Multimodal Virtual Assistant With Dynamic Transaction Schemas
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Communications between customer service associates and clients for simple tasks are inefficient and costly due to labor expenses, necessitating improved interaction methods.
Innovation Solution
A virtual assistant application that receives and processes multimodal inputs, adjusts a transaction service schema based on customer interactions, and responds with outputs tailored to the customer's preferences and history, utilizing machine learning to enhance reliability and adaptivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If customer service associates handle simple tasks manually, then customer interactions can be conducted, but labor costs increase and efficiency decreases
Solution Approach 1:
The virtual assistant enables customers to serve themselves by automatically handling transaction profiles, adjusting schemas based on customer interactions, and providing personalized responses without requiring manual intervention from customer service associates for simple tasks
Solution Approach 2:
The patent replaces the mechanical system of manual customer service handling with an automated virtual assistant system that uses machine learning models to process multimodal inputs, adjust transaction service schemas, and generate appropriate responses, thereby eliminating labor costs for simple tasks
2Adaptability or versatility
If a virtual assistant uses fixed interaction protocols, then the system is simple to implement, but adaptability to customer preferences decreases
Solution Approach 1:
The transaction service schema is made dynamic rather than fixed, automatically adjusting based on customer interactions and preferences. The system evolves its behavior through machine learning models that process multimodal inputs and refine responses over time, enabling adaptability without requiring complex manual reconfiguration
Solution Approach 2:
The system incorporates feedback loops where customer interactions with the virtual assistant are continuously monitored and used to refine the transaction service schema. Machine learning models analyze customer responses and adjust future interactions accordingly, improving personalization through iterative feedback rather than static programming
3Ease of operation
If the virtual assistant processes only single-modality inputs, then the system is simpler, but customer satisfaction decreases due to lack of personalization
Solution Approach 1:
The virtual assistant is designed to handle multiple input modalities (text, audio, visual) and processing modes within a single unified system. The machine learning framework accommodates diverse input types and combines them with transaction service schemas to generate personalized responses, providing universal functionality that enhances customer satisfaction without requiring separate specialized systems for each modality
Data Source
AI summary
A process for receiving, through a virtual assistant application, a first modality input related to a transaction profile. The process may involve identifying utterances from the first modality input. The process may further involve adjust a transaction service schema based on utterances identified from the first modality input and responding, through the virtual assistant application, with a first output derived from the transaction service schema. The process may further involve receiving, through the virtual assistant application, a second modality input related to the transaction profile. The process may then involve identifying utterances from the second modality input. The process may then involve adjusting the transaction service schema based on utterances identified from the second modality input. The process may conclude with responding, through the virtual assistant application, with a second output derived from the transaction service schema.