PATENT CLAIM ANALYSIS

Application Number: 16055114
Application Type: Utility
Filing Date: 2018-08
Publication Date: 2019-02
Patent Classification: ["455", "405000"]

Abstract:
A computer implemented method of predicting a rating score of a cellular service for a plurality of cellular subscribers, comprising obtaining one or more machine learning models trained with a plurality of feature vectors created for a subset of a plurality of cellular subscribers of a cellular operator participating in a survey to rate a cellular service provided by the cellular operator through a cellular network where each of the feature vectors is created by extracting a plurality of features of a respective cellular subscriber of the subset, each feature vector is associated with a rating score assigned by the respective cellular subscriber, predicting an estimated rating score of the cellular service for other cellular subscribers by applying the machine learning model(s) to the feature vector of extracted features of each of the other cellular subscribers and outputting the estimated rating score for each of the other cellular subscribers.

Claim (Index 18):
A software program product for predicting a rating score of a cellular product and/or service for a plurality of cellular subscribers, comprising:\n a non-transitory computer readable storage medium; first program instructions for obtaining at least one machine learning model trained with a plurality of feature vectors created for a subset of a plurality of cellular subscribers of a cellular operator participating in a survey to rate a cellular service provided by said cellular operator through a cellular network, each of the plurality of feature vectors is created by extracting a plurality of features of a respective cellular subscriber of said subset, said each feature vector is associated with a rating score assigned by said respective cellular subscriber; second program instructions for predicting an estimated rating score of said cellular service for at least some of said plurality of cellular subscribers by applying said at least one machine learning model to said feature vector of extracted features of each of said at least some cellular subscribers; and third program instructions for outputting said estimated rating score for each of said at least some cellular subscribers; wherein said first, second and third program instructions are executed by at least one processor from said non-transitory computer readable storage medium.

Metadata:
- Claim Count in Document: 22.0
- Percentile: 96.0
- Lexical Diversity: 2.51613
- Patent Class: 455.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15045043', '13702385', '15069617', '10863642', '10862930']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.6945044357816306
- 35 USC 102 Novelty (BERT): 0.4886361329966428
- Combined Prediction Score: 0.6739176055031318
- Mean Citation Score: 159.24431999999996
- Max Citation Score: 163.66965
- Similarity Product: 101.01712537872793

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

Dataset: test