PATENT CLAIM ANALYSIS

Application Number: 15927958
Application Type: Utility
Filing Date: 2018-03
Publication Date: 2019-07
Patent Classification: ["703", "011000"]

Abstract:
Provided herein are methods and apparatus for the identification of pathogenic and non-pathogenic microorganisms in food and environmental samples. The disclosure solves existing challenges encountered in identifying food borne pathogens, including pathogens of the  Salmonella, Campylobacter, Listeria , and  Escherichia  genera in a timely and efficient manner. The disclosure also provides methods for differentiating a transient versus a resident pathogen, correlating presence of non-pathogenic with pathogenic microorganisms, distinguishing live versus dead microorganisms by sequencing.

Claim (Index 18):
A method comprising:\n (a) sequencing a plurality of nucleic acid sequences from a food sample or from an environmental sample associated with said food sample for a period of time of less than 30 minutes; and (b) performing an assay on said food sample or said environment associated with said food sample if said sequencing for said period of time of less than 30 minutes identifies a threshold level of nucleic acid sequences from a microorganism in said food sample, wherein said threshold level is no more than 0.1% of said nucleic acid sequences in the entire genome of said microorganism from said microorganism.

Metadata:
- Claim Count in Document: 20.0
- Percentile: 90.0
- Lexical Diversity: 1.54717
- Patent Class: 703.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15927913', '15928023', '13707295', '14196999', '10240689']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.2562176316125276
- 35 USC 102 Novelty (BERT): 0.51727641300895
- Combined Prediction Score: 0.2823235097521698
- Mean Citation Score: 222.360736
- Max Citation Score: 302.33014
- Similarity Product: 209.1083812539649

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

Dataset: test