General Electric Company

Fmr Corp.

Debasis Bal

Gopi Subramanian

Giridhar M Prabhakar

Sudhanshu Rai

Paul August Quail

Ankush Chopra

Jayanth Kalle Marasanapalle

Babu Ozhur Narayanan

Ramasubramanian Gangaikondan Sundararajan

Abhinanda Sarkar

Rajagopolan Chandrasekharan

Roopesh Ranjan

Larry Weldon Dill

Awadhesh Kumar

Molli Anne Malcolmson

Sohom Ghosh

Methods and apparatuses are described for automated analysis of customer interaction text to generate customer intent information and a hierarchy of customer issues. A server captures computer text segments including a first portion comprising a transcript of an interaction and a second portion comprising notes about the interaction. The server generates interaction embeddings corresponding to the first portion of the computer text segment for a trained neural network. The server executes the neural network using the interaction embeddings to generate an interaction summary for each computer text segment. The server converts each interaction summary into a multidimensional vector and aggregates the multidimensional vectors into clusters based upon a similarity measure. The server aligns the clusters of vectors with attributes of the interaction summaries to generate a hierarchical mapping of customer issues.

Automated analysis of customer interaction text to generate customer intent information and hierarchy of customer issues

A system for predicting turbine rub includes a monitoring system configured to form operational values for a turbine based on information received from the turbine and a correlation engine configured to form at least one correlated value from the operating values that correlates a first operating value to a second operating value. The system also includes a variable deriver configured to form at least one derived variable from a one of the operating values and a rub predictor that forms a rub prediction based on the at least one correlated value and the at least one derived value.

System and method for predicting turbine rub

A system, method and computer program product for predicting collector flashover is disclosed. The method includes receiving a frame of measured data from a data acquisition system, fitting a statistical model to the measured data wherein the measured data includes a plurality of measured variables measured at multiple time instances. The statistical model and the measured data are used for estimating one or more parameters for the frame wherein the one or more parameters include at least one of field circuit impedance and field current noise. A flashover is predicted based, at least in part, upon the one or more estimated parameter for the frame.

Method and system for detection of collector flashover

A system for dynamically allocating resources in a computing grid is provided. The system comprises a resource management component, a service level agreement (SLA) management component and a job scheduling component. The resource management component is configured to determine availability of a set of resources in a computing grid, for executing job requests submitted by users of the computing grid. The SLA management component is configured to manage service level agreements (SLAs) corresponding to the users and perform a negotiation of resources for the users submitting the job requests, in real-time, based on a resource steerability attribute associated with the job requests. The job-scheduling component is configured to schedule the job requests for the users, based on the resource steerability attribute associated with the job requests. The job requests comprise at least one of a job having batch characteristics and a job having interactive characteristics. The computing grid is configured to process the job based on the resource steerability attribute associated with the job request.

System and method for dynamic allocation of resources in a computing grid

An integrated business decision-making system is provided. The system comprises a data processing component, a prediction component and an optimization component. The data processing component is configured to process observational data related to one or more members of a target population. The prediction component is configured to predict a business measure based on one or more selected combinations of marketing actions and the observational data related to one or more of the members of the target population. The optimization component is configured to determine an optimal marketing action based on one or more of the selected combinations of marketing actions and the predicted business measure associated with each of the marketing actions. The optimization component is further configured to determine an optimal predicted business measure for the members of the target population, based on the optimal marketing action.

Integrated business decision-making system and method

System, method and computer program product for optimal pricing of financial products. Data related to the financial product is extracted from at least one data source. A structured data file is generated from the data. The structured data file comprises a plurality of attributes and a plurality of price parameters related to the financial product. The plurality of price parameters comprise a price conversion probability measure and a business measure parameter associated with the financial product. Then, the optimal price associated with the financial product is estimated based on the plurality of attributes, the plurality of parameters, the price conversion probability measure and the business measure parameter.

System, method and computer product for optimal pricing of a financial product

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Bengaluru, India

Debasis Bal