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 55):
At least one non-transitory computer-readable storage medium storing computer-executable instructions that, when executed by at least one processor, cause the at least one processor to perform a method for identifying an amino acid sequence for a protein having an interaction with a target, the method comprising:\n querying a machine learning engine for a proposed amino acid sequence for a protein having a high interaction with the target, wherein the machine learning engine was trained using protein interaction information for different amino acid sequences with the target; and receiving from the machine learning engine the proposed amino acid sequence, the proposed amino acid sequence indicating a specific amino acid for each 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: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['11414742', '11566120', '13929338', '10153159', '11473745']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.1599201121032393
- 35 USC 102 Novelty (BERT): 0.499980240498378
- Combined Prediction Score: 0.1939261249427532
- Mean Citation Score: 195.733258
- Max Citation Score: 201.81108
- Similarity Product: 129.05218767663715

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