Focus Group Audio Insight Extraction with Tunable AI Reports
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Solution Overview
Problem
Conventional methods for analyzing audience sentiment in focus groups are inefficient and time-consuming, and existing natural language generation systems cannot produce variable outputs based on user-desired specifications, lacking the ability to effectively process and generate targeted marketing materials.
Innovation Solution
A system utilizing a combination of language processing, generative, and predictive AI modules to analyze audio data from focus groups, generating transcription, insight, and audience sentiment reports, which can be used to develop targeted marketing campaigns and product development.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional methods are used to analyze audience sentiment in focus groups, then human experts can identify topics and correlate sentiment, but the process is excessively time-consuming and inefficient
Solution Approach 1:
The patent replaces the mechanical human analysis process with an automated AI system comprising a language processing module, generative AI module, and predictive AI module. This substitution eliminates manual transcription and sentiment analysis, dramatically improving productivity while reducing time loss through automated processing of focus group data.
Solution Approach 2:
The system enables self-service analysis where the AI modules automatically process audio data, generate transcriptions, identify topics, and determine sentiment without requiring human intervention. The automated pipeline serves the analysis function independently, resolving the contradiction between efficiency and time consumption.
2Adaptability or versatility
If existing natural language generation systems are used, then text can be transformed using unsupervised NLP, but the systems cannot produce variable output based on user-desired tunable specifications
Solution Approach 1:
The patent introduces dynamic, tunable parameters that allow the NLG system to adapt its output based on user specifications. The system can adjust polarity of subjective opinion, sentiments, valence, emotions, formality, business tone, and readability levels. This dynamic adaptability resolves the contradiction by enabling customized outputs without requiring fundamentally different system architectures.
Solution Approach 2:
The system changes multiple parameters simultaneously to achieve desired output variations. By adjusting stylistic parameters such as sentiment polarity, formality level, and readability, the system produces variable outputs tailored to user needs while maintaining a unified underlying architecture, thus avoiding excessive complexity.
3Adaptability or versatility
If conventional NLG systems transform user text input using rule-based and machine learning classifiers, then tunable stylized text can be generated, but the systems are generally not readily extendable
Solution Approach 1:
The patent creates a universal AI-based platform that handles multiple functions including transcription, topic identification, sentiment analysis, and natural language generation. This multi-functional system replaces multiple separate conventional systems, improving extensibility while managing complexity through a unified architecture that can be extended to new tasks by adding or modifying AI modules.
Data Source
AI summary
Methods, apparatuses, and systems are described for generating one or more audience sentiment reports based on audio data of video files associated with one or more focus groups. Audience sentiment towards one or more topics discussed during one or more focus group sessions may be determined based on audio data associated with a focus group. The audio data may be processed via a language processing artificial intelligence (AI) module to generate transcription information. The transcription information may be processed via a generative AI module to generate insight information. The insight information may be processed via a predictive AI module to generate an audience sentiment report that includes transcription and diarization information, audience sentiment for each speaker and each topic, and/or future focus group questions associated with the focus group.