Gradient-Boosted Classifier for Lessons-Learned Incident Records
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Solution Overview
Problem
The manual identification and classification of lessons-learned incident records from a large volume of incident reports is impractical and labor-intensive, with challenges including imbalanced data, missing values, and unstructured data types, making it difficult to train accurate machine learning models for automated document management.
Innovation Solution
A system utilizing a deep neural network (DNN) and machine learning (ML) architecture to encode data entries into dense and sparse vector representations, concatenated to generate a representative vector, which is then classified by a gradient-boosted classifier network to identify lessons-learned records, and automatically transfer them to appropriate locations based on classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual identification and classification methods are used for lessons-learned incident records, then accuracy can be maintained through human judgment, but productivity is severely reduced due to labor-intensive processing of large volumes of incident reports
Solution Approach 1:
The patent replaces manual mechanical classification processes with an automated machine learning system. The system uses gradient-boosted classifier networks and deep neural networks to automatically identify and classify lessons-learned incident records, substituting human cognitive processes with computational algorithms that process incident reports at machine speed while maintaining consistent classification accuracy
Solution Approach 2:
The system enables self-service classification by training the machine learning model to autonomously identify lessons-learned records without human intervention. The gradient-boosted classifier automatically processes incident reports, extracts relevant features, and categorizes them based on learned patterns from training data, making the system self-sufficient for the classification task
2Measurement precision
If traditional machine learning models are trained on imbalanced data with missing values and unstructured data types, then model accuracy deteriorates, but collecting and preprocessing clean data is labor-intensive
Solution Approach 1:
The patent transforms the classification problem by changing the parameter representation of incident reports. The system converts unstructured text and various data types into structured feature vectors that the gradient-boosted classifier can process efficiently. This parameter transformation allows the model to handle imbalanced and incomplete data without requiring extensive manual preprocessing, as the algorithm learns to work with the inherent data variations
Solution Approach 2:
The system performs preliminary data encoding and feature extraction automatically as part of the machine learning pipeline. Before classification, the incident report data undergoes automated preprocessing steps including text vectorization, missing value handling, and feature engineering, which are executed by the system itself rather than requiring manual intervention, thus maintaining accuracy while reducing preprocessing time
3Productivity
If automated classification systems are implemented to reduce manual labor, then productivity increases, but the complexity of training accurate models on diverse data types increases
Solution Approach 1:
The patent segments the complex classification task into distinct processing stages handled by specialized components. The system divides incident report processing into separate feature extraction, data encoding, and classification phases, with the gradient-boosted classifier focusing specifically on classification while other components handle data preparation. This segmentation reduces the complexity burden on any single component while enabling high-level automation
Solution Approach 2:
The system introduces intermediary processing layers between the raw incident reports and the final classification decision. Deep neural networks and feature encoding mechanisms serve as intermediaries that transform diverse input data into a standardized format suitable for the gradient-boosted classifier, mediating the complexity between raw data variability and model training requirements
Data Source
AI summary
Systems and methods to classify incident report documents are disclosed, comprising inputting, a first type data entry of a document into a deep neural network (DNN); encoding, via the DNN, the first type data entry to output a densely embedded contextual vector representing contents of the first type data entry; generating, a list containing ordered data from a second type data entry of the document; encoding, via a machine learning network, the ordered data into a sparse vector representation of the second type data entry; concatenating, the densely embedded contextual vector with the sparse vector representation to generate a representative vector of the document; and training a gradient-boosted classifier network by using as training inputs the representative vector and a label associated with the document to generate a classification of the document.