Audio Data Conversion for Real-Time Expert Information Retrieval
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Solution Overview
Problem
Existing software systems fail to efficiently convert audio data from conference calls into actionable text data and associated computer code, leading to information gaps and inefficiencies in real-time collaboration due to time zone differences and scheduling conflicts, and they are not designed to provide information when key experts are absent.
Innovation Solution
A method and system that retrieves audio data from conference calls, converts it into text data, extracts specified information and code, detects user attributes, compares them with canonical documents, and executes digital actions, enabling an automated digital stand-in for subject matter experts and providing real-time information through machine learning and natural language processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If audio data from conference calls is converted into text data and code, then information accessibility is improved, but system complexity increases
Solution Approach 1:
The system segments the complex audio-to-code conversion process into distinct modules: audio retrieval module, audio-to-text conversion module, text-to-code extraction module, and information delivery module. Each module handles a specific transformation step, making the overall system more manageable and maintainable while improving information accessibility from conference calls.
Solution Approach 2:
The patent introduces text data as an intermediary representation between audio data and executable code. The audio transcript is converted into structured text that serves as a bridge, allowing natural language information to be systematically transformed into machine-executable code without requiring direct audio-to-code conversion.
2Productivity
If real-time information retrieval is implemented, then productivity is improved, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing audio transcripts and their associated code in a structured format during conference calls. This advance preparation allows the information retrieval system to quickly access and deliver relevant information without performing complex conversions in real-time, thus improving productivity while minimizing processing delays.
3Adaptability or versatility
If automated digital stand-in for experts is created, then availability is improved, but accuracy may deteriorate
Solution Approach 1:
The system creates a digital stand-in by copying and structuring the expert's conference call communications into accessible text and code formats. This digital replica captures the expert's information and can be retrieved automatically, improving availability while maintaining accuracy through faithful representation of the original expert content.
Data Source
AI summary
A method, system, and program product for selecting software is provided. The method includes retrieving audio data during a call with a subject matter expert (SME). The audio data is converted into a data training set and documents of the SME are converted into a document training set. Canonical documents generated by authors are analyzed and specified code is extracted from the text data training set and document training set. Attributes of individuals are detected. The attributes are compared with specified data and the canonical documents and it is determined that the individuals are requesting information associated with the text data. The information is provided to the individuals via the canonical documents or the documents of the SME and it is determined if a matched set of data exists between the attributes, the specified data, and the canonical documents. A digital action associated with results of the determination is executed.