Abstract: Systems, methods and/or computer program products maximizing answer coherence of virtual agents responding to multi-part user queries, using relationships among extracted chunks corresponding to support document(s). Relationships are extracted by mining entities from text and images of support document(s) and mapping entities to an otology. Induced subgraphs for chunks, based on extracted entities are created and compared. Depending on amounts of overlap and/or relationships between subgraphs, relationships among chunks are identified. Chunks relationships can be complementary, contained within one another, instances of one another, or not related at all. For a given multi-part query, a query answer graph is created comprising answer nodes corresponding to potential answer candidates associated with each chunk.

Abstract: Identifying an log anomaly resolution by generating a knowledge base linking each of a plurality of incidents with historical anomalous log lines, calculating a resolution specificity score for each knowledge base record, identifying a run-time anomalous log line using the knowledge base, predicting a category for the run-time anomalous log line, identifying resolutions according to the category, ranking the resolutions according to the resolution specificity scores, and recommending a resolution according to the ranking.

Abstract: One or more computer processors classify each log line in a plurality of unlabeled log lines as an erroneous log line or a non-erroneous log line. The one or more computer processors templatize each classified erroneous log line and non-erroneous log line in the plurality of unlabeled log lines. The one or more computer processors cluster erroneous log templates into erroneous log template clusters and the non-erroneous log templates into non-erroneous log template clusters. The one or more computer processors eliminate the erroneous log template clusters and the non-erroneous log template clusters that exceed a frequency threshold. The one or more computer processors train a log anomaly model utilizing=remaining erroneous log template clusters and remaining non-erroneous log template clusters. The one or more computer processors identify a subsequent log line as anomalous or non-anomalous utilizing the trained log anomaly model.

Abstract: Systems, methods and/or computer program products maximizing answer coherence of virtual agents responding to multi-part user queries, using relationships among extracted chunks corresponding to support document(s). Relationships are extracted by mining entities from text and images of support document(s) and mapping entities to an otology. Induced subgraphs for chunks, based on extracted entities are created and compared. Depending on amounts of overlap and/or relationships between subgraphs, relationships among chunks are identified. Chunks relationships can be complementary, contained within one another, instances of one another, or not related at all. For a given multi-part query, a query answer graph is created comprising answer nodes corresponding to potential answer candidates associated with each chunk.

Abstract: Identifying an log anomaly resolution by generating a knowledge base linking each of a plurality of incidents with historical anomalous log lines, calculating a resolution specificity score for each knowledge base record, identifying a run-time anomalous log line using the knowledge base, predicting a category for the run-time anomalous log line, identifying resolutions according to the category, ranking the resolutions according to the resolution specificity scores, and recommending a resolution according to the ranking.

Abstract: Methods, systems, and computer program products for iterative answer and supplemental information extraction for machine reading comprehension are provided herein. A computer-implemented method includes obtaining a user query and items of context information related to the user query; identifying at least one of the items of context information to be used in connection with generating supplemental information by processing the user query and at least a portion of the multiple items of context information; generating the answer using an artificial intelligence-based question answering system; generating a modified version of the user query based on keywords derived from the generated answer and keywords derived from the user query; generating items of supplemental information for the generated answer by processing the modified version of the user query and the at least one identified item of context information; and performing automated actions based on the items of supplemental information.

Abstract: One or more computer processors classify each log line in a plurality of unlabeled log lines as an erroneous log line or a non-erroneous log line; templatize each classified erroneous log line and non-erroneous log line in the plurality of unlabeled log lines; cluster erroneous log templates into erroneous log template clusters and non-erroneous log templates into non-erroneous log template clusters; identify one or more log lines as anomalous utilizing a plurality of factors including a log maturity, a number of encountered log template clusters, and a ratio of classified erroneous log lines to classified non-erroneous log lines; responsive to one or more identified anomalous log lines, validate the identified anomalous log lines utilizing a site reliability engineer and human-in-the-loop validation; train a log anomaly model utilizing one or more validated log lines; and identify a subsequent log line as anomalous utilizing the trained log anomaly model.

Abstract: One or more computer processors classify each log line in a plurality of unlabeled log lines as an erroneous log line or a non-erroneous log line. The one or more computer processors templatize each classified erroneous log line and non-erroneous log line in the plurality of unlabeled log lines. The one or more computer processors cluster erroneous log templates into erroneous log template clusters and the non-erroneous log templates into non-erroneous log template clusters. The one or more computer processors eliminate the erroneous log template clusters and the non-erroneous log template clusters that exceed a frequency threshold. The one or more computer processors train a log anomaly model utilizing=remaining erroneous log template clusters and remaining non-erroneous log template clusters. The one or more computer processors identify a subsequent log line as anomalous or non-anomalous utilizing the trained log anomaly model.

Abstract: Methods, systems, and computer program products for automatically classifying heterogeneous documents using machine learning techniques are provided herein. A computer-implemented method includes extracting multiple features from one or more sets of heterogeneous documents; arranging at least a portion of the extracted features into multiple hierarchically-defined classes; selecting one or more of the documents associated with each of the multiple hierarchically-defined classes; training at least one machine learning model using at least a portion of the selected documents; and automatically classifying at least one input document into one or more of the multiple hierarchically-defined classes by processing one or more portions of the at least one input document using the at least one trained machine learning model.

Abstract: An antenna system is provided for use in wireless communication, the antenna system is contained in a modular structure. The antenna system includes a plurality of co-located antennas, including at least one active modal antenna, each of the antennas being adapted for operation at a distinct frequency band. The antenna system further includes an active tuning module for tuning a frequency response of the co-located antennas.

Abstract: A method and apparatus for flexibly filtering upstream scheduling messages in a communication system is described. The inventive method and apparatus uses software to rapidly filter a MAP message comprising a plurality of information elements. In accordance with the inventive method, a communication device inputs a MAP message and outputs a filtered MAP message comprising upstream transmission information relevant only to the communication device. The communication device stores data relevant to its upstream transmit opportunities. This data is derived from information elements that are associated with the communication device. In one embodiment, the communication device stores a transmit time. In another embodiment, the communication device stores a transmit time and a transmit duration. In yet another embodiment, the communication device stores a transmit time, a transmit duration and an IUC.

Abstract: An interrupt driven interface coupling a programmable media access controller (MAC) and a process controller. The interrupt driven interface is operable within a cable modem system. The specification by which the cable modem operates to transfer data between the programmable media access controller (MAC) and the control processor is loaded into a memory location within the system, and the physical system is operable to provide for backward compatibility, in that, the addition of new messages and the deletion of old messages within the specification is performed via software upgrade. The necessity of a re-design and re-fabrication of the programmable media access controller (MAC) and the control processor, and the interface between them is completely obviated by the present invention.