Siemens Healthcare Diagnostics Gmbh

Educational Testing Service

Jeffrey Hoffman

Robert N Cichielo

Michael Heydlauf

Yue Zhang

Sandeep M Naik

Brian Heilig

Ahmet Tuysuzoglu

Luxi Zheng

Micheal Heydlauf

Methods of scheduling maintenance on a plurality of automated testing apparatus and possibly also on ancillary test processing apparatus are provided. The methods include inputting identification data on the plurality of automated testing apparatus to be maintained, inputting maintenance requirement data for the maintenance of each of the apparatus, inputting demand constraint data, operating on the identification data, maintenance requirement data, and demand constraint data using an optimization program, such as mixed integer linear programming (MILP), subject to demand constraints and at least one objective, and outputting an optimized maintenance schedule for a planning period. Systems and apparatus configured to carry out the methods are also provided, as are other aspects.

Methods and apparatus of maintenance scheduling in automated testing over a planning period

Methods of and apparatus controlling change-out inventory of an automated analyzer apparatus. The inventory control methods include receiving change-out inventory status from automated analyzer apparatus at a server, receiving an estimate of the planned workload over a specific time period at the server, analyzing with a decisioning module of the server first data of the change-out inventory status and second data of the planned workload over the specified time period, and producing a work plan itinerary as an output from the decisioning module. The work plan itinerary provides sequence instructions or commands for an operator (person and/or robotic mechanism) to follow for changing out of change-out inventory items. Inventory control apparatus configured to carry out the methods are provided, as are other aspects.

Predictive inventory control including scheduling and performing bio-fluid tests out of order based on reagent inventory expiration

Methods, apparatus, and systems for obtaining sensor-generated data in a diagnostic laboratory apparatus including one or more producers of data that include a sensor, a local communications network communicatively coupling the producers to a data router configured to translate data received from producers of data into a common format record, the common format including key-value pairs, and at least one subscriber that registers one or more channel-based subscriptions with the data router. Records remain in storage by the data router until to the subscriber acknowledges successful receipt, after which the data router manages its storage resources to provide storage for newer sensor-generated data. The data router is configured to install plug-ins as needed to translate data formats of newly-integrated producers with new formats.

Methods, apparatus, and systems for integration of diagnostic laboratory devices

A method and system for performing image mark recognition for a document is disclosed. A document is scanned into a digital image. Reference image marks are sensed from the digital image. The reference image marks may include trigger row marks and/or corner/crop marks. Coordinates denoting the location of cells within the digital image are determined base on the locations of the reference image marks. Response marks are evaluated for darkness, opacity, and/or grayness on a pixel-by-pixel basis. The response marks are each assigned a percentage value based on the ratio between a total color value for a cell and a maximum color value for a cell.

Method and system for performing image mark recognition

A method and system for performing image mark recognition for a document is disclosed. A document is scanned into a digital image. Reference image marks are sensed from the digital image. The reference image marks may include trigger row marks and/or corner/crop marks. Coordinates denoting the location of cells within the digital image are determined based on the locations of the reference image marks. Response marks are evaluated for darkness, opacity, and/or grayness on a pixel-by-pixel basis. The response marks are each assigned a percentage value based on the ratio between a total color value for a cell and a maximum color value for a cell.

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Lebanon, NJ, United States of America

Jeffrey Hoffman