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 57):
The at least one non-transitory computer-readable storage medium of  claim 56 , wherein the machine learning engine was trained to generate a model having a parameter representing a weight between the first characteristic and the second characteristic, and the predicting the proposed amino acid sequence further comprises using the parameter to identify a specific amino acid for at least one residue of 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): 0.1765647807799674
- 35 USC 102 Novelty (BERT): 0.502135591719239
- Combined Prediction Score: 0.2091218618738946
- Mean Citation Score: 195.733258
- Max Citation Score: 201.81108
- Similarity Product: 129.2698502192545

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