Patents by Inventor Madhan KUMAR
Madhan KUMAR has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
-
Patent number: 10922141Abstract: A multi-layer committed compute reservation stack may generate prescriptive reservation matrices for controlling static reservation for computing resources. A transformation layer of the committed compute reservation stack may generate a time-mapping based on historical utilization and tagging data. An iterative analysis layer may determine a consumption-constrained committed compute state of a distribution of static reservation and dynamic requisition that achieves one or more consumption efficiency goals. Once the consumption-constrained committed compute state is determined, the prescriptive engine layer may generate a reservation matrix that may be used to control computing resource static reservation prescriptively.Type: GrantFiled: March 15, 2018Date of Patent: February 16, 2021Assignee: Accenture Global Solutions LimitedInventors: Madhan Kumar Srinivasan, Arun Purushothaman, Manish Sharma Kolachalam, Michael S. Eisenstein
-
Patent number: 10906479Abstract: A door edge protector device includes a protector, a cam member and a link mechanism. Cam portions are formed in the cam member, and the cam member is rotated by an input corresponding to a close/open state of a door. The link mechanism includes cam followers which are engaged with the cam portions, and the protector is attached to the link mechanism. By the engagement between the cam portions and the cam followers, the link mechanism moves in an interlocking manner with rotation of the cam member. A locus T includes an accommodated position corresponding to the close state of the door and a set position covering a door edge corresponding to the close state of the door. The cam portions are configured so that the protector moves along the locus by movement of the link mechanism.Type: GrantFiled: October 25, 2016Date of Patent: February 2, 2021Assignee: U-SHIN LTD.Inventors: Manu Sharma, Himanshu Chaman, Madhan Kumar B, Toshikazu Making
-
Patent number: 10871917Abstract: A multi-layer rate sizing stack may generate prescriptive operation-rate tokens for controlling sizing adjustments for operation-rates. The input layer of the rate sizing stack may generate operation pattern data based on operation-rate data received via network connection. A prescriptive engine layer may determine a prescriptive allowed operation-rate based on the operation pattern data. Based on the prescriptive allowed operation-rate, the prescriptive engine layer may generate the operation-rate tokens that that may be used to control rate sizing adjustments prescriptively.Type: GrantFiled: February 26, 2019Date of Patent: December 22, 2020Assignee: Accenture Global Solutions LimitedInventors: Madhan Kumar Srinivasan, Arun Purushothaman, Guruprasad Pv, Kishore Kumar Gajula
-
Publication number: 20200382437Abstract: A multi-layer resource aggregation (RA) stack may generate prescriptive activation timetables for controlling activation states for computing resources. To facilitate operator control and adjustment, the RA stack may, at an aggregation engine layer, aggregate the computing resource into one or more resource aggregates. The computing resources within the resource aggregates may have similar individual activation prescription patterns. Machine learning techniques may be used by the RA stack to identify these related individual activation prescription patterns and aggregate the computing resources accordingly. Once aggregated, the RA stack may make a uniform activation determination for the aggregates as single units. Therefore, the computing resources within the aggregate may be controlled and/or adjust together. Thus, the RA stack increases the scalability of implementation of prescriptive computing resource activation state determinations.Type: ApplicationFiled: May 28, 2019Publication date: December 3, 2020Applicant: Accenture Global Solutions LimitedInventors: Madhan Kumar Srinivasan, Arun Purushothaman, Manish Sharma Kolachalam
-
PRESCRIPTIVE ANALYTICS BASED COMPUTE SIZING CORRECTION STACK FOR CLOUD COMPUTING RESOURCE SCHEDULING
Publication number: 20200348961Abstract: A multi-layer compute sizing correction stack may generate prescriptive compute sizing correction tokens for controlling sizing adjustments for computing resources. The input layer of the compute sizing correction stack may generate cleansed utilization data based on historical utilization data received via network connection. A prescriptive engine layer may generate a compute sizing correction trajectory detailing adjustments to sizing for the computing resources. Based on the compute sizing correction trajectory, the prescriptive engine layer may generate the compute sizing correction tokens that that may be used to control compute sizing adjustments prescriptively.Type: ApplicationFiled: July 20, 2020Publication date: November 5, 2020Applicant: Accenture Global Solutions LimitedInventors: Madhan Kumar Srinivasan, Arun Purushothaman, Guruprasad PV, Michael S. Eisenstein, Vijay Desai -
Patent number: 10764370Abstract: A cloud migration tool manages and monitors a cloud migration project that migrates data from a legacy environment to a target data center environment. The cloud migration tool includes an analytics engine that applies data regression models to generate a delay risk prediction for activities that are scheduled during the cloud migration project.Type: GrantFiled: April 30, 2018Date of Patent: September 1, 2020Assignee: Accenture Global Solutions LimitedInventors: Madhan Kumar Srinivasan, Arun Purushothaman, Guruprasad Pv
-
Publication number: 20200272355Abstract: A multi-layer rate sizing stack may generate prescriptive operation-rate tokens for controlling sizing adjustments for operation-rates. The input layer of the rate sizing stack may generate operation pattern data based on operation-rate data received via network connection. A prescriptive engine layer may determine a prescriptive allowed operation-rate based on the operation pattern data. Based on the prescriptive allowed operation-rate, the prescriptive engine layer may generate the operation-rate tokens that that may be used to control rate sizing adjustments prescriptively.Type: ApplicationFiled: February 26, 2019Publication date: August 27, 2020Applicant: Accenture Global Solutions LimitedInventors: Madhan Kumar Srinivasan, Arun Purushothaman, Guruprasad PV, Kishore Kumar Gajula
-
Patent number: 10742567Abstract: A multi-layer storage class placement stack may generate a token containing storage class placement prescriptions for controlling the placement of stored items within a selection of classes for storage. An input layer of the storage class placement stack may generate time-collated activity data based on historical access data, volume metric data, and/or tagging data. The time-collated activity data may include data groupings using timestamps or other timing indicators. A transformation layer may further process the time-collated activity data to generate defined-period summation data that provides summary detail for defined durations across a period of analysis. The defined-period summation data may be used by a prescriptive engine layer to generate prescriptions for placement of individual stored items by associating the prescriptions with storage identifiers for the individual items.Type: GrantFiled: December 13, 2018Date of Patent: August 11, 2020Assignee: Accenture Global Solutions LimitedInventors: Madhan Kumar Srinivasan, Arun Purushothaman, Guruprasad Pv, Manish Sharma Kolachalam
-
Patent number: 10719360Abstract: A system may support distributed multiple tier multi-node serverless analytics task execution. At a data ingestion tier, data ingestion serverless tasks may receive detail data for analytic processing. data integration serverless tasks, executing at a data integration and consolidation tier and initiated by the data ingestion serverless tasks, may sort the detail data and identify patterns within the detail data to generate grouped pre-processed data. The data integration serverless tasks may initiate partitioning serverless tasks which may divide the grouped pre-processed data into data chunks. Multi-node analytic serverless tasks at an analytic tier, at least some of which being initiated by the partitioning serverless tasks, may analyze the data chunks and generate prescriptive outputs.Type: GrantFiled: October 12, 2018Date of Patent: July 21, 2020Assignee: Accenture Global Solutions LimitedInventors: Madhan Kumar Srinivasan, Arun Purushothaman, Vijaya Tapaswi Achanta
-
Prescriptive analytics based compute sizing correction stack for cloud computing resource scheduling
Patent number: 10719344Abstract: A multi-layer compute sizing correction stack may generate prescriptive compute sizing correction tokens for controlling sizing adjustments for computing resources. The input layer of the compute sizing correction stack may generate cleansed utilization data based on historical utilization data received via network connection. A prescriptive engine layer may generate a compute sizing correction trajectory detailing adjustments to sizing for the computing resources. Based on the compute sizing correction trajectory, the prescriptive engine layer may generate the compute sizing correction tokens that that may be used to control compute sizing adjustments prescriptively.Type: GrantFiled: March 15, 2018Date of Patent: July 21, 2020Assignee: Acceture Global Solutions LimitedInventors: Madhan Kumar Srinivasan, Arun Purushothaman, Guruprasad Pv, Michael S. Eisenstein, Vijay Desai -
Patent number: 10712958Abstract: A system for elastic volume type selection and optimization is provided. The system may detect that a block storage volume was provisioned by a public cloud computing platform based on a first volume type identifier of a first volume type. The system may determine, based on a normalization model, a baseline operation rate and a baseline throughput rate for the provisioned block storage volume. The system may determine, based on a selected transition mode and historical performance measurements, a simulated operation rate and a simulated throughput rate. The system may communicate, in response to the simulated throughput being greater than the baseline throughput rate or the simulated operation rate being greater than the baseline operation rate, a provisioning instruction to re-provision the provisioned block storage volume on the cloud computing platform.Type: GrantFiled: October 8, 2018Date of Patent: July 14, 2020Assignee: Accenture Global Solutions LimitedInventors: Madhan Kumar Srinivasan, Arun Purushothaman, Guruprasad Pv
-
Publication number: 20200210443Abstract: A system may support multiple tier serverless data foundation creation to support large data set processing. At a data ingestion tier, data ingestion serverless tasks may receive source data for processing. The data integration serverless tasks may filter and group the source data into file-object stored items. Further, data integration serverless tasks may capture metadata that, when paired with the file-object stored items, establishes the data foundation. The data foundation facilitates database-like performance in data operations in a database-less system. At the processing tier, the processing serverless tasks access the data foundation by iterating across the file-object stored items to generate output-object stored items. At the directed storage tier, directed storage serverless tasks capture metadata for the output-object stored items to establish an output data foundation or prepare the output data for storage in a data warehouse.Type: ApplicationFiled: December 28, 2018Publication date: July 2, 2020Applicant: Accenture Global Solutions LimitedInventors: Madhan Kumar Srinivasan, Arun Purushothaman, Vijaya Tapaswi Achanta
-
Publication number: 20200195571Abstract: A multi-layer storage class placement stack may generate a token containing storage class placement prescriptions for controlling the placement of stored items within a selection of classes for storage. An input layer of the storage class placement stack may generate time-collated activity data based on historical access data, volume metric data, and/or tagging data. The time-collated activity data may include data groupings using timestamps or other timing indicators. A transformation layer may further process the time-collated activity data to generate defined-period summation data that provides summary detail for defined durations across a period of analysis. The defined-period summation data may be used by a prescriptive engine layer to generate prescriptions for placement of individual stored items by associating the prescriptions with storage identifiers for the individual items.Type: ApplicationFiled: December 13, 2018Publication date: June 18, 2020Applicant: Accenture Global Solutions LimitedInventors: Madhan Kumar Srinivasan, Arun Purushothaman, Guruprasad Pv, Manish Sharma Kolachalam
-
Publication number: 20200188952Abstract: A biocompatible polymer hybrid nanocomposite coating on a surface of a substrate, such as titanium and its alloys. The coating can be achieved by an electrostatic spray coating, preferably using ultra-high molecular weight polyethylene (UHMWPE) as a matrix for the coating. For example, up to 2.95 wt. % carbon nanotubes can be used as reinforcement, as can up to 4.95 wt. % hydroxyapatite. A dispersion of CNTs and HA in the coating is substantially uniform. The tribological performance of such coatings include high hardness, improved scratch resistance, excellent wear resistance, and corrosion resistance compared to pure UHMWPE coatings.Type: ApplicationFiled: September 12, 2019Publication date: June 18, 2020Applicant: KING FAHD UNIVERSITY OF PETROLEUM AND MINERALSInventors: Zahid Ahmed UWAIS, Abdul Samad MOHAMMED, Madhan KUMAR, Mohamed Abdrabou HUSSEIN, Nasser AL-AQEELI
-
Publication number: 20200034057Abstract: A system for elastic volume type selection and optimization is provided. The system may detect that a block storage volume was provisioned by a public cloud computing platform based on a first volume type identifier of a first volume type. The system may determine, based on a normalization model, a baseline operation rate and a baseline throughput rate for the provisioned block storage volume. The system may determine, based on a selected transition mode and historical performance measurements, a simulated operation rate and a simulated throughput rate. The system may communicate, in response to the simulated throughput being greater than the baseline throughput rate or the simulated operation rate being greater than the baseline operation rate, a provisioning instruction to re-provision the provisioned block storage volume on the cloud computing platform.Type: ApplicationFiled: October 8, 2018Publication date: January 30, 2020Applicant: Accenture Global Solutions LimitedInventors: Madhan Kumar Srinivasan, Arun Purushothaman, Guruprasad Pv
-
Patent number: 10523519Abstract: A multi-layer analytics service stack may generate forecasted utilization data. An input layer of the analytics service stack may receive input and designate cloud computing utilization data for analysis. A transformation layer of the analytics service stack may perform format transformations on the cloud computing utilization data, and the data may be prepared for analysis at a data treatment layer of the cloud computing utilization data. The treated and transformed cloud computing utilization data may be analyzed using multiple analytics models by a multi-forecasting layer of analytics service stack to generate the forecasted utilization data.Type: GrantFiled: June 29, 2017Date of Patent: December 31, 2019Assignee: Accenture Global Solutions LimitedInventors: Madhan Kumar Srinivasan, Guruprasad Pv
-
Patent number: 10459757Abstract: A multi-layer compute sizing correction stack may generate prescriptive compute sizing correction tokens for controlling sizing adjustments for computing resources. The input layer of the compute sizing correction stack may generate cleansed utilization data based on historical utilization data received via network connection. The input layer may receive one or more resource configurations that may be applied to implement the sizing correction. A prescriptive engine layer may generate a compute sizing correction trajectory indicative of a sizing adjustment to a computing resource. The compute sizing correction trajectory may account of historic processor, network, and memory utilization. Based on the compute sizing correction trajectory and a selected resource configuration, the prescriptive engine layer may generate the compute sizing correction tokens that that may be used to control compute sizing adjustments prescriptively.Type: GrantFiled: May 13, 2019Date of Patent: October 29, 2019Assignee: ACCENTURE GLOBAL SOLUTIONS LIMITEDInventors: Madhan Kumar Srinivasan, Guruprasad Pv, Arun Purushothaman
-
Publication number: 20190317808Abstract: A system may support distributed multiple tier multi-node serverless analytics task execution. At a data ingestion tier, data ingestion serverless tasks may receive detail data for analytic processing. data integration serverless tasks, executing at a data integration and consolidation tier and initiated by the data ingestion serverless tasks, may sort the detail data and identify patterns within the detail data to generate grouped pre-processed data. The data integration serverless tasks may initiate partitioning serverless tasks which may divide the grouped pre-processed data into data chunks. Multi-node analytic serverless tasks at an analytic tier, at least some of which being initiated by the partitioning serverless tasks, may analyze the data chunks and generate prescriptive outputs.Type: ApplicationFiled: October 12, 2018Publication date: October 17, 2019Applicant: Accenture Global Solutions LimitedInventors: Madhan Kumar Srinivasan, Arun Purushothaman, Vijaya Tapaswi Achanta
-
Patent number: 10445209Abstract: A multi-layer activation timetable stack may generate prescriptive activation timetables for controlling activation states for computing resources. An input layer of the activation timetable stack may generate time-scaled pattern data. A transformation layer may identify trends and variables at era timescales. A data treatment layer may flag activation states based on the trends identified at the era timescales. Once the activation states a flagged, the prescriptive engine layer may generate an activation timetable that may be used to control computing resource activation prescriptively.Type: GrantFiled: November 13, 2017Date of Patent: October 15, 2019Assignee: Accenture Global Solutions LimitedInventors: Madhan Kumar Srinivasan, Arun Purushothaman, Manish Sharma Kolachalam, Michael S. Eisenstein
-
Prescriptive Analytics Based Compute Sizing Correction Stack for Cloud Computing Resource Scheduling
Publication number: 20190205150Abstract: A multi-layer compute sizing correction stack may generate prescriptive compute sizing correction tokens for controlling sizing adjustments for computing resources. The input layer of the compute sizing correction stack may generate cleansed utilization data based on historical utilization data received via network connection. A prescriptive engine layer may generate a compute sizing correction trajectory detailing adjustments to sizing for the computing resources. Based on the compute sizing correction trajectory, the prescriptive engine layer may generate the compute sizing correction tokens that that may be used to control compute sizing adjustments prescriptively.Type: ApplicationFiled: March 15, 2018Publication date: July 4, 2019Applicant: Accenture Global Solutions LimitedInventors: Madhan Kumar Srinivasan, Arun Purushothaman, Guruprasad PV, Michael S. Eisenstein, Vijay Desai