PATENT CLAIM ANALYSIS

Application Number: 16182466
Application Type: Utility
Filing Date: 2018-11
Publication Date: 2019-03
Patent Classification: ["606", "246000"]

Abstract:
The disclosure herein relate to systems, methods, and devices for developing patient-specific spinal treatments, operations, and procedures. In some embodiments, systems, methods, and devices described herein for developing patient-specific spinal treatments, operations, and procedures can comprise an iterative virtuous cycle. The iterative virtuous cycle can further comprise pre-operative, intra-operative, and post-operative techniques or processes. For example, the iterative virtuous cycle can comprise imaging analysis, case simulation, implant production, case support, data collection, machine learning, and/or predictive modeling. One or more techniques or processes of the iterative virtuous cycle can be repeated.

Claim (Index 12):
The system of  claim 11 , wherein the desired surgical output curvature of the spine is determined based at least in part on one or more predicted post-operative parameters, wherein the system is further caused to generate a prediction of the one or more post-operative parameters by:\n analyzing the one or more medical images to determine one or more pre-operative variables relating to the spine of the patient, wherein the one or more pre-operative variables comprise at least one of UIL, LIL, age of the patient, pelvic incidence pre-operative values, pelvic tilt pre-operative values, lumbar lordosis pre-operative values, thoracic kyphosis pre-operative values, or sagittal vertical axis pre-operative values; and generating a prediction of one or more post-operative variables based at least in part on applying a predictive model, wherein the predictive model is generated by:\n accessing a dataset from an electronic database, the dataset comprising data collected from one or more previous patients and spinal surgical strategy employed for the one or more previous patients; \n dividing the dataset into one or more categories based on spinal surgery domain knowledge; \n standardizing the data in the first subcategory; selecting a model algorithm to the data in the first subcategory; inputting a first set of input values from the first subcategory into the model algorithm to train the predictive model based on a first set of output values from the first subcategory; inputting a second set of input values from the second subcategory into the trained predictive model and comparing results generated by the trained predictive model with a second set of output values from the second subcategory; and storing the trained predictive model for implementation, wherein the post-operative parameters comprise one or more of pelvic tilt, lumbar lordosis, thoracic kyphosis, or sagittal vertical axis.

Metadata:
- Claim Count in Document: 50.0
- Percentile: 98.0
- Lexical Diversity: 2.0
- Patent Class: 606.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15440991', '15344320', '13989854', '15027904', '15970637']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.7806316882571483
- 35 USC 102 Novelty (BERT): 0.4701066895928598
- Combined Prediction Score: 0.7495791883907195
- Mean Citation Score: 228.64778800000005
- Max Citation Score: 237.74556
- Similarity Product: 180.18441231946

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