The following patent family:
| Patent | Level | What it protects |
|---|---|---|
| 2016 (10437697) | Foundation | `Build + validate statistical models |
| 2019 (11243863) | Structured | Segment system into interaction types |
| 2021 (12007869) | Adaptive | Dynamically reconfigure system using models |
This progression covers:
✔ Observability / APM tools
- Modeling + correlation (Patent 1)
✔ Capacity planning systems
- Segmented workload modeling (Patent 2)
✔ Autonomous / AI ops (AIOps)
- Self-optimizing infrastructure (Patent 3)
👉 You effectively moved toward:
self-driving infrastructure based on statistical modeling
Those patents map very directly to modern AIOps, especially the parts around business/workload demand → resource utilization → model scoring → automated load-balancing/remapping.
Core patent family vs AIOps platform features
| Patent concept | Plain-English meaning | Modern AIOps equivalent |
|---|---|---|
| Interaction / transaction volume by type | Business workload demand, e.g. mobile banking, ATM, web traffic | Service traffic, request rate, user actions, business events |
| Device/resource utilization | CPU, memory, disk, network usage | Infrastructure + APM telemetry |
| Statistical / regression / multivariate models | Model relationship between workload and resource consumption | ML baselines, anomaly models, predictive analytics |
| Diagnostic scoring: R², RMSE, strength | Decide which models are reliable | Confidence/scoring of anomalies, correlations, RCA evidence |
| Filtering weak models | Keep only useful models | Noise reduction / alert suppression |
| Forecasts | Predict future demand/resource pressure | Bottleneck prediction, capacity forecasting |
| Remapping devices to interaction types | Use model output to change workload placement | Automated remediation, scaling, routing, load balancing |
The strongest overlap is not generic “anomaly detection.” It is business-demand-aware resource modeling that can drive infrastructure decisions.
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