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Analysis

1 / 2,836
1.20220180975METHODS 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.

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.

3.20200342307Swarm 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.

4.20240071569APPARATUSES, 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.

5.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.
6.WO/2018/226492ASYNCHRONOUS AGENTS WITH LEARNING COACHES AND STRUCTURALLY MODIFYING DEEP NEURAL NETWORKS WITHOUT PERFORMANCE DEGRADATION
WO - 13.12.2018
Int.Class G06N 3/02
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computing arrangements based on biological models
02Neural networks
 Appl.No PCT/US2018/035275 Applicant D5AI LLC Inventor BAKER, James K.
Methods and computer systems improve a trained base deep neural network by structurally changing the base deep neural network to create an updated deep neural network, such that the updated deep neural network has no degradation in performance relative to the base deep neural network on the training data. The updated deep neural network is subsequently training. Also, an asynchronous agent for use in a machine learning system comprises a second machine learning system ML2 that is to be trained to perform some machine learning task. The asynchronous agent further comprises a learning coach LC and an optional data selector machine learning system DS. The purpose of the data selection machine learning system DS is to make the second stage machine learning system ML2 more efficient in its learning (by selecting a set of training data that is smaller but sufficient) and/or more effective (by selecting a set of training data that is focused on an important task). The learning coach LC is a machine learning system that assists the learning of the DS and ML2. Multiple asynchronous agents could also be in communication with each others, each trained and grown asynchronously under the guidance of their respective learning coaches to perform different tasks.
7.20210110928ASSOCIATION OF PROGNOSTIC RADIOMICS PHENOTYPE OF TUMOR HABITAT WITH INTERACTION OF TUMOR INFILTRATING LYMPHOCYTES (TILS) AND CANCER NUCLEI
US - 15.04.2021
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 17065767 Applicant Case Western Reserve University Inventor Pranjal Vaidya

Embodiments discussed herein facilitate training and/or employing a machine learning model trained on radiomic features, quantitative histomorphometric features, and molecular expression to generate prognoses for treatment of tumors. One example embodiment can access a medical imaging scan of a tumor; segment a peri-tumoral region around the tumor; extract one or more radiomic features from the one or more of the tumor or the peri-tumoral region; provide the one or more radiomic features to a machine learning model trained based on the one or more radiomic features of a training set, one or more quantitative histomorphometric (QH) features of the training set, and a molecular expression of the training set; and receive a prognosis associated with the tumor from the machine learning model.

8.WO/2023/126832DIGITAL MEDICINE COMPANION FOR CDK INHIBITOR MEDICATIONS FOR CANCER PATIENTS
WO - 06.07.2023
Int.Class G16H 10/40
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
10ICT specially adapted for the handling or processing of patient-related medical or healthcare data
40for data related to laboratory analysis, e.g. patient specimen analysis
 Appl.No PCT/IB2022/062810 Applicant PFIZER INC. Inventor CAI, Xuemei
The present disclosure relates to a digital medicine companion for patients undergoing oncology treatments. A patient may enter symptoms on a prescription software. A wearable device may passively collect other healthcare data such as biological data and/or physical activity data. The patient may therefore be monitored using the prescription software and/or wearables. Furthermore, the prescription software may be integrated with biofluid testing systems. For example, an at-home biofluid monitoring kit and/or a laboratory system may communicate with the prescription software and/or its backend server. The healthcare data collected through the monitoring and the biofluid testing may be fed into a machine learning model, which may output whether the patient is likely to develop side effects such as cytopenia. One or more alert notifications, e.g., to a clinician dashboard and/or to the prescription software, may be triggered when the machine learning model determines a higher likelihood of such side effects.
9.20250174318DIGITAL MEDICINE COMPANION FOR CDK INHIBITOR MEDICATIONS FOR CANCER PATIENTS
US - 29.05.2025
Int.Class G16H 10/40
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
10ICT specially adapted for the handling or processing of patient-related medical or healthcare data
40for data related to laboratory analysis, e.g. patient specimen analysis
 Appl.No 18725376 Applicant PFIZER INC. Inventor Anthony LAMBROU

The present disclosure relates to a digital medicine companion for patients undergoing oncology treatments. A patient may enter symptoms on a prescription software. A wearable device may passively collect other healthcare data such as biological data and/or physical activity data. The patient may therefore be monitored using the prescription software and/or wearables. Furthermore, the prescription software may be integrated with biofluid testing systems. For example, an at-home biofluid monitoring kit and/or a laboratory system may communicate with the prescription software and/or its backend server. The healthcare data collected through the monitoring and the biofluid testing may be fed into a machine learning model, which may output whether the patient is likely to develop side effects such as cytopenia. One or more alert notifications, e.g., to a clinician dashboard and/or to the prescription software, may be triggered when the machine learning model determines a higher likelihood of such side effects.

10.4163833DEEP NEURAL NETWORK MODEL DESIGN ENHANCED BY REAL-TIME PROXY EVALUATION FEEDBACK
EP - 12.04.2023
Int.Class G06N 3/04
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
 Appl.No 22186944 Applicant INTEL CORP Inventor CUMMINGS DANIEL J
The present disclosure is related to artificial intelligence (AI), machine learning (ML), and Neural Architecture Search (NAS) technologies, and in particular, to Deep Neural Network (DNN) model engineering techniques that use proxy evaluation feedback. The DNN model engineering techniques discussed herein provide near real-time feedback on model performance via low-cost proxy scores without requiring continual training and/or validation cycles, iterations, epochs, etc. In conjunction with the proxy-based scoring, semi-supervised learning mechanisms are used to map proxy scores to various model performance metrics. Other embodiments may be described and/or claimed.
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