Digital Response Validation via Synthetic Test Profiles
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Solution Overview
Problem
Existing digital response systems lack effective methods for validating the accuracy of their responses across the entire customer interaction lifecycle, which can lead to user frustration and reduced system effectiveness.
Innovation Solution
A validation platform that includes a processor, non-transitory memory, and a machine-learning engine, which generates a test profile, authentication data, and simulated conversations to validate the accuracy of digital response systems by scoring responses against intended requests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a digital response system is implemented to respond to customer inquiries, then service efficiency and cost-effectiveness are improved, but response accuracy may be insufficient leading to user frustration
Solution Approach 1:
The patent implements a feedback mechanism where the digital response system's answers are evaluated against ground truth data. The system receives feedback on accuracy metrics and uses this to iteratively improve its response quality through retraining on corrected data, resolving the contradiction between automated efficiency and response accuracy.
Solution Approach 2:
The system performs self-validation and self-improvement by automatically evaluating its own responses against test datasets generated from historical interactions. This self-service capability allows the system to maintain and improve accuracy without requiring constant human intervention, preserving efficiency while improving precision.
2Measurement precision
If validation methods are added to ensure response accuracy, then response quality is improved, but system complexity increases
Solution Approach 1:
The validation system creates synthetic test profiles that copy the structure and characteristics of real user profiles without requiring actual user data. These copied profiles serve as test avatars to validate system responses, improving accuracy measurement while avoiding the complexity of managing real user interactions for validation purposes.
Solution Approach 2:
The system performs preliminary validation by pre-generating test profiles and test queries before actual deployment. This preliminary action allows the system to validate its responses in advance against known correct answers, ensuring accuracy without adding complexity to the real-time interaction flow.
3Reliability
If comprehensive validation across the complete lifecycle is implemented, then validation thoroughness is improved, but computational resources and time are consumed
Solution Approach 1:
The validation system implements periodic validation at key stages of the customer interaction lifecycle rather than continuous validation of every single interaction. This periodic approach maintains thoroughness by validating at critical points (initial contact, intermediate steps, final resolution) while reducing overall time consumption compared to continuous validation of every interaction detail.
Data Source
AI summary
Systems and methods for validating the accuracy of an authenticated, end-to-end, digital response system are provided. Methods may include curating a database of training data, including historical profile data and historical interaction data. Profile data may include a name, an identifier, and a set of financial instruments for a plurality of system users. Interaction data may include records of multi-step interactions between the system users and the digital response system. Methods may include generating, via a machine-learning (ML) engine and based on the training data: a test profile including a fictitious name, a fictitious identifier, and a fictitious set of financial instruments; authentication data for the test profile including a username and password that are operational for authenticating the test profile to the digital response system; and a simulated conversation for the test profile including an utterance that is associated with an intended request. Methods may include: initiating a validation session by logging the test profile into the digital response system using the authentication data; feeding the simulated conversation as an input to the digital response system; receiving a response from the digital response system; scoring the accuracy of the response vis-à-vis the intended request; generating an accuracy report based on the accuracy score; and submitting the accuracy report to a system administrator.