IP Library › Granted Patent US 12,536,595
Granted Patent B2
US 12,536,595 · App. 18/132,109 · Granted Jan 27, 2026

Mobile supplementation, extraction, and analysis of health records

Inventors: Shane Colley (Chicago, IL); Nike Beaubier (Chicago, IL); Robert Tell (Chicago, IL); Kevin White (Singapore, SG); Eric Lefkofsky (Glencoe, IL)
Assignee: TEMPUS AI, INC.
G06Q40/08G06F18/217G06F18/2431G06V10/75G06V30/19013G06V30/416G06V30/418G16H30/40G16H50/20G16H50/70G06V30/10G16H15/00
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,536,595
App. No.
18/132,109
Granted
Jan 27, 2026
Kind
B2
Abstract

A method includes displaying a cohort report; receiving a request to determine a therapy identification; causing information to be transmitted to a remote cloud server; receiving clinical trial data; and updating the cohort report. A computing system includes a processor; and a memory having stored thereon computer-executable instructions that, when executed by the one or more processors, cause the computing system to: display a cohort report; receive a request to determine a therapy identification; cause information to be transmitted to a remote cloud server; receive clinical trial data; and update the cohort report. A computer-readable medium having stored thereon a set of computer-executable instructions that, when executed by one or more processors, cause a computer to: display a cohort report; receive a request to determine a therapy identification; cause information to be transmitted to a remote cloud server; receive clinical trial data; and update the cohort report.

Claims (60)

1 . A computer-implemented method for improving therapy identification for a cancer patient via a mobile computing device, the method comprising:

displaying, via a display screen of the mobile computing device, information identifying the cancer patient;

receiving, at the mobile computing device, a request to determine a therapy identification for the cancer patient;

responsive to receiving the request, causing the information identifying the cancer patient and medical history information of the cancer patient to be transmitted to a remote cloud server having a stateless bioinformatics pipeline configured to:

process the information identifying the cancer patient and the medical history information of the cancer patient to extract a plurality of medical concept candidates,

rank the plurality of medical concept candidates based on a frequency of occurrence, within the medical history information of the cancer patient, of each of the plurality of medical concept candidates,

based on ranking the plurality of medical concept candidates, preserve a subset of the plurality of medical concept candidates, and

determine, based on the subset of the plurality of medical concept candidates, a recommended clinical trial as the therapy identification for the cancer patient;

responsive to the stateless bioinformatics pipeline determining the recommended clinical trial, receiving from the remote cloud server clinical trial data corresponding to the recommended clinical trial; and

displaying, via the display screen of the mobile computing device, a cohort report associated with a cohort representing a plurality of cancer patients including the cancer patient, wherein the cohort report comprises at least the clinical trial data corresponding to the recommended clinical trial.

2 . The computer-implemented method of claim 1 ,

wherein receiving, at the mobile computing device, the request to determine a therapy indication for the cancer patient is performed after receiving, at the mobile computing device, an electronic notification to the cancer patient, the electronic notification being at least one of a direct push notification to the mobile computing device, an SMS message to the mobile computing device, a text message to the mobile computing device, an in-application notification to the mobile computing device, or an email message sent to the mobile computing device.

3 . The computer-implemented method of claim 1 , further comprising:

determining, via one or more processors of the mobile computing device, location data corresponding to the recommended clinical trial from the received clinical trial data;

determining a distance between a location associated with the mobile computing device and the location data corresponding to the recommended clinical trial; and

updating, via the display screen of the mobile computing device, the cohort report to include the distance.

4 . The computer-implemented method of claim 1 , wherein causing the information identifying the cancer patient and the medical history information of the cancer patient to be transmitted to the remote cloud server having the stateless includes:

causing the information identifying the patient and/or the medical history information of the cancer patient to be received at a database containing the molecular information; and

causing the molecular information to be transmitted from the database to the remote cloud server.

5 . The computer-implemented method of claim 1 , wherein the cohort report comprises molecular information corresponding to the cancer patient and to a next generation sequencing of at least one specimen of the cancer patient, and wherein the molecular information comprises at least one of gene data, mutation data, somatic variant data, germline variant data, expression data, gain of function data, loss of function data, or allele fraction data.

6 . The computer-implemented method of claim 1 , wherein the cohort report comprises molecular information corresponding to the cancer patient, and wherein the molecular information and the information identifying the cancer patient are stored in a common and structured data format in a memory of the mobile computing device.

7 . The computer-implemented method of claim 1 , wherein the clinical trial data includes one or more of gene data for the recommended clinical trial, mutation data for the recommended clinical trial, and/or variant data for the recommended clinical trial.

8 . The computer-implemented method of claim 7 , further comprising:

updating, via the display screen of the mobile computing device, the cohort report, to include mutation data for the recommended clinical trial, and/or variant data for the recommended clinical trial that matches with molecular information corresponding to the cancer patient.

9 . The computer-implemented method of claim 8 , further comprising:

updating, via the display screen of the mobile computing device, the cohort report to include a percentage match of the recommended clinical trial and the cancer patient.

10 . The computer-implemented method of claim 8 , further comprising:

updating, via the display screen of the mobile computing device, the cohort report to include allele fraction data for the cancer patient.

11 . The computer-implemented method of claim 10 , further comprising:

updating, via the display screen of the mobile computing device, the cohort report to include a graphic representation of the allele fraction data for the cancer patient.

12 . The computer-implemented method of claim 1 , wherein the cohort report comprises a name of the recommended clinical trial, one or more treatments associated with the recommended clinical trial, and/or inclusion/exclusion criteria associated with the recommended clinical trial.

13 . The computer-implemented method of claim 1 , further comprising:

performing, at the mobile computing device, an authentication of the information identifying the cancer patient prior to causing the information identifying the cancer patient and the medical history information of the cancer patient to be transmitted to the remote cloud server.

14 . The computer-implemented method of claim 1 , wherein molecular information corresponding to the cancer patient is determined from next generation sequencing of at least one normal sample and at least one tumor sample.

15 . The computer-implemented method of claim 14 , where the molecular information generated by the next generation sequencing comprises ribonucleic acid sequences.

16 . The computer-implemented method of claim 15 , where the molecular information generated by the next generation sequencing comprises ribonucleic acid sequences indicating fusion mRNAs.

17 . The computer-implemented method of claim 1 , further comprising:

requesting a determination of whether molecular information corresponding to the cancer patient meets a quality threshold level of a number of read pairs of ribonucleic acid sequences.

18 . A computing system for improving therapy identification for a cancer patient via a mobile computing device, comprising:

one or more processors; and

one or more memories having stored thereon computer-executable instructions that, when executed by the one or more processors, cause the computing system to:

display, via a display screen of a mobile computing device, information identifying the cancer patient;

receive, at the mobile computing device, a request to determine a therapy identification for the cancer patient;

responsive to receiving the request, cause the information identifying the cancer patient and medical history information of the cancer patient to be transmitted to a remote cloud server having a stateless bioinformatics pipeline configured to:

process the information identifying the cancer patient and the medical history information of the cancer patient to extract a plurality of medical concept candidates,

rank the plurality of medical concept candidates based on a frequency of occurrence, within the medical history information of the cancer patient, of each of the plurality of medical concept candidates,

based on ranking the plurality of medical concept candidates, preserve a subset of the plurality of medical concept candidates, and

determine, based on the subset of the plurality of medical concept candidates, a recommended clinical trial as the therapy identification for the cancer patient;

responsive to the stateless bioinformatics pipeline determining the recommended clinical trial, receive from the remote cloud server clinical trial data corresponding to the recommended clinical trial; and

display, via the display screen of the mobile computing device, a cohort report associated with a cohort representing a plurality of cancer patients including the cancer patient, wherein the cohort report comprises at least the clinical trial data corresponding to the recommended clinical trial.

19 . A computer-readable medium having stored thereon a set of computer-executable instructions that, when executed by one or more processors, cause a computer to:

display, via a display screen of a mobile computing device, information identifying the cancer patient;

receive, at the mobile computing device, a request to determine a therapy identification for the cancer patient;

responsive to receiving the request, cause the information identifying the cancer patient and medical history information of the cancer patient to be transmitted to a remote cloud server having a stateless bioinformatics pipeline configured to:

process the information identifying the cancer patient and the medical history information of the cancer patient to extract a plurality of medical concept candidates,

rank the plurality of medical concept candidates based on a frequency of occurrence, within the medical history information of the cancer patient, of each of the plurality of medical concept candidates,

based on ranking the plurality of medical concept candidates, preserve a subset of the plurality of medical concept candidates, and

determine, based on the subset of the plurality of medical concept candidates, a recommended clinical trial as the therapy identification for the cancer patient;

responsive to the stateless bioinformatics pipeline determining the recommended clinical trial, receive from the remote cloud server clinical trial data corresponding to the recommended clinical trial; and

display, via the display screen of the mobile computing device, a cohort report associated with a cohort representing a plurality of cancer patients including the cancer patient, wherein the cohort report comprises at least the clinical trial data corresponding to the recommended clinical trial.

Assignments (5)
RELEASE OF SECURITY INTEREST Recorded May 14, 2026
From: ARES CAPITAL CORPORATION, AS COLLATERAL AGENT
To: TEMPUS AI, INC. (F/K/A TEMPUS LABS, INC.)
Reel/Frame 074653/0918 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Dec 23, 2025
From: LEFKOFSKY, ERIC
To: TEMPUS LABS
Reel/Frame 073304/0670 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Dec 23, 2025
From: COLLEY, SHANE; BEAUBIER, NIKE; TELL, ROBERT; WHITE, KEVIN
To: TEMPUS LABS, INC.
Reel/Frame 073304/0891 →
CHANGE OF NAME Recorded Feb 9, 2024
From: TEMPUS LABS, INC.
To: TEMPUS AI, INC.
Reel/Frame 066544/0110 →
SECURITY INTEREST Recorded Oct 13, 2023
From: TEMPUS LABS, INC.
To: ARES CAPITAL CORPORATION, AS COLLATERAL AGENT
Reel/Frame 065209/0711 →
Continuity (7)
Continuation 17680292 · Feb 25, 2022
Continuation 17157974 · Jan 25, 2021
Continuation 16531005 · Aug 2, 2019
Continuation 16289027 · Feb 28, 2019
Provisional Application 62774854 · Dec 3, 2018
Provisional Application 62746997 · Oct 17, 2018
Related Publication 20230267553A1 · Aug 24, 2023
References Cited (238)
US 5804656A · Eichenauer et al. · 1998 [cited by applicant]
US 6804656B1 · Rosenfeld et al. · 2004 [cited by applicant]
US 9031926B2 · Milward et al. · 2015 [cited by applicant]
US 9208217B2 · Milward et al. · 2015 [cited by applicant]
US 9424532B1 · Abedini et al. · 2016 [cited by applicant]
US 20010051353A1 · Kornblith · 2001 [cited by applicant]
US 20020173702A1 · Lebel et al. · 2002 [cited by applicant]
US 20030143572A1 · Lu et al. · 2003 [cited by applicant]
US 20030144886A1 · Taira · 2003 [cited by applicant]
US 20040122790A1 · Walker et al. · 2004 [cited by applicant]
US 20040220895A1 · Carus et al. · 2004 [cited by applicant]
US 20050125256A1 · Schoenberg et al. · 2005 [cited by applicant]
US 20070005621A1 · Lesh et al. · 2007 [cited by applicant]
US 20070294111A1 · Settimi · 2007 [cited by applicant]
US 20080162393A1 · Iliff · 2008 [cited by applicant]
US 20080195600A1 · Deakter · 2008 [cited by applicant]
US 20090319244A1 · West et al. · 2009 [cited by applicant]
US 20100029498A1 · Gnirke et al. · 2010 [cited by applicant]
US 20100076780A1 · Mahesh et al. · 2010 [cited by applicant]
US 20110117545A1 · Stacey et al. · 2011 [cited by applicant]
US 20110255790A1 · Duggan et al. · 2011 [cited by applicant]
US 20110288877A1 · Ofek et al. · 2011 [cited by applicant]
US 20120110016A1 · Phillips · 2012 [cited by applicant]
US 20120123184A1 · Otto et al. · 2012 [cited by applicant]
US 20120173585A1 · Pan et al. · 2012 [cited by applicant]
US 20120179696A1 · Charlot et al. · 2012 [cited by applicant]
US 20120265544A1 · Hwang et al. · 2012 [cited by applicant]
US 20120310899A1 · Wasserman et al. · 2012 [cited by applicant]
US 20130024382A1 · Dala · 2013 [cited by examiner]
US 20130060793A1 · Bandyopadhyay et al. · 2013 [cited by applicant]
US 20130073214A1 · Hyland · 2013 [cited by examiner]
US 20130096947A1 · Shah et al. · 2013 [cited by applicant]
US 20130246049A1 · Mirhaji · 2013 [cited by applicant]
US 20130268474A1 · Nizzari et al. · 2013 [cited by applicant]
US 20130332191A1 · Hoffman · 2013 [cited by examiner]
US 20140122117A1 · Masarie, Jr. et al. · 2014 [cited by applicant]
US 20140244625A1 · Seghezzi et al. · 2014 [cited by applicant]
US 20140249761A1 · Carroll · 2014 [cited by applicant]
US 20140278461A1 · Artz · 2014 [cited by applicant]
US 20140280353A1 · Delaney et al. · 2014 [cited by applicant]
US 20140330583A1 · Madhavan et al. · 2014 [cited by applicant]
US 20140364481A1 · Elenitoba-Johnson et al. · 2014 [cited by applicant]
US 20140365242A1 · Neff · 2014 [cited by applicant]
US 20150006558A1 · Leighton et al. · 2015 [cited by applicant]
US 20150106125A1 · Farooq et al. · 2015 [cited by applicant]
US 20150178386A1 · Oberkampf et al. · 2015 [cited by applicant]
US 20150213194A1 · Wolf et al. · 2015 [cited by applicant]
US 20150324527A1 · Siegel et al. · 2015 [cited by applicant]
US 20150331909A1 · Sundquist et al. · 2015 [cited by applicant]
US 20160019351A1 · Ober, Jr. · 2016 [cited by applicant]
US 20160019365A1 · Ober, Jr. · 2016 [cited by applicant]
US 20160019666A1 · Amarasingham et al. · 2016 [cited by applicant]
US 20160224760A1 · Peták · 2016 [cited by examiner]
US 20160224893A1 · Parker et al. · 2016 [cited by applicant]
US 20170046425A1 · Tonkin et al. · 2017 [cited by applicant]
US 20170103163A1 · Emanuel et al. · 2017 [cited by applicant]
US 20170109502A1 · Labkoff et al. · 2017 [cited by applicant]
US 20170177597A1 · Asimenos · 2017 [cited by applicant]
US 20170177822A1 · Fogel · 2017 [cited by applicant]
US 20170193175A1 · Madabhushi et al. · 2017 [cited by applicant]
US 20170193197A1 · Randhawa et al. · 2017 [cited by applicant]
US 20170199965A1 · Dekel · 2017 [cited by applicant]
US 20170237805A1 · Worley · 2017 [cited by applicant]
US 20170351816A1 · Fink et al. · 2017 [cited by applicant]
US 20180045727A1 · Spetzler · 2018 [cited by examiner]
US 20180046753A1 · Shelton · 2018 [cited by examiner]
US 20180046764A1 · Katwala et al. · 2018 [cited by applicant]
US 20180060482A1 · Nadauld · 2018 [cited by examiner]
US 20180060523A1 · Farh et al. · 2018 [cited by applicant]
US 20180068083A1 · Cohen et al. · 2018 [cited by applicant]
US 20180085015A1 · Crowder et al. · 2018 [cited by applicant]
US 20180101584A1 · Pattnaik et al. · 2018 [cited by applicant]
US 20180121618A1 · Smith et al. · 2018 [cited by applicant]
US 20180144003A1 · Formoso et al. · 2018 [cited by applicant]
US 20180211725A1 · Purdie et al. · 2018 [cited by applicant]
US 20180311224A1 · Hedley et al. · 2018 [cited by applicant]
US 20180358125A1 · Bagaev · 2018 [cited by examiner]
US 20180373844A1 · Ferrandez-Escamez et al. · 2018 [cited by applicant]
US 20190006024A1 · Kapoor · 2019 [cited by applicant]
US 20190006048A1 · Gupta et al. · 2019 [cited by applicant]
US 20190050530A1 · De La Vega · 2019 [cited by applicant]
US 20190057774A1 · Velez et al. · 2019 [cited by applicant]
US 20190108898A1 · Gulati · 2019 [cited by applicant]
US 20190108912A1 · Spurlock, III et al. · 2019 [cited by applicant]
US 20190206524A1 · Baldwin et al. · 2019 [cited by applicant]
US 20190214145A1 · Kurek et al. · 2019 [cited by applicant]
US 20190304574A1 · Weinstock · 2019 [cited by examiner]
US 20200005461A1 · Yip · 2020 [cited by applicant]
US 20200005906A1 · Wang et al. · 2020 [cited by applicant]
US 20200065374A1 · Gao et al. · 2020 [cited by applicant]
US 20200075139A1 · Master et al. · 2020 [cited by applicant]
US 20200118644A1 · Khan et al. · 2020 [cited by applicant]
US 20200126642A1 · Kenna et al. · 2020 [cited by applicant]
US 20200176098A1 · Lucas et al. · 2020 [cited by applicant]
US 20200219619A1 · Feczko et al. · 2020 [cited by applicant]
US 20200234801A1 · Mao et al. · 2020 [cited by applicant]
US 20210043275A1 · Landau et al. · 2021 [cited by applicant]
US 20210115519A1 · Pitroda · 2021 [cited by examiner]
US 20210174025A1 · Hu et al. · 2021 [cited by applicant]
US 20210208131A1 · Kretzschmar et al. · 2021 [cited by applicant]
WO 2020023420A2 · 2020 [cited by applicant]
Shu Y, Wu X, Tong X, Wang X, Chang Z, Mao Y, Chen X, Sun J, Wang Z, Hong Z, Zhu L, Zhu C, Chen J, Liang Y, Shao H, Shao YW. Circulating Tumor DNA Mutation Profiling by Targeted Next Generation Sequencing Provides Guidan… [cited by examiner]
Damen et al., PASTEL: A Semantic Platform for Assisted Clinical Trial Patient Recruitment, 2013, IEEE International Conference on Healthcare Informatics (Year: 2013). [cited by applicant]
Lee et al., A Semantic Framework for Intelligent Matchmaking for Clinical Trial Eligibility Critiera, Sep. 2013, ACM Transactions on Intelligent Systems and Technology, vol. 4, No. 4, Article 71 (Year: 2013). [cited by applicant]
Kopcke, Employing Computers for the Recruitment into Clinical Trials: A Comprehensive Systematic Review, Jul. 2014, J Med Internet Res 16(7) (Year: 2014). [cited by applicant]
Ni et al., Increasing the efficiency of trail-patient matching: automated clinical trial eligibility Pre-screening for pediatric oncology patients, 2015, BMC Medical Informatics and Decision Making (Year: 2015). [cited by applicant]
Gkotsis et al., Don't Let Notes Be Misunderstood: A Negation Detection Method for Assessing Risk of Suicide in Mental Health Records, 2016, Proceedings of the 3rd Workshop on Computational Linguistics and Clinical Psych… [cited by applicant]
AACR Project GENIE Consortium_ AACR Project GENIE: Powering Precision Medicine through an International consortium. Cancer Discov. 7, 818-831 (2017). [cited by applicant]
Alexandrov, L. B. et al. Signatures of mutational processes in human cancer. Nature 500, 415-421 (2013). [cited by applicant]
Allen, J. et al. Barriers to Patient Enrollment in Therapeutic Clinical Trials for Cancer: A Landscape Report. (2018). [cited by applicant]
Ayers, M, et at “IFN-?-related mRNA profile predicts clinical response to PD-1 blockade.” The Journal of clinical investigation 127.8 (2017): 2930-2940. [cited by applicant]
Beaubier, N. et al. Clinical Validation of the Tempus xT Next-Generation Sequencing Targeted Oncology Assay Oncotarget 10, 2384-2396 (2019). [cited by applicant]
Bertsimas, D., et at “Personalized diabetes management using electronic medical records.” Diabetes care 40.2(2017): 210-217. [cited by applicant]
Caris Life Sciences. Code: Comprehensive Oncology Data Explorer. Slideshow May 2017. Accessed on Dec. 16, 2020 at https://www.karmanos.org/Uploads/PubliciDocuments/Karmanos/CODE%20slides_5.2017.ppbc%20. [cited by applicant]
Chae, Y. K. et al. Association of tumor mutational burden with DNA repair mutations and response to anti-PD-1/PD-L1 therapy in non-small cell lung cancer. Clin. Lung Cancer (2018). doi:10.1016/J.CLLC.2018.09.008. [cited by applicant]
Chatterjee, P. et al. The TMPRSS2-ERG Gene Fusion Blocks XRCC4-Mediated Nonhomologous End-Joining Repair and Radiosensitizes Prostate Cancer Cells to PARP Inhibition. Mol. Cancer Ther. 14, 1896-1906 (2015). [cited by applicant]
Conway, J. R., et al. UpSetR: an R package for the visualization of intersecting sets and their properties. Bioinformatics 33, 2938-2940 (2017). [cited by applicant]
Coutinho, A. D., et al. “Real-world treatment patterns and outcomes of patients with small cell lung cancer progressing after 2 lines of therapy.” Lung Cancer 127 (2019): 53-58. [cited by applicant]
Cristescu, R. et al. Pan-tumor genomic biomarkers for PD-1 checkpoint blockade-based immunotherapy. Science 362, eaar3593 (2018). [cited by applicant]
Darvin, P., et al. Immune checkpoint inhibitors: recent progress and potential biomarkers. Exp. Mol. Med. 50, 165 (2018). [cited by applicant]
Dempster, AP. et al. (1977). “Maximum Likelihood from Incomplete Data via the EM Algorithm”. Journal of the Royal Statistical Society, Series B. 39 (1): 1-38. [cited by applicant]
Desrichard, A., et al. Cancer Neoantigens and Applications for Immunotherapy. (2016). doi:10.1158/1078-0432.2. CCR-14-3175. [cited by applicant]
Dhir, M. et al. Impact of genomic profiling on the treatment and outcomes of patients with advanced gastrointestinal malignancies. Cancer Med. 6, 195-206 (2017). [cited by applicant]
Dienstmann, R. et al. Standardized decision support in next generation sequencing reports of somatic cancer variants. Mol. Oncol. 8, 859-873 (2014). [cited by applicant]
Dienstmann, R., et al. “Database of genomic biomarkers for cancer drugs and clinical targetability in solid tumors” Cancer discovery 5.2 (2015): 118-123. [cited by applicant]
DNANEXUS. Working with UK Biobank: A Research Guide. Accessed online on Dec. 31, 2020. Available at https://web.archive.org/save/https://dna-nexus-prod-s3-assets-51tcpaqcp0pd.s3.amazonaws.com/images/files/DNAnexus_White… [cited by applicant]
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15-21 (2013). [cited by applicant]
Faust, G. G., et al. “SAMBLASTER: fast duplicate marking and structural variant read extraction.” Bioinformatics 30.17 (2014): 2503-2505. [cited by applicant]
Fernandes, G. et al. Next-generation Sequencing-based genomic profiling: Fostering innovation in cancer care? Clinics 72, 588-594 (2017). [cited by applicant]
Finan, C. et al. The druggable genome and support for target identification and validation in drug development. Sci. Trans !. Med. 9, eaag1166 (2017). [cited by applicant]
Flaig, T. W., et al. “Treatment evolution for metastatic castration-resistant prostate cancer with recent introduction of novel agents: retrospective analysis of real-world data.” Cancer Medicine 5.2 (2016): 182. [cited by applicant]
Forbes, S. A. et al. COSMIC: somatic cancer genetics at high-resolution. Nucleic Acids Res. 45, D777-D783 (2017). [cited by applicant]
Gangul, R. Line of Therapy Analytics: A key to commercial drug success. Feb. 15, 2017. Available online https://www.saama.com/blog/line-therapy-analytics-key-commercial-drug-success/. [cited by applicant]
Goldman, M. et al. The UCSC Xena platform for public and private cancer genomics data visualization and interpretation. bioRxiv 326470 (2019). doi:10.1101/326470. [cited by applicant]
Gong, J. et al. Value-based genomics. Oncotarget 9, 15792-15815 (2018). [cited by applicant]
Goodman, A. M. et al. Tumor Mutational Burden as an Independent Predictor of Response to Immunotherapy in Diverse Cancers. Mol. Cancer Ther. 16, 2598-2608 (2017). [cited by applicant]
Griffith, M. et al. CIVIC is a community knowledgebase for expert crowdsourcing the clinical interpretation of variants in cancer Nat_Genet. 49, 170-174 (2017). [cited by applicant]
Han, M.-E. et al. Overexpression of NRG1 promotes progression of gastric cancer by regulating the self-renewal of cancer stem cells. J. Gastroenterol. 50, 645-656 (2015). [cited by applicant]
Hartmaier, R. J. et al. High-Throughput Genomic Profiling of Adult Solid Tumors Reveals Novel Insights into Cancer Pathogenesis. Cancer Res. 77, 2464-2475 (2017). [cited by applicant]
Hegde, G. V. et al. Blocking NRG1 and Other Ligand-Mediated Her4 Signaling Enhances the Magnitude and Duration of the Chemotherapeutic Response of Non-Small Cell Lung Cancer. Sci. Transl. Med. 5, 171ra18-171ral8 (2013). [cited by applicant]
Institute of Medicine of the National Academies. Clinical Trials in Cancer. Chapter 6 in Transforming Clinical Research in the United States: Challenges and Opportunities: Workshop Summary (ed. Institute of Medicine (US… [cited by applicant]
International Searching Authority, International Search Report and Written Opinion for application PCT/US2019/056713. Mailed on Feb. 27, 2020. [cited by applicant]
International Searching Authority, International Search Report and Written Opinion for application PCT/US2019/064329. Mailed on Apr. 1, 2020. [cited by applicant]
International Searching Authority, International Search Report and Written Opinion for application PCT/US2020/047704. Mailed on Nov. 20, 2020. [cited by applicant]
International Searching Authority. International Search Report and Written Opinion for application PCT/US2021/017517. Mailed on Apr. 29, 2021. 11 pages. [cited by applicant]
Lau, D., et al. RNA Sequencing of the Tumor Microenvironment in Precision Cancer Immunotherapy. Trends in Cancer 5, 149-156 (2019). [cited by applicant]
Lawrence, M. S. et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 499, 214-218 (2013). [cited by applicant]
Layer, R. M., et al. “LUMPY: a probabilistic framework for structural variant discovery.” Genome biology 15.6 (2014): R84. [cited by applicant]
Le, D. T. et al. Mismatch repair deficiency predicts response of solid tumors to PD-1 blockade. Science 357, 409-413 (2017). [cited by applicant]
Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285-291 (2016). [cited by applicant]
Li, FL et al. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754-1760 (2009). [cited by applicant]
Li, M. M. et al. Standards and Guidelines for the Interpretation and Reporting of Sequence Variants in Cancer. J. Mol. Diagnostics 19, 4-23 (2017). [cited by applicant]
Liao, Y., et al. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923-930 (2014). [cited by applicant]
Lonsdale, J. et al. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 45, 580-585 (2013). [cited by applicant]
Luraghi, P. et al. A Molecularly Annotated Model of Patient-Derived Colon Cancer Stem-Like Cells to Assess Genetic and Nongenetic Mechanisms of Resistance to Anti-EGFR Therapy. Clin. Cancer Res. 24, 807-820 (2018). [cited by applicant]
Madhavan, S. et al. ClinGen Cancer Somatic Working Group—standardizing and democratizing access to cancer molecular diagnostic data to drive translational research. Pac. Symp. Biocomput. 23, 247-258 (2018). [cited by applicant]
Malangone-Monaco, E., et al. “Prescribing patterns of oral antineoplastic therapies observed in the treatment of patients with advanced prostate cancer between 2012 and 2014: results of an oncology EMR analysis.” Clinic… [cited by applicant]
Maxwell, K N. et al. BRCA locus-specific loss of heterozygosity in germline BRCA1 and BRCA2 carriers. Nat. Commun. 8, 319 (2017). [cited by applicant]
Mendell, J. et al. Clinical Translation and Validation of a Predictive Biomarker for Patritumab, an Anti-human Epidermal Growth Factor Receptor 3 (HER3) Monoclonal Antibody, in Patients With Advanced Non-small Cell Lung… [cited by applicant]
Miller, A. et al. High somatic mutation and neoantigen burden are correlated with decreased progression-free survival in multiple myeloma_Blood Cancer J. 7, e612 (2017). [cited by applicant]
Newman, A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 12, 453-7 (2015). [cited by applicant]
Newton, Y. et al. TumorMap: Exploring the Molecular Similarities of Cancer Samples in an Interactive Portal. Cancer Res. 77, e111-e114 (2017). [cited by applicant]
OPTUM. Determining Lines of Therapy (LOT) in Oncology in Claims Databases. 2017. Available online at https://cdnaem.optum.comicontentidam/optum3/optum/eniresources/white-papers/w1520768_guidelines-for-determining-lines-… [cited by applicant]
Peng, L. et al. Large-scale RNA-Seq Transcriptome Analysis of 4043 Cancers and 548 Normal Tissue Controls across 12 TCGA Cancer Types. Sci. Rep. 5, 13413 (2015). [cited by applicant]
Radovich, M. et al. Clinical benefit of a precision medicine based approach for guiding treatment of refractory Cancers. Oncotarget 7, 56491-56500 (2016). [cited by applicant]
Rajkumar, S. V., et al. “Guidelines for determination of the number of prior lines of therapy in multiple myeloma.” Blood, The Journal of the American Society of Hematology 126.7 (2015): 921-922. [cited by applicant]
Reiman, D. et al. Integrating RNA expression and visual features for immune infiltrate prediction. Biocomputing 2019, 284-295 (2018). [cited by applicant]
Richards, S. et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Patho… [cited by applicant]
Robinson, D. R. et al. Integrative clinical genomics of metastatic cancer. Nature 548, 297-303 (2017). [cited by applicant]
Rokach L., Maimon O., Decision Trees, 2005, Data Mining and Knowledge Discovery Handbook, pp. 165-192 (Year: 2005). [cited by applicant]
Rooney, M. S., et al. Molecular and Genetic Properties of Tumors Associated with Local Immune Cytolytic Activity. Cell 160, 48-61 (2015). [cited by applicant]
Rosenthal, R., et al. deconstructSigs: delineating mutational processes in single tumors distinguishes DNA repair deficiencies and patterns of carcinoma evolution. Genome Biol. 17, (2016). [cited by applicant]
Roufas, C. et al. The Expression and Prognostic Impact of Immune Cytolytic Activity-Related Markers in Human Malignancies: A Comprehensive Meta-analysis. Front. Oncol. 8, 27 (2018). [cited by applicant]
Savova, et al., “Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications”, J Am Med Inform Assoc 2010;17: 507-513., retrieved from the internet at: https… [cited by applicant]
Sheng, Q. et al. An Activated ErbB3/NRG1 Autocrine Loop Supports In Vivo Proliferation in Ovarian Cancer Cells. Cancer Cell 17, 298-310 (2010). [cited by applicant]
Solomon, B., et al. ALK Gene Rearrangements: A New Therapeutic Target in a Molecularly Defined Subset of Non-small Cell Lung Cancer. J. Thorac. Oncol. 4, 1450-1454 (2009). [cited by applicant]
Szolek, A, et al. “OptiType: precision HLA typing from next-generation sequencing data” Bioinformatics 30.23 (2014): 3310-3316. [cited by applicant]
Teer, J. K. et al. Evaluating somatic tumor mutation detection without matched normal samples. Hum. Genomics 11, 22 (2017). [cited by applicant]
The ASCO Post. 2018 ASCO: Impact Trial Matches Treatment to Genetic Changes in the Tumor to Improve Survival Across Multiple Cancer Types—The ASCO Post_ Jun. 6, 2018 (2018). Available at: http://www_ascopost.com/News/58… [cited by applicant]
Tomlins, S. A. et al. Recurrent fusion of TMPRSS2 and ETS transcription factor genes in prostate cancer. Science 310, 6/1,1 618 (2005). [cited by applicant]
Tsang, J-P. Deploying Machine Learning for Commercial Analytics. PMSA.net. Version accessed Dec. 15, 2018. Available online at https://web.archive.org/web/20181215152550/http://www.pmsa.net/jpmsa-vol06-article09. [cited by applicant]
Unger, J. M., et al. Systematic Review and Meta-Analysis of the Magnitude of Structural, Clinical, and Physician and Patient Barriers to Cancer Clinical Trial Participation. J. Natl. Inst. 111, 245-255 (2019). [cited by applicant]
Wang, Z. et al. Significance of the TMPRSS2:ERG gene fusion in prostate cancer. Mol. Med. Rep. 16, 5450-5458 (2017). [cited by applicant]
Wheler, J. J. et al. Cancer Therapy Directed by Comprehensive Genomic Profiling: A Single Center Study. Cancer Res. 76, 3690-3701 (2016). [cited by applicant]
Wilson, T. R., et al. Neuregulin-1—Mediated Autocrine Signaling Underlies Sensitivity to HER2 Kinase Inhibitors in a Subset of Human Cancers. Cancer Cell 20, 158-172 (2011). [cited by applicant]
Yan, M. et al. HER2 expression status in diverse cancers: review of results from 37,992 patients. Cancer Metastasis Rev. 34, 157-64 (2015). [cited by applicant]
Yang, L. et al. NRG1-dependent activation of HER3 induces primary resistance to trastuzumab in HER2-overexpressing breast cancer cells. Int. J. Oncol. 51, 1553-1562 (2017). [cited by applicant]
Yonesaka, K. et al. Activation of ERBB2 Signaling Causes Resistance to the EGFR-Directed Therapeutic Antibody Cetuximab. Sci. Transl. Med. 3, 99ra86-99ra86 (2011). [cited by applicant]
Yong, W Px, et al. “The role of pharmacogenetics in cancer therapeutics.” British journal of clinical pharmacology 62.1 (2006): 35-46. [cited by applicant]
Yun, S. et al. Clinical significance of overexpression of NRG1 and its receptors, HER3 and HER4, in gastric cancer patients. Gastric Cancer 21, 225-236 (2018). [cited by applicant]
Algorithm Implementation/Strings/Levenshtein distance, Wikibooks, last edited on Jan. 5, 2019, 39 pages. Retrieved from Internet. URL: https://en.wikibooks.org/wiki/Algorithm_Implementation/Strings/Levenshtein_distance#… [cited by applicant]
Amazon Textract—Easily extract text and data from virtually any document, Amazon, Nov. 28, 2018, 6 pages. Retrieved from Internet. URL: https://aws.amazon.comitextract/. [cited by applicant]
CliNER, Text Machine Lab 2014, Nov. 10, 2017, 2 pages. Retrieved from Internet. URL: http://text-machine.cs.umLedu/cliner/. [cited by applicant]
Clinithink CLiX CNLP (Harness unstructured data to drive value across the healthcare continuum), Clinithink Limited, 2015, 1 page. Retrieved from Internet. URL: http://clinithink.com/wp-content/uploads//2015/01/Clinithi… [cited by applicant]
Evernote Scannable on the App Store, Jun. 18, 2018, 3 pages. Retrieved from Internet. URL: https://itunes.apple.com/us/app/evemote-scannable/id883338188?mt=8. [cited by applicant]
12E: The world's leading Text Mining platform, 2019 LINGUAMATICS, Aug. 14, 2018, 5 pages. Retrieved from Internet. URL: https://www.linguamatics.com/products-servicestabout-i2e. [cited by applicant]
PHP: levenshtein—Manual, latest comment dated in 2014, 32 pages. Retrieved from Internet. URL: http://www.php.net/levenshtein. [cited by applicant]
Talk:Algorithm Implementation/Strings/Levenshtein distance, Wikibooks, last edited on Nov. 2, 2016, 2 pages. Retrieved from Internet. URL: https://en_wikibooks_org/wiki/Talk:Algorithm_Implementation/Strings/Levenshtein_… [cited by applicant]
Why cTAKES? See the animation below for a sampling of cTAKES capabilities, The Apache Software Foundation, Apr. 25, 2017, 1 page. Retrieved from Internet.URL: http://ctakes.apache.org/whycTAKES.html. [cited by applicant]
Aronson, Alan “MetaMap—A Tool For Recognizing UMLS Concepts in Text,” Jan. 11, 2019, 2 pages. Retrieved from Internet. URL: https://metamap.nlm.nih.gov/. [cited by applicant]
Balatero, David (dbalatero/levenshtein-ffi) “Fast string edit distance computation, using the Damerau-Levenshtein algorithm,” GitHub, Inc., latest comment dated on Aug. 10, 2014, 2 pages. Retrieved from Internet. URL: h… [cited by applicant]
Chen, Huizhong et al. “Robust Text Detection in Natural Images With Edge-Enhanced Maximally Stable Extremal Regions,” 2011 18th IEEE International Conference on Image Processing, Sep. 11-14, 2011, 4 pages. [cited by applicant]
Choi, Edward et al. “Doctor AI: Predicting Clinical Events via Recurrent Neural Networks,” Proceedings of Machine Learning for Healthcare 2016, JMLR W&C Track vol. 56, 2016, pp. 1-18. [cited by applicant]
Choi, Edward et al. “Coarse-to-Fine Question Answering for Long Documents,” Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, Vancouver, Canada, Jul. 30-Aug. 4, 2017, pp. 209-220. [cited by applicant]
Collobert, Ronan et al. “Natural Language Processing (Almost) from Scratch,” Journal of Machine Learning Research 12, (2011) pp. 2493-2537. [cited by applicant]
Conneau, Alexis et al. “Very Deep Convolutional Networks for Text Classification,” arXiv:1606.01781 [cs.CL], Jan. 27, 2017, 10 pages. [cited by applicant]
Das, Rajarshi et al. “Chains of Reasoning over Entities, Relations, and Text using Recurrent Neural Networks,” Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics:… [cited by applicant]
De Bruijn, Berry et al. “Machine-learned solutions for three stages of clinical information extraction: the state of the art at i2b2 2010,” 18 J. Am. Med. Inform. Assoc. 2011, May 12, 2011, pp. 557-562. [cited by applicant]
Dozat, Timothy et al. “Deep Biaffine Attention for Neural Dependency Parsing,” Published as a conference paper at ICLR 2017, arXiv:1611.01734 [cs.CL], Mar. 10, 2017, pp. 1-8. [cited by applicant]
Genthial, Guillaum “Sequence Tagging with Tensorflow (bi-LSTM + CRF with character embeddings for NER and POS),” Guillaume Genthial blog, Apr. 5, 2017, 23 pages. [cited by applicant]
He, Dafang et al. “Multi-scale Multi-task FCN for Semantic Page Segmentation and Table Detection,” IEEE, 2017 14th IAPR International Conference on Document Analysis and Recognition, Nov. 9-15, 2017, pp. 254-261. [cited by applicant]
He, Luheng et al. “Deep Semantic Role Labeling: WhatWorks and What's Next” Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, Vancouver, Canada, Jul. 30-Aug. 4, 2017, pp. 473-483. [cited by applicant]
Kavasidis, I. et al. “A Saliency-based Convolutional Neural Network for Table and Chart Detection in Digitized Documents,” Submitted to IEEE Transactions on Multimedia, Apr. 17, 2018, pp. 1-13. [cited by applicant]
Kim, Youngjun et al. “A Study of Concept Extraction Across Different Types of Clinical Notes,” AMIA Annu Symp Proc. 2015; Nov. 5, 2015, pp. 737-746. [cited by applicant]
Lafferty, John et al. “Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data,” Proceedings of the 18th International Conference on Machine Learning 2001 (ICML 2001), Jun. 28, 2001, pp… [cited by applicant]
Lample, Guillaume et al. “Neural Architectures for Named Entity Recognition,” Association for Computational Linguistics, Proceedings of NAACL-HLT 2016, San Diego, California, Jun. 12-17, 2016, pp. 260-270. [cited by applicant]
Lang, Francois-Michel et al. “Increasing UMLS Coverage and Reducing Ambiguity via Automated Creation of Synonymous Terms: First Steps toward Filling UMLS Synonymy Gaps,” Semantic Scholar, 2017, pp. 1-26. [cited by applicant]
Laserson, Jonathan et al. “TextRay: Mining Clinical Reports to Gain a Broad Understanding of Chest X-rays,” arXiv:1806.02121 [cs_CV], Jun. 6, 2018, 13 pages. [cited by applicant]
Lee, Kenton et al. “End-to-end Neural Coreference Resolution,” arXiv:1707.07045 [cs.CL], Dec. 15, 2017, 10 pages. [cited by applicant]
Lei, Tao et al. “Rationalizing Neural Predictions,” arXiv:1606.04155 [cs.CL], Nov. 2, 2016, 11 pages. [cited by applicant]
Ling, Yuan et al. “Learning to Diagnose: Assimilating Clinical Narratives using Deep Reinforcement Learning,” Proceedings of the The 8th International Joint Conference on Natural Language Processing, Taipei, Taiwan, Nov… [cited by applicant]
Lipton, Zachary et al. “Learning to Diagnose With LSTM Recurrent Neural Networks,” ICLR 2016, arXiv:1511.03677 [cs_LG], pp. 1-18. [cited by applicant]
Ma, Xuezhe et al. “End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF,” arXiv:1603.01354v5 [cs.LG], May 29, 2016, 12 pages. [cited by applicant]
Mackenzie, Andrei (andrei-m/levenshteinjs) “Levenshtein distance between two given strings implemented in JavaScript and usable as a Nodejs module,” GitHub, Inc., latest comment dated on Feb. 2, 2018, 9 pages. Retrieved… [cited by applicant]
Mihalcea, Rada et al. “Part IV Graph-Based Natural Language Processing” excerpted from a book “Graph-Based Natural Language Processing and Information Retrieval,” Cambridge University Press, First published 2011 (Reprin… [cited by applicant]
Mysore, Sheshera S. “Segmentation of Non-text Objects with Connected Operators” Msheshera, May 21, 2017, 2 pages. Retrieved from Internet. URL: http://msheshera.github.io/non-text-segment-connected-operators.htm. [cited by applicant]
Narasimhan, Karthik et al. “Improving Information Extraction by Acquiring External Evidence with Reinforcement Learning,” arXiv:1603.07954v3 [cs.CL], Sep. 27, 2016, 12 pages. [cited by applicant]
Neelakantan, Arvind et al. “Learning Dictionaries for Named Entity Recognition using Minimal Supervision,” Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, Got… [cited by applicant]
Peters, Matthew. E. et al. “Semi-supervised sequence tagging with bidirectional language models,” arXiv:1705.00108v1 [cs.CL], Apr. 29, 2017, 10 pages. [cited by applicant]
Prakash, Aaditya et al. “Condensed Memory Networks for Clinical Diagnostic Inferencing,” arXiv:1612.01848v2 [cs.CL], Jan. 3, 2017, 8 pages. [cited by applicant]
Sager, Naomi et al. “Natural Language Processing and the Representation of Clinical Data,” Journal of the American Medical Informatics Association, vol. 1, No. 2, Mar./Apr. 1994, pp. 142-160. [cited by applicant]
Sambyal, Nitigya et al. “Automatic Text Extraction and Character Segmentation Using Maximally Stable Extremal Regions,” arXiv:1608.03374 [cs_CV], Aug. 11, 2016, 6 pages. [cited by applicant]
Tsou, Ching-Huei et al. “Watson for Patient Record Analytics (aka Watson EMRA),” IBM Research, Aug. 2017, 5 pages. Retrieved from Internet. URL: https://researcher.watson.ibm.com/researcher/view_group.php?id=7664. [cited by applicant]
Wu, Yonghui et al. “A Study of Neural Word Embeddings for Named Entity Recognition in Clinical Text,” AMIA Annual Symp. Proc. 2015; Published online Nov. 5, 2015, pp. 1326-1333. [cited by applicant]
Zhang, Yuan et al. “Aspect-augmented Adversarial Networks for Domain Adaptation” sentiarXiv:1701.00188v2 [cs.CL] Sep. 25, 2017, 14 pages. [cited by applicant]
Zehir, A. et al. Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 batients. Nat. Med. 23, 703-713 (2017). [cited by applicant]
Zeng, et al., “Extracting principal diagnosis, co-morbidity and smoking status for asthma research: evaluation of a natural language processing system”, BMC Medical Informatics and Decision Making 2006, 6:30, retrieved … [cited by applicant]
Sayitoglu M. Clinical Interpretation of Genomic Variations. Genomik Varyasyonlann Klinik Yorumlanmasi. Turk J Haematol. 2016;33(3):172-179. doi:10.4274/tjh.2016.0149 (Year: 2016). [cited by applicant]