Ambient Audio Collection via NLP Keyword Detection
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Solution Overview
Problem
Current Natural Language Processing (NLP) implementations in information handling systems are limited in collecting and correlating audio data, as they typically rely on pre-defined dictionaries and are not configured to gather or analyze key words, text strings, or meta-data during user interactions with websites, restricting their ability to provide comprehensive customer insights.
Innovation Solution
A system and method that utilizes NLP-enabled devices to collect audio data during website browsing sessions, converting it to text, identifying relevant keywords, and analyzing them based on business-specific requirements, by integrating a business-provided user interface code and dictionary to enhance data collection and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If NLP APIs use pre-defined dictionaries to detect certain key words, then detection capability is provided, but the ability to collect and correlate words, text strings, and meta-data is limited
Solution Approach 1:
The NLP API is enhanced to perform multiple functions: it not only detects pre-defined keywords using traditional dictionaries but also collects and correlates words, text strings, and meta-data generated during NLP processing. This multi-functional approach allows the system to maintain precise keyword detection while expanding data collection capabilities to include ambient audio data and user interactions.
Solution Approach 2:
The system dynamically adjusts its data collection scope by integrating with the operating system's NLP engine to capture real-time audio data, converted text, and associated meta-data during website browsing sessions. This dynamic collection adapts to user behavior and provides comprehensive data for analysis beyond static pre-defined dictionaries.
2Loss of information
If audio data is collected during website browsing sessions and converted to text, then customer insights can be obtained, but system complexity increases
Solution Approach 1:
The system uses the device's existing NLP engine and operating system as intermediaries to handle audio collection, text conversion, and initial processing. By leveraging these existing components rather than building proprietary solutions, the system obtains comprehensive customer insights while minimizing the complexity of the data collection infrastructure.
Solution Approach 2:
The NLP-enabled device performs self-service by automatically collecting audio data, converting it to text, and generating meta-data during normal website browsing operations. This self-service approach eliminates the need for separate dedicated hardware or complex manual data collection processes, reducing overall system complexity while maintaining high information quality.
Data Source
AI summary
A system, method, and computer-readable medium are disclosed for improved audio collection of website ambient data. In various embodiments, when a user visits a website, audio data is collected during a website browsing session. The audio data is converted to text by Natural Language Processing (NLP) enabled device used for browsing. From the text, certain key words are identified that are relevant to a business. The identified key words can be analyzed per requirements of the business.