PATENT CLAIM ANALYSIS

Application Number: 16458376
Application Type: Utility
Filing Date: 2019-07
Publication Date: 2019-10
Patent Classification: ["702", "019000"]

Abstract:
The present disclosure provides a HTP microbial genomic engineering platform that is computationally driven and integrates molecular biology, automation, and advanced machine learning protocols. This integrative platform utilizes a suite of HTP molecular tool sets to create HTP genetic design libraries, which are derived from, inter alia, scientific insight and iterative pattern recognition. The HTP genomic engineering platform described herein is microbial strain host agnostic and therefore can be implemented across taxa. Furthermore, the disclosed platform can be implemented to modulate or improve any microbial host parameter of interest.

Claim (Index 1):
A high-throughput method for engineering a host cell to have improved phenotypic performance, comprising:\n a. accessing a training data set containing one or more genetic alteration input variables and one or more measured phenotypic performance output variables,\n i. wherein the one or more genetic alteration input variables represent one or more genetic alterations that have been introduced into a host cell through application of one or more libraries, and \n ii. wherein the one or more measured phenotypic performance output variables represent one or more phenotypic performance measurements that are associated with the introduced genetic alterations; \n b. developing a predictive machine learning model that is populated with the training data set; c. generating, in silico, a pool of design candidate host cells incorporating the one or more genetic alterations; d. utilizing the predictive machine learning model to predict the expected phenotypic performance of each member of the pool of design candidate host cells,\n i. wherein at least one design candidate host cell comprises a consolidated combination of genetic alterations from among the genetic alterations of step (a), in a genomic sequence, said combination being uncharacterized for improving the phenotypic performance at the time that step (d) is carried out; \n ii. wherein the expected phenotypic performance predicted by the machine learning model is based upon the introduced genetic alterations and their associated phenotypic performance measurements of step (a); and \n e. providing a subset of the design candidate host cells for use in creating engineered host cells.

Metadata:
- Claim Count in Document: 17.0
- Percentile: 100.0
- Lexical Diversity: 1.45588
- Patent Class: 702.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15923527', '15396230', '15923555', '15923543', '16313613']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.2883188350761628
- 35 USC 102 Novelty (BERT): 0.7400755899081187
- Combined Prediction Score: 0.3334945105593584
- Mean Citation Score: 604.2083819999998
- Max Citation Score: 794.4581
- Similarity Product: 770.9245055334449

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

Dataset: test