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    PREDICTING USER BODY VOLUME TO MANAGE MEDICAL TREATMENT AND MEDICATION
    1.
    发明申请
    PREDICTING USER BODY VOLUME TO MANAGE MEDICAL TREATMENT AND MEDICATION 有权

    公开(公告)号：US20250029710A1

    公开(公告)日：2025-01-23

    申请号：US18908466

    申请日：2024-10-07

    Applicant: Advanced Health Intelligence Ltd.

    Inventor： Vlado Bosanac ,  Amar El-Sallam

    IPC: G16H30/40 ,  G06F18/214 ,  G06N20/00 ,  G16H20/10 ,  G16H70/40

    Abstract: In some examples, an apparatus, such as a mobile phone may include a camera, a processor, and computer readable medium. The camera capture one or more images of a person. The processor may use a machine learning model to predict the body volume of the person based on the captured images. The model may be trained based at a training data set comprising at least a plurality of user images. The apparatus may transmit the predicted body volume to a medication and medical treatment management system and receive from the same an adjusted medication or medical treatment plan. The apparatus may further execute the adjusted medication or medical treatment plan.

    Pharmaceutical content generation based on user type
    2.
    发明授权
    Pharmaceutical content generation based on user type 有权

    公开(公告)号：US12205693B2

    公开(公告)日：2025-01-21

    申请号：US17170067

    申请日：2021-02-08

    Applicant: ACTO Technologies Inc.

    Inventor： Kumar Karthik Erramilli ,  Parth Khanna ,  Kapil Kalra

    IPC: G16H15/00 ,  G06F40/174 ,  G06F40/186 ,  G06Q10/10 ,  G16H20/10 ,  G16H40/20 ,  G16H50/70 ,  G16H70/40

    Abstract: Various implementations disclosed herein include devices, systems, and methods for tailoring information regarding a pharmaceutical article to user types. In various implementations, a device includes a non-transitory memory and a processor coupled with the non-transitory memory. In some implementations, a method includes obtaining a request to synthesize a plurality of pharmaceutical content items for respective user types. In some implementations, the plurality of pharmaceutical content items provides information regarding a pharmaceutical article. In some implementations, the method includes determining, for the respective user types, corresponding expected engagement values indicative of expected engagement with the subject. In some implementations, the method includes determining, based on the corresponding expected engagement values, respective content templates for the plurality of pharmaceutical content items. In some implementations, the method incudes synthesizing the plurality of pharmaceutical content items by populating the respective content templates with information regarding the pharmaceutical article.

    METHODS AND APPARATUS FOR MAKING BIOLOGICAL PREDICTIONS USING A TRAINED MULTI-MODAL STATISTICAL MODEL
    4.
    发明申请
    METHODS AND APPARATUS FOR MAKING BIOLOGICAL PREDICTIONS USING A TRAINED MULTI-MODAL STATISTICAL MODEL 有权

    公开(公告)号：US20240420850A1

    公开(公告)日：2024-12-19

    申请号：US18640453

    申请日：2024-04-19

    Applicant: Quantum-Si Incorporated

    Inventor： Marylens Hernandez ,  Umut Eser ,  Michael Meyer ,  Henri Lichenstein ,  Tian Xu ,  Jonathan M. Rothberg

    IPC: G16H70/40 ,  G16H50/20 ,  G16H50/50

    Abstract: Methods and apparatus for predicting an association between input data in a first modality and data in a second modality using a statistical model trained to represent interactions between data having a plurality of modalities including the first modality and the second modality, the statistical model comprising a plurality of encoders and decoders, each of which is trained to process data for one of the plurality of modalities, and a joint-modality representation coupling the plurality of encoders and decoders. The method comprises selecting, based on the first modality and the second modality, an encoder/decoder pair or a pair of encoders, from among the plurality of encoders and decoders, and processing the input data with the joint-modality representation and the selected encoder/decoder pair or pair of encoders to predict the association between the input data and the data in the second modality.

    METHODS AND SYSTEMS OF FACILITATING MANAGING MEDICATION FOR A PATIENT
    5.
    发明申请
    METHODS AND SYSTEMS OF FACILITATING MANAGING MEDICATION FOR A PATIENT 有权

    公开(公告)号：US20240420816A1

    公开(公告)日：2024-12-19

    申请号：US18453786

    申请日：2023-08-22

    Applicant: Tyler Lee Edwards

    Inventor： Tyler Lee Edwards

    IPC: G16H20/10 ,  G16H10/60 ,  G16H40/40 ,  G16H70/40

    Abstract: Disclosed herein is a method of facilitating managing medication for a patient. Accordingly, the method may include receiving a prescription information from a patient device, receiving a medication information corresponding to at least one medication from the patient device, analyzing the medication information and the prescription information, generating a medication consumption information for consuming the medication, obtaining a current timing information associated with a current time of the patient, analyzing the current timing information and the medication consumption information, determining a match of the current timing information and the medication consumption information, selecting a first medication from the at least one medication, generating a notification based on the selecting, and transmitting the notification to the patient device.

    Method and apparatus for constructing drug knowledge graph
    7.
    发明授权
    Method and apparatus for constructing drug knowledge graph 有权

    公开(公告)号：US12170149B2

    公开(公告)日：2024-12-17

    申请号：US18016896

    申请日：2021-04-22

    Applicant: Beijing Jingdong Tuoxian Technology Co., Ltd.

    Inventor： Shuai Yang ,  Pei Xie ,  Hao Wen ,  Lei Han ,  Ya Zhang

    IPC: G16H70/40

    Abstract: A method and apparatus for constructing a drug knowledge graph are provided. The method may include: identifying entities in a drug text; replacing medical key entities among the entities with character strings that conform to a preset rule, to obtain a replaced text; restoring the character strings in a word segmentation result, determined based on the replaced text, to the medical key entities replaced by the character strings; forming a linear entity relationship between the entities based on the entities; and generating a drug knowledge graph according to a parsing result obtained by syntactically parsing the linear entity relationship.

    Drug Sensitivity Prediction and Model Training Method, Storage Medium and Device
    8.
    发明申请
    Drug Sensitivity Prediction and Model Training Method, Storage Medium and Device 有权

    公开(公告)号：US20240387060A1

    公开(公告)日：2024-11-21

    申请号：US18034071

    申请日：2022-05-20

    Applicant: BOE Technology Group Co., Ltd.

    Inventor： Pi CAO ,  Shuobin LIANG

    IPC: G16H70/40 ,  G16B20/50

    Abstract: A method for predicting drug sensitivity includes: acquiring gene expression information of a cell line to be tested, gene mutation information of the cell line to be tested, and structural information of a drug to be tested; calculating first correlation information between the structural information of the drug to be tested and the gene expression information based on a first attention model; calculating second correlation information between the structural information of the drug to be tested and the gene mutation information based on a second attention model; splicing the first correlation information and the second correlation information to obtain a splicing result; and performing a prediction processing on the splicing result based on a drug sensitivity prediction model to obtain sensitivity information of the cell line to be tested for the drug to be tested.

    MACHINE LEARNING FOR DESIGNING ANTIBODIES AND NANOBODIES IN-SILICO
    10.
    发明申请
    MACHINE LEARNING FOR DESIGNING ANTIBODIES AND NANOBODIES IN-SILICO 有权

    公开(公告)号：US20240379248A1

    公开(公告)日：2024-11-14

    申请号：US18615028

    申请日：2024-03-25

    Applicant: MarWell Bio Inc.

    Inventor： Zara Hemmatian ,  Adrian Alireza Chavosh

    IPC: G16H70/40

    Abstract: A computer-implemented method for generating a set of candidate variant amino acid sequences of an antibody, a nanobody, or a fragment thereof, having binding ability to a target protein, may comprise: (a) obtaining a set of seed amino acid sequences; and (b) processing the set of seed amino acid sequences using a first trained machine learning algorithm to generate the set of candidate amino acid sequences, wherein the first trained machine learning algorithm is trained with first training data comprising a set of training amino acid sequences for the target protein, wherein the first trained machine learning algorithm is further trained through a transfer learning method using a second trained machine learning algorithm, wherein the second trained machine learning algorithm is trained with second training data comprising a set of training amino acid sequences for a second target protein, wherein the second target protein is different from the target protein.

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