Language Model Contextualization for User-Specific Transaction Features
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Solution Overview
Problem
Existing data contextualization systems fail to accurately and efficiently identify user-specific features from transaction data, leading to unreliable and inaccurate data management and support, particularly in applications like credit underwriting and financial reporting, due to their agnostic and static labeling approaches.
Innovation Solution
Employing language models, specifically fine-tuned using the Low-Rank Adaptation (LoRA) algorithm, to transform and identify user-specific features from transaction data by inserting grammatical patterns and text phrases, enabling dynamic and precise mapping of data to customized reports.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional agnostic and static labeling approaches are used for data contextualization, then the system complexity is reduced, but the accuracy and reliability of identifying user-specific features deteriorates
Solution Approach 1:
The patent applies parameter changes by transforming the data representation through grammatical pattern insertion and text phrase addition, converting raw transaction data into a structured format that language models can process. This transformation enables reliable feature identification while managing system complexity through standardized data modification protocols
Solution Approach 2:
The patent introduces an intermediary approach by using language models as a mediator between raw transaction data and feature identification. The language model processes the grammatically modified data and extracts user-specific features, thereby improving reliability without requiring direct complex analysis of the original data
2Measurement precision
If language models with grammatical patterns are used to transform data, then the accuracy of feature identification is improved, but the processing time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by pre-processing the transaction data through grammatical pattern insertion and text phrase addition before feeding it to the language model. This preparation step structures the data in advance, enabling the language model to focus on accurate feature identification rather than parsing unstructured data, thus improving accuracy while managing processing time
Solution Approach 2:
The patent segments the data processing into distinct stages: data collection, grammatical transformation, language model processing, and feature extraction. This segmentation allows each stage to be optimized independently, improving overall efficiency and reducing total processing time while maintaining high accuracy
3Adaptability or versatility
If dynamic mapping of data to customized reports is implemented, then the adaptability and user-specific customization are improved, but the system complexity and computational requirements increase
Solution Approach 1:
The patent applies universality by using a single language model framework that can handle multiple types of transactions and generate various customized reports for different users. The grammatical pattern insertion creates a universal data representation that works across different business contexts, enabling high adaptability without proportionally increasing system complexity
Solution Approach 2:
The patent implements dynamics through the ability to adaptively map data to different report categories and formats based on the specific transaction type and user requirements. The system dynamically adjusts the mapping process rather than using fixed templates, improving versatility while managing complexity through flexible, rule-based adaptation
Data Source
AI summary
A system configured for contextualizing data. The system may receive first data, and may transform the first data into modified first data. The system may train a first language model to identify first feature(s) from the modified first data to create a trained first language model. The system may receive second data, and may transform the second data into modified second data. The system may identify, via the trained first language model, the first feature(s) from a first portion of the modified second data. The system may dynamically map the first portion of the modified second data to one or more first categories. The system may generate a first customized report based on one or more of the modified second data, the first feature(s), the one or more first categories, or combinations thereof.