Predictive Model for Unpaired Observation Identifier Generation
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Solution Overview
Problem
Manually pairing recorded observations with identifiers is tedious and time-consuming, especially in network environments where observations often lack corresponding identifiers.
Innovation Solution
A computer-implemented method that uses a training dataset to train a predictive model, which generates identifiers for unpaired recorded observations by analyzing the data and ranking potential identifiers based on likelihood of acceptance, leveraging natural language processing to convert unstructured data into structured formats as needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual pairing of recorded observations with identifiers is performed, then accuracy of pairing can be maintained, but time consumption and labor effort increase significantly
Solution Approach 1:
The system performs preliminary actions by training a predictive model on historical paired data before actual pairing is needed. The model learns patterns and relationships between observation features and identifiers in advance, so that when new unpaired observations arrive, the pairing can be performed automatically and quickly without manual intervention, thus reducing time consumption while maintaining accuracy through the pre-learned patterns.
Solution Approach 2:
The patent replaces the manual mechanical pairing process with an automated machine learning system. The predictive model uses natural language processing and pattern recognition to automatically match observations with identifiers, substituting human manual work with computational automation. This maintains pairing accuracy through algorithmic consistency while dramatically reducing time consumption and labor effort.
2Productivity
If automated methods are used to generate identifiers for unpaired observations, then productivity increases, but system complexity increases
Solution Approach 1:
The predictive model serves multiple functions: it performs natural language processing on observation data, identifies relevant features, generates predicted identifiers, and ranks multiple candidate identifiers. This multi-functionality consolidates what would otherwise require separate systems into a single automated solution, increasing productivity while managing system complexity through functional integration.
Solution Approach 2:
The system introduces a training dataset as an intermediary between the raw observation data and the predictive model. This training dataset acts as a bridge that captures the relationship patterns between observations and identifiers, allowing the model to learn from historical data without requiring complex hard-coded rules. This intermediary approach simplifies the overall system architecture while enabling automated high-productivity pairing.
3Measurement precision
If predictive models are trained on extensive training datasets, then identification accuracy improves, but data processing requirements and computational resources increase
Solution Approach 1:
The system performs the computationally intensive model training as a preliminary action that is executed once or periodically on historical training data. After training is complete, the model can be deployed for inference on new observations with much lower computational requirements. This separates the heavy computational workload from the ongoing pairing operations, improving prediction accuracy through thorough training while managing energy consumption during actual use.
Data Source
AI summary
In some examples, a computer-implemented method is provided. The computer-implemented method includes accessing a training dataset including a plurality of data pairs. Each data pair of the plurality of data pairs includes first data from a first recorded observation for a first dependent user and second data from a first identifier for the first dependent user. The first identifier corresponds to the first recorded observation. The computer-implemented method also includes training a predictive model by using the training data set and accessing an unpaired recorded observation for a second dependent user. The unpaired recorded observation is missing a corresponding identifier. The computer-implemented method further includes using the trained predictive model to generate at least one paired identifier for the unpaired recorded observation, and outputting the at least one paired identifier.