International Business Machines Corporation

Omnyx, LLC

Curtis Stratman

Scott Kevin Reynolds

Jacob Couch

Jeremy Espenshade

Zhen Zeng

Timothy Mark McVaugh

Anthony J Melanson

Mark Lotter

Steve Selzer

Marina Virnik

Joshua Todd Ehlke

Joshua Micah Weihnacht

Martin Shelly

Narasimha Rao Khandavilli

Systems, methods, and products are described that provide for a histology workflow management system and associated functions. One aspect provides for accessing one or more digital specimen images; generating one or more patient records and one or more case records; matching the one or more digital specimen images to the one or more case records based on one or more image properties of the one or more digital specimen images; and facilitating one or more image review functions comprising image quality, case matching, tissue block matching, and case completeness functions. Other aspects and embodiments are also described herein.

Histology workflow management system

A digital pathology system has a central workflow server hosting digital pathology application services and supporting one or more pathology workstations. The digital pathology system may include one or more image servers, providing digital images of sample specimen slides that are associated with medical cases. Residing at and executing on each pathology workstation is a digital pathology application client, which is the counterpart of digital pathology application services at the central workflow server. The combination of digital pathology application services at the central workflow server and digital pathology application client at each pathology workstation support a pathology workflow software module and a slide viewer software module. The present disclosure also describes a method of operation and/or use of the digital pathology system.

Digital pathology system

A system for generating a sequence of learning objects comprising an e-Learning course is provided. The system can include one or more processors and a memory unit for electronically storing data comprising a plurality of learning objects. The system further can include a sequence builder configured to execute on the at least one processor. The sequence builder, more particularly, can be configured to generate a sequence comprising at least a portion of the plurality of learning objects based upon a measurable user relevancy and/or a measurable query relevancy of each learning object contained in the sequence.

System and methods for sequencing learning objects based upon query and user relevancy

In a method and system for matching learning objects with a user profile, a top-level concept structure, including a keyword hierarchy for each of one or more top-level concepts, can be used to determine the complexity of a learning object. The learning object can include one or more learning object keywords related to one or more top-level concepts contained in the top-level concept structure. The learning object keywords can be assigned a complexity score based upon their location within the keyword hierarchy, and the complexity scores for all of the learning object keywords relating to a top-level concept can be aggregated to generate a learning object top-level concept complexity score. The learning object top-level concept complexity score can be compared with a user's experience rating for that top-level concept and be used to select an appropriate learning object.

Matching learning objects with a user profile using top-level concept complexity

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Pittsburgh, PA, United States of America

Curtis Stratman