Generative ML Telemetry Processing with Semantic Event Indexing
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Solution Overview
Problem
Deriving meaningful insights from telemetry data is challenging due to its volume and complexity, making it difficult for developers to diagnose software bugs, identify usability issues, and improve user experience, especially for large numbers of users and devices.
Innovation Solution
Utilizing a generative machine learning model induced by a prompt that includes a semantic event index, which defines events with descriptions and context, to interpret telemetry data and facilitate conversations about it, enabling developers to understand user interactions and software/hardware issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review of telemetry data is performed, then understanding of user experience and software issues is improved, but the process becomes impractical or impossible for large numbers of users and devices due to data volume
Solution Approach 1:
The patent introduces natural language processing and generative AI models as intermediaries between raw telemetry data and human analysts. These intermediaries automatically interpret and summarize telemetry data, converting large volumes of structured data into actionable insights that can be understood by developers and product managers without manual review of the underlying data
Solution Approach 2:
The patent replaces the mechanical process of manual data review with automated computational systems. Instead of human analysts directly examining telemetry data, the system uses AI models to perform the analysis, generating natural language summaries and insights that preserve the quality of human understanding while eliminating the scalability limitations of manual processing
2Quantity of substance
If comprehensive telemetry data is collected from multiple users and devices, then the quantity and variety of insights is improved, but the complexity and difficulty of analysis increases
Solution Approach 1:
The patent segments the complex analysis task into distinct components handled by different AI models and processing stages. The system divides telemetry data analysis into separate functions such as event detection, pattern recognition, natural language generation, and insight prioritization, allowing each component to specialize in specific aspects of the analysis while working together to solve the overall problem
Solution Approach 2:
The patent creates a universal analysis framework that can handle multiple types of telemetry data from various sources (different users, devices, applications) using the same AI-powered processing pipeline. This multi-functional system adapts to different data formats and analysis requirements without requiring separate manual analysis processes for each data type
3Measurement precision
If detailed context information is provided for each event in telemetry data, then accuracy of issue identification is improved, but the volume of data to be processed increases
Solution Approach 1:
The patent performs preliminary processing and filtering of telemetry data before detailed analysis. The system pre-processes incoming data to extract relevant features, filter out noise, and organize information in ways that prepare it for more efficient processing by the AI models, reducing the burden of detailed context analysis while maintaining accuracy
Data Source
AI summary
Aspects of the present application relate to telemetry data processing using generative machine learning (ML). In examples, a prompt is generated that induces the generative ML model to interpret telemetry data according to the prompt. For instance, the prompt includes a semantic event index that defines a set of events relating to one or more issues, wherein each event is associated with a description and/or other context information for the event. An indication of the telemetry data may thus be provided for processing by the generative ML model. The generative ML model generates model output relating to the telemetry data, for example responsive to natural language input. Accordingly, the disclosed aspects may enable a developer or other user to converse with the generative ML model about telemetry data as though the model were the user.