PATENT CLAIM ANALYSIS

Application Number: 15973498
Application Type: Utility
Filing Date: 2018-05
Publication Date: 2018-11
Patent Classification: ["704", "243000"]

Abstract:
A mathematical model may be trained to diagnose a medical condition of a person by processing acoustic features and language features of speech of the person. The performance of the mathematical model may be improved by appropriately selecting the features to be used with the mathematical model. Features may be selected by computing a feature selection score for each acoustic feature and each language feature, and then selecting features using the scores, such as by selecting features with the highest scores. In some implementations, stability determinations may be computed for each feature and features may be selected using both the feature selection scores and the stability determinations. A mathematical model may then be trained using the selected features and deployed. In some implementations, prompts may be selected using computed prompt selection scores, and the deployed mathematical model may be used with the selected prompts.

Claim (Index 15):
One or more non-transitory computer-readable media comprising computer executable instructions that, when executed, cause at least one processor to perform actions comprising:\n obtaining a training corpus comprising speech data items, wherein each speech data item is labelled with a diagnosis value; obtaining speech recognition results for each speech data item using automatic speech recognition, wherein the speech recognition results for a speech data item comprise a transcription of the speech data item; computing a plurality of acoustic features for each speech data item in the training corpus, wherein the plurality of acoustic features is computed from the speech data item and wherein computation of the plurality of acoustic features does not use the speech recognition results of the speech data item; computing a plurality of language features for each speech data item in the training corpus by processing the speech recognition results; computing a feature selection score for each feature of the plurality of acoustic features and each feature of the plurality of language features, wherein:\n the feature selection score for a feature indicates a usefulness of the feature for detecting a medical condition, and \n the feature selection score is computed using, for each speech data item, a value of the feature and the diagnosis value corresponding to the speech data item; \n selecting a plurality of features from the plurality of acoustic features and the plurality of language features using the feature selection scores; training a mathematical model for detecting the medical condition using the selected plurality of features for each speech data item of the training corpus; deploying a computer program product or computer service for detecting the medical condition using the mathematical model; presenting, by the computer program product or computer service, a prompt to a person; receiving, by the computer program product or computer service, a speech data item corresponding to speech of a person in response to the prompt; computing a medical diagnosis score by processing the received speech data item using the mathematical model; and displaying, by the computer program product or computer service, one or more of the medical diagnosis score or a medical diagnosis based on the medical diagnosis score.

Metadata:
- Claim Count in Document: 50.0
- Percentile: 93.0
- Lexical Diversity: 2.87037
- Patent Class: 704.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15973504', '14640955', '15850106', '13959171', '15055417']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.2166112415319312
- 35 USC 102 Novelty (BERT): 0.5107550462608714
- Combined Prediction Score: 0.2460256220048252
- Mean Citation Score: 196.13005
- Max Citation Score: 273.94684
- Similarity Product: 199.28233845026017

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