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AIfunctionalapplicationsSpeechProcessingSpeechRecognition

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Analysis

1 / 3,525
1.20200342307Swarm fair deep reinforcement learning
US - 29.10.2020
Int.Class G06N 3/08
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computing arrangements based on biological models
02Neural networks
08Learning methods
 Appl.No 16395187 Applicant International Business Machines Corporation Inventor Aaron K. Baughman

Fair deep reinforcement learning is provided. A microstate of an environment and reaction of items in a plurality of microstates within the environment are observed after an agent performs an action in the environment. Semi-supervised training is utilized to determine bias weights corresponding to the action for the microstate of the environment and the reaction of the items in the plurality of microstates within the environment. The bias weights from the semi-supervised training are merged with non-bias weights using an artificial neural network. Over time, it is determined where bias is occurring in the semi-supervised training based on merging the bias weights with the non-bias weights in the artificial neural network. A deep reinforcement learning model that decreases reliance on the bias weights is generated based on determined bias to increase fairness.

2.12274503Myopia 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.

3.20200202436Method and system using machine learning for prediction of stocks and/or other market instruments price volatility, movements and future pricing by applying random forest based techniques
US - 25.06.2020
Int.Class G06N 3/08
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computing arrangements based on biological models
02Neural networks
08Learning methods
 Appl.No 16783457 Applicant Dhruv Siddharth Krishnan Inventor Dhruv Siddharth Krishnan

A method for providing stock predictive information by a cloud-based computing system implementing a random forest algorithm via a machine learning model by receiving a set of stock data from multiple sources of stock data wherein the set of stock data at least comprises stock prices at the open and close of a market, changes in stock prices during the open and close of a market, and real-time stock data; defining a range in time contained in a window defined of an initial selected month, a day or real-time period and an end of the selected month, day and real-time period; applying the random forest model to the set of stock data by creating multiple decision trees to predict a stock price in a quantified period, amount or percentage change in a stock price; and presenting the predicted stock price in a graphic user interface to an user.

4.20210287664Machine learning used to detect alignment and misalignment in conversation
US - 16.09.2021
Int.Class G06N 3/08
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computing arrangements based on biological models
02Neural networks
08Learning methods
 Appl.No 16817944 Applicant Palo Alto Research Center Incorporated Inventor Evgeniy Bart

Digitized media is received that records a conversation between individuals. Cues are extracted from the digitized media that indicate properties of the conversation. The cues are entered as training data into a machine learning module to create a trained machine learning model. The trained machine learning model is used in a processor to detect other misalignments in subsequent digitized conversations.

5.20230419170SYSTEM AND METHOD FOR EFFICIENT MACHINE LEARNING
US - 28.12.2023
Int.Class G06N 20/00
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
20Machine learning
 Appl.No 17887056 Applicant Fractal Analytics Private Limited Inventor Abhishek Chopde

Systems and methods employ knowledge distillation for efficient machine learning. Systems and methods integrate self-supervised learning, supervised learning, semi-supervised learning and active learning, each of which learning is executed in an iterative fashion. The system comprises three main components: a database server, a data analytics system and a standard dashboard. The database server contains real-time inventory images as well as historical images of each product type. The data analytics system is executed by a computer processor configured to apply a multi-head self-supervised learning-based deep neural network. The standard dashboard is configured to output a report regarding the object information.

6.12112752Cohort 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.

7.20220139498APPARATUSES, SYSTEMS, AND METHODS FOR EXTRACTING MEANING FROM DNA SEQUENCE DATA USING NATURAL LANGUAGE PROCESSING (NLP)
US - 05.05.2022
Int.Class G16B 40/00
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
BBIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
40ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
 Appl.No 17088734 Applicant BASF CORPORATION Inventor Erin Marie Davis

Apparatuses, systems, and methods are provided that may analyze deoxyribonucleic add (DNA) sequence data using a natural language processing (NLP) model to, for example, identify genetic elements such as known and/or novel cis-regulatory elements (e.g., known and/or putative novel drought-responsive cis-regulatory elements (DREs)). Apparatuses, systems, and methods are also provided that may identify transcriptional regulators (e.g., upstream transcriptional regulators of a novel putative DRE) based on natural language processing (NLP) model data and expression genome-wide association study (eGWAS) data. Apparatuses, systems, and methods are also provided that may verify putative novel cis-regulatory elements based on a comparison of natural language processing (NLP) model output data and other model output data.

8.20240071569APPARATUSES, SYSTEMS, AND METHODS FOR EXTRACTING MEANING FROM DNA SEQUENCE DATA USING NATURAL LANGUAGE PROCESSING (NLP)
US - 29.02.2024
Int.Class G16B 40/00
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
BBIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
40ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
 Appl.No 18034417 Applicant BASF CORPORATION Inventor Erin Marie Davis

Apparatuses, systems, and methods are provided that may analyze deoxyribonucleic add (DNA) sequence data using a natural language processing (NLP) model to, for example, identify genetic elements such as known and/or novel cis-regulatory elements {e.g., known and/or putative novel drought-responsive cis-regulatory elements (DREs)). Apparatuses, systems, and methods are also provided that may identify transcriptional regulators {e.g., upstream transcriptional regulators of a novel putative DRE) based on natural language processing (NLP) model data and expression genome-wide association study (eGWAS) data. Apparatuses, systems, and methods are also provided that may verify putative novel cis-regulatory elements based on a comparison of natural language processing (NLP) model output data and other model output data.

9.WO/2022/098588APPARATUSES, SYSTEMS, AND METHODS FOR EXTRACTING MEANING FROM DNA SEQUENCE DATA USING NATURAL LANGUAGE PROCESSING (NLP)
WO - 12.05.2022
Int.Class C12Q 1/68
CCHEMISTRY; METALLURGY
12BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
QMEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
1Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
68involving nucleic acids
 Appl.No PCT/US2021/057491 Applicant BASF CORPORATION Inventor DAVIS, Erin, Marie
Apparatuses, systems, and methods are provided that may analyze deoxyribonucleic add (DNA) sequence data using a natural language processing (NLP) model to, for example, identify genetic elements such as known and/or novel cis-regulatory elements {e.g., known and/or putative novel drought-responsive cis-regulatory elements (DREs)). Apparatuses, systems, and methods are also provided that may identify transcriptional regulators {e.g., upstream transcriptional regulators of a novel putative DRE) based on natural language processing (NLP) model data and expression genome-wide association study (eGWAS) data. Apparatuses, systems, and methods are also provided that may verify putative novel cis-regulatory elements based on a comparison of natural language processing (NLP) model output data and other model output data.
10.2498015COMBINING ACTIVE AND SEMI-SUPERVISED LEARNING FOR SPOKEN LANGUAGE UNDERSTANDING
CA - 02.09.2005
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 2498015 Applicant AT&T CORP. Inventor
Combined active and semi-supervised learning to reduce an amount of manual labeling when training a spoken language understanding model classifier. The classifier may be trained with human-labeled utterance data. Ones of a group of unselected utterance data may be selected for manual labeling via active learning. The classifier may be changed, via semi- supervised learning, based on the selected ones of the unselected utterance data.
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