Opt-Out Intent Inference via ML Sentiment Analysis
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Solution Overview
Problem
Existing systems struggle to accurately infer an entity's intent to opt-out of communications, particularly in cases where replies do not comply with standard keywords or phrases, or when messages are received erroneously due to changes in phone numbers.
Innovation Solution
A processing server is configured to assess messages and replies from entities, using a machine learning component to infer intent based on sentiment and context, including historical behavior and message content, to selectively terminate or continue communications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If keyword-based opt-out detection is used, then implementation is simple, but accuracy is low when replies do not comply with standard keywords
Solution Approach 1:
The patent replaces the mechanical keyword-matching system with a machine learning-based intent detection system. The ML model analyzes semantic meaning, context, and sentiment of replies rather than relying on fixed keyword patterns, enabling accurate detection of opt-out intent even when users do not use standard keywords or phrases.
Solution Approach 2:
The system changes the detection parameters from binary keyword presence/absence to continuous sentiment analysis and contextual understanding. By transforming the detection approach from discrete keyword matching to continuous semantic analysis, the system can accurately interpret nuanced user responses that express opt-out intent without using standard keywords.
2Reliability
If automated intent inference is implemented, then compliance with communication laws is improved, but false positives may occur due to erroneous messages or number reassigned
Solution Approach 1:
The system incorporates feedback mechanisms where the ML model continuously learns from actual user behavior patterns. When a user responds in an unexpected way or when the inferred intent seems contradictory to subsequent behavior, the system can adjust its inference, reducing false positives caused by erroneous messages or number reassigned.
Solution Approach 2:
The system performs preliminary verification steps before finalizing opt-out decisions, such as analyzing the context of the reply, checking for patterns consistent with erroneous messages, and considering the user's communication history. This preliminary analysis helps distinguish between genuine opt-out intent and false indicators.
3Reliability
If strict opt-out enforcement is applied, then legal compliance is improved, but legitimate communications may be blocked
Solution Approach 1:
The system applies different levels of strictness to different communication contexts. By analyzing the local quality of each reply (sentiment, context, pattern), the system can determine when strict opt-out enforcement is necessary versus when communications should continue. This allows legal compliance to be maintained while preserving legitimate communication effectiveness.
Data Source
AI summary
A processing server assists in selectively removing or opting-out a client device from further communications. The processing server includes a hardware processor, and memory storing computer instructions that when executed perform transmitting a message to an entity, indicating directions to opt-out of receiving further communications, receiving, in response to the transmitting of the message, a reply, from the entity, inferring an intent of the reply pertaining to opting-out of receiving communications, and selectively terminating the communications based on the inferred intent.