Emc IP Holding Company LLC

Idan Richman Goshen

Avitan Gefen

Shiri Gaber

Amihai Savir

Anat Parush Tzur

Eder Soares

Christopher Schneider

Daniel Coutinho

Lisa Woo

Joao Adalino Lopes De Moraes, Jr

Guilherme Toralles Darley

Emer Fitzpatrick

Lidiane Silva

Noam Kalush

Balaji Dhamodharan

Carlos Augusto Barra

Skye Jones

Eder Crespo

A method is provided comprising: obtaining a counterparty contract, the counterparty contract including a contract that is being proposed by a counterparty to a user; performing a segmentation of the counterparty contract to identify a plurality of sentence clusters, each of the sentence clusters corresponding to a different provision in the counterparty contract; generating a plurality of counterparty provision vectors based on the counterparty contract, each of the counterparty provision vectors being generated based on a different one of the plurality of sentence clusters; retrieving a user provision vector, the user provision vector corresponding to a user provision; calculating a plurality of similarity scores for the user provision vector; detecting whether the plurality of similarity scores satisfies a condition that is associated with the user provision; and outputting a notification associated with the user provision when the condition is satisfied.

Method and apparatus for contract analysis

Techniques are provided for automatic discovery of data records. One method comprises obtaining data records each corresponding to a different item and comprising features extracted from a data source, wherein the data records identify related items identified using a collaborative filter that relates items based on user preferences; generating an item network comprising multiple nodes each corresponding to a different item, where two nodes are connected by an edge based on: (i) an item type of the two nodes, (ii) a ratio of numerical values associated with the two nodes, and/or (iii) a pairwise configuration similarity score for the two nodes; clustering the nodes into node clusters based on topological properties of the item network; and identifying items related to a given item that (i) share an edge with the given item and (ii) are in a node cluster comprising a node of the given item.

Automatic discovery of related data records

One example method includes transcribing a portion of the audio component to create a transcription file that includes text, searching the text of the transcription file and identifying information in the text that may include personal information, defining a textual window that includes the information, evaluating the text in the textual window to identify personal information, and masking the personal information in the audio component of the recording. The personal information may be masked with information of a non-personal nature.

Masking personal information in audio recordings

Techniques are provided for evaluating multiple machine learning models to identify issues with a communication. One method comprises applying an audio signal associated with a communication to at least two of: (i) a trigger word analysis module that evaluates contextual information to determine if a trigger word is detected in the audio signal; (ii) an audio activity pattern analysis module that determines if a silence pattern anomaly is detected; and (iii) a communication application analysis module that evaluates features provided by a communication application relative to applicable thresholds; and combining results of the at least two of the trigger word analysis module, the audio activity pattern analysis module and the communication application analysis module to identify a communication issue. The combining may evaluate an accuracy of the trigger word analysis module, the audio activity pattern analysis module and/or the communication application analysis module to combine the results.

Communication issue detection using evaluation of multiple machine learning models

Techniques are provided for fraud mitigation using enhanced spatial features. One method comprises obtaining transaction data associated with a transaction; obtaining a machine learning module trained using training transaction data for multiple geographic areas to learn a correlation of the training transaction data with fraudulent activity for each geographic area; extracting a transaction address from the transaction data; determining a given geographic area for the transaction using the transaction address; determining values for a predefined spatial feature for a predefined region that includes the transaction address in the given geographic area using a query of an external online data source; applying the determined values for the predefined spatial feature to the machine learning module to obtain an anomaly score for the transaction; and initiating a predefined remedial step and/or a predefined mitigation step when the transaction is determined to be a predefined anomaly based on the anomaly score.

Fraud mitigation using one or more enhanced spatial features

Recommending applications is disclosed. An application graph is disclosed that represents applications. Each node of the graph corresponds to an application and edges relate applications that can handle the same file type. When an input application is identified, the graph can be used to recommend other applications that may be a suitable replacement for the input application. The recommendation is based on the graph and its links and on characteristics of the organization.

Smart software recommendation using an application network

Growing community of inventors

Beer Sheva, Israel

Idan Richman Goshen