Conversational Agent Macro Recording for Personalization
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Solution Overview
Problem
Conversational agents or chatbots often become cumbersome for users as interactions become repetitive, especially after the user becomes familiar with the task, due to a one-size-fits-all approach that does not account for user proficiency levels or preferences.
Innovation Solution
A system and method that allows users to record, replay, and parameterize their interactions with a chatbot as macros, enabling personalized conversations by extending the conversational system for macro recording, which captures and reuses dialog steps efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If chatbots use fine-grained prompts to reduce misunderstanding, then reliability is improved, but ease of operation deteriorates due to repetitive interactions
Solution Approach 1:
The system performs preliminary action by recording the sequence of fine-grained prompts and responses during an initial conversation session. This recorded macro captures the complete interaction pattern, allowing the user to invoke the entire sequence with a single command later, eliminating repetitive typing while preserving the reliable interaction pattern.
Solution Approach 2:
The system creates a copy of the proven reliable interaction sequence (the macro) that can be reused multiple times. Instead of re-executing the same reliable prompt sequence, the user invokes a copied version stored in the system, maintaining reliability while reducing operational effort.
2Device complexity
If chatbots use one-size-fits-all approach, then device complexity is reduced, but adaptability deteriorates as user proficiency increases
Solution Approach 1:
The system transitions from a static one-size-fits-all approach to a dynamic adaptive system. It automatically detects when a user becomes familiar with certain tasks by analyzing conversation patterns, and then adapts by offering macro recording options. The system complexity increases only when needed, providing adaptability while maintaining simplicity for new users.
Solution Approach 2:
The system changes the interaction parameter from detailed step-by-step prompts to condensed macro invocations based on user proficiency. When the system detects repeated similar tasks, it transforms the interaction mode, allowing the same simple system to serve both novice and expert users effectively.
3Measurement precision
If chatbots require detailed prompt sequences, then measurement precision is improved for task execution, but loss of time increases due to repetitive input
Solution Approach 1:
The system performs the detailed prompt sequencing action in advance during the first execution and stores it as a macro. Subsequent executions simply invoke the pre-prepared sequence, maintaining measurement precision while eliminating the time-consuming repetitive input.
Solution Approach 2:
The system creates a copy of the precise prompt sequence that preserves the exact wording and structure needed for accurate task execution. This copied macro can be invoked repeatedly without re-typing, maintaining measurement precision while reducing time loss.
Data Source
AI summary
A computer-implemented conversational agent engages in a natural language conversation with a user, interpreting the natural language conversation by parsing and tokenizing utterances in the natural language conversation. Based on interpreting, a set of utterances in the natural language conversation to be recorded as a macro is determined. The macro is stored in a database with an associated macro identifier. Replaying of the macro executes a function specified in the set of utterances.