Abstract: Techniques are provided for predicting firmware installation failure reasons using machine learning techniques. One method comprises obtaining log data for a user device, wherein the log data is obtained following a failure of a firmware installation on the user device; extracting a plurality of features from the obtained log data; applying the extracted features to a trained machine learning model to obtain a prediction of whether the firmware installation failure is caused by a hardware-related failure or a software-related failure; and performing an automated remedial action based on a result of the prediction. The trained machine learning model can be trained using historical data for multiple user devices that experienced a firmware installation failure, where the historical data comprises a label indicating whether a given failure comprises a hardware-related failure or a software-related failure. The trained machine learning model can be trained and tested using cross-validation techniques.

Abstract: Techniques are provided for predicting firmware installation failure reasons using machine learning techniques. One method comprises obtaining log data for a user device, wherein the log data is obtained following a failure of a firmware installation on the user device; extracting a plurality of features from the obtained log data; applying the extracted features to a trained machine learning model to obtain a prediction of whether the firmware installation failure is caused by a hardware-related failure or a software-related failure; and performing an automated remedial action based on a result of the prediction. The trained machine learning model can be trained using historical data for multiple user devices that experienced a firmware installation failure, where the historical data comprises a label indicating whether a given failure comprises a hardware-related failure or a software-related failure. The trained machine learning model can be trained and tested using cross-validation techniques.

Abstract: The present invention relates to systems and methods for real-time multi-party recommendation in a peer to peer communication. The system (200) comprises a transceiver (202) that receives a selection for selecting a first electronic media from a first set of electronic media stored in the first user equipment (102), and transmit the same to the second user equipment (104). The system (200) further comprises a metadata generator unit (204) to generate first electronic media metadata; and a recommendation unit (206) to determine first recommendation metadata based on an analysis of first electronic media metadata and a first set of electronic media metadata. The transceiver (202) of the system (200) transmits the first electronic media metadata and the first recommendation metadata to the second user equipment (104), and receives, a second recommendation metadata based on an analysis of the first electronic media metadata and a second set of electronic media metadata.

Abstract: The present invention relates to systems and methods for real-time multi-party recommendation in a peer to peer communication. The system (200) comprises a transceiver (202) that receives a selection for selecting a first electronic media from a first set of electronic media stored in the first user equipment (102), and transmit the same to the second user equipment (104). The system (200) further comprises a metadata generator unit (204) to generate first electronic media metadata; and a recommendation unit (206) to determine first recommendation metadata based on an analysis of first electronic media metadata and a first set of electronic media metadata. The transceiver (202) of the system (200) transmits the first electronic media metadata and the first recommendation metadata to the second user equipment (104), and receives, a second recommendation metadata based on an analysis of the first electronic media metadata and a second set of electronic media metadata.