Device telemetry processing system with probabilistic classification and predictive health modeling

A computer-implemented method for predictive routing of mobile devices, the method comprising:

• extracting, upon intake of a mobile device, initial telemetry data from the mobile device, the initial telemetry data comprising low-level telemetry including kernel panic logs and voltage fluctuation data;
• applying a probabilistic classifier to the initial telemetry data, the probabilistic classifier configured to generate uncertainty scores for fault predictions;
• determining, based on the uncertainty scores, whether the mobile device has a high confidence classification of no fault found;
• responsive to determining that the mobile device has the high confidence classification of no fault found, routing the mobile device to a bypass processing pathway that bypasses a standard testing pipeline;
• and
• responsive to determining that the mobile device does not have the high confidence classification of no fault found, routing the mobile device to the standard testing pipeline comprising triage, testing, and repair stages.

A computer-implemented method for multi-modal device data analysis, the method comprising:

• receiving multi-modal device data for a mobile device, the multi-modal device data comprising structured telemetry data, diagnostic logs, and device photographs;
• processing the multi-modal device data using an LLM-driven intelligence layer comprising a cross-modal reasoning engine;
• correlating, using a multimodal large language model of the cross-modal reasoning engine, the diagnostic logs with the device photographs to identify relationships between functional issues indicated in the diagnostic logs and physical conditions visible in the device photographs;
• and
• generating a unified device condition assessment based on the correlating.