Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media for summarizing a call. One of the methods includes generating text corresponding to processing audio produced during an interaction between two participants by executing natural language processing logic. The method includes identifying one or more topics by providing the generated text to a machine learning system, the machine learning system trained to identify topics based on text. The method also includes generating a summary of the interaction based on the one or more topics and the text.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media for summarizing a call. One of the methods includes generating text corresponding to processing audio produced during an interaction between two participants by executing natural language processing logic. The method includes identifying one or more topics by providing the generated text to a machine learning system, the machine learning system trained to identify topics based on text. The method also includes generating a summary of the interaction based on the one or more topics and the text.

Abstract: The technique herein substantially improves productivity of Annotator developers by providing methods and systems to develop and test Annotators without having to run a full pipeline every time changes are made to a particular Annotator. To this end, preferably a running pipeline is instrumented to enable automated recording of static configuration and dynamically-generated event data as the pipeline is executed. Based on these data, a reusable data model is generated that captures code and other dependencies in the pipeline (e.g., configuration parameters, intermediary CASes, program flow, annotations, and the like). The data model is then used to facilitate testing of Annotators without using the full pipeline (or even major sub-pipelines therein).

Abstract: The technique herein substantially improves productivity of Annotator developers by providing methods and systems to develop and test Annotators without having to run a full pipeline every time changes are made to a particular Annotator. To this end, preferably a running pipeline is instrumented to enable automated recording of static configuration and dynamically-generated event data as the pipeline is executed. Based on these data, a reusable data model is generated that captures code and other dependencies in the pipeline (e.g., configuration parameters, intermediary CASes, program flow, annotations, and the like). The data model is then used to facilitate testing of Annotators without using the full pipeline (or even major sub-pipelines therein).

Abstract: The technique herein substantially improves productivity of Annotator developers by providing methods and systems to develop and test Annotators without having to run a full pipeline every time changes are made to a particular Annotator. To this end, preferably a running pipeline is instrumented to enable automated recording of static configuration and dynamically-generated event data as the pipeline is executed. Based on these data, a reusable data model is generated that captures code and other dependencies in the pipeline (e.g., configuration parameters, intermediary CASes, program flow, annotations, and the like). The data model is then used to facilitate testing of Annotators without using the full pipeline (or even major sub-pipelines therein).

Abstract: A pipeline development environment includes a toolset that includes a visual design editor. The editor comprises a display interface having a palette of known Annotators that may be selected by a developer. The pipeline development environment also includes or has associated therewith a data repository. The data repository stores datasets. A particular dataset is associated with an Annotator and comprises dependency data generated from execution of a pipeline (or some portion thereof). The repository typically stores datasets from many pipeline runs, including runs of other pipelines, multiple runs of a given pipeline with different inputs, etc. Using the editor, a developer creates a visual representation of the pipeline. As Annotators are added into the pipeline, system tooling dynamically generates the descriptor files and other configuration parameters (for the new pipeline), preferably based on the dependency data associated with the individual Annotators and retrieved from the repository.

Abstract: The technique herein substantially improves productivity of Annotator developers by providing methods and systems to develop and test Annotators without having to run a full pipeline every time changes are made to a particular Annotator. To this end, preferably a running pipeline is instrumented to enable automated recording of static configuration and dynamically-generated event data as the pipeline is executed. Based on these data, a reusable data model is generated that captures code and other dependencies in the pipeline (e.g., configuration parameters, intermediary CASes, program flow, annotations, and the like). The data model is then used to facilitate testing of Annotators without using the full pipeline (or even major sub-pipelines therein).

Abstract: A pipeline development environment includes a toolset that includes a visual design editor. The editor comprises a display interface having a palette of known Annotators that may be selected by a developer. The pipeline development environment also includes or has associated therewith a data repository. The data repository stores datasets. A particular dataset is associated with an Annotator and comprises dependency data generated from execution of a pipeline (or some portion thereof). The repository typically stores datasets from many pipeline runs, including runs of other pipelines, multiple runs of a given pipeline with different inputs, etc. Using the editor, a developer creates a visual representation of the pipeline. As Annotators are added into the pipeline, system tooling dynamically generates the descriptor files and other configuration parameters (for the new pipeline), preferably based on the dependency data associated with the individual Annotators and retrieved from the repository.

Abstract: An improved precision cleaning method includes introducing a solvent to a cleaning vessel, placing a workpiece in the cleaning vessel and contacting the workpiece with the solvent during a residence period. The cleaning vessel is then pressurized to a target pressure by adding carbon dioxide to the vessel, and additional carbon dioxide is pumped into the vessel while maintaining the target pressure to flush contents of the vessel. Cleaning may be enhanced by pressurizing to above the supercritical point of the carbon dioxide, and cleaning is further enhanced by application of sonic energy or mechanical agitation.