Automated Agent Decision Logic Imputation via Backward Interaction Paths
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Solution Overview
Problem
Existing automated agents lack the ability to efficiently determine and supplement interaction records with optimal decision logic, leading to suboptimal performance in client interactions.
Innovation Solution
A system that accesses interaction records, API specifications, and agent architecture specifications to determine an optimal logical path by working backward from the end point, supplementing the interaction record with additional information on the steps and states taken by the automated agent.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated agents use basic interaction recording without decision logic supplementation, then the system complexity remains low, but the response accuracy and decision quality deteriorate
Solution Approach 1:
The system performs preliminary action by pre-defining decision logic, states, and steps in the agent architecture specification before interactions occur. This allows the interaction record to be supplemented with optimal decision paths based on pre-analyzed logic rather than requiring complex real-time analysis, thereby improving response accuracy without proportionally increasing system complexity
Solution Approach 2:
The patent introduces an intermediary component (the decision logic supplementation mechanism) that bridges the gap between simple interaction recording and complex real-time decision analysis. By working backward from interaction endpoints and supplementing records with pre-determined optimal paths, the intermediary enables accurate decision tracking without requiring the full complexity of real-time optimization
2Productivity
If the system processes interaction records without optimal path determination, then the processing speed remains high, but the decision quality and effectiveness deteriorate
Solution Approach 1:
The system performs preliminary analysis to determine optimal logical paths and decision logic before processing interaction records. By pre-computing decision paths and supplementing the agent architecture specification with this information, the system enables fast processing of interaction records while maintaining high decision quality, as the complex optimization work is done in advance rather than in real-time
Solution Approach 2:
The patent creates a supplemented copy of the interaction record that includes decision logic, states, and steps. This copying approach allows the system to maintain the original fast-processing interaction record while generating a supplemented version with enhanced decision quality information, thereby achieving both high processing speed and reliable decision-making without requiring complex real-time processing of the original records
3Loss of information
If the system supplements interaction records with complete decision logic, then the information completeness improves, but the data processing time increases
Solution Approach 1:
The system performs preliminary action by pre-determining decision logic, states, and steps that will be needed for interaction record supplementation. This advance preparation allows the system to complete information supplementation efficiently during actual processing, as the complex analytical work has already been performed and stored in the agent architecture specification
Solution Approach 2:
The patent creates a supplemented copy of the interaction record that includes complete decision logic information. By working backward from the endpoint and identifying optimal paths, the system generates this enriched copy without requiring extensive processing time during actual interaction handling, as the supplementation leverages pre-analyzed decision logic stored in the agent architecture specification
Data Source
AI summary
A system automatically imputes automated agent decision logic. An interaction record is used with other data to determine an optimal and efficient logical path from a starting point in an interaction record to the end of the interaction record between an automated agent and a client. The logical path can be determined using a process that works backward, from the end point to the starting point, to determine the optimal path. Once complete, the resulting interaction record is supplemented with additional information regarding what states and/or steps were taken in the agent architecture specification to navigate through the interaction record.