Ambient Analytics Using Real-Time Transcription And LLMs
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Solution Overview
Problem
Existing data analytics systems struggle to provide real-time, ambient analytics information that leverages user interactions for driving relevant data visualizations and insights.
Innovation Solution
A system and method that utilizes a real-time transcription of user interactions combined with a large language model and knowledge service to surface relevant data visualizations and analytics information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If real-time transcription and large language models are integrated into data analytics systems, then ambient analytics information and user interaction capabilities are enhanced, but system complexity and computational resource requirements increase
Solution Approach 1:
The system is divided into distinct functional modules: a transcription service that converts speech to text, a large language model processing layer, and a data analytics core. This segmentation allows each component to be optimized independently and reduces overall system complexity by distributing functionality across separate services.
Solution Approach 2:
A transcription service acts as an intermediary between user speech and the data analytics system. This mediator converts unstructured speech into structured text that can be processed by the large language model, bridging the gap between natural human interaction and computational analysis without requiring direct integration of all system components.
2Productivity
If real-time transcription and large language models are used to provide ambient analytics information, then relevant data visualizations are surfaced faster, but computational resource consumption and processing time increase
Solution Approach 1:
The system processes only the most relevant portions of user interactions using the computationally intensive large language model. By identifying key phrases and questions in the transcription, the system applies advanced NLP selectively rather than processing entire conversations, reducing computational overhead while maintaining high productivity in delivering relevant analytics.
Solution Approach 2:
The transcription service performs preliminary processing of user speech into text format before the data reaches the large language model. This preliminary action prepares the data in advance, allowing the computationally intensive LLM to focus only on semantic understanding and query generation rather than basic speech processing, thereby optimizing resource usage.
Data Source
AI summary
Embodiments described herein are generally related to computer data analytics, and computer-based methods of providing business intelligence or other data, and are particularly related to a system and method for providing ambient analytics information, for use with data analytics environments. In accordance with an embodiment, the system can leverage a real-time transcription of an interaction between one or more users, for example as part of a conversation, in combination with a large language model or knowledge service, to drive the surfacing of relevant data visualizations or other analytics information.