PATENT CLAIM ANALYSIS

Application Number: 16101302
Application Type: Utility
Filing Date: 2018-08
Publication Date: 2019-02
Patent Classification: ["703", "006000"]

Abstract:
A multi-step iterative process for simulating complex agricultural situations where limited sets of data are available for such problems first predicts an outcome for each situation in a particular dataset, using initial assumptions of an applied primary model. The process then uses the errors across these situations to identify where opportunities exist among relevant predictive variables for the model to make changes to a response to such predictor variables to reduce the errors when averaged across all situations. The process then develops a correction model to identify adjustments based on combinations of the predictive variables, and applies the adjustments to the primary model to induce an altered outcome.

Claim (Index 21):
A system for simulating complex agricultural processes, comprising:\n a computing environment including at least one computer-readable storage medium having program instructions stored therein and a computer processor operable to execute the program instructions to generate a simulation model for an agricultural process using available datasets within a plurality of data processing modules, the plurality of data processing modules including: one or more modules configured to develop a primary model for simulating an agricultural process, the agricultural process dependent upon predictive variables derived from input data relative to the agricultural process that includes one or more of weather conditions, soil conditions, crop and seed properties, and process-specific metadata over a period of time; one or more modules configured to calculate errors in the primary model using one or more validating observations relative to the agricultural process during the period of time; and one or more modules configured to develop a correction model to identify adjustments to an output of the primary model based on one or more combinations of predictive variables impacting an outcome of the primary model, and apply the adjustments as identified by the correction model to the outputs of primary model to induce an altered response from the primary model to yield an overall reduction of the errors, and iteratively repeat an interoperation of the primary model and the correction model until the errors from the primary model reach an apparent minimum.

Metadata:
- Claim Count in Document: 80.0
- Percentile: 96.0
- Lexical Diversity: 1.63768
- Patent Class: 703.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['14853593', '15187963', '14603383', '14463111', '15883017']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.2656825978686512
- 35 USC 102 Novelty (BERT): 0.4770949146280415
- Combined Prediction Score: 0.2868238295445903
- Mean Citation Score: 216.523014
- Max Citation Score: 222.74457
- Similarity Product: 193.12941637732388

Labels:
- Claim Label 101: 1
- Claim Label 102: 1
- Claim Label 103: 0
- Claim Label 112: 0
- Combined Label: 1
- Label 101 Adjusted: 1

Dataset: test