Omnichannel AI Context and Intent Determination
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Solution Overview
Problem
Traditional customer conversation systems are static, one-size-fits-all platforms that lack proactive and adaptive approaches to understanding customer context and intent, leading to suboptimal customer experiences and inefficient interactions.
Innovation Solution
A machine learning-based artificial intelligence (AI) system that determines context and intent in omnichannel communications, providing personalized, dynamic conversations across multiple channels, enabling organizations to respond intuitively and efficiently to customer needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional static customer conversation systems are used, then system simplicity and ease of implementation are maintained, but customer experience quality and adaptability deteriorate
Solution Approach 1:
The patent implements dynamic intent classification by training machine learning models on historical conversation data to adapt to evolving customer communication patterns. The system continuously learns from new interactions to improve its understanding of customer intent across different channels, transforming from a static to a dynamic system that evolves with customer behavior changes.
Solution Approach 2:
The system incorporates feedback mechanisms where conversation outcomes and customer responses are fed back into the machine learning models for continuous retraining. This feedback loop enables the system to learn from actual customer interactions and refine its intent classification accuracy over time, improving adaptability without requiring manual reconfiguration.
2Productivity
If machine learning based AI techniques are implemented to determine context and intent, then customer interaction quality and responsiveness are improved, but computational resources and system complexity increase
Solution Approach 1:
The system performs preliminary processing by pre-training machine learning models on historical data and pre-processing customer communication data into structured formats. This preparation work is done in advance, allowing the trained models to quickly classify intent in real-time conversations without requiring heavy computational resources during actual customer interactions.
Solution Approach 2:
The patent uses data copying and replication strategies where historical conversation data is replicated and processed to train multiple versions of the intent classification model. These pre-trained model copies can then serve multiple customers simultaneously, distributing the computational burden and improving efficiency through model reuse rather than creating new models for each interaction.
3Loss of information
If omnichannel communication data is collected and analyzed, then customer journey understanding is improved, but data security risks and privacy concerns increase
Solution Approach 1:
The system extracts only the necessary information from omnichannel communication data for training purposes, separating useful patterns from sensitive personal details. By extracting only the features needed for intent classification and discarding or anonymizing personally identifiable information, the system reduces data security risks while maintaining customer journey understanding capabilities.
Solution Approach 2:
The patent introduces data anonymization and aggregation as intermediary steps between raw customer communication data and the machine learning models. These intermediaries process and transform the data, removing direct identifiers and combining individual records into aggregated patterns, thereby reducing privacy exposure while preserving the insights needed for improving customer interactions.
Data Source
AI summary
A system for determining context and intent in a conversation using machine learning (ML) based artificial intelligence (AI) in omnichannel data communications is disclosed. The system may comprise a data store to store and manage data within a network, a server to facilitate operations using information from the one or more data stores, and a ML-based AI subsystem to communicate with the server and the data store in the network. The ML-based AI subsystem may comprise a data access interface to receive data associated with a conversation with a user via a communication channel. The ML-based AI subsystem may comprise a processor to provide a proactive, adaptive, and intelligent conversation by applying hierarchical multi-intent data labeling framework, training at least one model with training data, and generating and deploying a production-ready model based on the trained and retained at least one model.