PATENT CLAIM ANALYSIS

Application Number: 15923527
Application Type: Utility
Filing Date: 2018-03
Publication Date: 2018-08
Patent Classification: ["703", "011000"]

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, 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 one or more 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 prior knowledge regarding the underlying function of a given genetic alteration is not required to predict the expected phenotypic performance; \n e. selecting a subset of the design candidate host cells based upon their predicted phenotypic performance; f. manufacturing host cells from the subset of the design candidate host cells to thereby create engineered host cells; g. measuring, in an in vitro assay, the phenotypic performance of the engineered host cells; and h. adding to the training data set of (a):\n i. one or more genetic alteration input variables representing one or more genetic alterations that were introduced into the engineered host cells, and \n ii. one or more measured phenotypic performance output variables representing the phenotypic performance measurements of the engineered host cells.

Metadata:
- Claim Count in Document: 47.0
- Percentile: 90.0
- Lexical Diversity: 1.45588
- Patent Class: 703.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15396230', '15923555', '15923543', '16313613', '16313612']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.2949586195727255
- 35 USC 102 Novelty (BERT): 0.623971676161252
- Combined Prediction Score: 0.3278599252315782
- Mean Citation Score: 490.724338
- Max Citation Score: 693.2911
- Similarity Product: 501.57818576396704

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

Dataset: test