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 1):
A method of cross-project learning for improvement of a subject project, the method comprising:\n finding one or more similar projects to a subject project, the finding including:\n accessing, from a candidate target project database, features including feature information of one or more candidate target projects; \n accessing, from a server, features including feature information of the subject project, wherein the candidate target projects and the subject project are software programs; \n determining a similarity score between the feature information of each of the candidate target projects and the feature information of the subject project, wherein the similarity score is based on a term frequency, an inverse document frequency, and term weighting of the feature information of each feature of each of the candidate target projects; \n aggregating the similarity scores of each of the features in the candidate target projects to create an aggregate similarity score for each of the candidate target projects; \n sorting the candidate target projects by the aggregate similarity scores; \n filtering the candidate target projects that have an aggregate similarity score below a particular threshold; and \n generating a set of similar target projects that includes the candidate target projects that have an aggregate similarity score equal to or above the particular threshold; \n identifying a defect in the subject project based on the similar target projects; recommending code, based on the similar target projects, to repair the defect in the subject project; and modifying the subject project by implementing the recommended code in the subject project to repair the defect.

Metadata:
- Claim Count in Document: 40.0
- Percentile: 88.0
- Lexical Diversity: 2.8
- Patent Class: 717.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['13479648', '15275494', '12033308', '12041629', '14219613']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3817899144836701
- 35 USC 102 Novelty (BERT): 0.5033933401081155
- Combined Prediction Score: 0.3939502570461147
- Mean Citation Score: 202.216488
- Max Citation Score: 222.40976
- Similarity Product: 158.2788367665863

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