PATENT CLAIM ANALYSIS

Application Number: 16011043
Application Type: Utility
Filing Date: 2018-06
Publication Date: 2018-11
Patent Classification: ["506", "009000"]

Abstract:
In various implementations, a noninvasive, veterinary diagnostic test may be utilized to identify one or more immunologic food sensitivities. A fecal sample may be obtained. The veterinary diagnostic test may be performed on the obtained fecal sample. One or more immunologic food sensitivities may be identified based on the veterinary diagnostic test.

Claim (Index 1):
A method to diagnose immunologic food sensitivity in nonhuman animals, the method comprising:\n obtaining a fecal sample from an animal, wherein the animal is an animal type included in a set of nonhuman animals; determining whether a testing portion of the obtained fecal sample includes at least one of IgA associated with a set of food sensitivities or IgA antibody associated with a set of food sensitivities, wherein the testing portion comprises a portion of the fecal sample; and determining that one or more food sensitivities of a set of immunologic food sensitivities is present in the animal if a determination is made that at least one of IgA associated with a set of food sensitivities or IgA antibody associated with a set of food sensitivities is present in the testing portion of the fecal sample of the animal.

Metadata:
- Claim Count in Document: 2.0
- Percentile: 94.0
- Lexical Diversity: 1.8125
- Patent Class: 506.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['16011236', '11899670', '12995037', '12545041', '12960031']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.5961344190200608
- 35 USC 102 Novelty (BERT): 0.5333370608968536
- Combined Prediction Score: 0.5898546832077401
- Mean Citation Score: 272.142342
- Max Citation Score: 370.03073
- Similarity Product: 321.2518590833348

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

Dataset: test