Abstract: Systems and methods for detecting, classifying, and managing impediments are disclosed. For example, embodiments may be related to impediments in project management. The proposed systems and methods are configured to evaluate data harvested from multiple different sources (in different formats), identify potential impediments that may be described or present in the data, and classify said impediments based on whether the impediment is non-technical or technical. In addition, the proposed systems implement a technical solution of active learning combined with reinforcement learning to produce a feedback loop that, over each iteration, improves the accuracy of the impediment classification. The impediment management assistant is configured to identify impediments from various inputs sources across industries with an AI-based self-learning capability, providing a robust and accurate model even with only a limited training dataset.

Abstract: A device may receive audio data identifying a plurality of speakers and may process the audio data, with a plurality of clustering models, to identify a plurality of speaker segments. The device may determine a plurality of diarization error rates for the plurality of speaker segments and may identify a plurality of errors in the plurality of speaker segments. The device may select rectification models to rectify the plurality of errors and may segment and/or re-segment the audio data with the rectification models to generate re-segmented audio data. The device may determine a plurality of modified diarization error rates for the plurality of speaker segments based on the re-segmented audio data and may select one of the plurality of speaker segments based on the plurality of modified diarization error rates. The device may calculate an empathy score based on the selected speaker segment and may perform actions based on the empathy score.

Abstract: Systems and methods for facilitating candidate acquisition are disclosed. The systems may include a candidate acquisition orchestration engine. The engine may include a candidate engagement optimizer, which may receive, from a database storing profiles attributes of a plurality of candidates, an expanded dataset having one or more filtered attributes pertaining to a set of candidates from the plurality of candidates. The optimizer may receive, from an entity intending to engage at least one candidate, inputs associated with preferred parameters for the at least one candidate. The optimizer may process the expanded dataset and the entity inputs through a plurality of classifiers to generate candidate predictions. A bias identification engine may optimize the candidate predictions to remove inherent bias therein so as to generate, for each classifier, optimized candidate predictions. A stack classifier may process the optimized candidate predictions to generate final candidate predictions.

Abstract: A system for solution architecture prediction may identify a previous solution from a data source and may create a historical solution evaluation matrix by mapping a plurality of concern categories with the previous solution. The system may identify a plurality of solution components preponderant to deriving a solution associated with the solution architecture prediction and create a potential solution evaluation matrix therefrom. The system may evaluate the historical solution evaluation matrix and the potential solution evaluation matrix to determine a credibility score for each solution comprised therein. Based on the evaluation, a solution prediction data may be generated including a previous solution, a potential solution, and the associated credibility score. A service solution may be selected from the solution prediction data to resolve the solution prediction requirement.

Abstract: A device may receive audio data identifying a plurality of speakers and may process the audio data, with a plurality of clustering models, to identify a plurality of speaker segments. The device may determine a plurality of diarization error rates for the plurality of speaker segments and may identify a plurality of errors in the plurality of speaker segments. The device may select rectification models to rectify the plurality of errors and may segment and/or re-segment the audio data with the rectification models to generate re-segmented audio data. The device may determine a plurality of modified diarization error rates for the plurality of speaker segments based on the re-segmented audio data and may select one of the plurality of speaker segments based on the plurality of modified diarization error rates. The device may calculate an empathy score based on the selected speaker segment and may perform actions based on the empathy score.

Abstract: A system for obtaining optimized regular expression may convert a received input data into a plurality of embeddings. The system may receive a generated regular expression corresponding to the plurality of embeddings, wherein the generated regular expression is at least one of an existing regular expression from a database and a newly generated regular expression. The system may parse the generated regular expression into a plurality of sub-blocks. The system may classify the plurality of sub-blocks to obtain a plurality of classified sub-blocks. The system may evaluate a quantifier class for each classified sub-block to identify a corresponding computationally expensive class. The system may perform an iterative analysis to obtain a plurality of optimized sub-blocks associated with a minimum computation time. The system may combine the plurality of optimized sub-blocks to obtain the optimized regular expression.

Abstract: A device may receive input data identifying a technical architecture diagram, a design document, an interface specification document, and technical architecture icons, and may process the input data identifying the technical architecture diagram, with a model, to determine hierarchical objects from the technical architecture diagram. The device may perform OCR and NLP of the hierarchical objects to determine blocks of data, and may compare the blocks of data and the input data identifying the design document to identify functionalities of applications. The device may compare the blocks of data and the input data identifying the interface specification document to identify attributes, and may compare the blocks of data and the input data identifying the technical architecture icons to identify icons. The device may consolidate the blocks of data, the functionalities, the attributes, and the icons into a final document, and may perform actions based on the final document.

Abstract: A system for solution architecture prediction may identify a previous solution from a data source and may create a historical solution evaluation matrix by mapping a plurality of concern categories with the previous solution. The system may identify a plurality of solution components preponderant to deriving a solution associated with the solution architecture prediction and create a potential solution evaluation matrix therefrom. The system may evaluate the historical solution evaluation matrix and the potential solution evaluation matrix to determine a credibility score for each solution comprised therein. Based on the evaluation, a solution prediction data may be generated including a previous solution, a potential solution, and the associated credibility score. A service solution may be selected from the solution prediction data to resolve the solution prediction requirement.

Abstract: A device may receive input data identifying a technical architecture diagram, a design document, an interface specification document, and technical architecture icons, and may process the input data identifying the technical architecture diagram, with a model, to determine hierarchical objects from the technical architecture diagram. The device may perform OCR and NLP of the hierarchical objects to determine blocks of data, and may compare the blocks of data and the input data identifying the design document to identify functionalities of applications. The device may compare the blocks of data and the input data identifying the interface specification document to identify attributes, and may compare the blocks of data and the input data identifying the technical architecture icons to identify icons. The device may consolidate the blocks of data, the functionalities, the attributes, and the icons into a final document, and may perform actions based on the final document.

Abstract: Systems and methods for email validation are disclosed. The email validation includes transforming format of emails to a predefined format understandable the present system and application of text mining component on the transformed format. The email validation further includes obtaining details from a repository related to a historical pattern associated with an email validation requirement and a cognitive learning operation employed for the historical email validation to ascertain an outcome of the historical validation for similar emails. The email validation also includes predicting misdirection of the email and change in configuration of the email account based on the validation of the email.