Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis operations. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations by generating a hybrid class for a multi-party communication transcript data object associated with a predictive entity utilizing a hybrid space classification machine learning model, generating a machine learning-based risk score utilizing a hybrid-class-based risk scoring machine learning model, and generating a hierarchical-workflow risk score using a hierarchical risk score adjustment workflow.

Abstract: There is a need for more effective, efficient, and accurate computer text comprehension. This need is addressed by applying unique text processing techniques to identify and remove irrelevant sentences from a narrative. The text processing techniques include a machine-learning based model that is trained using automatically generated training data that is tailored to a particular circumstance. A method for machine narrative comprehension includes receiving a narrative data object comprising one or more sentences; determining, using a machine-learning based irrelevant classifier model, a relevance of at least one of the one or more sentences; responsive to a determination that at least one sentence is irrelevant, generating a pertinent summary by removing the at least one sentence from the narrative; and generating, based at least in part on the pertinent summary, an output indicia data object for the narrative data object.

Abstract: There is a need for more effective and efficient predictive natural language summarization. This need is addressed by applying hybrid extractive and abstractive summarization techniques in a unique processing pipeline to generate a cohesive and comprehensive summary of a multi-party interaction.

Abstract: As described herein, various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing natural language processing operations for generating guided summaries using summarization templates that are mapped to hybrid classes of a hybrid classification space for a hybrid classification machine learning model. In some embodiments, by using summarization templates, a proposed summarization framework is able to vastly reduce the computational complexity of performing summarization on an input document data object, such as an input multi-party communication transcript data object, by defining the set of dynamic data fields that apply to the input document data object based at least in part on an assigned class/category of the input document data object.

Abstract: Various embodiments provide automatic extraction of interpretable and entity-specific data from unstructured/semi-structured data. In some embodiments, a method to extract label-value pairs from an input data record is provided. The method includes identifying label data tokens and value data tokens within the input data record and generating a spatial coordinate set for each thereof within a spatial coordinate scheme associated with the input data record. For example, the spatial coordinate sets may be generated with respect to a rendered format of the input data record. The method further includes, for each label data token, generating coordinate vectors positioned in relation to the value data tokens and selecting a value data token for pairing with the label data token based at least in part on the coordinate vectors and using a vector classification machine learning model that is generated based at least in part on automatic annotation of historical data records.

Abstract: There is a need for more effective and efficient predictive data analysis solutions and/or more effective and efficient solutions for generating an emotional sentiment score without the use of labelled data. In one example, embodiments comprise receiving an input text sequence, generating an intermediate emotional sentiment score object based at least in part on the input text sequence and by utilizing an emotional sentiment machine learning model, generating an overall emotional sentiment score based at least in part on the intermediate sentiment score object and by utilizing an emotional sentiment score transformation object, and performing one or more prediction-based actions based at least in part on the overall emotional sentiment score.

Abstract: The present disclosure relates to a comprehensive model for expression of recombinant peptides by Pichia pastoris. The model uses an easily controllable variable called ‘critical nutrient ratio’ for obtaining a right balance between product synthesis and it's degradation during the fermentation process. The extra cellular concentration of precursor could be increased by about 10 folds and the degradation constants could be reduced by about 10-20 folds for intracellular and extracellular cases respectively by controlling critical nutrient ratio and addition of soya flour hydrolysate and EDTA.

Abstract: The present disclosure relates to a comprehensive model for expression of recombinant peptides by Pichia pastoris. The model uses an easily controllable variable called ‘critical nutrient ratio’ for obtaining a right balance between product synthesis and it's degradation during the fermentation process. The extra cellular concentration of precursor could be increased by about 10 folds and the degradation constants could be reduced by about 10-20 folds for intracellular and extracellular cases respectively by controlling critical nutrient ratio and addition of soya flour hydrolysate and EDTA.