ML-Based Unknown Object Engagement for Network Efficiency
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Solution Overview
Problem
Existing systems face inefficiencies in sending communications to large groups of users, as many communications go unanswered and unread, leading to computational expenses.
Innovation Solution
A system utilizing a machine learning model to engage a reduced set of unknown objects by processing interaction data, developing interaction maps, creating profiles for unknown objects, and triggering communications to those most likely to reciprocate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If communications are sent to a large group of users, then the coverage and potential reach is improved, but the computational cost and resource consumption increases
Solution Approach 1:
The system segments the user base into known objects (with historical interaction data) and unknown objects (without historical data). By processing known objects first and using their interaction patterns to predict unknown object behavior, the system divides the communication task into manageable segments, reducing unnecessary computations on unlikely responders while maintaining broad coverage potential.
Solution Approach 2:
The system performs preliminary actions by developing interaction maps and profiles for known objects before sending communications to unknown objects. Machine learning models predict which unknown objects are likely to reciprocate based on known object patterns, allowing the system to pre-filter the target audience and avoid computational waste on communications that will not be received.
2Loss of energy
If communications are sent to reduce computational cost, then resource efficiency is improved, but the likelihood of missing potential responders increases
Solution Approach 1:
The system implements feedback loops by recording which unknown objects receive communications and which reciprocate. This feedback is fed back into the interaction maps and machine learning models, continuously improving prediction accuracy. Over time, the system learns from actual responses to refine its filtering, ensuring that reduced communication targets maintain or improve response rates while reducing computational waste.
Solution Approach 2:
The system changes parameters by dynamically adjusting communication targets based on predicted reciprocation likelihood. Instead of using fixed thresholds or random selection, the machine learning models continuously evaluate and adjust which unknown objects are most likely to respond, optimizing the balance between computational efficiency and response rate based on evolving data patterns.
3Measurement precision
If machine learning processing is applied to all unknown objects, then prediction accuracy is improved, but processing time and complexity increases
Solution Approach 1:
The system applies partial action by using machine learning processing selectively rather than on all unknown objects. It first processes known objects to build interaction maps, then uses these maps to predict and filter which unknown objects warrant full machine learning analysis. This partial approach focuses computational resources on the most promising candidates, reducing overall processing time while maintaining high prediction accuracy for the final communication targets.
Data Source
AI summary
A system for improving distributed network data flow efficiency by using a machine learning model to engage a reduced set of unknown objects is disclosed. The system includes at least one processor, a communication interface communicatively coupled to the at least one processor, and a memory device storing executable code that, when executed causes the at least one processor to receive interaction data corresponding to interactions between known objects and unknown objects, develop an interaction map, develop a profile for each unknown object, process each unknown object using a machine learning algorithm to generate a reduced set of unknown objects most likely to reciprocate and identify properties of each unknown object, and trigger a communication to each of the reduced set of unknown objects.