Dialog Act Recommendation Model for Conversation Objective Probability
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Solution Overview
Problem
Current prediction analytics in communication do not optimize the probability of achieving a specific outcome in conversations between participants, lacking measures to guide effective dialogue acts.
Innovation Solution
A computer-implemented method that receives textual data from conversations, identifies participant objectives, and determines optimal dialog acts using a model to score and select actions that maximize the probability of achieving those objectives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current prediction analytics are used in communication, then basic conversation functionality is maintained, but the probability of achieving specific conversation outcomes is not optimized
Solution Approach 1:
The system segments the conversation analysis into distinct components: textual data reception, objective identification, dialog act generation, scoring of multiple proposed dialog acts, and selection of optimal dialog act. This modular approach enables optimized outcome prediction while maintaining manageable system complexity through divided functional responsibilities.
Solution Approach 2:
The system performs preliminary analysis by generating multiple proposed dialog acts before final selection, scoring each proposal based on its likelihood to achieve the conversation objective. This advance evaluation enables optimized outcome prediction by pre-assessing multiple potential paths before committing to a specific dialog act.
2Measurement precision
If multiple proposed dialog acts are scored and evaluated, then the quality of dialog act selection is improved, but the processing time and computational resources increase
Solution Approach 1:
The system generates multiple proposed dialog acts (excessive action) but scores and evaluates them selectively based on their relevance to the conversation objective. By focusing computational resources on scoring only the most promising proposals rather than all possible dialog acts, the system achieves accurate selection while controlling processing time through targeted evaluation.
3Productivity
If a model-based approach with scoring multiple dialog acts is implemented, then the probability of achieving conversation objectives is maximized, but the device complexity and computational requirements increase
Solution Approach 1:
The model-based system serves multiple functions: it receives textual data, identifies conversation objectives, generates proposed dialog acts, scores each proposal based on objective achievement probability, and selects the optimal dialog act. This multi-functional approach maximizes conversation outcome effectiveness while consolidating complexity into a single unified system rather than requiring separate systems for each function.
Data Source
AI summary
A computer-implemented method according to one embodiment includes receiving, utilizing a processor, textual data associated with a conversation between a first participant and a second participant; receiving, utilizing the processor, an objective of the first participant for the conversation between the first participant and the second participant, where the objective is separate from the conversation; determining, utilizing the processor, a dialog act to be entered by the first participant that meets the objective, utilizing a model, including scoring a plurality of proposed dialog acts based on an amount that each proposed dialog act will change a probability of the objective being achieved during the conversation, and determining the dialog act to be entered, based on the scoring; and returning, utilizing the processor, the dialog act to the first participant.