PATENT CLAIM ANALYSIS

Application Number: 15939621
Application Type: Utility
Filing Date: 2018-03
Publication Date: 2018-08
Patent Classification: ["706", "012000"]

Abstract:
Systems and methods are provided for predicting treatment-regimen-related outcomes (e.g., risks of regimen-related toxicities). A predictive model is determined for predicting treatment-regimen-related outcomes and applied to a plurality of datasets. An ensemble algorithm is applied on result data generated from the application of the predictive model. Treatment-regimen-related outcomes are predicted using the predictive model. A combination of machine learning prediction and patient preference assessment is provided for enabling informed consent and precise treatment decisions.

Claim (Index 25):
A processor-implemented system for predicting regimen-related outcomes, the system comprising:\n one or more processors configured to:\n generate one or more training datasets and one or more testing datasets based at least in part on clinical data or gene feature data of a plurality of patients; \n determine one or more initial predictive models using one or more machine learning algorithms based at least in part on the one or more training datasets; \n apply the one or more initial predictive models on the one or more training datasets to generate result data; \n perform an ensemble algorithm on the result data to generate ensemble data; \n determine one or more final predictive models based at least in part on the ensemble data; \n evaluate performance of the one or more final predictive models based at least in part on the one or more test datasets; and \n predict regimen-related outcomes using the one or more final predictive models; \n one or more non-transitory machine-readable storage media for storing a computer database having a database schema that includes and interrelates clinical data fields, gene feature data fields, result data fields, ensemble data fields and predictive model data fields, the clinical data fields storing the clinical data, the gene feature data fields storing the gene feature data, the result data fields storing the result data, the ensemble data fields storing the ensemble data, and the predictive model data fields storing parameter data of the initial predictive models and the final predictive models.

Metadata:
- Claim Count in Document: 5.0
- Percentile: 90.0
- Lexical Diversity: 1.51852
- Patent Class: 706.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['14458575', '10955591', '11371511', '13246596', '12349628']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3896326535945186
- 35 USC 102 Novelty (BERT): 0.4897436988179374
- Combined Prediction Score: 0.3996437581168605
- Mean Citation Score: 190.618108
- Max Citation Score: 207.23244
- Similarity Product: 139.17636467009066

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

Dataset: test