Declarative Specification for Automated Agent Response Accuracy
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Solution Overview
Problem
Customer service assistant applications often struggle to correctly parse user inquiries, generate relevant responses, and provide timely assistance, leading to unhelpful or irrelevant interactions.
Innovation Solution
The system generates a declarative specification for an automated agent based on previous conversation data, combining general and specific policies to improve response accuracy and relevance. This specification is used to determine actions and responses during customer interactions, with the option to audit and evaluate responses against policy checklists.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a customer service assistant application interacts with a customer through a chat application, then the agent and customer can exchange text messages, but the application often fails to correctly parse user inquiries, generate relevant responses, or provide timely assistance
Solution Approach 1:
The patent introduces a declarative specification as an intermediary layer between the customer service assistant application and the customer interactions. This specification acts as a mediator that translates complex policy requirements into structured guidelines, enabling the application to generate accurate responses without directly embedding complex parsing and decision-making logic throughout the system.
Solution Approach 2:
The patent applies preliminary action by generating the declarative specification before customer interactions occur. The specification is created in advance based on previous conversation data and policy requirements, allowing the customer service assistant application to follow pre-established guidelines during actual interactions, thereby improving response accuracy without adding complexity during real-time operations.
2Speed
If the customer service assistant application processes user inquiries in real-time, then responses can be provided quickly, but the responses may not be relevant or helpful due to incorrect parsing
Solution Approach 1:
The declarative specification is generated in advance based on analysis of previous conversation data, allowing the system to have pre-process knowledge about effective response patterns. During real-time interactions, the application can quickly reference this pre-generated specification to maintain both speed and relevance without sacrificing one for the other.
Solution Approach 2:
The system uses feedback from previous conversation data to continuously improve the declarative specification. By analyzing past interactions and their outcomes, the specification is refined to better guide future responses, ensuring that real-time responses remain relevant and helpful while maintaining quick response times.
3Reliability
If the system uses previous conversation data to generate policies and declarative specifications, then response accuracy improves, but the time required to create and update policies increases
Solution Approach 1:
The system applies self-service by automatically generating the declarative specification from previous conversation data without requiring extensive manual intervention. The application autonomously analyzes past interactions, identifies patterns, and creates the specification, significantly reducing the time and effort needed compared to manual policy creation while maintaining high response accuracy.
Solution Approach 2:
The specification generation is performed as a preliminary action that can be done in advance and updated periodically rather than in real-time for each interaction. This approach allows the system to invest time in creating accurate specifications upfront, thereby improving response accuracy without incurring continuous time costs during customer service operations.
4Reliability
If the system audits and evaluates automated agent responses against policy checklists, then compliance with established policies is ensured, but the complexity of the system increases
Solution Approach 1:
The audit system is segmented into modular components: a checklist generator that creates evaluation criteria from the declarative specification, and an auditor that systematically checks responses against these criteria. This segmentation allows the compliance verification function to be added without overwhelming system complexity, as each component has a specific, well-defined role in the audit process.
Data Source
AI summary
The system provides improved controls for an automated agent by generating a declarative specification for an automated agent. The declarative specification is generated based at least in part from previous conversation data associated with interactions between an automated agent and a customer. The policies may include general policies and specific policies. A general policy is one that is applied to all automated agents. After creating the declarative specification, an automated agent can interact with a customer in an interaction based on the specification. The automated agent responses are evaluated based on checklists associated with the policies to determine if a response was proper.