Machine Learning Recommendation Engine for Unstructured Data Analysis
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Solution Overview
Problem
Current data analysis and rule generation methods for decision-making processes are inefficient in handling structured, semi-structured, and unstructured data, requiring manual intervention and struggling to adapt to changes such as new research or policy updates, and lack context-awareness and adaptability.
Innovation Solution
A computer-implemented method and system that dynamically extracts data points and generates rules by analyzing structured, semi-structured, and unstructured data, using a machine learning model to create a recommendation structure with feature sets and calculating feature worthiness scores, allowing for real-time updates and context-driven decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual preparation of comma separated values file is used, then recommendation accuracy can be maintained, but time consumption and manual intervention increase
Solution Approach 1:
The system automatically extracts features from unstructured data sources (chat transcripts, emails, documents) and generates recommendation rules without requiring manual preparation of structured data files. The machine learning model processes unstructured data autonomously to create actionable recommendations, eliminating the need for manual CSV file preparation while maintaining recommendation accuracy.
Solution Approach 2:
The patent replaces the manual mechanical process of preparing comma separated values files with an automated machine learning-based system. The machine learning model automatically extracts features, identifies patterns, and generates recommendations, substituting the manual data preparation mechanism with an intelligent automated system that achieves the same accuracy without human intervention.
2Reliability
If manual data preparation is used, then data quality can be controlled, but adaptability to changes is reduced
Solution Approach 1:
The system dynamically adapts to changes in data sources and requirements through the machine learning model. When new data types emerge or policies change, the model automatically learns from the unstructured data and updates its recommendations accordingly, providing continuous adaptation without requiring manual reconfiguration of data preparation processes.
Solution Approach 2:
The machine learning model automatically adjusts feature extraction parameters and recommendation criteria based on changing data characteristics and organizational requirements. The system can adapt to new data formats, emerging patterns, and changing policies by retraining or fine-tuning the model, thereby maintaining data quality while improving adaptability.
3Productivity
If structured data processing is used, then processing speed is improved, but ability to handle diverse data types is reduced
Solution Approach 1:
The machine learning model serves as a universal processor that handles multiple data types (text, email, documents, chat transcripts) through a single unified system. The model automatically detects and processes different data formats and structures, extracting relevant features and generating recommendations across diverse data sources without requiring separate processing pipelines for each data type.
Solution Approach 2:
The machine learning model acts as an intermediary layer between diverse unstructured data sources and the recommendation generation process. It translates various data formats into a unified feature representation that can be processed efficiently, serving as a bridge that maintains both processing speed and versatility across different data types.
4Measurement precision
If context-aware analysis is implemented, then recommendation relevance is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary feature extraction and pattern identification during the training phase, where the machine learning model analyzes historical data and learns contextual relationships. This preliminary learning enables the system to automatically capture context-aware patterns during operation without requiring complex real-time analysis, thereby improving recommendation relevance while managing system complexity through pre-computed knowledge.
Data Source
AI summary
Provided are techniques for data analysis and rule generation for providing a recommendation. A recommendation structure is built from features of data, where the recommendation structure comprises a plurality of rules, and where each rule of the plurality of rules is associated with a recommendation. The recommendation structure is sent to a model. A feature set comprising a feature and one or more related features is created from the features. In response to determining that a feature worthiness score of the feature set exceeds a threshold, the feature set is input to the model. A rule of the plurality of rules is received from the model, where the rule includes the feature, the one or more related features, and the associated recommendation. In response to receiving a set of values for the feature and the one or more related features, the rule is applied to the set of values to provide the recommendation.