PATENT CLAIM ANALYSIS

Application Number: 15908346
Application Type: Utility
Filing Date: 2018-02
Publication Date: 2019-08
Patent Classification: ["717", "120000"]

Abstract:
The disclosed method may include accessing features including feature information of one or more candidate target projects and of a subject project, in which the candidate target projects and the subject project are software programs. The method may include determining a similarity score between the feature information of each of the candidate target projects and the feature information of the subject project, in which a similarity score is determined for each feature of each of the candidate target projects. The method may include aggregating the similarity scores of the feature information of each feature in the candidate target projects to create an aggregate similarity score for each of the candidate target projects and generate a set of similar target projects. The method may include modifying the subject project by implementing recommended code, based on the similar target projects, in the subject project to repair a defect.

Claim (Index 12):
The non-transitory computer-readable medium of  claim 11 , wherein the operations further comprise one or both of:\n generating new data to test the modified subject project; and predicting another defect in the subject project.

Metadata:
- Claim Count in Document: 40.0
- Percentile: 88.0
- Lexical Diversity: 2.8
- Patent Class: 717.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['13479648', '15275494', '12033308', '12041629', '14219613']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.4087180221628666
- 35 USC 102 Novelty (BERT): 0.5121407871207557
- Combined Prediction Score: 0.4190602986586555
- Mean Citation Score: 202.216488
- Max Citation Score: 222.40976
- Similarity Product: 145.92416481567383

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

Dataset: test