PATENT CLAIM ANALYSIS

Application Number: 15934596
Application Type: Utility
Filing Date: 2018-03
Publication Date: 2018-08
Patent Classification: ["424", "093210"]

Abstract:
The present invention is relevant to the generation of multi-specific antibodies, antibodies that are distinguished by their ability to bind to multiple antigens with specificity and with affinity. In particular, the present invention is related to bi-specific antibodies.

Claim (Index 15):
A method of identifying and modifying a multi-specific antibody, the method comprising:\n a. generating a library of antibodies; b. screening the library to identify a multi-specific antibody that binds to two or more epitopes; c. evolving the multi-specific antibody to produce a set of modified antibodies; d. screening the modified antibodies for optimized binding to one or more of the two or more epitopes; and e. modifying the multi-specific antibody such that the multi-specific antibody comprises an organic moiety, wherein step (c) employs one or more of comprehensive positional evolution (CPE); comprehensive positional insertion evolution (CPI); comprehensive positional deletion evolution (CPD); comprehensive positional evolution (CPE) followed by combinatorial protein synthesis (CPS); and comprehensive positional deletion evolution (CPD) followed by combinatorial protein synthesis (CPS).

Metadata:
- Claim Count in Document: 55.0
- Percentile: 90.0
- Lexical Diversity: 1.4
- Patent Class: 424.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['14399617', '15593721', '13298559', '15751169', '13826126']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.7274269368467168
- 35 USC 102 Novelty (BERT): 0.550333286253426
- Combined Prediction Score: 0.7097175717873877
- Mean Citation Score: 275.173576
- Max Citation Score: 422.22406
- Similarity Product: 325.80243299199344

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