Sociolinguistic Virtual Assistant for Low-Resourced Dialects

The patent introduces an intermediary layer of psych-sociological categorization frameworks that mediate between the input text and the NLP processing. This framework provides structured categories (cultural values, social norms, communication styles) that guide the analysis of low-resourced dialects, enabling accurate interpretation even with limited training data by mapping dialectal expressions to universal psych-sociological concepts

The system dynamically adjusts processing parameters based on the detected dialect type and available data. When processing low-resourced dialects, the system modifies its approach by relying more heavily on psych-sociological categorizations and contextual analysis rather than traditional statistical methods, thereby maintaining accuracy despite limited corpora

The patent segments the complex task of analyzing unstructured qualitative data into distinct modular components: data ingestion module, psych-sociological categorization module, sentiment analysis module, and query processing module. Each module handles a specific aspect of the data flow, making the overall system more manageable and maintainable while achieving comprehensive data utilization

The system implements a universal psych-sociological categorization framework that can process multiple types of unstructured data (transcripts, survey responses, focus group notes) through a single integrated architecture. This multi-functional approach enables the system to handle diverse qualitative data formats without requiring separate processing pipelines for each data type