Network Device Reporting Optimization via Dynamic Scheduling
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Solution Overview
Problem
Current networked multifunction peripherals (MFPs) face challenges in optimizing reporting schedules due to factors like user-powered off devices, network congestion, and varying regional policies, leading to missed communication windows and data staleness, which existing algorithms like CARP fail to adapt to effectively.
Innovation Solution
An AI-driven system utilizing machine learning models analyzes historical and real-time data to determine optimal communication times for MFPs, considering factors such as device location, network conditions, and user behaviors, to predict high success rates and adapt to changes, thereby providing updated reporting schedules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fixed reporting schedules are used for MFPs, then network traffic can be predictable and managed, but communication success rates decrease due to network congestion and device power-saving restrictions
Solution Approach 1:
The patent implements dynamic reporting schedules that automatically adjust communication times based on learned patterns of network congestion and device availability. Instead of fixed schedules, the system adapts reporting times in real-time to match optimal windows when devices are powered on and networks are less congested, thereby improving communication success rates while maintaining manageable complexity through automated adaptation.
Solution Approach 2:
The system incorporates feedback mechanisms where reporting outcomes (success/failure) are monitored and used to refine future scheduling decisions. The CARP algorithm learns from historical reporting data to identify patterns in device power cycles and network conditions, continuously optimizing the reporting schedule to avoid periods of high congestion or device unavailability, thus improving reliability without requiring complex manual intervention.
2Loss of information
If reporting attempts are made during all possible time windows, then data freshness can be maximized, but network congestion increases and power consumption rises
Solution Approach 1:
The system employs periodic reporting actions scheduled at optimized intervals rather than continuous or frequent attempts. By using the CARP algorithm to determine optimal reporting periods based on learned patterns of device activity and network conditions, the system achieves adequate data freshness while minimizing unnecessary communication events that would consume network bandwidth and device energy, thus balancing information currency with energy efficiency.
Solution Approach 2:
The patent dynamically changes reporting parameters (timing, frequency, interval) based on learned patterns from historical data. The system adjusts these parameters to match optimal windows when devices are active and networks are less congested, reducing overall energy consumption while maintaining acceptable data freshness. This adaptive parameter adjustment allows the system to achieve efficient operation without sacrificing critical information updates.
3Adaptability or versatility
If existing algorithms like CARP are used for schedule optimization, then some adaptation can be achieved, but effectiveness is limited due to inability to fully adapt to regional policies and user behaviors
Solution Approach 1:
The system performs preliminary learning and analysis of regional policies, user behaviors, and network patterns before implementing optimized reporting schedules. By pre-processing historical data to identify patterns in device power cycles, user activity, and network congestion, the CARP algorithm can proactively adjust schedules to avoid problematic periods, thereby improving both adaptability to local conditions and communication success rates before actual reporting attempts are made.
Solution Approach 2:
The patent implements self-service capabilities where the reporting system automatically adapts to regional policies and user behaviors without requiring manual configuration or intervention. The CARP algorithm autonomously learns from observed patterns in device usage and network conditions, automatically adjusting reporting schedules to match local preferences and constraints, thereby achieving high adaptability while maintaining reliable communication through data-driven optimization.
Data Source
AI summary
A system and method for a device reporting optimization includes a processor and associated memory and a network interface for data communication with a multifunction peripheral. Reporting schedule data is sent from the memory the multifunction peripheral via the network interface. The processor receives a plurality of service file sets from the multifunction peripheral via the network interface in accordance with the scheduled reporting. The processor determines a timing of receipt of each of the service file sets relative to timing specified the reporting schedule data and generates updated reporting schedule data in accordance with a determined timing. The processor then sends the updated reporting schedule data to the multifunction peripheral via the network interface.