Microsoft Technology Licensing, LLC

Rajeev Dubey

Navendu Jain

David A Machaj

Adam James Betz

Huy P M Dao

Jason Michael Deakins

Bill A Haase

Dump file content and other event data is pruned and prioritized to assist analysis of hangs, crashes, and other circumstances. An event data pruner condenses or excludes certain event data. A cluster creator creates clusters from pruned and vectorized event data, using a clustering algorithm and a similarity metric, without any prior specification of the desired number of clusters. A cluster ranker ranks clusters according to event data volume and thread importance, thus prioritizing the event data for inspection. A results presenter configures a user interface to present ranked clusters, their associated data, data reduction statistics, regression analysis results, and other data reduction processing results. Thus, the innovative functionality assists analysis and prioritized inspection of event data by an analyst, surfacing organized event data that is relevant to the circumstance under investigation, or supporting comparison of clusters from before and after a change in the circumstance, or doing both.

Pruning and prioritizing event data for analysis

A knowledge base can include a dictionary associated with classes of a model, e.g., an ontology. A text segment that is not found in the dictionary can be received. Feature(s) can be determined for the text segment and, based partly on providing the feature(s) to a classifier, a set of values can be determined. The distribution can include values respectively corresponding to the classes. One of the values can be greater than a predetermined threshold. That value can correspond to a class. An indication identifying the class can be presented via a user interface having functionality to provide input that the text segment is associated with the class, is not associated with the class, or is associated with another class. Based at least partly on adding a new class to the ontology, a precedence table indicating priorities between motifs defining relationships between classes of the ontology can be updated.

Knowledge base for analysis of text

User feedback may be analyzed with semantic hierarchies. In some instances, the user feedback includes free-form text. The user feedback may be mapped to one or more semantic hierarchies that include multiple levels of nodes, where each node corresponds to a class. Information of the one or more semantic hierarchies may be mapped to an ontology model. The mapped information of the ontology model may be used to identify an actionable item for the user feedback, such as a problem, suggestion, question, or other issue. Information regarding the actionable item may be made available to an individual for evaluation of the actionable item.

Utilizing semantic hierarchies to process free-form text

Free-form text in a document can be analyzed using natural-language processing to determine actionable items specified by users in the text or to provide recommendations, e.g., by automatically analyzing texts from multiple users. Words or phrases of the text can be mapped to classes of a model. An actionable item can be determined using the mapped words or phrases that match a selected grammar pattern. Items can be ranked, e.g., based on frequency across multiple documents. In some examples, the classes can include a suggestion-indicator class or a modal-indicator class, and the selected grammar pattern can include one of those classes. In some examples, the mapping can use a dictionary. A new term not in the dictionary can be automatically associated with classes based on attributes of the new term and of terms in the dictionary, e.g., the new term's part of speech or neighboring terms.

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Rajeev Dubey