Accenture Global Solutions Limited

Accenture Global Services Gmbh

Marin Grace

Vidya Rajagopal

Divya Kilari Chandrababu Naidu

Subhasish Roy

Amritendu Majumdar

Sunjeet Gupta

Bibin George Thottikkara

Sudhir Kudva

Matthew D LeClaire

Jeff Ivany

Raghu Polisetty

This disclosure is directed generally to an automatic intelligent electronic data processing system, platform, and method for computerized multi-facet data pattern recognition and ranking, and particularly to intelligently personalizing recommendation of data items for consumption by a particular entity based on past data consumption history of the entity and/or other entities via machine recognition of intra and/or inter-entity data item selection correlations. Such personalized recommendation may be based on a multi-facet ranking of the data items by integrating various intra-entity and inter-entity correlations and patterns in data item consumption into a quantifiable entity-specific ranking score for each data item that may potentially be selected for consumption by a particular entity.

Intelligent data ranking system based on multi-facet intra and inter-data correlation and data pattern recognition

A mobile application portfolio management system that estimates performance of a mobile application portfolio on various metrics and provides suggestions for improvements is disclosed. The mobile application portfolio manager calculates different KPIs for estimating performance of the mobile applications within the mobile application portfolio. The KPIs that underperform their respective thresholds are analyzed to isolate the elements of the KPIs that result in subpar performances by the KPIs. Various corrective actions to improve the performance of the elements of the KPIs can be derived based on the rules configured within the mobile application portfolio management system.

Testing and improving performance of mobile application portfolios

An electronic control unit (ECU) is tested by an automated D-bus testing tool in a first device. The test tool establishes one or more secure shells between the first device and the ECU. The tool reads test input data from an Excel input file. The ECU comprises a software stack including a test client, a Bluetooth middle layer and a hardware abstraction layer, that communicate internally relative to the ECU, via a D-bus. The test tool sends function calls and parameters via the secure shells to the ECU to test execution of Bluetooth functions and/or Bluetooth profiles. The function calls enable simulation of a human machine interface by the test client. The tool monitors API call returns and logs D-bus communications via the one or more secure shells. The test tool outputs test verdict information and/or D-bus communication logs as text in an Excel file.

Automation of D-bus communication testing for bluetooth profiles

An electronic control unit (ECU) of a hands-free module may be tested by an automated voice based testing tool in a first device. The tool reads test input data from an Excel input file. The tool generates simulated audible voice commands in a specified language, accent, pitch, volume or speed to test the hands-free module. The voice commands are transmitted via a speaker to a hands-free module microphone. The hands-free ECU is coupled to a CAN bus and the tool receives CAN bus information corresponding to hands-free module operations. The tool outputs test verdict information and/or CAN bus message logs as text in an Excel file.

Voice based automation testing for hands free module

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Bengaluru, India

Marin Grace