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Analysis

1 / 14,533
1.20140188462System and method for analyzing ambiguities in language for natural language processing
US - 03.07.2014
Int.Class G06F 17/00
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
17Digital computing or data processing equipment or methods, specially adapted for specific functions
 Appl.No 14201974 Applicant Zadeh Lotfi A. Inventor Zadeh Lotfi A.

Specification covers new algorithms, methods, and systems for artificial intelligence, soft computing, and deep learning/recognition, e.g., image recognition (e.g., for action, gesture, emotion, expression, biometrics, fingerprint, facial, OCR (text), background, relationship, position, pattern, and object), large number of images (“Big Data”) analytics, machine learning, training schemes, crowd-sourcing (using experts or humans), feature space, clustering, classification, similarity measures, optimization, search engine, ranking, question-answering system, soft (fuzzy or unsharp) boundaries/impreciseness/ambiguities/fuzziness in language, Natural Language Processing (NLP), Computing-with-Words (CWW), parsing, machine translation, sound and speech recognition, video search and analysis (e.g. tracking), image annotation, geometrical abstraction, image correction, semantic web, context analysis, data reliability (e.g., using Z-number (e.g., “About 45 minutes; Very sure”)), rules engine, control system, autonomous vehicle, self-diagnosis and self-repair robots, system diagnosis, medical diagnosis, biomedicine, data mining, event prediction, financial forecasting, economics, risk assessment, e-mail management, database management, indexing and join operation, memory management, and data compression.

2.20220180975METHODS AND SYSTEMS FOR DETERMINING GENE EXPRESSION PROFILES AND CELL IDENTITIES FROM MULTI-OMIC IMAGING DATA
US - 09.06.2022
Int.Class G16B 40/30
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
30Unsupervised data analysis
 Appl.No 17553691 Applicant The Broad Institute, Inc. Inventor Aviv Regev

The present disclosure relates to systems and method of determining transcriptomic profile from omics imaging data. The systems and methods train machine learning methods with intrinsic and extrinsic features of a cell and/or tissue to define transcriptomic profiles of the cell and/or tissue. Applicants utilize a convolutional autoencoder to define cell subtypes from images of the cells.

3.20210097682Disease characterization and response estimation through spatially-invoked radiomics and deep learning fusion
US - 01.04.2021
Int.Class G06T 7/00
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
7Image analysis
 Appl.No 17038934 Applicant Case Western Reserve University Inventor Anant Madabhushi

Embodiments discussed herein facilitate training and/or employing a combined model employing machine learning and deep learning outputs to generate prognoses for treatment of tumors. One example embodiment can extract radiomic features from a tumor and a peri-tumoral region; provide the intra-tumoral and peri-tumoral features to two separate machine learning models; provide the segmented tumor and peri-tumoral region to two separate deep learning models; receive predicted prognoses from each of the machine learning models and each of the deep learning models; provide the predicted prognoses to a combined machine learning model; and receive a combined predicted prognosis for the tumor from the combined machine learning model.

4.20140201126Method and system for feature detection
US - 17.07.2014
Int.Class G06N 7/00
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
7Computing arrangements based on specific mathematical models
 Appl.No 14218923 Applicant Lotfi A. Zadeh Inventor Lotfi A. Zadeh

Specification covers new algorithms, methods, and systems for artificial intelligence, soft computing, and deep learning/recognition, e.g., image recognition (e.g., for action, gesture, emotion, expression, biometrics, fingerprint, facial, OCR (text), background, relationship, position, pattern, and object), Big Data analytics, machine learning, training schemes, crowd-sourcing (experts), feature space, clustering, classification, SVM, similarity measures, modified Boltzmann Machines, optimization, search engine, ranking, question-answering system, soft (fuzzy or unsharp) boundaries/impreciseness/ambiguities/fuzziness in language, Natural Language Processing (NLP), Computing-with-Words (CWW), parsing, machine translation, sound and speech recognition, video search and analysis (e.g. tracking), image annotation, geometrical abstraction, image correction, semantic web, context analysis, data reliability, Z-number, Z-Web, Z-factor, rules engine, control system, autonomous vehicle, self-diagnosis and self-repair robots, system diagnosis, medical diagnosis, biomedicine, data mining, event prediction, financial forecasting, economics, risk assessment, e-mail management, database management, indexing and join operation, memory management, data compression, event-centric social network, Image Ad Network.

5.12274503Myopia 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.

6.20240071569APPARATUSES, 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.

7.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.
8.20220139498APPARATUSES, 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.

9.10303771Utilizing machine learning models to identify insights in a document
US - 28.05.2019
Int.Class G06F 17/27
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
17Digital computing or data processing equipment or methods, specially adapted for specific functions
20Handling natural language data
27Automatic analysis, e.g. parsing, orthograph correction
 Appl.No 15896922 Applicant Capital One Services, LLC Inventor Joni Bridget Jezewski

A device receives document information associated with a document, and receives a request to identify insights in the document information. The device performs, based on the request, natural language processing on the document information to identify words, phrases, and sentences in the document information, and utilizes a first machine learning model with the words, the phrases, and the sentences to identify information indicating abstract insights, concrete insights, and non-insights in the document. The device utilizes a second machine learning model to match the abstract insights with particular concrete insights that are different than the concrete insights, and utilizes a third machine learning model to determine particular insights based on the non-insights. The device generates an insight document that includes the concrete insights, the abstract insights matched with the particular concrete insights, and the particular insights determined based on the non-insights.

10.20160329044Semi-supervised learning of word embeddings
US - 10.11.2016
Int.Class G06F 17/27
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
17Digital computing or data processing equipment or methods, specially adapted for specific functions
20Handling natural language data
27Automatic analysis, e.g. parsing, orthograph correction
 Appl.No 14707720 Applicant International Business Machines Corporation Inventor Liangliang Cao

Software that trains an artificial neural network for generating vector representations for natural language text, by performing the following steps: (i) receiving, by one or more processors, a set of natural language text; (ii) generating, by one or more processors, a set of first metadata for the set of natural language text, where the first metadata is generated using supervised learning method(s); (iii) generating, by one or more processors, a set of second metadata for the set of natural language text, where the second metadata is generated using unsupervised learning method(s); and (iv) training, by one or more processors, an artificial neural network adapted to generate vector representations for natural language text, where the training is based, at least in part, on the received natural language text, the generated set of first metadata, and the generated set of second metadata.

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