Blast Electronic Activity Detection via Machine Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The challenge lies in efficiently managing and synchronizing large volumes of heterogeneous electronic communications across systems of record, which is time-consuming, error-prone, and requires manual input, leading to inaccuracies and inefficiencies in data management.
Innovation Solution
A method and system utilizing machine learning models to detect 'blast electronic activities' by generating features from electronic activities, assigning weights, and determining performance scores, which automatically synchronizes data with systems of record, reducing manual effort and improving data accuracy through real-time or near-real-time data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual data entry is used to input electronic communication information into a system of record, then data can be entered into the system, but the process becomes time-consuming, error-prone, and inefficient
Solution Approach 1:
The patent replaces manual mechanical data entry operations with an automated electronic system that uses machine learning models to detect blast electronic activities, extract features, and automatically input data into the system of record, thereby eliminating the time-consuming and error-prone manual process
Solution Approach 2:
The system enables self-service by automatically detecting, processing, and entering electronic communication data without human intervention. The machine learning model autonomously identifies blast activities, extracts relevant features, and populates the system of record, making the data entry process self-executing
2Reliability
If manual data entry is used for electronic communications, then data can be input into the system, but errors increase and accuracy decreases
Solution Approach 1:
The patent replaces manual data entry operations with an automated electronic system that uses machine learning models to detect blast electronic activities, extract features, and automatically input data into the system of record, thereby eliminating the time-consuming and error-prone manual process
Solution Approach 2:
The system implements feedback mechanisms where the machine learning model continuously learns from labeled blast and non-blast electronic activities, improving its detection accuracy over time. The model receives feedback from the labeling process and uses it to refine feature extraction and classification, thereby increasing data accuracy
3Productivity
If a machine learning model is used to detect blast electronic activities and automatically synchronize data, then data management efficiency is enhanced and errors are reduced, but system complexity increases
Solution Approach 1:
The patent segments the complex data management system into distinct functional modules: a machine learning model for detecting blast electronic activities, a feature extraction component for generating features from detected activities, and an automated data synchronization component for inputting data into the system of record. This modular segmentation manages complexity by organizing functions into separate, manageable units
Solution Approach 2:
The patent introduces a machine learning model as an intermediary between raw electronic communication data and the system of record. This intermediary automatically detects blast activities, extracts relevant features, and prepares data for synchronization, thereby enhancing efficiency while managing complexity through a dedicated intermediate processing layer
Data Source
AI summary
The present disclosure relates to determining detecting blast electronic activities. A method can include identifying a plurality of first electronic activities transmitted by a first electronic account of a data source provider. For each first electronic activity of the plurality of first electronic activities, a plurality of features can be extracted. For at least one first electronic activity of the plurality of first electronic activities, a blast probability score can be generated indicating a likelihood that the at least one first electronic activity is a blast electronic activity. The blast probability score can be generated using a machine learning model trained using features extracted from second electronic activities labeled as blast electronic activities and features extracted from third electronic activities labeled as non-blast electronic activities. An association between the at least one first electronic activity and the blast probability score can be stored in a data structure.