Emc IP Holding Company LLC

Trax Technology Solutions Pte Ltd.

Emc Corporation

Alon Grubshtein

Raphael Cohen

Yair Adato

Aisling J Crowley

Peter R Elliot

Youval Bronicki

Omer Sagi

Daniel Shimon Cohen

Nimrod Milo

David M Gottlieb

Shlomi Dayan

Galit Peled

Amihai Savir

Philip Derbeko

Shiri Gaber

Oshry Ben-Harush

Ofri Masad

Dolev Pomeranz

Ziv Mhabary

Ran Taig

Itai Lishner

Aviv Eisenschtat

Yotam Michael

Mark Cook

Nir Hemed

Yonatan Adar

Maria Kushnir

Dror Yashpe

Haim Halbfinger

Osnat Yanushevsky

Anton Pavlinov

Lena Tenenboim-Chekina

Danny Croitoru

Paul Yudkin

Yohai Devir

A search engine responding to a user query to find relevant data assets in a federation business data lake (FBDL) system by monitoring and recording all of the interactions of users interacting with data assets in the FBDL system, providing all of the user interactions to a recommendation engine, calculating relevance of information in the FBDL system to each user, and recommending one or more new data assets to a target user based on the relevance of the information. The relevance comprises the target user's past interactions with the data assets based and the cumulative interactions of other users with the data assets, such that if one or more of the other users has similar interaction behavior to the target user, then knowledge of the one or more other users can impact the relevance of the information with regard to the one or more new data assets suggested to the target user.

Recommendation system for data assets in federation business data lake environments

The present disclosure relates to systems and methods for identifying products and monitoring planogram compliance using analysis of image data. In one implementation, the method may include accessing at least one planogram describing a desired placement of a plurality of product types on shelves of a plurality of retail stores; receiving image data from the plurality of retail stores; analyzing the image data to determine an actual placement of the plurality of product types on the shelves of the plurality of retail stores; determining at least one characteristic of planogram compliance based on detected differences between the at least one planogram and the actual placement of the plurality of product types on the shelves of the plurality of retail stores; and receiving checkout data from the plurality of retail stores reflecting sales of at least one product type from the plurality of product types.

Comparing planogram compliance to checkout data

A system for reducing product shortage durations in retail stores based on analysis of image data is provided. The system may comprise: a communication interface configured to receive image data from retail stores indicative of a product shortage of a product type relative to information describing a placement of products of a product type on a store shelf; and at least one processor configured to: analyze the image data to detect occurrences of product shortages of the product type in the retail stores and determine durations associated with the occurrences; identify a common factor contributing to the duration of part of the occurrences of the product shortages; determine an action, associated with the at least one common factor, for potentially reducing product shortage durations of future shortages of the product type in the retail stores; and provide information associated with the identified action to an entity.

Aggregating product shortage information

A system for automatically addressing detected conditions within the retail store is provided. The system may include at least one processor configured to receive a plurality of images depicting a plurality of products displayed on a plurality of store shelves; retrieve reference data specifying a desired state of the retail store; use the reference data to detect an indicator of a first service-improvement condition; use the reference data to detect an indicator of a second service-improvement; determine a first product-related task for addressing the first service-improvement condition; determine a second product-related task for addressing the second service-improvement condition; access a database storing information indicative of a first employee and a second employee on duty during a time interval over which the plurality of images was received; and assign the first product-related task to the first employee and assign the second product-related task to the second employee.

Prioritizing shelf-oriented tasks

A system for identifying products and tracking inventory in a retail store based on analysis of image data is provided. The system may comprise at least one processor configured to receive image data from a plurality of image sensors mounted in a retail store; analyze the image data to estimate a current inventory of at least one product type in the retail store; receive product supply information associated with the at least one product type in the retail store; determine that an online order from a virtual store made during a first time period will be fulfilled by an employee of the retail store during a second time period; determine a predicted inventory of the at least one product type during the second time period; and provide information to the virtual store regarding the predicted inventory of the at least one product type during the second time period.

Updating online store inventory based on physical store inventory

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

A method in one embodiment comprises extracting features from each of a plurality of software investigation log sets, generating representations for respective ones of the software investigation log sets based at least in part on the corresponding extracted features, and storing the representations in a knowledge base. In conjunction with obtaining at least one additional software investigation log set, the method generates a representation of the additional software investigation log set, identifies one or more of the representations previously stored in the knowledge base that exhibit at least a specified similarity to the representation of the additional software investigation log set in accordance with one or more statistical models, and presents information characterizing the one or more software investigation log sets corresponding to respective ones of the identified one or more representations in a user interface. The method is illustratively implemented in a machine learning system of a processing platform.

Similarity-based retrieval of software investigation log sets for accelerated software deployment

A System, Computer Program Product, and Computer-executable method for testing a production system, the System, Computer Program Product, and Computer-executable method including receiving information related to the production system, receiving production data from the production system, creating a virtual production system based off the production system using the received information and the received production data, and analyzing the production system by performing tests on the virtual production system.

Evaluation of security in a cyber simulator

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.

Automated content inference system for unstructured text data

Growing community of inventors

Lehavim, Israel

Alon Grubshtein