PATENT CLAIM ANALYSIS

Application Number: 15890385
Application Type: Utility
Filing Date: 2018-02
Publication Date: 2019-05
Patent Classification: ["717", "101000"]

Abstract:
Methods are provided for software build outcome prediction. For example, one method may comprise obtaining feature data associated with a software build, the feature data including one or more of the following: first data identifying a user to perform a modification on a set of one or more software artifacts, second data identifying the set of one or more software artifacts, and third data identifying a set of one or more reviewers to review the modification. The method may also comprise determining first probability data associated with the software build being unsuccessful given the feature data and second probability data associated with the software build being successful given the feature data; and predicting a software build outcome associated with the software build based on the first probability data and the second probability data.

Claim (Index 8):
A non-transitory computer-readable storage medium that includes a set of instructions which, in response to execution by a processor of a computer system, cause the processor to perform software build outcome prediction, wherein the computer system implements a machine learning classifier and the method comprises:\n obtaining feature data associated with a software build, wherein the feature data includes one or more of the following: first data identifying a user to perform a modification on a set of one or more software artifacts, second data identifying the set of one or more software artifacts, and third data identifying a set of one or more reviewers to review the modification; determining, using the machine learning classifier, first probability data associated with the software build being unsuccessful given the feature data and second probability data associated with the software build being successful given the feature data, wherein the first probability data and the second probability data are conditional independent from each other; and predicting, using the machine learning classifier, a software build outcome associated with the software build based on the first probability data and the second probability data.

Metadata:
- Claim Count in Document: 15.0
- Percentile: 88.0
- Lexical Diversity: 2.62963
- Patent Class: 717.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: True
- Related Applications: ['13589180', '14188005', '15362343', '14308450', '14450164']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3728434087631391
- 35 USC 102 Novelty (BERT): 0.4934003188327243
- Combined Prediction Score: 0.3848990997700977
- Mean Citation Score: 132.161096
- Max Citation Score: 154.41745
- Similarity Product: 104.86081857915222

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