Utterance Recommendation via Multi-Source Data Enrichment
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Solution Overview
Problem
Conventional utterance recommendation systems fail to provide contextually personalized interactions, struggle with complex queries, and are limited to unidirectional data, often providing static responses that lack intelligence when interactions deviate from predicted paths.
Innovation Solution
A system that integrates multiple data sources, including user portfolio and profile data, to provide dynamic utterance recommendations using a processor, data aggregator, and response utterance recommender, which includes a data enricher, multi-fold solution recommender, and parallel consensus aggregator, to analyze user interactions and preferences, and rank potential responses for optimized personalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional utterance recommendation systems use pre-set responses based on predicted paths, then the system structure is simple and easy to implement, but the system cannot provide contextually personalized interactions and fails when conversations deviate from predefined paths
Solution Approach 1:
The system transitions from static pre-set responses to dynamic utterance recommendations that adapt in real-time based on conversation context, user profile data, and interaction history. The recommendation engine continuously updates suggested responses based on actual conversation flow, enabling the system to handle deviations from predicted paths while maintaining personalization.
Solution Approach 2:
The system performs preliminary actions by pre-processing and storing user profile data, conversation history, and contextual information before the actual conversation occurs. This allows the recommendation system to quickly retrieve and apply relevant information during interactions, reducing the computational burden during real-time conversation while maintaining high adaptability.
2Adaptability or versatility
If conventional systems handle only first-level or preliminary questions, then the system complexity is low, but the system cannot handle complex queries and multi-level questions
Solution Approach 1:
The system segments complex queries into multiple levels of processing. The conversation manager identifies the primary intent and routes to appropriate handling mechanisms, while the recommendation engine processes sub-questions and contextual nuances separately. This segmentation allows the system to handle complex multi-level questions without overwhelming the entire system at once.
Solution Approach 2:
The system implements a nested architecture where multiple processing layers are contained within each other. The conversation manager contains the recommendation engine, which in turn contains the data processing components. This nested structure allows complex queries to be processed through multiple nested layers of analysis, each handling specific aspects of the query systematically.
3Adaptability or versatility
If conventional systems provide unidirectional recommendations catering to only one kind of data, then the system is simple and fast, but the system cannot provide multifaceted personalized recommendations
Solution Approach 1:
The system merges multiple data sources including user profile data, conversation history, transactional data, and contextual information into a unified recommendation output. The data aggregator consolidates information from various sources and the recommendation engine processes this merged data to generate comprehensive personalized recommendations that consider multiple facets of user behavior and context.
Solution Approach 2:
The recommendation engine is designed with multi-functionality to handle different types of data and generate appropriate recommendations for various conversation scenarios. The same core recommendation mechanism can process user profile data, conversation context, and external information to provide tailored responses across different domains and interaction types.
Data Source
AI summary
An utterance recommendation system may obtain a plurality of utterance influencing attributes influencing a response recommended for a user query from a plurality of sources. The system may collate the utterance influencing attributes to provide enriched user data and may identify a set of preconfigured potential utterance options associated with the user query. The system may implement a plurality of utterance recommendation techniques to analyze the enriched user data based on associated predefined rules, and provide a preferential rank ordering of the preconfigured potential utterance options to be recommended based on the analysis. The system may determine a single optimized rank ordering of the preconfigured potential utterance options representing a ranking order of the preconfigured potential utterance options. The system may provide the response to the user query, based on a potential utterance option ranked highest in the single optimized rank ordering.