Emc IP Holding Company LLC

Emc Corporation

Raphael Cohen

Alon Grubshtein

Peter R Elliot

Aisling J Crowley

David M Dionisio

Amihai Savir

Shiri Gaber

Anat Parush Tzur

Oshry Ben-Harush

Ofri Masad

Otniel Van-Handel

Eslam ElNakib

Lena Tenenboim-Chekina

Phu Son Le

Hengli Liang

Jenny L Beazley

Lina Al Farouk

An apparatus comprises a processing platform configured to implement a machine learning system for automated classification of documents comprising text data of at least one database. The machine learning system comprises a clustering module configured to assign each of the documents to one or more of a plurality of clusters corresponding to respective topics identified from the text data in accordance with at least one topic model, and an interface configured to present portions of documents assigned to a particular one of the clusters by the clustering module and to receive feedback regarding applicability of the corresponding topic to each of one or more of the presented portions on a per-portion basis. The topic model is updated based at least in part on the received feedback. The feedback may comprise, for example, selection of a confidence level for applicability of the topic to a given one of the presented portions.

Topic model based clustering of text data with machine learning utilizing interface feedback

An apparatus comprises a processing platform configured to implement a machine learning system for automated generation of predicted reliability measures and associated early warning indicators for product and part combinations. The machine learning system comprises a data aggregation module configured to extract product and part data from a big data repository, and a reliability predictor configured to generate predicted reliability measures for respective ones of the product and part combinations utilizing a shared model that is determined based at least in part on the extracted product and part data. The machine learning system processes the predicted reliability measures to generate early warning indicators relating to particular ones of the product and part combinations having predicted reliability measures that fail to meet one or more specified criteria. The machine learning system illustratively provides the early warning indicators to a visualization interface so as to facilitate user adjustment of a product line.

Predicting reliability of product and part combinations using machine learning based on shared model

An apparatus comprises a processing platform configured to implement an analysis and visualization tool utilizing a mixture of multiple reliability measures to characterize each of a plurality of product and part combinations. The analysis and visualization tool comprises a data aggregation module configured to extract product and part data from a big data repository, a reliability measure generator configured to process the extracted product and part data to generate a plurality of reliability measures for each of a plurality of different product and part combinations, a mixture model module configured to compute a score from the plurality of reliability measures for each of the different product and part combinations, and a visualization module configured to generate at least one visualization as a function of the scores computed for the respective different product and part combinations. The generated visualization may comprise a quadrant plot view visualization displaying the computed scores.

Analysis and visualization tool utilizing mixture of multiple reliability measures for product and part combinations

An apparatus comprises a processing platform configured to implement an expert recommender engine. The expert recommender engine receives information relating to a communication from a user device, and identifies at least one subject matter expert for the communication based on the received information and unstructured text data of a service events database. The expert recommender engine is associated with a clustering module that separates the unstructured text data into topic clusters. The expert recommender engine comprises a collaborative filtering module that receives the topic clusters from the clustering module and utilizes the topic clusters to identify the subject matter expert. The user device is connected with an expert device corresponding to the identified subject matter expert. The expert recommender engine may utilize structured data, social media data and customer satisfaction survey data in combination with the received information and the topic clusters to identify the subject matter expert.

Expert recommendation leveraging topic clusters derived from unstructured text data

An apparatus comprises a processing platform configured to implement an analysis and visualization tool for combined processing of structured and unstructured service event data. The structured service event data comprises service event data stored in one or more structured data fields of a service events database, and the unstructured service event data includes documents comprising unstructured text data of the service events database. The analysis and visualization tool is associated with a clustering module that assigns each of the documents to one or more clusters corresponding to respective topics. The analysis and visualization tool comprises an interface that permits selection of one or more of the structured data fields, and a visualization module configured to generate at least one visualization as a function of the selected one or more structured data fields and particular ones of the cluster topics that relate to the selected one or more structured data fields.

Analysis and visualization tool with combined processing of structured and unstructured service event data

An apparatus comprises a processing platform configured to implement an analytic engine for evaluation of at least one of a converged infrastructure environment and one or more components of the converged infrastructure environment. The analytic engine comprises an extraction module configured to extract one or more features corresponding to the converged infrastructure environment, a learning and modeling module configured to predict an expected quantitative performance value of at least one of the converged infrastructure environment and the one or more components of the converged infrastructure environment based on the extracted one or more features, and comparison and ranking modules. The comparison module is configured to calculate a difference between an actual quantitative performance value of at least one of the converged infrastructure environment and the one or more components of the converged infrastructure environment and the expected quantitative performance value. The ranking module determines anomalies based on the difference.

Data driven converged infrastructure components evaluation

An apparatus comprises a processing platform configured to implement a cluster labeling system for documents comprising unstructured text data. The cluster labeling system comprises a clustering module and a visualization module. The clustering module implements a topic model generator and is configured to assign each of the documents to one or more of a plurality of clusters based at least in part on one or more topics identified from the unstructured text data using at least one topic model provided by the topic model generator. The visualization module comprises multiple view generators configured to generate respective distinct visualizations of a selected one of the clusters. The multiple view generators include at least a bigram view generator configured to provide a visualization of a plurality of term pairs from the selected cluster, and a summarization view generator configured to provide a visualization of representative term sequences from the selected cluster.

Cluster labeling system for documents comprising unstructured text data

An apparatus comprises a processing platform configured to implement an automated content inference system for unstructured text data. The automated content inference system comprises a data indexer, a dictionary generator, a topic model generator, and a visualization generator. The data indexer is configured to process the unstructured text data to construct a term index for a given document. The dictionary generator is configured to process the term index to construct an in-domain dictionary. The topic model generator is configured to process the in-domain dictionary to construct a topic model. The visualization generator is configured to provide an output display showing assignment of one or more topics to the given document based on the topic model. The unstructured text data illustratively comprises unstructured service request summaries from a service events database. The unstructured service request summaries may comprise problem summaries and corresponding solution summaries relating to respective service events.

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Beer-Sheva, Israel

Raphael Cohen