System and method for providing video query

A system for performing a video query, the system comprising:

• a memory;
• and
• a processor,
• wherein the system is configured to perform:
• acquiring context information via a user interface of a software life cycle tool, wherein the context information is based on a current task being processed by a user and amount of time expanded for the current task;
• training a first machine learning (ML) model using historical data of the software life cycle tool;
• removing, by the first ML model executed by the processor, extraneous information and noise from the acquired context information;
• generating, by the first ML model executed by the processor, a query, the query including at least one keyword;
• executing, by a second ML model executed by the processor, the generated query to retrieve at least one video segment of a video file from a learning repository, wherein the video file has a plurality of segments, and each segment of the plurality of segments corresponds to a different topic;
• scoring, by the second ML model executed by the processor, the at least one video segment based on a number of matches between the at least one keyword and transcription of the at least one video segment;
• ranking, by the second ML model executed by the processor, the at least one video segment based on the scoring;
• transmitting, by the second ML model executed by the processor and to the user interface of the software life cycle tool, the ranking of the at least one video segment;
• and
• displaying, via the user interface of the software life cycle tool, the ranked at least one video segment.

A non-transitory computer readable storage medium that stores a computer program for performing a video query, the computer program, when executed by a processor, causing a system to perform a plurality of processes comprising:

• acquiring context information via a user interface of a software life cycle tool, wherein the context information is based on a current task being processed by a user and amount of time expanded for the current task;
• training a first machine learning (ML) model using historical data of the software life cycle tool;
• removing, by the first ML model executed by the processor, extraneous information and noise from the acquired context information;
• generating, by the first ML model executed by the processor, a query, the query including at least one keyword;
• executing, by a second ML model executed by the processor, the generated query to retrieve at least one video segment of a video file from a learning repository, wherein the video file has a plurality of segments, and each segment of the plurality of segments corresponds to a different topic;
• scoring, by the second ML model executed by the processor, the at least one video segment based on a number of matches between the at least one keyword and transcription of the at least one video segment;
• ranking, by the second ML model executed by the processor, the at least one video segment based on the scoring;
• transmitting, by the second ML model executed by the processor and to the user interface of the software life cycle tool, the ranking of the at least one video segment;
• and
• displaying, via the user interface of the software life cycle tool, the ranked at least one video segment.