Recurring Data Entry Prediction Using Precursor Pattern Detection
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Solution Overview
Problem
Existing systems fail to efficiently predict and mitigate recurring data entries, leading to resource inefficiencies and security threats due to unintended or malicious duplication of data, which are typically addressed after the fact, resulting in irreversible effects.
Innovation Solution
A computer-based system that utilizes a recurring entry classifier model to identify precursor data entries, generate a library of recognized recurring data entries, and provide a graphical user interface for user interaction to accept, decline, or schedule alerts for future entries, updating the model based on user feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If recurring data entries are addressed after-the-fact, then the problem is eventually resolved, but resource usage and harmful effects to users cannot be reversed
Solution Approach 1:
The system performs preliminary action by predicting recurring data entries before they occur. The machine learning model analyzes historical data patterns to identify precursor entries that will lead to unwanted recurrences, allowing the system to take preventive measures (such as blocking or alerting) before the harmful recurring entries are created, thus avoiding wasted resources and irreversible effects
2Ease of manufacture
If traditional after-the-fact solutions are used for recurring data entries, then implementation is simple, but resource efficiency deteriorates due to unavoidable duplication
Solution Approach 1:
The system implements preliminary prediction using machine learning models that analyze historical data to identify patterns leading to recurring entries. By detecting precursor entries and predicting future recurrences before they happen, the system prevents resource waste from duplicate data processing while maintaining implementation feasibility through automated model-based detection
Solution Approach 2:
The system incorporates feedback mechanisms where user interactions with predictions (confirming or correcting predicted recurring entries) are used to retrain and improve the machine learning model. This feedback loop enhances prediction accuracy over time, improving resource efficiency while the automated nature of the system maintains implementation simplicity
3Device complexity
If no prediction system is implemented, then system complexity remains low, but security deteriorates due to undetected malicious recurring entries
Solution Approach 1:
The system performs preliminary security analysis by predicting potentially malicious recurring entries before they execute. The machine learning model identifies patterns characteristic of fraudulent or harmful recurring transactions, allowing security interventions to be applied in advance, thus preventing security breaches while keeping the added complexity manageable through automated prediction
Solution Approach 2:
The system applies preliminary anti-action by proactively counteracting potential security threats. When the prediction system identifies entries that resemble known fraudulent patterns or abnormal recurring behaviors, it takes preventive measures (such as blocking or requiring additional verification) before the harmful action can occur, thus enhancing security against malicious recurring entries
Data Source
AI summary
Systems and methods of the present disclosure enable a processor to automatically predict a sequence of recurring data entries by accessing a history of electronic activity and executing a recurring entry classifier model to generate a library of recognized recurring data entries, where each recognized recurring data entry in the library includes: a precursor period associated with a precursor data entry, a recurrence period associated with a recurring value, and a recurring entity identifier. An electronic activity data entry is received and identified as preceding a recurring data entry based on the electronic activity value being a nominal electronic activity value. The electronic activity data entry is matched to a recognized recurring data entry in the library using the entity identifier. The processor notifies a user of the matching sequence of recurring data entries as a sequence of recurring data entries to commence after the precursor period.