Abstract: Disclosed is a monitoring system for a people mover system, having: a controller for controlling an electronic component of the people mover system, wherein the controller is configured to: determine a first operational status of the component, while the component is operating with first operational parameters; and transmit first controller data to a BOT service, directly or via a mobile device, wherein the first controller data is indicative of the first operational status.

Abstract: Methods, systems, and computer program products for repeat execution of RCA logic through run-time discovered topology pattern maps are provided herein. A computer-implemented method includes determining one or more topology paths of configuration items from a collection of multiple views of a target system; identifying one or more patterns from the one or more topology paths of configuration items; comparing the one or more identified patterns against a collection of data comprising mappings between (i) multiple root cause analysis logical expressions and (ii) multiple system architecture patterns, thereby identifying one or more of the multiple root cause analysis logical expressions to be executed on the one or more topology paths of configuration items; and generating a user-accessible link via a uniform resource locator for direct invocation of the one or more identified multiple root cause analysis logical expressions executed on the one or more topology paths of configuration items.

Abstract: Methods, systems, and computer program products for repeat execution of RCA logic through run-time discovered topology pattern maps are provided herein. A computer-implemented method includes determining one or more topology paths of configuration items from a collection of multiple views of a target system; identifying one or more patterns from the one or more topology paths of configuration items; comparing the one or more identified patterns against a collection of data comprising mappings between (i) multiple root cause analysis logical expressions and (ii) multiple system architecture patterns, thereby identifying one or more of the multiple root cause analysis logical expressions to be executed on the one or more topology paths of configuration items; and generating a user-accessible link via a uniform resource locator for direct invocation of the one or more identified multiple root cause analysis logical expressions executed on the one or more topology paths of configuration items.

Abstract: According to example configurations, a speech processing system can include a syntactic parser, a word extractor, word extraction rules, and an analyzer. The syntactic parser of the speech processing system parses the utterance to identify syntactic relationships amongst words in the utterance. The word extractor utilizes word extraction rules to identify groupings of related words in the utterance that most likely represent an intended meaning of the utterance. The analyzer in the speech processing system maps each set of the sets of words produced by the word extractor to a respective candidate intent value to produce a list of candidate intent values for the utterance. The analyzer is configured to select, from the list of candidate intent values (i.e., possible intended meanings) of the utterance, a particular candidate intent value as being representative of the intent (i.e., intended meaning) of the utterance.

Abstract: According to example configurations, a speech processing system can include a syntactic parser, a word extractor, word extraction rules, and an analyzer. The syntactic parser of the speech processing system parses the utterance to identify syntactic relationships amongst words in the utterance. The word extractor utilizes word extraction rules to identify groupings of related words in the utterance that most likely represent an intended meaning of the utterance. The analyzer in the speech processing system maps each set of the sets of words produced by the word extractor to a respective candidate intent value to produce a list of candidate intent values for the utterance. The analyzer is configured to select, from the list of candidate intent values (i.e., possible intended meanings) of the utterance, a particular candidate intent value as being representative of the intent (i.e., intended meaning) of the utterance.

Abstract: Techniques for identifying one or more inconsistencies between an unstructured document and a back-end fact-base are provided. The techniques include automatically parsing a query document and comparing the document with a back-end fact-base comprising facts relevant to the document, identifying one or more inconsistencies between information mentioned in the document and the facts stored in the back-end fact-base, and providing a response to the query document, wherein the response additionally includes the one or more identified inconsistencies.

Abstract: Determining synonyms of words in a set of documents. Particularly, when provided with a word or phrase as input, in exemplary embodiments there is afforded the return of a predetermined number of “top” synonym words (or phrases) for an input word (or phrase) in a specific collection of text documents. Further, a user is able to provide ongoing and iterative positive or negative feedback on the returned synonym words, by manually accepting or rejecting such words as the process is underway.

Abstract: Techniques for identifying one or more inconsistencies between an unstructured document and a back-end fact-base are provided. The techniques include automatically parsing a query document and comparing the document with a back-end fact-base comprising facts relevant to the document, identifying one or more inconsistencies between information mentioned in the document and the facts stored in the back-end fact-base, and providing a response to the query document, wherein the response additionally includes the one or more identified inconsistencies.

Abstract: The embodiments of the invention provide a method for the automatic identification of changing subtopics within topics. The method begins by receiving customer satisfaction data having unstructured data objects. Next, the data objects are automatically categorized into pre-defined topics, wherein the pre-defined topics do not change throughout the customer satisfaction analysis. The pre-defined topics can be automatically defined based on a history of customer satisfaction data. Following this, a clustering analysis is automatically performed to identify subtopics of the data objects within the pre-defined topics. The subtopics are more specific than the pre-defined topics, and the subtopics can change. Further, the clustering analysis can include extracting features from the data objects and grouping the features into the subtopics. Each of the subtopics includes features having a predetermined degree of similarity.