Pre-executing Idiosyncratic Computation via Neural Network Prediction
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Solution Overview
Problem
Server latency in processing data transaction requests leads to increased user frustration and negatively impacts user experience, necessitating a reduction in response latency tailored to individual users.
Innovation Solution
A computational logic for predicting and pre-executing anticipated data calls by receiving and storing identification information, retrieving in-session and historical transaction steps, and using neural networks to provisionally complete transactions, thereby reducing latency through pre-fetching related information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If server processes data transaction requests in real-time, then processing accuracy is maintained, but response latency increases
Solution Approach 1:
The system performs preliminary actions by predicting user transaction patterns using neural networks and pre-executing anticipated data calls before the user actually submits the transaction request. This proactive approach retrieves necessary data in advance, thereby reducing response latency when the user makes the actual request while maintaining processing accuracy through validation against predicted patterns.
2Loss of time
If the system pre-executes anticipated transactions, then response latency is reduced, but system complexity increases
Solution Approach 1:
The system incorporates feedback mechanisms where neural networks continuously learn from actual user transaction patterns to improve prediction accuracy. The system monitors prediction outcomes and adjusts its modeling accordingly, enabling it to handle increasingly complex transaction scenarios while maintaining manageable system complexity through adaptive learning rather than hard-coded rules.
3Measurement precision
If the system uses historical transaction patterns for prediction, then prediction accuracy is improved, but data processing load increases
Solution Approach 1:
The system applies partial action by selectively pre-executing only those transactions that the neural network predicts with high confidence based on historical patterns. Rather than pre-executing all possible transactions or using excessive computational resources on low-probability predictions, the system focuses computational effort on high-confidence predictions, thereby improving prediction accuracy while controlling data processing load.
Data Source
AI summary
Aspects of the disclosure relate to a machine-learning transaction-prediction engine for seasoning an anticipated manual transaction. The transaction may occur in a transaction session. The seasoning may occur prior to execution of the anticipated manual transaction. The transaction-prediction engine may include a receiving/storage module configured to receive and store identification information of the transactor. The engine may also include a step-retrieval module configured to retrieve a set of in-session transaction steps associated with the transactor. In addition, the engine may include a history-retrieval module configured to retrieve, based on the stored identification information, historical transactional information associated with the transactor. The historical transaction information comprising a plurality of historical transaction patterns associated with the transactor. The engine may also include a processor module configured to initiate the anticipated manual transaction by predicting and provisionally completing the in-session transaction steps. The predicting may be based on the set of in-session transaction associated with the transactor and on the plurality of historical patterns.