Numerify, Inc.

Digital.ai Software, Inc.

Microsoft Technology Licensing, LLC

Mimosa Systems, Inc.

Microsoft Corporation

Rahul Kapoor

Gaurav Rewari

Sadanand Sahasrabudhe

Renu Chintalapati

Nigel Robin Ellis

Satish Ramchandra Thatte

Rolando Jimenez Salgado

Florian Schouten

Anuj Bansal

Prakash Sundaresan

Aravind Sridharan

Cesar Alejandro Galindo-Legaria

Todd J Abel

Sandhya D Jain

Umakanthan Diwakaran

Abhijeet Anant Joshi

LiHui V Xu

Ravishankar Muniasamy

David Shenk

Srinivas M Vedagiri

Sunny Tulsi Sreedhar Murthy

Joseph Patrick Foley

Surajit J Chaudhuri

Parul Manek

Venkatesh Ganti

Burra Gopal

Sherif Botros

Xiaoyu Wu

Alexandru Croicu

Theodore Vassilakis

Ricard Roma I Dalfó

Rohan Aranha

Yi Mao

Kaushik Raj

Oleg Ovanesyan

Abha Jain

Saji Varkey

Peiwei Cao

A Seby

Heena Hussain

Ryan O'Shea

Sameer H Ranade

Srikanth Gopalaswami

Muthiah K Annamalai

Sharon E Edelstein

Michael Fodor

Shalini Sinha

Govind Saria

Ravi Kumar

Kari Clement

Cesar Galindo-Legaria

Generating an output prioritized list of computerized work-items for prioritizing work based on a set of prioritized computer-executable rules includes a structured way of associating contributions and expenses with computerized work-items and enhancing current technological processes of selecting computerized work-items to meet prioritized computer-executable rules. The method includes determining an electronic priority order of each of contribution sub-components and expense sub-components based on prioritized computer-executable rules, generating a tuple having contribution sub-components and expense sub-components for each of the computerized work-items, sorting the computerized work-items of the input master list in descending order of the contribution and expense sub-component tuples to obtain a sorted list of computerized work-items, and traversing the sorted list of computerized work-items to generate the output prioritized list of computerized work-items for prioritizing work based on prioritized computer-executable rules.

Computerized work-item selection and progress tracking based on a set of prioritized computer-executable rules

Generating an output prioritized list of computerized work-items for prioritizing work based on a set of quantified computer-executable rules includes a structured way of associating contributions and expenses with computerized work-items and enhancing current technological processes of selecting computerized work-items to meet quantified computer-executable rules. The method includes determining a priority order of each sub-component based on quantified computer-executable rules associated with those sub-components, generating tuples having sub-components for each computerized work-item that are arranged based on the priority order, sorting the computerized work-items in descending order of the tuples, and traversing the sorted list of computerized work-items to generate the output prioritized list of computerized work-items for prioritizing work based on quantified computer-executable rules. The method provides the ability of tracking progress of the computerized work-items using standardized or comparable bucket values and measuring progress against the quantified computer-executable rules.

Computerized work-item selection and progress tracking based on a set of quantified computer-executable rules

An analytics server for scalable predictive analysis for analytics as a software service in multi-tenant environment is provided. The analytics server automatically validates portability of a predictive model from a first tenant to a second tenant by comparing value distribution of parameters between data inputs of the first tenant and the second tenant. The analytics server further automatically detects source data changes over a configurable time horizon as relevant to predictive model inputs, by comparing value distribution of parameters between two data inputs from a same tenant separated by a selected time horizon.

Aids for porting predictive models across tenants and handling impact of source changes on predictive models

Approaches for a model-driven tool-agnostic reporting approach are provided. In one example, a report request is received from a business intelligence reporting application, from which a graphical template and query template are created, wherein the graphical template comprises the visual presentation of data responsive to the request and the query template specifies how the data responsive to the request is to be computed. The report created by the approaches described herein is compatible with multiple business intelligence reporting applications.

Model driven reporting

A system that suggests and refines priority, severity, historical resolution, resolution time and assignment group to resolve auto-generated incidents is provided. The system leverages an incident characterization list tracking essence key phrases associated with historical incidents generated by an application performance management (APM) system and a machine data (MD) analysis system. The system automatically obtains a first auto-generated incident, from the APM system or the MD analysis system, based on any specified search criteria being met, or detection of a deviation in the value of key metrics from a specified threshold value. The system further automatically extracts essence key phrases of the first auto-generated incident and matches it with the essence key phrases of the incident characterization list to suggest priority, severity, historical resolution and resolution time, relevant knowledge base articles and assignment group for the first auto-generated incident to aid in resolution of the first auto-generated incident.

Computer incident scoring

An automated system for recommending predictor variable values for improving predictive outcomes of a predictive model is provided. The automated system recommends appropriate predictor variable values for changeable predictor variables that improve a predictive outcome of the predictive model by (i) computing predictive outcomes for each input record during a batch ETL process and (ii) determining appropriate predictor variable values that lead to improved predictive outcomes, using the code generated extended ETL jobs updated to perform rescoring using a combination of different values of the changeable predictor variables while honoring constraints between the changeable predictor variables, or by enabling an end user to perform said rescoring by changing values of the changeable predictor variables on the fly to determine most suitable predictor variable values that lead to improved predictive outcomes.

Automatic recommendation of predictor variable values for improving predictive outcomes

A method of generation of a unified view of a software development process that spans a plurality of distinct software development applications having distinct software models is provided. The method includes enhancing a software development application selected as primary to provide the unified view of the software development process, or alternatively using a data warehouse to provide the unified view of the software development process via reports and dashboards created based on a unified software model. The method provides end to end visibility, auditability, and support for calculation of one or more flow metrics, not only for the software development process, but also for a set of stages, individual stages and sub-stages of the software development process. The method also provides an ability to relate the one or more flow metrics to business outcomes.

Unified view, analytics, and auditability for software development processes spanning distinct software development applications

A method for enabling performance of a protected computer operation on a protected item without pop-up confirmation dialogs being generated, is provided. The method includes performing a first action on a first element of a user interface of a computing device, the first action is associated with the protected computer operation by processing a first response that is a first input into the computing device. Performing the first action on the first element of the user interface enables a second element of the user interface to perform the protected computer operation. The protected computer operation is performed by processing a second response that is a second input into the computing device.

Pop-up free protected operations

Systems and methods for training a machine learning (ML) model for predicting probabilities for binary outcomes to automatically generate positive class predictions close to an ideal one probability and negative class predictions close to an ideal zero probability are disclosed. The method includes generating a predictive probability (PP) curve based on a ML algorithm and transforming the PP curve into a curve with probabilities spread close to ideal one probability for positive class predictions (PCP) indicating successful prediction and close to ideal zero probability for negative class predictions (NCP) indicating failed prediction, thereby introducing a valley in the transformed probability curve separating PCP from NCP. The PP curve is transformed by one of (1) minimizing distance between: (a) ideal one probability and PP value of PCP; and (b) ideal zero probability and PP value of NCP, and (2) maximizing distance of PP values from center of PP curve.

System and method of training machine learning models to generate intuitive probabilities

Computer incident scoring and correlation

A method for automatically determining one or more cross customer metrics from a cross customer store for cross customer analytics is provided. The method includes the steps of: (i) automatically standardizing dimensions associated with the one or more customers to create one or more cross customer metrics; (ii) automatically determining incremental data from one or more customer specific stores by tracking (a) a time stamp of a last pull time (T) for customer data stored in the one or more customer specific stores, and (b) a number of items involved (I); (iii) automatically updating the one or more cross customer metrics in the cross customer store with the incremental data; and (iv) automatically comparing the one or more cross customer metrics of a customer with one or more peer groups created at run time based on customer selected values for one or more parameters.

Efficient cross customer analytics

Growing community of inventors

Bellevue, WA, United States of America

Rahul Kapoor