Data-Aware Agents for Dialog Latency and Cost Optimization
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Solution Overview
Problem
Conventional dialog systems face challenges in optimizing user experience due to up-front data loading and on-demand data retrieval, which can lead to latency and increased costs in developing and maintaining asynchronous data collection heuristics, especially when dealing with large data sets and slow information systems.
Innovation Solution
The implementation of data-aware agents that connect virtual robots to enterprise systems using a knowledge representation and reasoning process to determine the necessity and cost of data retrieval, allowing for on-demand data collection and optimizing dialog paths to reduce interaction with external data sources, while employing Query Blocks for asynchronous query execution and spectral graph analysis to identify cost-effective data retrieval strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If up-front data loading is used to provide virtual agents with vast amounts of data, then the agents can perform dialog tasks, but data integration costs and memory requirements increase significantly
Solution Approach 1:
The system performs preliminary analysis of user input and dialog context to identify only the specific data elements that will be needed for the current task. Instead of loading all possible data upfront, the reasoning process determines in advance which data retrievals are necessary, and pre-loads only those specific elements before the dialog task begins.
Solution Approach 2:
The system extracts and retrieves only the specific data elements that are necessary for completing the current dialog task, rather than providing the virtual agent with access to the entire database. The data-aware knowledge base stores metadata about data elements including their relative costs, and the system extracts only those elements with the lowest retrieval cost that are relevant to the current user intent.
2Quantity of substance
If on-demand data retrieval is implemented to reduce data loading, then data integration costs decrease, but latency increases due to asynchronous data collection
Solution Approach 1:
The system performs preliminary analysis of user input and dialog context to identify and queue the specific data elements that will be needed. By determining in advance which data retrievals are necessary and preparing retrieval requests before they are actually executed, the system minimizes the time during which data is being fetched asynchronously, thereby reducing dialog latency while maintaining on-demand retrieval benefits.
3Adaptability or versatility
If asynchronous data collection heuristics are used to manage data retrieval, then data access flexibility improves, but system complexity and maintenance costs increase
Solution Approach 1:
The system changes the parameters used to evaluate data retrieval decisions by incorporating a data-aware knowledge base that stores metadata about data elements including their relative costs, availability, and relevance. This structured parameter framework provides clear guidelines for data retrieval decisions, reducing the complexity of managing asynchronous data collection while maintaining flexibility. The reasoning process uses these parameters to make deterministic decisions about which data to retrieve.
4Reliability
If virtual agents are preloaded with vast amounts of data to handle undefined situations, then reliability improves, but data integration and maintenance costs increase
Solution Approach 1:
The system extracts and retrieves only the specific data elements needed to handle undefined or unexpected situations in the dialog, rather than preloading all possible data. When the virtual agent encounters an undefined situation, the reasoning process analyzes the context and retrieves only the relevant data elements from the data-aware knowledge base, maintaining reliability while minimizing data maintenance costs.
Solution Approach 2:
The system implements self-service data retrieval where the reasoning process autonomously determines what data is needed and retrieves it on-demand without requiring preloading. The data-aware knowledge base automatically tracks which data elements have been retrieved and their costs, enabling the system to serve itself with minimal human intervention for data management while handling undefined situations effectively.
Data Source
AI summary
A system and method for data gathering system, comprising a data-aware knowledge base storing knowledge on relative costs of obtaining various data items; and a data retrieval decision-making processor operative, when an individual data element is sought to be retrieved, to determine whether or not to retrieve the data element by comparing at least one parameter representing need for the data element, also termed herein a utility value, with at least one parameter, retrieved from the data-aware knowledge base, which represents relative cost of obtaining the data element.