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Analysis

1 / 18,256
1.20250045566SYSTEMS AND METHODS FOR CONVOLUTIONAL NEURAL NETWORK AND TRANSFORMER-BASED TIME SERIES MODELING
US - 06.02.2025
Int.Class G06N 3/0455
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computing arrangements based on biological models
02Neural networks
04Architecture, e.g. interconnection topology
045Combinations of networks
0455Auto-encoder networks; Encoder-decoder networks
 Appl.No 18365808 Applicant JPMORGAN CHASE BANK, N.A. Inventor Zhen ZENG

In some aspects, the techniques described herein relate to a method including: receiving, at a forecasting platform, a time series; partitioning the time series into a plurality of partitions; processing the time series with a convolutional neural network machine learning model; generating, by the convolutional neural network machine learning model, a plurality of tokens, wherein the plurality of tokens are based on the time series; processing the plurality of tokens with a transformer machine learning model; generating, by the transformer machine learning model, a transformer vector, wherein the transformer vector is based on relationships among the plurality of tokens determined by the transformer machine learning model; and assigning, by a multilayer perceptron classifier, a classification to the transformer vector.

2.WO/2015/066564METHODS OF IDENTIFICATION AND DIAGNOSIS OF LUNG DISEASES USING CLASSIFICATION SYSTEMS AND KITS THEREOF
WO - 07.05.2015
Int.Class G01N 33/48
GPHYSICS
01MEASURING; TESTING
NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
33Investigating or analysing materials by specific methods not covered by groups G01N1/-G01N31/131
48Biological material, e.g. blood, urine; Haemocytometers
 Appl.No PCT/US2014/063594 Applicant CANCER PREVENTION AND CURE, LTD. Inventor SZUSTKIEWICZ, Christopher, P.
The invention provides biomarkers and combinations of biomarkers useful in diagnosing lung diseases such as non-small cell lung cancer or reactive airway disease. Measurements of these biomarkers are inputted into a classification system such as a support vector machine or AdaBoost to assist in determining the likelihood that an individual has a lung disease. Kits comprising agents for detecting the biomarkers and combination of biomarkers, as well as systems that assist in diagnosing lung diseases are also provided.
3.20220180975METHODS 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.

4.WO/2022/155555SYSTEMS AND METHODS FOR DERIVING HEALTH INDICATORS FROM USER-GENERATED CONTENT
WO - 21.07.2022
Int.Class G16H 50/20
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
HHEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
50ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
20for computer-aided diagnosis, e.g. based on medical expert systems
 Appl.No PCT/US2022/012645 Applicant MY LUA LLC Inventor CONWARD, Michael
The present disclosure relates to systems and methods for generating priority lists and/or predictions or identifications of root causes of acute or chronic conditions. In one exemplary embodiment, a method comprises aggregating data corresponding to a plurality of individuals, the data comprising, for each individual, user-generated content and/or biometric data; generating, from a machine learning model that utilizes the aggregated user-generated content and/or biometric data as input, one or more of a priority list for the plurality of individuals, or, for each individual, a prediction, diagnosis, or identification of one or more root causes of one or more acute or chronic conditions of the individual.
5.WO/2023/059663SYSTEMS AND METHODS FOR ASSESSMENT OF BODY FAT COMPOSITION AND TYPE VIA IMAGE PROCESSING
WO - 13.04.2023
Int.Class A61B 5/00
AHUMAN NECESSITIES
61MEDICAL OR VETERINARY SCIENCE; HYGIENE
BDIAGNOSIS; SURGERY; IDENTIFICATION
5Measuring for diagnostic purposes ; Identification of persons
 Appl.No PCT/US2022/045706 Applicant THE BROAD INSTITUTE, INC. Inventor KHERA, Amit
The subject matter disclosed herein relates to utilizing the silhouette of an individual to measure body fat volume and distribution. Particular examples relates to providing a system, a computer-implemented method, and a computer program product to utilize a binary outline, or silhouette, to predict the individual's fat depot volumes with machine learning models.
6.20190056715Framework for rapid additive design with generative techniques
US - 21.02.2019
Int.Class G05B 19/4099
GPHYSICS
05CONTROLLING; REGULATING
BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
19Programme-control systems
02electric
18Numerical control , i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form
4097characterised by using design data to control NC machines, e.g. CAD/CAM
4099Surface or curve machining, making 3D objects, e.g. desktop manufacturing
 Appl.No 15678653 Applicant General Electric Company Inventor Arun Karthi Subramaniyan

According to some embodiments, a system may include a design experience data store containing electronic records associated with prior industrial asset item designs. A deep learning model platform, coupled to the design experience data store, may include a communication port to receive constraint and load information from a designer device. The deep learning platform may further include a computer processor adapted to automatically and generatively create boundaries and geometries, using a deep learning model associated with an additive manufacturing process, for an industrial asset item based on the prior industrial asset item designs and the received constraint and load information. According to some embodiments, the deep learning model computer processor is further to receive design adjustments from the designer device. The received design adjustments might be for example, used to execute an optimization process and/or be fed back to continually re-train the deep learning model.

7.20190072554Methods of Identification and Diagnosis of Lung Diseases Using Classification Systems and Kits Thereof
US - 07.03.2019
Int.Class G01N 33/574
GPHYSICS
01MEASURING; TESTING
NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
33Investigating or analysing materials by specific methods not covered by groups G01N1/-G01N31/131
48Biological material, e.g. blood, urine; Haemocytometers
50Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
53Immunoassay; Biospecific binding assay; Materials therefor
574for cancer
 Appl.No 15961307 Applicant Joel Michalek Inventor Joel Michalek

The invention provides biomarkers and combinations of biomarkers useful in diagnosing lung diseases such as non-small cell lung cancer or reactive airway disease. Measurements of these biomarkers are inputted into a classification system such as a support vector machine or AdaBoost to assist in determining the likelihood that an individual has a lung disease. Kits comprising agents for detecting the biomarkers and combination of biomarkers, as well as systems that assist in diagnosing lung diseases are also provided.

8.201937044042PLASMA BASED PROTEIN PROFILING FOR EARLY STAGE LUNG CANCER PROGNOSIS
IN - 02.10.2020
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 201937044042 Applicant LUNG CANCER PROTEOMICS, LLC Inventor GOEBEL, Cherylle
The invention provides biomarkers and combinations of biomarkers useful in diagnosing non-small cell lung cancer. Measurements of these biomarkers are inputted into a classification system such as Random Forest to assist in determining the likelihood that an individual has non-small cell lung cancer. Kits comprising agents for detecting the biomarkers and combination of biomarkers as well as systems that assist in diagnosing non-small cell lung cancer are also provided.
9.12274503Myopia 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.

10.20200342307Swarm 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.

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