected job listing raw term, wherein the formulation includes accessing one or more stores.

Assignments (6)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Dec 23, 2025
From: MONSTER WORLDWIDE, LLC
To: MCB BERMUDA LTD.
Reel/Frame 073303/0055 →
SECURITY INTEREST Recorded Sep 17, 2024
From: MONSTER WORLDWIDE, LLC (F/K/A: MONSTER WORLDWIDE, INC.)
To: RANDSTAD MWW SOLUTIONS INC.
Reel/Frame 068610/0395 →
SECURITY INTEREST Recorded Sep 17, 2024
From: MONSTER WORLDWIDE, LLC (F/K/A: MONSTER WORLDWIDE, INC.)
To: RANDSTAD MWW SOLUTIONS INC.
Reel/Frame 068610/0404 →
SECURITY INTEREST Recorded Sep 17, 2024
From: MONSTER WORLDWIDE, LLC (F/K/A: MONSTER WORLDWIDE, INC.)
To: RANDSTAD MWW SOLUTIONS INC.
Reel/Frame 068610/0413 →
ENTITY CONVERSION Recorded Sep 17, 2024
From: MONSTER WORLDWIDE, INC.
To: MONSTER WORLDWIDE, LLC
Reel/Frame 068974/0543 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Oct 18, 2023
From: MUHAMMEDALI, JAVID
To: MONSTER WORLDWIDE, INC.
Reel/Frame 065263/0338 →
Continuity (5)
Continuation 15465478 · Mar 21, 2017
Continuation In Part 14711336 · May 13, 2015
Provisional Application 62345719 · Jun 3, 2016
Provisional Application 61992816 · May 13, 2014
Related Publication 20230026252A1 · Jan 26, 2023
Cited By (1)
US 12,725,127