Patents by Inventor Robert Michael WARD
Robert Michael WARD has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 11989504Abstract: Disclosed is a platform that manages worker users in a temporary staffing environment via an artificial machine learning model. The temporary staffing platform matches available workers to available shifts/gigs. Additional features include generating provisional or near-miss matches and informing workers how to turn those near-misses into full matches, plotting a gig-career path to develop additional skills, gamify development, and automatically generate resumes. The platform generates a set of skill tags associated with each shift/gig performed by the user. Designing of resume text files by the artificial machine learning model includes procedurally generated descriptions of experience the a user has based on the recording of each shift/gig performed by the user and the skill tags associated with each recorded shift/gig, wherein a format of the resume text file is formulated by the artificial machine learning model evaluating a mix of skill tags and employers amassed by the user.Type: GrantFiled: March 3, 2021Date of Patent: May 21, 2024Assignee: TRUEBLUE, INC.Inventors: Christopher A. Kapcar, Richard Paul Betori, Jeffrey Howard Rash, Robert Michael Ward, Jeffrey S. Dirks, Jeroen Anton Decker, Shawn David Dillenbeck
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Publication number: 20240135090Abstract: Disclosed is a platform that manages worker users in a temporary staffing environment via an artificial machine learning model. The temporary staffing platform matches available workers to available shifts/gigs. Additional features include generating provisional or near-miss matches and informing workers how to turn those near-misses into full matches, plotting a gig-career path to develop additional skills, gamify development, and automatically generate resumes. The platform generates a set of skill tags associated with each shift/gig performed by the user. Designing of resume text files by the artificial machine learning model includes procedurally generated descriptions of experience the user has based on the recording of each shift/gig performed by the user and the skill tags associated with each recorded shift/gig, wherein a format of the resume text file is formulated by the artificial machine learning model evaluating a mix of skill tags and employers amassed by the user.Type: ApplicationFiled: October 15, 2023Publication date: April 25, 2024Inventors: Christopher A. KAPCAR, Richard Paul BETORI, Jeffrey Howard RASH, Robert Michael WARD, Jeffrey S. DIRKS, Jeroen Anton DECKER, Shawn David DILLENBECK
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Publication number: 20240123736Abstract: An example method comprising requesting authorization to reprocess a replaceable supply component using first data stored in memory of the replaceable supply component and an interface, the first data including original manufacturing data for the replaceable supply component. The method further comprises, in response to the request, receiving second data using the interface, and appending the original manufacturing data stored in the memory with the second data to designate the replaceable supply component as reprocessed.Type: ApplicationFiled: February 12, 2021Publication date: April 18, 2024Inventors: Steven T CASTLE, Paul L JERAN, Jefferson P WARD, Alex Michael OAKS, Jesse Otto SUTHERLAND, III, Robert TAYLOR
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Patent number: 11954619Abstract: Apparatuses, systems, and methods described include receiving data related to an availability for a shift, automatically triggering initiation of a communications session related to the shift, conducting the communications session, and receiving and storing a plurality of audio or audio and visual signals from the communications session. Machine learning (ML) sentiment analysis is performed on data of the communications session and based on the sentiment analysis, a reliability score is determined.Type: GrantFiled: January 12, 2022Date of Patent: April 9, 2024Assignee: TRUEBLUE, INC.Inventors: Christopher A. Kapcar, Carlos A. Lara Maldonado, Robert Michael Ward, Jeffrey S Dirks
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Publication number: 20240012991Abstract: Disclosed is a platform that manages worker users in a temporary staffing environment via an artificial machine learning model. The temporary staffing platform matches available workers to available shifts/gigs. Additional features include generating provisional or near-miss matches and informing workers how to turn those near-misses into full matches, plotting a gig-career path to develop additional skills, gamify development, and automatically generate resumes. The platform generates a set of skill tags associated with each shift/gig performed by the user. Designing of resume text files by the artificial machine learning model includes procedurally generated descriptions of experience the a user has based on the recording of each shift/gig performed by the user and the skill tags associated with each recorded shift/gig, wherein a format of the resume text file is formulated by the artificial machine learning model evaluating a mix of skill tags and employers amassed by the user.Type: ApplicationFiled: September 6, 2023Publication date: January 11, 2024Inventors: Christopher A. KAPCAR, Richard Paul BETORI, Jeffrey Howard RASH, Robert Michael WARD, Jeffrey S. DIRKS, Jeroen Anton DECKER, Shawn David DILLENBECK
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Patent number: 11822881Abstract: Disclosed is a platform that manages worker users in a temporary staffing environment via an artificial machine learning model. The temporary staffing platform matches available workers to available shifts/gigs. Additional features include generating provisional or near-miss matches and informing workers how to turn those near-misses into full matches, plotting a gig-career path to develop additional skills, gamify development, and automatically generate resumes. The platform generates a set of skill tags associated with each shift/gig performed by the user. Designing of resume text files by the artificial machine learning model includes procedurally generated descriptions of experience the a user has based on the recording of each shift/gig performed by the user and the skill tags associated with each recorded shift/gig, wherein a format of the resume text file is formulated by the artificial machine learning model evaluating a mix of skill tags and employers amassed by the user.Type: GrantFiled: January 26, 2021Date of Patent: November 21, 2023Assignee: TRUEBLUE, INC.Inventors: Christopher A. Kapcar, Richard Paul Betori, Jeffrey Howard Rash, Robert Michael Ward, Jeffrey S. Dirks, Jeroen Anton Decker, Shawn David Dillenbeck
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Patent number: 11790163Abstract: Disclosed is a platform that manages worker users in a temporary staffing environment via an artificial machine learning model. The temporary staffing platform matches available workers to available shifts/gigs. Additional features include generating provisional or near-miss matches and informing workers how to turn those near-misses into full matches, plotting a gig-career path to develop additional skills, gamify development, and automatically generate resumes. The platform generates a set of skill tags associated with each shift/gig performed by the user. Designing of resume text files by the artificial machine learning model includes procedurally generated descriptions of experience the a user has based on the recording of each shift/gig performed by the user and the skill tags associated with each recorded shift/gig, wherein a format of the resume text file is formulated by the artificial machine learning model evaluating a mix of skill tags and employers amassed by the user.Type: GrantFiled: March 3, 2021Date of Patent: October 17, 2023Assignee: TrueBlue, Inc.Inventors: Christopher A. Kapcar, Richard Paul Betori, Jeffrey Howard Rash, Robert Michael Ward, Jeffrey S. Dirks, Jeroen Anton Decker, Shawn David Dillenbeck
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Publication number: 20230101734Abstract: Disclosed is a platform that makes use of hybrid model employing both heuristic and machine learning models to adaptively generate recommendations based on requested circumstances in a temporary staffing platform. The hybrid model is based on a set of training data surrounding historical temporary staffing outcomes. The heuristic model portion identifies matches between current queries to past outcomes and the machine learning model portion trains to derive new recommendations where no match exists. Queries are received and executed upon in real-time as opposed to pre-computing based on the frequency of changes to the recommendation to what would otherwise be the same query. The hybrid model is therefore configured to optimize for real-time responses to individual queries. The data surrounding the historical temporary staffing outcomes includes data relating to users, data relating to shifts, and data derived from a combination of both.Type: ApplicationFiled: September 22, 2022Publication date: March 30, 2023Inventors: Robert Michael Ward, Christopher A. Kapcar, Carlos Lara Maldonado
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Publication number: 20230019856Abstract: Disclosed is a platform that manages worker users in a temporary staffing environment via an AI machine learning model. The machine learning model predicts dispatch outcomes of a plurality of pairings of worker users to potential shifts. A dispatch outcome predicts whether a worker will show up for and work a given shift. The machine learning model is based on a set of training data surrounding historical dispatch outcomes. The data surrounding the historical dispatch outcomes includes data relating to users, data relating to shifts, and data derived from a combination of both. An implementation of the machine learning model stitches together multiple shifts for up to a schedule horizon based on predicted dispatch outcomes.Type: ApplicationFiled: July 19, 2021Publication date: January 19, 2023Inventors: Carlos Lara Maldonado, Robert Michael Ward, Jeffrey S. Dirks, Christopher A. Kapcar
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Publication number: 20210342528Abstract: Disclosed is a platform that manages worker users in a temporary staffing environment via an artificial machine learning model. The temporary staffing platform matches available workers to available shifts/gigs. Additional features include generating provisional or near-miss matches and informing workers how to turn those near-misses into full matches, plotting a gig-career path to develop additional skills, gamify development, and automatically generate resumes. The platform generates a set of skill tags associated with each shift/gig performed by the user. Designing of resume text files by the artificial machine learning model includes procedurally generated descriptions of experience the a user has based on the recording of each shift/gig performed by the user and the skill tags associated with each recorded shift/gig, wherein a format of the resume text file is formulated by the artificial machine learning model evaluating a mix of skill tags and employers amassed by the user.Type: ApplicationFiled: March 3, 2021Publication date: November 4, 2021Inventors: Christopher A. KAPCAR, Richard Paul BETORI, Jeffrey Howard RASH, Robert Michael WARD, Jeffrey S. DIRKS, Jeroen Anton DECKER, Shawn David DILLENBECK
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Publication number: 20210342527Abstract: Disclosed is a platform that manages worker users in a temporary staffing environment via an artificial machine learning model. The temporary staffing platform matches available workers to available shifts/gigs. Additional features include generating provisional or near-miss matches and informing workers how to turn those near-misses into full matches, plotting a gig-career path to develop additional skills, gamify development, and automatically generate resumes. The platform generates a set of skill tags associated with each shift/gig performed by the user. Designing of resume text files by the artificial machine learning model includes procedurally generated descriptions of experience the a user has based on the recording of each shift/gig performed by the user and the skill tags associated with each recorded shift/gig, wherein a format of the resume text file is formulated by the artificial machine learning model evaluating a mix of skill tags and employers amassed by the user.Type: ApplicationFiled: March 3, 2021Publication date: November 4, 2021Inventors: Christopher A. KAPCAR, Richard Paul BETORI, Jeffrey Howard RASH, Robert Michael WARD, Jeffrey S. DIRKS, Jeroen Anton DECKER, Shawn David DILLENBECK
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Publication number: 20210334763Abstract: Disclosed is a platform that provides temporary staff along with administrative infrastructure to employers who have temporary staffing needs. Administrative issues are monitored via dynamically generated geofences and mobile devices (mobile phones) carried by worker-users. The geofences serve as time clock and behavioral monitor zone.Type: ApplicationFiled: March 3, 2021Publication date: October 28, 2021Inventors: Richard Paul BETORI, Eric Eugene LAWSON, Christopher A. KAPCAR, Robert Michael WARD, Jeffrey S. DIRKS, Jeroen Anton DECKER