Cross-Channel Session Data Caching for Seamless Communication Transitions
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Solution Overview
Problem
Existing communication systems face challenges when switching between different channels or entities during customer support sessions, leading to delays, interruptions, and loss of communication history, resulting in sub-optimal message delivery and miscommunication.
Innovation Solution
Implementing a system that uses machine learning models to process and store session data across multiple channels, allowing seamless transitions between channels and entities by caching computations and using universal logging to maintain a unified record of interactions, thereby ensuring continuity and accessibility of communication history.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a conversing entity switches between different communication channels or entities during customer support sessions, then the system can handle diverse communication needs and provide flexible support options, but delays and interruptions occur during transitions, communication history may be lost, and miscommunication may result
Solution Approach 1:
The system performs preliminary actions by pre-processing and caching computations from the first channel before the switch occurs. This includes computing and storing representations of communication history, entity states, and contextual information in advance, so that when the channel switch happens, the second channel can immediately access these pre-computed results without waiting for real-time processing during the transition.
Solution Approach 2:
The system creates copies of communication history and contextual data from the first channel and transfers them to the second channel. By copying relevant session data, entity states, and computed representations, the system ensures that the conversing entity in the second channel has immediate access to the same information without needing to re-process or re-request it during the channel transition.
2Adaptability or versatility
If channel switching occurs without proper data transfer, then system flexibility is maintained, but communication history is lost or forgotten leading to sub-optimal message delivery
Solution Approach 1:
The system merges communication history and contextual information from the first channel with the session data in the second channel. By combining these data sources into a unified representation that the conversing entity can access across channels, the system prevents information loss and ensures continuous contextual awareness during channel transitions.
Solution Approach 2:
The system creates and transfers copies of communication history, session data, and contextual representations from the first channel to the second channel. This copying mechanism ensures that all relevant information is preserved and made accessible to the conversing entity in the new channel, preventing any loss of communication context during the switch.
3Device complexity
If the conversing entity is not aware of the channel switch, then system simplicity is maintained, but messages may be missed or miscommunicated requiring repetition
Solution Approach 1:
The system introduces an intermediary mechanism that automatically manages channel transitions and informs the conversing entity of the switch. This intermediary component handles the complexity of channel switching, data transfer, and entity state maintenance in the background, while the conversing entity simply receives notifications of channel changes without needing to understand or manage the underlying complexity, thus maintaining high message delivery efficiency.
4Device complexity
If previous communications are not accessible in the new channel format, then system simplicity is maintained, but understanding previous messages and providing appropriate responses becomes more difficult
Solution Approach 1:
The system implements a universal data representation format that can be accessed across different channel types. By creating a channel-agnostic representation of communication history and contextual information that the conversing entity can interpret regardless of the source channel, the system enables the entity to understand previous messages and generate appropriate responses in the new channel format without requiring channel-specific processing logic.
Data Source
AI summary
A company may implement automated workflows for convenience of users or to reduce support costs. For example, allowing a user to change an address using an automated workflow may be faster or less expensive than with a human agent. In some instances, a first communications session may be started between a first user and a second user. During the first communications session, one or more communications may be processed to select an intent of the first user and a value of an information item communicated by the first user. An automated workflow may be selected to continue assisting the first user, and the first user may be transferred to a second communications session with the automated workflow. The automated workflow may be initialized with the value of the information item that was provided during the first communications session so that the first user does not need to repeat information.