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AIfunctionalapplicationsSpeechProcessingSpeechProcessingGeneral

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Analysis

1 / 1,832
1.12274503Myopia ocular predictive technology and integrated characterization system
US - 15.04.2025
Int.Class A61B 3/14
AHUMAN NECESSITIES
61MEDICAL OR VETERINARY SCIENCE; HYGIENE
BDIAGNOSIS; SURGERY; IDENTIFICATION
3Apparatus for testing the eyes; Instruments for examining the eyes
10Objective types, i.e. instruments for examining the eyes independent of the patients perceptions or reactions
14Arrangements specially adapted for eye photography
 Appl.No 18778027 Applicant COGNITIVECARE INC. Inventor Venkata Narasimham Peri

According to an embodiment, disclosed is a system comprising a processor wherein the processor is configured to receive an input data comprising an image of an ocular region of a user, clinical data of the user, and external factors; extract, using an image processing module comprising adaptive filtering techniques, ocular characteristics, combine, using a multimodal fusion module, the input data to determine a holistic health embedding; detect, based on a machine learning model and the holistic health embedding, a first output comprising likelihood of myopia, and severity of myopia; predict, based on the machine learning model and the holistic health embedding, a second output comprising an onset of myopia and a progression of myopia in the user; and wherein the machine learning model is a pre-trained model; and wherein the system is configured for myopia prognosis powered by multimodal data.

2.12112752Cohort determination in natural language processing
US - 08.10.2024
Int.Class G10L 15/22
GPHYSICS
10MUSICAL INSTRUMENTS; ACOUSTICS
LSPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
15Speech recognition
22Procedures used during a speech recognition process, e.g. man-machine dialog
 Appl.No 17688279 Applicant Amazon Technologies, Inc. Inventor Rahul Gupta

Devices and techniques are generally described for cohort determination in natural language processing. In various examples, a first natural language input to a natural language processing system may be determined. The first natural language input may be associated with a first account identifier. A first machine learning model may determine first data representing one or more words of the first natural language input. A second machine learning model may determine second data representing one or more acoustic characteristics of the first natural language input. Third data may be determined, the third data including a predicted performance for processing the first natural language input by the natural language processing system. The third data may be determined based on the first data representation and the second data representation.

3.WO/2019/028279METHODS AND SYSTEMS FOR OPTIMIZING ENGINE SELECTION USING MACHINE LEARNING MODELING
WO - 07.02.2019
Int.Class G06N 99/00
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
99Subject matter not provided for in other groups of this subclass
 Appl.No PCT/US2018/045051 Applicant VERITONE, INC. Inventor STEELBERG, Chad
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.
4.3937170SPEECH ANALYSIS FOR MONITORING OR DIAGNOSIS OF A HEALTH CONDITION
EP - 12.01.2022
Int.Class G10L 25/66
GPHYSICS
10MUSICAL INSTRUMENTS; ACOUSTICS
LSPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
25Speech or voice analysis techniques not restricted to a single one of groups G10L15/-G10L21/129
48specially adapted for particular use
51for comparison or discrimination
66for extracting parameters related to health condition
 Appl.No 20185364 Applicant NOVOIC LTD Inventor WESTON JACK
The invention relates to a computer-implemented method of training a machine learning model for performing speech analysis for monitoring or diagnosis of a health condition. The method uses training data comprising audio speech data and comprises obtaining one or more linguistic representations that each encode a sub-word, word, or multiple word sequence, of the audio speech data; obtaining one or more audio representations that each encode audio content of a segment of the audio speech data; combining the linguistic representations and audio representations into an input sequence comprising: linguistic representations of a sequence of one or more words or sub-words of the audio speech data; and audio representations of segments of the audio speech data, where the segments together contain the sequence of the one or more words or sub-words. The method further includes training a machine learning model using unsupervised learning to map the input sequence to a target output to learn combined audio-linguistic representations of the audio speech data for use in speech analysis for monitoring or diagnosis of a health condition.
5.20230320642SYSTEMS AND METHODS FOR TECHNIQUES TO PROCESS, ANALYZE AND MODEL INTERACTIVE VERBAL DATA FOR MULTIPLE INDIVIDUALS
US - 12.10.2023
Int.Class A61B 5/16
AHUMAN NECESSITIES
61MEDICAL OR VETERINARY SCIENCE; HYGIENE
BDIAGNOSIS; SURGERY; IDENTIFICATION
5Measuring for diagnostic purposes ; Identification of persons
16Devices for psychotechnics; Testing reaction times
 Appl.No 18130947 Applicant The Trustees of Columbia University in the City of New York Inventor Baihan Lin

Disclosed are methods, systems, and other implementations for processing, analyzing, and modelling psychotherapy data. The implementations include a method for analyzing psychotherapy data that includes obtaining transcript data representative of spoken dialog in one or more psychotherapy sessions conducted between a patient and a therapist, extracting speech segments from the transcript data related to one or more of the patient or the therapist, applying a trained machine learning topic model process to the extracted speech segments to determine weighted topic labels representative of semantic psychiatric content of the extracted speech segments, and processing the weighted topic labels to derive a psychiatric assessment for the patient.

6.20190043487METHODS AND SYSTEMS FOR OPTIMIZING ENGINE SELECTION USING MACHINE LEARNING MODELING
US - 07.02.2019
Int.Class G10L 15/16
GPHYSICS
10MUSICAL INSTRUMENTS; ACOUSTICS
LSPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
15Speech recognition
08Speech classification or search
16using artificial neural networks
 Appl.No 15922802 Applicant Veritone, Inc. Inventor Steven Neal Rivkin

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.

7.WO/2021/094522REINFORCEMENT LEARNING FOR ACTIVE SEQUENCE PROCESSING
WO - 20.05.2021
Int.Class G06N 3/00
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computing arrangements based on biological models
 Appl.No PCT/EP2020/082041 Applicant DEEPMIND TECHNOLOGIES LIMITED Inventor PATRAUCEAN, Viorica
A system that is configured to receive a sequence of task inputs and to perform a machine learning task is described. The system includes a reinforcement learning (RL) neural network and a task neural network. The RL neural network is configured to: generate, for each task input of the sequence of task inputs, a respective decision that determines whether to encode the task input or to skip the task input, and provide the respective decision of each task input to the task neural network. The task neural network is configured to: receive the sequence of task inputs, receive, from the RL neural network, for each task input of the sequence of task inputs, a respective decision that determines whether to encode the task input or to skip the task input, process each of the un-skipped task inputs in the sequence of task inputs to generate a respective accumulated feature for the un-skipped task input, wherein the respective accumulated feature characterizes features of the un-skipped task input and of previous un-skipped task inputs in the sequence, and generate a machine learning task output for the machine learning task based on the last accumulated feature generated for the last un-skipped task input in the sequence.
8.3662981METHOD AND SYSTEM FOR GENERATING A RECORDING OF VIDEO GAME GAMEPLAY
EP - 10.06.2020
Int.Class A63F 13/497
AHUMAN NECESSITIES
63SPORTS; GAMES; AMUSEMENTS
FCARD, BOARD OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
13Video games, i.e. games using an electronically generated display having two or more dimensions
45Controlling the progress of the video game
49Saving the game status; Pausing or ending the game
497Partially or entirely replaying previous game actions
 Appl.No 19196958 Applicant SONY INTERACTIVE ENTERTAINMENT INC Inventor DAREDDY SASHIKANTH
A method of generating a recording of video game gameplay is provided. The method comprising receiving video game data generated during the playing of a video game, the video game including at least a video and corresponding audio signal. The method also comprises inputting each signal in the received video game data into a respective machine learning model and determining based on the output of each model, whether a highlight event has occurred during the playing of the video game. At least some of the frames in the video and or audio signal are then selected for inclusion in a recording of the video game gameplay, based on a determination that a highlight event has occurred and a recording is generated that includes the selected video and or audio frames. A method for training the models is also provided, along with systems for training and executing the trained models.
9.WO/2022/008739SPEECH ANALYSIS FOR MONITORING OR DIAGNOSIS OF A HEALTH CONDITION
WO - 13.01.2022
Int.Class G10L 25/66
GPHYSICS
10MUSICAL INSTRUMENTS; ACOUSTICS
LSPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
25Speech or voice analysis techniques not restricted to a single one of groups G10L15/-G10L21/129
48specially adapted for particular use
51for comparison or discrimination
66for extracting parameters related to health condition
 Appl.No PCT/EP2021/069221 Applicant NOVOIC LTD. Inventor WESTON, Jack
The invention relates to a computer-implemented method of training a machine learning model for performing speech analysis for monitoring or diagnosis of a health condition. The method uses training data comprising audio speech data and comprises obtaining one or more linguistic representations that each encode a sub-word, word, or multiple word sequence, of the audio speech data; obtaining one or more audio representations that each encode audio content of a segment of the audio speech data; combining the linguistic representations and audio representations into an input sequence comprising: linguistic representations of a sequence of one or more words or sub-words of the audio speech data; and audio representations of segments of the audio speech data, where the segments together contain the sequence of the one or more words or sub-words. The method further includes training a machine learning model using unsupervised learning to map the input sequence to a target output to learn combined audio- linguistic representations of the audio speech data for use in speech analysis for monitoring or diagnosis of a health condition.
10.3185590SPEECH ANALYSIS FOR MONITORING OR DIAGNOSIS OF A HEALTH CONDITION
CA - 13.01.2022
Int.Class G10L 25/30
GPHYSICS
10MUSICAL INSTRUMENTS; ACOUSTICS
LSPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
25Speech or voice analysis techniques not restricted to a single one of groups G10L15/-G10L21/129
27characterised by the analysis technique
30using neural networks
 Appl.No 3185590 Applicant NOVOIC LTD. Inventor WESTON, JACK
The invention relates to a computer-implemented method of training a machine learning model for performing speech analysis for monitoring or diagnosis of a health condition. The method uses training data comprising audio speech data and comprises obtaining one or more linguistic representations that each encode a sub-word, word, or multiple word sequence, of the audio speech data; obtaining one or more audio representations that each encode audio content of a segment of the audio speech data; combining the linguistic representations and audio representations into an input sequence comprising: linguistic representations of a sequence of one or more words or sub-words of the audio speech data; and audio representations of segments of the audio speech data, where the segments together contain the sequence of the one or more words or sub-words. The method further includes training a machine learning model using unsupervised learning to map the input sequence to a target output to learn combined audio- linguistic representations of the audio speech data for use in speech analysis for monitoring or diagnosis of a health condition.
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