Abstract: A system and a method for dynamically optimizing an instrument system workflow based on operational monitoring and managing of a workflow for a hardware system. The system includes instrument resources and sample chambers, each resource and chamber with a dedicated sensor configured to acquire data. The system further includes a computing device communicatively connected to the instrument resources and sample chambers. The computing device includes a software application or program comprising a workflow builder, an execution engine, an analytics engine, a virtual system modeling engine, and an optional machine learning engine.

Abstract: A system and a method for dynamically optimizing an instrument system workflow based on operational monitoring and managing of a workflow for a hardware system. The system includes instrument resources and sample chambers, each resource and chamber with a dedicated sensor configured to acquire data. The system further includes a computing device communicatively connected to the instrument resources and sample chambers. The computing device includes a software application or program comprising a workflow builder, an execution engine, an analytics engine, a virtual system modeling engine, and an optional machine learning engine.

Abstract: Disclosed are methods, systems, and articles of manufacture for performing a process on biological samples. An analysis of biological samples in multiple regions of interest in a microfluidic device and a timeline correlated with the analysis may be identified. One or more region-of-interest types for the multiple regions of interest may be determined; and multiple characteristics may be determined for the biological samples based at least in part upon the one or more region-of-interest types. Associated data that respectively correspond to the multiple regions of interest in a user interface for at least a portion of the biological samples in the user interface based at least in part upon the multiple identifiers and the timeline. A count of the biological samples in a region of interest may be determined based at least in part upon a class or type of data using a convolutional neural network (CNN).

Abstract: Disclosed are methods, systems, and articles of manufacture for performing a process on biological samples. An analysis of biological samples in multiple regions of interest in a microfluidic device and a timeline correlated with the analysis may be identified. One or more region-of-interest types for the multiple regions of interest may be determined; and multiple characteristics may be determined for the biological samples based at least in part upon the one or more region-of-interest types. Associated data that respectively correspond to the multiple regions of interest in a user interface for at least a portion of the biological samples in the user interface based at least in part upon the multiple identifiers and the timeline. A count of the biological samples in a region of interest may be determined based at least in part upon a class or type of data using a convolutional neural network (CNN).

Abstract: Disclosed are methods, systems, and articles of manufacture for performing a process on biological samples. An analysis of biological samples in multiple regions of interest in a microfluidic device and a timeline correlated with the analysis may be identified. One or more region-of-interest types for the multiple regions of interest may be determined; and multiple characteristics may be determined for the biological samples based at least in part upon the one or more region-of-interest types. Associated data that respectively correspond to the multiple regions of interest in a user interface for at least a portion of the biological samples in the user interface based at least in part upon the multiple identifiers and the timeline. A count of the biological samples in a region of interest may be determined based at least in part upon a class or type of data using a convolutional neural network (CNN).

Abstract: Disclosed are methods, systems, and articles of manufacture for performing a process on biological samples. An analysis of biological samples in multiple regions of interest in a microfluidic device and a timeline correlated with the analysis may be identified. One or more region-of-interest types for the multiple regions of interest may be determined; and multiple characteristics may be determined for the biological samples based at least in part upon the one or more region-of-interest types. Associated data that respectively correspond to the multiple regions of interest in a user interface for at least a portion of the biological samples in the user interface based at least in part upon the multiple identifiers and the timeline. A count of the biological samples in a region of interest may be determined based at least in part upon a class or type of data using a convolutional neural network (CNN).

Abstract: A system and a method for dynamically optimizing an instrument system workflow based on operational monitoring and managing of a workflow for a hardware system. The system includes instrument resources and sample chambers, each resource and chamber with a dedicated sensor configured to acquire data. The system further includes a computing device communicatively connected to the instrument resources and sample chambers. The computing device includes a software application or program comprising a workflow builder, an execution engine, an analytics engine, a virtual system modeling engine, and an optional machine learning engine.