Accenture Global Solutions Limited

Biocon Limited

Sanjay Tiwari

Badarayan Panigrahi

Mathangi Sandilya

Gaurav Khanduri

Shantha Maheswari

Mayur Kolhe

Harish Iyer

Mukesh Babuappa Patale

Anuj Goel

Surya Kumar Ivg

Tarang Kumar

Shubhashis Sengupta

Ramakrishnan Melarkode

Shrikumar Suryanarayan

Ramavana Gururaja

Nithyanandan Periasamy Dhanapal

Surya Kumar Venkata Gangadhara Idumudi

Surekha K Prabhu

Chittnalli Ramegowda Naveen Kumar

Marcio Miranda Theme

Kedarnath Nanjund Sastry

Nagaraj Govindappa

Laxmi Adhikary

Anand Khedkar

Ranjana Bhalchandra Narawane

Nitin Patil

Partha Hazra

Nitesh Dave

Rakesh Bhaiyyaram Mendhe

Mayank Kumar Garg

Madhav Kulkarni

Madenahally Channabasappa Shivakumar

Prijyajit Sengupta

Chetan Thukral

Mohit Mahajan

Shihab Ponnampadikkal Abdul Rahiman

Indranil Choudhuri

Saurabh Garg

Sulekha Joshi

Bimal Kumar

Gokul Jothiraman

Anshul Kapoor

Krishnamurthy Venkatesan

Carl Matthew Dukatz

Vinu Varghese

Saran Prasad

Balaji Janarthanam

Kung-Chuan Hsu

Komal N Kanojia

Nita Roy

Goldy Sachdev

Deepthy Sathyanathan

Nikhil Varshney

Pinakee Devi Sharma

Nithyanandan Kandaswamy

Bhavika Sehgal

Dinesh Vijayakumar

Gourav Awasthi

Rajesh Gala

Amol Nandkishor Joshi

Trupti Meghshyam Shirodkar

Arun Chandavarkar

Dan Malagari

Nishant Shyam Patriker

Sanmati Jain

Sandeep Bhatnagar

Arti Kumari

Ashwini Purushothaman

Binit Agrawal

Mohit Chawla

Suraj Singh

Neha Shishoo

Subrat Kumar Pradhan

Chandrasekarapuram Venkatesh Gangadharan

Nandini Nataraj

Vivekanandan Kannan

Anoop Vasudevan

Juhi Mandot

Devendra Singh Dhakarey

Anupama Jagadish

Devendra Gautam

Madenahalli Channabasappa Shivakumar

Komal Kanojia

M C Shivakumar

A device may receive source code and a database to be migrated to a cloud computing environment, and may perform a first stage analysis of the source code to generate a first report. The device may cause a second stage analysis of the source code to be performed based on the first report and to generate refactored and rewritten code, and may perform a third stage analysis of the database to generate a second report. The device may cause a fourth stage analysis of the database to be performed and to generate a refactored and rewritten database, and may perform the first stage analysis of the refactored and rewritten code and the third stage analysis of the refactored and rewritten database to generate a final report. The device may generate a migration strategy based on the final report and may perform actions based on the migration strategy.

Utilizing multiple analyses to migrate an application to a cloud computing environment

Provided are systems, methods, and computer-readable storage media that leverage artificial intelligence and machine learning to identify, diagnose, and mitigate occurrences of network faults or incidents within a network. Historical network incidents may be used to generate a model that may be used to evaluate real-time occurring network incidents, such as to identify a cause of the network incident. Clustering algorithms may be used to identify portions of the model that share similarities with a network incident and then actions taken to resolve similar network incidents in the past may be identified and proposed as candidate actions that may be executed to resolve the cause of the network incident. Execution of the candidate actions may be performed under control of a user or automatically based on execution criteria and the configuration of the fault mitigation system.

Intelligent network operation platform for network fault mitigation

Embodiments of the present disclosure provide systems, methods, and computer-readable storage media that leverage artificial intelligence and machine learning to identify, diagnose, and mitigate occurrences of network faults or incidents within a network. Historical network incidents may be used to generate a model that may be used to evaluate real-time occurring network incidents, such as to identify a cause of the network incident. Clustering algorithms may be used to identify portions of the model that share similarities with a network incident and then actions taken to resolve similar network incidents in the past may be identified and proposed as candidate actions that may be executed to resolve the cause of the network incident. Execution of the candidate actions may be performed under control of a user or automatically based on execution criteria and the configuration of the fault mitigation system.

A device may receive audio data identifying a plurality of speakers and may process the audio data, with a plurality of clustering models, to identify a plurality of speaker segments. The device may determine a plurality of diarization error rates for the plurality of speaker segments and may identify a plurality of errors in the plurality of speaker segments. The device may select rectification models to rectify the plurality of errors and may segment and/or re-segment the audio data with the rectification models to generate re-segmented audio data. The device may determine a plurality of modified diarization error rates for the plurality of speaker segments based on the re-segmented audio data and may select one of the plurality of speaker segments based on the plurality of modified diarization error rates. The device may calculate an empathy score based on the selected speaker segment and may perform actions based on the empathy score.

Utilizing machine learning models to provide cognitive speaker fractionalization with empathy recognition

A device determines, from a network slice template associated with a network slice, a quality of service (QoS) profile for the network slice that includes performance metrics for corresponding QoS parameters associated with providing a service. The device monitors performance of the network slice in association with the QoS profile, and determines, based on the performance, that a performance indicator for a QoS parameter of the network slice is outside a threshold range of a performance metric. The device determines, based on the performance information and the QoS profile, a slice modification to the network slice template for the network slice, where the slice modification is configured to cause the performance indicator to be within the threshold range of the performance metric. The device causes a network slice orchestrator to update an instantiation of the network slice according to the slice modification and the network slice template.

Quality of service manager for network slices of a network

Methods, systems, and apparatus for root cause analysis in a communication network. In one aspect, a method includes providing a quantum computer with data representing a topology of the communication network, the topology comprising a graph of vertices representing network devices and edges representing connections between network devices; receiving, from the quantum computer, data representing a first subset of network devices, wherein the first subset comprises a dominating set of vertices or a vertex cover for the graph; monitoring network devices in the first subset to generate alarm data representing triggered network device alarms; providing the alarm data to a quantum computer; receiving, from the quantum computer, data representing a second subset of network devices, wherein the second subset comprises a set cover for the alarm data and the network devices in the second subset comprise diagnosed sources of failures in the communication network.

Quantum computing in root cause analysis of 5G and subsequent generations of communication networks

A device may receive site data identifying a site associated with a telecommunications network. The device may receive RF drive test data identifying signal strengths of the site at different location points. The device may receive network KPI data and throughput data associated with the site. The device may assign unique identifiers to the different location points. The device may determine, based on the site data, the RF drive test data, the network KPI data, and the unique identifiers, at least one of reference signal strength data for each of the different location points or downlink channel data for each of the different location points. The device may determine degraded signal strength data identifying a degraded signal strength of each of the different location points, if the site is decommissioned and based on the at least one of the reference signal strength data or the downlink channel data.

Analyzing a communication network device, based on coverage analyses associated with decommissioning the communication network device, to determine next actions for 5G rollout

A device may receive site data identifying a site associated with a telecommunications network. The device may determine handover data identifying handovers of user equipment associated with the site. The device may receive traffic data identifying traffic generated by the user equipment associated with the site. The device may calculate, based on the site data, the handover data, and the traffic data, a plurality of key performance indicators (KPIs) for the site, for a predefined time period, and if the site is decommissioned, wherein the plurality of KPIs include one or more of: resource block utilization data, determining KPI satisfaction data identifying whether one or more of the plurality of KPIs satisfy one or more of a plurality of KPI thresholds, and performing one or more actions based on the KPI satisfaction data.

Analyzing a communication network device, based on capacity analyses associated with decommissioning the communication network device, to determine next actions

The present disclosure provides a framework for quantitatively testing network services and their supporting APIs. To facilitate the testing, a testing script may be generated. The testing script may be parameterized with input data derived from a historical database. The testing script may be executed and responses received from the network during testing may be analyzed to determine metrics associated with execution of the testing script. The metrics may indicate successfully tested conditions, unsuccessfully tested conditions, untested conditions, other testing metrics, or a combination thereof. After the initial testing cycle a determination is made regarding whether a stop condition has been satisfied and if it has not been satisfied, another testing cycle may be executed. The next testing cycle may utilize a different set of input data that may be dissimilar to the input data used in the previous testing cycle so that additional conditions and functionality may be tested.

Quantitative network testing framework for 5G and subsequent generation networks

The vulnerability of network devices may be predicted by performing a survival analysis on big data. A prediction algorithm may be built by considering historical data from heterogeneous data sources. The operating state of the network devices on a network may be predicted. The services potentially affected by a predicted outage may be determined and displayed. Alternatively or in addition, the number of clients potentially affected by a predicted outage may be determined and displayed.

Network device vulnerability prediction

A device receives first network information associated with a first portion of a network, and second network information associated with a second portion of the network, and determines, based on the first network information and the second network information, an alarm condition for the network. The device determines, based on correlation rules, whether the alarm condition relates to a currently handled alarm condition or a previously handled alarm condition. The device correlates the alarm condition with network inventory information, network topology information, and network service information, when the alarm condition does not relate to the currently handled alarm condition or the previously handled alarm condition, to generate a correlated alarm condition. The device automatically generates a resolution for the correlated alarm condition based on the correlated alarm condition, and automatically performs one or more actions based on the resolution for the correlated alarm condition.

Automatic monitoring, correlation, and resolution of network alarm conditions

A system for migrating data from a legacy system to a target system includes an input/output (IO) processor configured to receive legacy data from a plurality of different types of legacy systems and to communicate target data to a plurality of different types of target systems; a staging area database configured to store legacy data according to a common database schema; a localized database configured to store target data according to a target schema that is associated with a target system type; a processor in communication with the interface, the staging area database, and the localized database; and non-transitory computer readable media in communication with the processor that stores instruction code. The instruction codes is executed by the processor and causes the processor to: determine a target schema associated with a target system type; convert the legacy data stored in the staging area database to localized data according to the determined target schema; store the localized data in the localized database; and communicate the localized data to the target system.

Data migration system

A computer-implemented method for correlating alarms that are generated with relation to a computer network is disclosed. The alarms represent faults of the computer network and/or faults of components of the computer network. The computer-implemented method comprises: displaying, on a display device, a graphical user interface that allows a user to select a type of correlation between one or more alarms and a type of fault which causes the one or more alarms from among a plurality of types of correlation; receiving, via the graphical user interface, a user input indicating the type of correlation selected by the user; storing, in a database, at least one rule based on the selected correlation; receiving, by a server, a plurality of alarms generated with relation to the computer network; correlating, by a correlation engine, the plurality of alarms received by the server according to the at least one rule stored in the database to identify a fault which has caused the one or more alarms; and displaying, on the display device, the at least one identified fault.

On-demand fault reduction framework

A device identifies a subset of network performance indicator (NPI) values that measure network performance during peak usage time periods, selects particular NPI values associated with the peak usage time periods, based on one or more of the subset of NPI values satisfying particular threshold network performance values, and compares the particular NPI values with threshold upper and lower bound network performance values to determine weighted scores for the particular NPI values. The device determines, by processing the weighted scores, a weighted mean score that represents a mean of the weighted scores, and determines, based on an analysis of the particular NPI values and the weighted mean score, an overall site score that represents a viability level in upgrading the cell site. The device generates, based on the overall site score, a plan to upgrade one or more network technologies associated with the cell site.

Identifying a cell site as a target for utilizing 5th generation (5G) network technologies and upgrading the cell site to implement the 5G network technologies

Growing community of inventors

Bengaluru, India

Sanjay Tiwari