PATENT CLAIM ANALYSIS

Application Number: 15922802
Application Type: Utility
Filing Date: 2018-03
Publication Date: 2019-02
Patent Classification: ["704", "232000"]

Abstract:
A system for optimizing selection of transcription engines using a combination of selected machine learning models. The system includes a plurality of preprocessors that generate a plurality of features from a media data set. The system further includes a deep learning neural network model, a gradient boosted machine model and a random forest model used in generating a ranked list of transcription engines. A transcription engine is selected from the ranked list of transcription engines to generate a transcript for the media dataset.

Claim (Index 11):
A computer-implemented method for optimizing the selection of transcription engines using a combination of selected machine learning models, comprising:\n one or more network-connected servers, each including a processor and non-transitory computer readable memory storing instructions that, when executed by the processor: generate, by one or more preprocessors, a plurality of features from a selected media data set of one or more media data sets; improve, by a deep learning neural network model, detection of patterns in the plurality of features and to improve generation of classified categories; improve, by a gradient boosted machine model, prediction of patterns in the plurality of features and to improve generation of multiclass classified categories; improve, by a random forest model, prediction of patterns in a first classification data and to improve generation of multiclass classified categories; generate a ranked list of transcription engines based on improvements learned from the deep learning neural network model, the gradient boosted machine model, and the random forest model; and select a transcription engine from the ranked list of transcription engines, configured to ingest the plurality of features and to generate a transcript for the selected media data set.

Metadata:
- Claim Count in Document: 50.0
- Percentile: 90.0
- Lexical Diversity: 1.79592
- Patent Class: 704.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['14291720', '15604473', '15162444', '15878113', '14231222']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.2068198836155295
- 35 USC 102 Novelty (BERT): 0.4804004924065444
- Combined Prediction Score: 0.234177944494631
- Mean Citation Score: 157.737416
- Max Citation Score: 166.08577
- Similarity Product: 119.96023619816064

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

Dataset: test