PATENT CLAIM ANALYSIS

Application Number: 16240610
Application Type: Utility
Filing Date: 2019-01
Publication Date: 2019-06
Patent Classification: ["702", "003000"]

Abstract:
In an approach, a computer receives an observation dataset that identifies one or more ground truth values of an environmental variable at one or more times and a reforecast dataset that identifies one or more predicted values of the environmental variable produced by a forecast model that correspond to the one or more times. The computer then trains a climatology on the observation dataset to generate an observed climatology and trains the climatology on the reforecast dataset to generate a forecast climatology. The computer identifies observed anomalies by subtracting the observed climatology from the observation dataset and forecast anomalies by subtracting the forecast climatology from the reforecast dataset. The computer then models the observed anomalies as a function of the forecast anomalies, resulting in a calibration function, which the computer can then use to calibrate new forecasts received from the forecast model.

Claim (Index 7):
The method of  claim 5 , wherein the Nonhomogeneous Gaussian Regression uses one or more covariants representing one or more of: seasonal bias, ensemble mean, ensemble standard deviation, recent anomalies, forecasts from previous run times, or forecasts at other lead times.

Metadata:
- Claim Count in Document: 50.0
- Percentile: 99.0
- Lexical Diversity: 2.66071
- Patent Class: 702.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['15066958', '15153392', '11695353', '15450897', '15092227']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.2294611419951356
- 35 USC 102 Novelty (BERT): 0.655046396501506
- Combined Prediction Score: 0.2720196674457726
- Mean Citation Score: 312.49354
- Max Citation Score: 634.9811
- Similarity Product: 614.1381146873294

Labels:
- Claim Label 101: 1
- Claim Label 102: 1
- Claim Label 103: 1
- Claim Label 112: 1
- Combined Label: 1
- Label 101 Adjusted: 1

Dataset: test