The difference between a sensor and a clinical intelligence system is not the quality of the hardware — it is the data architecture between the raw signal and the actionable output. This paper describes Barkley's four-stage data transformation pipeline: from raw sensor output to normalized behavioral features, to temporally-structured DogGraph, to clinical intelligence. It explains why each transformation stage is necessary and architecturally novel, and why the principal failure of current pet-technology platforms is not sensor quality but data architecture — the LLM Veneer problem: conversational fluency masking diagnostic incompetence. The paper details individual-referenced normalization, three-tier temporal decomposition, knowledge graph construction, and the specific deep learning architectures (TCN, GNN, Bayesian hierarchical models) appropriate to each clinical inference task. Barkley AI | Precision Behavioral Intelligence Series | No. 06.
Elodie P. Remoissenet (2026) studied this question.