Voice Communication Notification Bot for Topic Monitoring
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Solution Overview
Problem
Business professionals often spend excessive time and resources on multiple voice communication sessions, with only a small portion relevant to their specific interests, leading to unnecessary costs and inefficiencies.
Innovation Solution
A notification bot that automatically monitors voice communication sessions, transcribes conversations using natural language processing, compares topics of interest, and provides users with summaries and connectivity options to join relevant calls, allowing efficient participation in specific areas of interest.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a person participates in multiple voice communication sessions to ensure coverage of all potential relevant topics, then the probability of catching relevant information is improved, but the time and cost spent on unnecessary calls increases significantly
Solution Approach 1:
The patent replaces manual monitoring and participation in voice communications with an automated AI bot that uses natural language processing to analyze calls. The bot transcribes speech, identifies topics, and determines relevance automatically, substituting human mechanical participation with automated digital processing. This allows comprehensive monitoring without requiring human presence in all calls.
Solution Approach 2:
The AI bot acts as an intermediary between the voice communication sessions and the user. It monitors multiple calls, analyzes content, and selectively connects the user only to relevant portions or notifications, serving as a filter that prevents direct human involvement in all communications while ensuring relevant information is captured.
2Loss of information
If a person joins all voice communication sessions where their topic of interest might be discussed, then complete information retrieval is achieved, but resource efficiency deteriorates due to participation in irrelevant calls
Solution Approach 1:
The system performs preliminary analysis of voice communications by transcribing and analyzing call content before determining user relevance. The AI bot proactively identifies topics and assesses relevance in advance, allowing the user to be notified or connected only when relevant, rather than requiring continuous participation in all calls.
Solution Approach 2:
The system continuously monitors voice communications, compares detected topics against user interests, and provides feedback by notifying or connecting the user only when relevance is detected. This closed-loop feedback mechanism ensures complete information retrieval for relevant topics while avoiding resource waste on irrelevant communications.
3Measurement precision
If automated monitoring and transcription of multiple voice communication sessions is implemented, then topic detection accuracy is improved, but system complexity increases
Solution Approach 1:
The AI bot is designed as a universal system that performs multiple functions: monitoring multiple voice communications simultaneously, transcribing speech to text, identifying topics, comparing against user interests, and managing user connections. This multi-functional design consolidates what would otherwise require separate systems into a single integrated solution.
Solution Approach 2:
The system uses voice-to-text transcription to create a textual copy of voice communications, which can then be analyzed by the AI bot. This copying approach allows the system to work with text representations of calls rather than requiring direct real-time audio processing, simplifying the technical implementation while maintaining accuracy.
Data Source
AI summary
A method and system are provided. The method includes automatically monitoring one or more voice communication sessions occurring on multiple communication devices to transcribe into text, by a processor-based voice-to-text transcriber, voices uttered by two or more respective participants other than a user in each of the voice communications sessions. The method further includes comparing, for each of the voice communication sessions, the topic of interest to conversation features derived from the text to detect a match there between. The method also includes providing the user with a summary of each of the voice communication sessions for which the match has been detected by said comparing step, the summary being based on the text. The method additionally includes automatically connecting the user to at least one of the voice communication sessions for which the summary thereof has been provided to the user.