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公开(公告)号：US20230335135A1
公开(公告)日：2023-10-19
申请号：US18338747
申请日：2023-06-21
申请人： Tempus Labs, Inc.
发明人： Eric Lefkofsky , Jonathan Ozeran
IPC分类号： G10L15/22 , G16H10/60 , G16H50/20 , G16H70/60 , G16H50/50 , G16H40/20 , G06F16/632 , H04B1/3827 , G16H70/20 , G06F3/0481 , G06F3/16
CPC分类号： G10L15/22 , G16H10/60 , G16H50/20 , G16H70/60 , G16H50/50 , G16H40/20 , G06F16/634 , H04B1/3827 , G16H70/20 , G06F3/0481 , G06F3/167 , G10L2015/223
摘要： A method and system of audibly broadcasting responses to a user based on user queries about a specific patient report, the method comprising receiving an audible query from the user to a microphone coupled to a collaboration device, identifying at least one intent associated with the audible query, identifying at least one data operation associated with the at least one intent, associating each of the at least one data operations with a first set of data presented on the report, executing each of the at least one data operations on a second set of data to generate response data, generating an audible response file associated with the response data and providing the audible response file for broadcasting via a speaker coupled to the collaboration device.
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32.发明授权公开(公告)号：US11769572B2
公开(公告)日：2023-09-26
申请号：US17139931
申请日：2020-12-31
申请人： Tempus Labs, Inc.
IPC分类号： G16H10/20 , G16H50/70 , G16H50/20 , G06N20/00 , G06F17/18 , G16H10/60 , G16H30/00 , G16H80/00 , G06F21/62 , G06F18/22 , G06F18/24 , G06F18/214 , G06F18/2115 , G06F18/243 , G06N5/01
CPC分类号： G16H10/20 , G06F17/18 , G06F18/214 , G06F18/2115 , G06F18/22 , G06F18/24 , G06F18/24323 , G06F21/62 , G06N5/01 , G06N20/00 , G16H10/60 , G16H30/00 , G16H50/20 , G16H50/70 , G16H80/00
摘要： A system and method for analyzing a data store of de-identified patient data to generate one or more dynamic user interfaces usable to predict an expected response of a particular patient population or cohort when provided with a certain treatment. The automated analysis of patterns occurring in patient clinical, molecular, phenotypic, and response data, as facilitated by the various user interfaces, provides an efficient, intuitive way for clinicians to evaluate large data sets to aid in the potential discovery of insights of therapeutic significance.
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公开(公告)号：US11756688B2
公开(公告)日：2023-09-12
申请号：US17829351
申请日：2022-05-31
发明人： Alvaro E. Ulloa-Cerna , Noah Zimmerman , Greg Lee , Christopher M. Haggerty , Brandon K. Fornwalt , Ruijun Chen , John Pfeifer , Christopher Good
CPC分类号： G16H50/30 , A61B5/0006 , A61B5/28 , A61B5/318 , A61B5/7275 , G16H50/20 , G06F18/2155
摘要： A method for determining cardiology disease risk from electrocardiogram trace data and clinical data includes receiving electrocardiogram trace data associated with a patient, receiving the patient's clinical data, providing both sets of data to a trained machine learning composite model that is trained to evaluate the data with respect to each disease of a set of cardiology diseases including three or more of cardiac amyloidosis, aortic stenosis, aortic regurgitation, mitral stenosis, mitral regurgitation, tricuspid regurgitation, abnormal reduced ejection fraction, or abnormal interventricular septal thickness, generating, by the model and based on the evaluation, a composite risk score reflecting a likelihood of the patient being diagnosed with one or more of the cardiology diseases within a predetermined period of time from when the electrocardiogram trace data was generated, and outputting the composite risk score to at least one of a memory or a display.
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34.发明授权公开(公告)号：US11727674B2
公开(公告)日：2023-08-15
申请号：US17549040
申请日：2021-12-13
申请人： TEMPUS LABS, INC.
IPC分类号： G16C20/70 , G06V20/69 , G06V10/774
CPC分类号： G06V10/7747 , G06V20/698 , G16C20/70
摘要： A system and method are provided for training and using a machine learning model to analyze hematoxylin and eosin (H&E) slide images, where the machine learning model is trained using a training data set comprising a plurality of unmarked H&E images and a plurality of marked H&E images, each marked H&E image being associated with one unmarked H&E image and each marked H&E image including a location of one or more molecules determined by analyzing a multiplex IHC image having at least two IHC stains, each IHC stain having a unique color and a unique target molecule. Predicted molecules and locations identified with the machine learning model result in an immunotherapy response class being assigned to the H&E slide image.
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公开(公告)号：US20230245788A1
公开(公告)日：2023-08-03
申请号：US18298371
申请日：2023-04-11
申请人： Tempus Labs, Inc.
发明人： Shane Colley , Isaiah Simpson , Brian Reuter , Robert Tell , Hailey Lefkofsky , Hunter Lane , Kevin White , Nike Beaubier , Stephen Bush , Aly Khan , Denise Lau , Kaanan Shah , Eric Lefkofsky
摘要： A method for data intake and consumption includes the steps of: storing a plurality of micro-service programs, operational user application programs, and analytical user application programs in at least one computer system, storing system data received from a plurality of different sources in a database, the system data includes clinical records data in original forms, the clinical records data including cancer state information, treatment types, and treatment efficacy information, consuming, by each of the micro-service programs, defined subsets of the system data to generate a new data product, storing the new data product in a second database, and consuming the new data product by others of the micro-service programs or the operational or analytical user application programs.
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36.发明公开公开(公告)号：US20230187074A1
公开(公告)日：2023-06-15
申请号：US18167812
申请日：2023-02-10
申请人： Tempus Labs, Inc.
发明人： Frank Austin Nothaft , Mateo Paredes Sepulveda , Jesus Pedrosa Guerrero , Abigail Michelle Lammers , Carin Fishel Queen , Daniel Philip Leavitt , Lorenzo Carlo Grego , Sowmya Gowri Ballakur , Peter Raynesford Halloran
摘要： A system and method for analyzing a data store of de-identified patient data to generate one or more dynamic user interfaces usable to predict an expected response of a particular patient population or cohort when provided with a certain treatment. The automated analysis of patterns occurring in patient clinical, molecular, phenotypic, and response data, as facilitated by the various user interfaces, provides an efficient, intuitive way for clinicians to evaluate large data sets to aid in the potential discovery of insights of therapeutic significance.
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公开(公告)号：US20230187070A1
公开(公告)日：2023-06-15
申请号：US17982399
申请日：2022-11-07
申请人： Tempus Labs, Inc.
发明人： Jackson Michuda , Kyle Ashley Beauchamp , Joshuah Kapilivsky , Calvin McCarter , Nike Tsiapera Beaubier , Martin Christian Stumpe , Catherine Igartua , Joshua SK Bell , Timothy Taxter , Raphael Pelossof
摘要： Systems and methods are provided for identifying a diagnosis of a cancer condition for a somatic tumor specimen of a subject. The method receives sequencing information comprising analysis of a plurality of nucleic acids derived from the somatic tumor specimen. The method identifies a plurality of features from the sequencing information, including two or more of RNA, DNA, RNA splicing, viral, and copy number features. The method provides a first subset of features and a second subset of features from the identified plurality of features as inputs to a first classifier and a second classifier, respectively. The method generates, from two or more classifiers, two or more predictions of cancer condition based at least in part on the identified plurality of features. The method combines, at a final classifier, the two or more predictions to identify the diagnosis of the cancer condition for the somatic tumor specimen of the subject.
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公开(公告)号：US20230148456A9
公开(公告)日：2023-05-11
申请号：US17829356
申请日：2022-05-31
申请人： Tempus Labs, Inc.
发明人： Noah Zimmerman , Brandon Fornwalt , John Pfeifer , Ruijun Chen , Arun Nemani , Greg Lee , Steve Steinhubl , Christopher Haggerty , Sushravya Raghunath , Alvaro Ulloa-Cerna , Linyuan Jing , Thomas Morland
CPC分类号： A61B5/7275 , A61B5/318 , G16H50/30 , G16H50/20
摘要： A method and system for predicting the likelihood that a patient will suffer from a cardiac event is provided. The method includes receiving electrocardiogram data associated with the patient, providing at least a portion of the electrocardiogram data to a trained model, receiving a risk score indicative of the likelihood the patient will suffer from the cardiac event within a predetermined period of time from when the electrocardiogram data was generated, and outputting the risk score to at least one of a memory or a display for viewing by a medical practitioner or healthcare administrator. The system includes at least one processor executing instructions to carry out the steps of the method.
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公开(公告)号：US20230036156A1
公开(公告)日：2023-02-02
申请号：US17816395
申请日：2022-07-29
申请人： TEMPUS LABS, INC.
发明人： Chi-Sing Ho , Madhavi Kannan , Sonal Khare , Brian Larsen , Brandon Mapes , Ameen Salahudeen , Jagadish Venkataraman
IPC分类号： G01N33/50 , G06T7/00 , G06T3/00 , G06V20/69 , G06T7/11 , G06V10/774 , G16H15/00 , G16H30/40 , G01N21/64
摘要： A method for characterizing cancer organoid response to an immune cell based therapy, includes providing a panel of different combinations of cancer organoid cells and immune cells to culturing wells and culturing the different combination under conditions that support organoid growth. Brightfield and corresponding fluorescence images of the culturing wells are captured and provided to one or more trained machine learning algorithms that identify and distinguish cancer organoid cells from immune cells and characterize cancer organoid morphology changes caused by an immune cell based therapies, from which an analytical report including a characterization of cancer organoid cell death caused by the immune cell based therapy is provided.
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公开(公告)号：US11561178B2
公开(公告)日：2023-01-24
申请号：US17301975
申请日：2021-04-20
申请人： Tempus Labs, Inc.
发明人： Madhavi Kannan , Brian Larsen , Aly A. Khan , Ameen Salahudeen
IPC分类号： G01N21/64 , G01N33/50 , G02B21/12 , G02B21/00 , G01N21/552 , G06T7/00 , G06N20/00 , G16H30/00 , G06V20/69
摘要： The disclosure provides a method of generating an artificial fluorescent image of cells is provided. The method includes receiving a brightfield image generated by a brightfield microscopy imaging modality of at least a portion of cells included in a specimen, applying, to the brightfield image, at least one trained model, the trained model being trained to generate the artificial fluorescent image based on the brightfield image, receiving the artificial fluorescent image from the trained model
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