Accenture Global Solutions Limited

Emmanuel Benbinuto Antonio

Raye Willson Paez Raymundo

Yvonne Jean Martinez

Darwin Endaya Flores

Joseph Christian Policarpio Tamayo

Yvonne Jean Balsamo Martinez

Elpidio J C Dalupang

John Carlo Canlubo De Guzman

Donna Clarisse Lemence Calma

Franklin Chuatiu Yan

Ray Jeremiah Tagalo Omapoy

Marvin G Tulagan

Maria Carlita Grospe

Michael D G Agito

Jonathan Cioco

A device may receive input data identifying variables and features associated with a target process automation, and may receive historical data associated with the target process. The device may process the input data, with a model, to calculate a future process score, a future integration score, a current process score, and a current integration score, and may calculate a combined score based on the scores. The device may calculate a calibrated combined score based on the combined score and a calibration factor, and may calculate an initial fit-for-purpose score for the target process automation based on the calibrated combined score and a probability metric. The device may generate a refined fit-for-purpose score based on the initial fit-for-purpose score and the historical data, and may determine recommendations for the target process automation based on the refined fit-for-purpose score. The device may perform one or more of the recommendations.

Determining a fit-for-purpose rating for a target process automation

A device may receive time series data from one or more data sources. The device may pre-process the time series data to generate a respective time series chart for multiple classifications included in the time series data. The device may randomly select a pair of respective time series charts and a correlation detection technique after pre-processing the time series data. The device may determine a correlation for the pair of respective time series charts based on using the correlation detection technique. The device may determine a score for the pair of respective time series charts based on the correlation of the pair of respective time series charts. The score may indicate an extent to which the pair of respective time series charts is correlated with each other. The device may perform one or more actions after determining the score for the pair of respective time series charts.

Detecting correlation among sets of time series data

A device may process configuration data to identify category data that identifies possible categories of data included in the configuration data. The device may process the category data, after identifying the category data, using a natural language processing technique to generate a set of terms to be processed using an artificial intelligence module. The device may utilize the artificial intelligence module to process the set of terms to predict a set of configuration actions to be performed with respect to the configuration data and to determine an order in which to perform the set of configuration actions. The artificial intelligence module may be associated with translating the configuration data into a computer-readable form. The device may generate an execution script based on the set of configuration actions or the order in which to perform the set of configuration actions. The device may perform the set of configuration actions.

Generating an execution script for configuration of a system

A participant node of a distributed ledger network may detect a tool event token stored on a blockchain. The tool event token may be generated by another participant node of the distributed ledger network. The tool event token may represent execution of a devops tool in a toolchain for an integrated devops environment. The participant node may generate, in response to detection of the tool event token, a new tool path. The participant node may aggregate the new tool path with a previously generated tool path. The optimization node may execute a fitness logic to generate a fitness metric based the aggregated path. The optimization node may prioritize the aggregated tool path with other unique tool paths. The optimization node may select an optimal tool path and communicate an instruction to configure the integrated devops environment with devops tools identified in the optimal tool path.

Decentralized development operations blockchain system

In some possible implementations, an operations platform may include one or more memories; and one or more processors, communicatively coupled to the one or more memories, to: monitor a client platform without interacting with a user interface of the client platform; detect an issue associated with the client platform based on monitoring the client platform; determine whether the operations platform has access to a bot to resolve the issue; generate the bot when the operations platform does not have access to the bot; and deploy the bot with regard to the client platform to resolve the issue, wherein the bot is configured to resolve the issue without interacting with the user interface of the client platform.

Automated issue detection and resolution framework for enterprise resource planning

Growing community of inventors

Mandaluyong, Philippines

Emmanuel Benbinuto Antonio