PATENT CLAIM ANALYSIS

Application Number: 16171596
Application Type: Utility
Filing Date: 2018-10
Publication Date: 2019-02
Patent Classification: ["702", "019000"]

Abstract:
Described herein are techniques for more precisely identifying antibodies that may have a high affinity to an antigen. The techniques may be used in some embodiments for synthesizing entirely new antibodies for screening for affinity, and for more efficiently synthesizing and screening antibodies by identifying, prior to synthesis, antibodies that are predicted to have a high affinity to the antigen. In some embodiments, a machine learning engine is trained using affinity information indicating a variety of antibodies and affinity of those antibodies to an antigen. The machine learning engine may then be queried to identify an antibody predicted to have a high affinity for the antigen.

Claim (Index 83):
The system of  claim 79 , wherein the predicting the proposed amino acid sequence further comprises:\n receiving, from the machine learning engine, an output amino acid series and values associated with different amino acids for each residue of the output amino acid series, wherein the values for each amino acid for each residue correspond to predictions of the machine learning engine regarding levels of the first characteristic and the second characteristic if the amino acid is selected for the residue; identifying a discrete version of the output amino acid series by selecting, for each residue, an amino acid from among the different amino acids for the residue based on the values; and receiving, as an output of identifying the discrete version, the proposed amino acid sequence.

Metadata:
- Claim Count in Document: 53.0
- Percentile: 97.0
- Lexical Diversity: 2.15094
- Patent Class: 702.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['11414742', '11566120', '13929338', '10153159', '11473745']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.1753803709116668
- 35 USC 102 Novelty (BERT): 0.5102955581346651
- Combined Prediction Score: 0.2088718896339666
- Mean Citation Score: 195.733258
- Max Citation Score: 201.81108
- Similarity Product: 116.80638116042375

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

Dataset: test