Segmentation Platform Using Feature Label Pairs
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Solution Overview
Problem
Conventional systems face challenges in efficiently managing and analyzing large amounts of electronic data, leading to storage space and computational resource inefficiencies, as well as limitations in predictive analysis due to the need for extensive historical data.
Innovation Solution
A segmentation platform comprising a behavior service and a predictive service that determines segments from datasets by identifying significant feature and label pairs, using a training model to predict future behaviors and initiate actions such as recommendations or alerts, while storing data in a condensed form to minimize storage needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional systems store and analyze large amounts of electronic data, then comprehensive data analysis is achieved, but storage space and computing resources are wasted
Solution Approach 1:
The patent extracts only the significant features and labels from the complete dataset to form a condensed representation. The training model learns from this extracted subset rather than processing all raw data, thereby reducing storage requirements while maintaining predictive accuracy.
Solution Approach 2:
The patent segments the dataset into feature data and label data, then further segments them into significant and insignificant portions. Only the significant segments are retained in the condensed form, allowing efficient storage and processing while preserving essential information patterns.
2Reliability
If conventional systems use historical data for predictive analysis, then predictions about specific behaviors are made, but the system cannot effectively analyze new or unknown behaviors
Solution Approach 1:
The patent performs preliminary training of the model using condensed feature-label pairs that capture essential behavioral patterns. This pre-trained model can then generalize to new behaviors without requiring extensive historical data for each specific behavior, enabling both reliability and adaptability.
Solution Approach 2:
The patent transforms the predictive approach by changing from behavior-specific historical data requirements to a generalized model trained on condensed feature-label representations. This parameter transformation allows the system to adapt to new behaviors while maintaining predictive accuracy through the learned patterns in the condensed data.
3Loss of information
If conventional systems attempt to conduct analyses on large amounts of data, then comprehensive analysis is attempted, but the system is inefficient and cannot provide results within a reasonable amount of time
Solution Approach 1:
The patent extracts only the essential feature-label pairs from the complete dataset, creating a condensed representation that retains the most informative patterns. This extraction dramatically reduces the data volume that needs to be processed during analysis, thereby reducing computation time while preserving analysis completeness.
Solution Approach 2:
The patent performs preliminary processing to condense the dataset into significant feature-label pairs before the actual analysis is conducted. This preliminary action prepares the data in an optimized format that enables faster processing during the analysis phase without losing essential information.
Data Source
AI summary
A segmentation platform enables a system that comprises a behavior service and a predictive service for determining a segment from a dataset. The behavior service can analyze data to determine information about behavior that has already occurred. The predictive service can analyze data to determine information about the predicted propensity for certain behavior to occur in the future. In some cases, the predictive service can determine the information by utilizing a training model that indicates predictions related to potential relationships among properties of a dataset. The segmentation platform also enables an interactive user interface that can be utilized to configure attributes of the segment, analyze information associated with the segment, and deliver the information to another device.