PATENT CLAIM ANALYSIS

Application Number: 15971952
Application Type: Utility
Filing Date: 2018-05
Publication Date: 2019-01
Patent Classification: ["706", "025000"]

Abstract:
Systems and methods are provided for performing predictive assignments pertaining to genetic information. One embodiment is a system that includes a genetic prediction server. The genetic prediction server includes an interface that acquires records that each indicate one or more genetic variants determined to exist within an individual, and a controller. The controller selects one or more machine learning models that utilize the genetic variants as input, and loads the machine learning models. For each individual in the records: the controller predictively assigns at least one characteristic to that individual by operating the machine learning models based on at least one genetic variant indicated in the records for that individual. The controller also generates a report indicating at least one predictively assigned characteristic for at least one individual, and transmits a command via the interface for presenting the report at a display.

Claim (Index 3):
The system of  claim 1  wherein:\n genetic variants are each assigned a location with respect to other genetic variants, and the neural networks include a layer that generates an output based on locations of genetic variants with respect to each other.

Metadata:
- Claim Count in Document: 50.0
- Percentile: 93.0
- Lexical Diversity: 2.08219
- Patent Class: 706.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['14739432', '15489564', '15404052', '14568456', '14465599']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3353759723893783
- 35 USC 102 Novelty (BERT): 0.5006864298572148
- Combined Prediction Score: 0.351907018136162
- Mean Citation Score: 166.90926799999997
- Max Citation Score: 197.37247
- Similarity Product: 127.34146171426832

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