Omnichannel Negotiation Assistant with Sentiment Analysis
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Solution Overview
Problem
Current negotiation processes lack automated or semi-automated tools to assist human negotiators effectively, often relying on lengthy long-form contracts that divert focus from key business terms, leading to suboptimal negotiation outcomes.
Innovation Solution
A negotiation platform utilizing an omnichannel listener for real-time sentiment analysis and AI-driven feedback, combined with a semantic term extractor to convert contracts or communications into negotiable term sheets, and an intervention generator providing context-sensitive assistance based on game theory and negotiation best practices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automated tools are introduced to assist negotiators, then negotiation effectiveness is improved, but system complexity increases
Solution Approach 1:
The negotiation assistant system is divided into distinct functional modules: omnichannel listener for data collection, semantic term extractor for processing communications, sentiment analyzer for emotional assessment, and intervention generator for providing guidance. This segmentation allows each component to perform its specific function independently, improving overall system reliability while managing complexity through modular design.
Solution Approach 2:
The system introduces an intermediary AI assistant that mediates between negotiators and negotiation outcomes. This intermediary processes communications, analyzes sentiments, and generates interventions without directly participating in the negotiation, thereby improving effectiveness while maintaining a manageable system architecture through a clear intermediary layer.
2Reliability
If real-time sentiment analysis and AI feedback are implemented, then negotiation outcomes are enhanced, but computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing communications through the semantic term extractor and maintaining structured representations of negotiation terms and sentiments. This preliminary processing reduces the computational burden during real-time analysis, as the heavy lifting of parsing and structuring data is completed in advance, allowing faster and more energy-efficient real-time sentiment analysis and intervention generation.
Solution Approach 2:
The system applies partial action by focusing computational resources on analyzing only the most relevant aspects of negotiations - specifically sentiment patterns and key term discussions - rather than processing every aspect of communications equally. This selective analysis approach enhances negotiation outcomes through targeted insights while reducing overall computational resource consumption by ignoring less critical data.
3Loss of information
If multiple communication channels are monitored, then negotiation data completeness is improved, but processing complexity increases
Solution Approach 1:
The omnichannel listener is designed with multi-functionality to handle multiple communication channels (email, chat, video conferencing, phone) through a unified interface. This universal approach allows the system to collect data from diverse sources without requiring separate processing pipelines for each channel, thereby improving data completeness while managing processing complexity through a single versatile component rather than multiple specialized ones.
Solution Approach 2:
The semantic term extractor performs the function of extracting and isolating relevant negotiation terms and communications from the multi-channel data stream. By taking out only the essential negotiation-related information from various communication channels and organizing it into a structured format, the system achieves complete negotiation data capture while reducing processing complexity by filtering out irrelevant information early in the pipeline.
Data Source
AI summary
An omnichannel intelligent negotiation assistant for generating timely, contextual negotiation assistance to a negotiator. The invention includes a semantic term extractor for converting a contract document into a negotiable term sheet. An omnichannel listener captures all negotiation inputs associated with a negotiation event, sequences each negotiation input by time, and analyzes the sentiment of the negotiation inputs in the context of a term sheet. The resulting annotated negotiation input stream is processed by an intervention generator that includes models of the parties and the negotiation itself as well as a referent negotiation model. The intervention generator includes a game theoretic model that, in concert with a trade-off matrix, allows the intervention generator to produce timely contextual interventions to the negotiator that assist in achieving a superior resulting negotiated agreement.