Chat Content Subset Identification Using Geographic ML Vectors
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing communication platforms lack the ability to effectively analyze and utilize contextual information from interactions to align user practices with organizational policies and provide personalized responses, leading to inefficiencies and potential misalignment with regulatory requirements.
Innovation Solution
Implementing a machine learning (ML) framework that analyzes chat conversations to identify contextual attributes, generate vectorized representations, and label them with metadata, enabling the detection of policy drifts and generation of personalized responses or training materials to ensure compliance with organizational policies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine learning models are executed on interaction content to determine contextual values, then the ability to provide personalized responses is improved, but the processing time and computational resources increase
Solution Approach 1:
The system pre-processes and stores interaction content with contextual values in a database before actual queries are needed. This allows rapid retrieval during operational time without re-executing ML models, thus maintaining personalization capability while reducing real-time processing delays
Solution Approach 2:
The patent creates a database copy of processed interaction content and contextual values, allowing the system to query pre-processed data rather than processing raw data in real-time. This copying approach maintains adaptability while significantly reducing computational time during actual operations
2Measurement precision
If interaction content is aggregated with previously received and annotated content, then the training data volume increases improving model accuracy, but the data storage requirements and processing complexity increase
Solution Approach 1:
The system extracts only the essential contextual values and annotated information from large volumes of interaction content, storing only these condensed representations in the database. This extraction approach maintains the accuracy benefits of comprehensive training data while significantly reducing storage requirements and processing complexity
Solution Approach 2:
The patent transforms raw interaction content into structured contextual values and annotations, changing the data parameters from unstructured text to organized metadata. This parameter transformation maintains information richness for accurate training while simplifying data structure and reducing processing complexity
3Adaptability or versatility
If the system identifies subsets of content corresponding to different geographic locations, then the ability to provide location-specific responses is improved, but the data processing and classification complexity increases
Solution Approach 1:
The system segments interaction content into distinct subsets based on geographic locations, organizing data by region. This segmentation approach enables location-specific responses while simplifying processing by handling each geographic subset independently rather than processing all content uniformly
Solution Approach 2:
The patent applies different processing and response strategies to different geographic subsets of content, tailoring the system behavior to local requirements. This local quality approach improves geographic adaptability while managing complexity by applying specialized processing only where needed rather than universally
Data Source
AI summary
An example operation may include one or more of receiving communication content from an interaction session between participants of an organization, identifying a plurality of subsets of content within the communication content that correspond to a plurality of different geographic locations based on execution of a machine learning (ML) model on the communication content, converting the plurality of subsets of content into a plurality of vectors and labelling the plurality of vectors with the plurality of different geographic locations, respectively, and identifying a subset of content within the interaction session that is directed to a common topic based on the execution of the ML model, wherein the identifying comprises identifying a plurality of subsets of posted content that correspond to the plurality of different geographic locations within the subset of content that is directed to the common topic.