PATENT CLAIM ANALYSIS

Application Number: 15990032
Application Type: Utility
Filing Date: 2018-05
Publication Date: 2019-07
Patent Classification: ["717", "124000"]

Abstract:
Machine learning techniques are used to determine the viability of user data measuring the behavior of a new version of the program when compared with user data that measured the behavior of a previous version of the program. The machine learning techniques utilize statistical techniques in a non-conventional manner to train a system to learn from data obtained from the usage of both a new version of the program and a previous version that accounts for the variability in the user population, time variability of the results of the previous version, and feature coverage between the two test results in order to ensure the suitability of the user data in making estimations or predictions about the performance and reliability of the new version.

Claim (Index 8):
A method, comprising:\n formulating, on a computing device, matched pairs of users, a matched pair including a user having tested a previous version of a program and a user having tested a beta version of the program, the matched pair having similar usage features; averaging values of a first metric from the matched pairs of users having tested the previous version and averaging values of the first metric from the matched pairs of users having tested the beta version; comparing the average value of the first metric from the previous version with the average value of the first metric from the beta version to determine a significance of the difference between both average values; and generating a reliability of the first metric based on a significance of the difference between both average values.

Metadata:
- Claim Count in Document: 46.0
- Percentile: 93.0
- Lexical Diversity: 2.04839
- Patent Class: 717.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['14198284', '14198271', '14865384', '14877923', '13796923']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3537597164399519
- 35 USC 102 Novelty (BERT): 0.4972446160854447
- Combined Prediction Score: 0.3681082064045012
- Mean Citation Score: 146.21510999999995
- Max Citation Score: 154.44724
- Similarity Product: 98.93654002253533

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

Dataset: test