Prediction Model for Agent Resource Limits
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Solution Overview
Problem
Existing techniques struggle to accurately predict the outcome of information exchanges, especially when an agent lacks sufficient prior activity for analysis, making it difficult to establish a trust threshold.
Innovation Solution
A system that determines resource limits for each agent based on their request history, using a function that identifies local extrema points to predict the outcome of information exchanges and adjust resource limits dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional trust analysis methods are used, then agents with sufficient prior activity can be evaluated, but agents lacking sufficient history cannot establish a trust threshold
Solution Approach 1:
The system performs preliminary actions by establishing default resource limits and using synthetic data generation before actual information exchanges occur. This allows the system to operate with agents who have insufficient prior activity history by pre-configuring trust parameters and generating simulated exchange data to initialize their profiles.
Solution Approach 2:
The patent introduces an intermediary mechanism through synthetic data generation and simulation environments. This intermediary layer creates virtual information exchange records that mirror real exchanges, allowing agents with limited real history to accumulate virtual experience data that can be used for trust assessment.
2Measurement precision
If individualized resource limits are determined for each agent, then prediction accuracy improves, but system complexity increases
Solution Approach 1:
The system dynamically adjusts resource limit parameters based on agent-specific characteristics, exchange types, and contextual factors. By changing parameters such as resource allocation thresholds, evaluation criteria, and trust weights according to specific conditions, the system achieves individualized predictions without requiring completely separate analysis systems for each agent.
Solution Approach 2:
The patent creates a universal prediction model that can handle multiple agent types, exchange categories, and scenario variations through a single integrated framework. The model uses generalized feature extraction and pattern recognition mechanisms that work across different contexts, reducing the need for separate specialized systems while maintaining high prediction accuracy.
3Productivity
If automated resource limit determination is implemented, then trust establishment speed increases, but measurement and detection difficulty increases
Solution Approach 1:
The system replaces manual, mechanical trust assessment processes with automated computational models that use algorithms for pattern recognition, statistical analysis, and machine learning. This substitution enables rapid automated evaluation of agent reliability by transforming complex behavioral analysis into computational operations that can be executed efficiently.
Solution Approach 2:
The patent implements self-service mechanisms where agents automatically generate and update their own profiles, exchange histories, and resource limit configurations. Agents perform self-evaluation and self-adjustment of their parameters, reducing the need for external manual analysis while maintaining comprehensive and accurate trust assessments.
Data Source
AI summary
Disclosed methods include maintaining a database of resource limits for a plurality of agents. A resource limit may be usable for predicting a result of a given request from a given agent. Maintaining the database may include determining an updated resource limit for a particular agent based on identifying an extrema point of a function of resource limit. The maintaining may further include updating the database using the updated resource limit, as well as selecting, from the database, a subset of the plurality of agents that are selected based on associated parameter values compared to parameter values associated with the particular agent. The maintaining may also include updating corresponding resource limits for the subset of the plurality of agents based on the updated resource limit. The method may further include receiving a request from the particular agent, and predicting, using the updated resource limit, a result of the request.