1DETAILED ACTION
This is responsive to application 18/250,607 filed on 04/26/2023 in which claims 1-20 are presented for examination.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1:
Step 1: Is the claim to a process, machine, manufacture or composition of matter?” Yes, it’s a method(process).
Step 2a Prong 1 (judicial exception)
Step 2A (1): “Does the claim recite an abstract idea, law of nature, or natural phenomenon? Yes , the claim comes under mental processes.
Claim 1 recites:
“A method comprising: retrieving, by a computing system comprising one or more processors and a memory with instructions executable by the one or more processors, from an electronic health records (EHR) system, for a patient with a medical condition, a structured dataset and an unstructured dataset, the structured dataset comprising demographic and clinical data for the patient, and the unstructured dataset comprising a report with free-form text of a clinician with respect to a medical procedure; analyzing, by the computing system, the structured dataset and the unstructured dataset to generate a plurality of health indicators for the patient, wherein analyzing the structured dataset and the unstructured dataset comprises applying natural language processing to the free-form text in the report to extract one or more of the plurality of indicators; generating, by the computing system, based on the plurality of health indicators, one or more categorizations corresponding to the medical condition; determining, by the computing system, a treatment regimen based on drug orders in the structured dataset; performing, by the computing system, survival modeling to generate, by the computing system, based on (i) the plurality of health indicators, (ii) the one or more categorizations, and (iii) the treatment regimen, a prediction corresponding to a survival of the patient following administration of a treatment to the patient for the medical condition; and providing, by the computing system, a report comprising the prediction to one or more users for determining whether to administer the treatment to the patient for the medical condition, wherein providing the report comprises at least one of (1) transmitting the report to a computing device, (2) displaying the report on a display screen, or (3) storing the report in a non-volatile computer-readable storage medium for access via a server.
All the limitations above are abstract idea related to the mental process (concepts performed in the human mind (including an observation, evaluation, judgment, opinion)) with the exception of bold and underlined limitations. Claim language pertains to analyzing demographics(structured ) and clinical notes/report(unstructured) to determine health indicators( e.g. single nucleotide polymorphism (SNIP) array) and categorizations(e.g. molecular category, histology etc.). A drug treatment regimen can be determined based on demographics. Patient’s survival probability can be estimated using health indicators, categorizations and treatment regimen. A report can be generated based on administering drug treatment regimen . All of this can be done mentally or on paper.
Step 2A(2): Prong Two: evaluate whether the claim recites additional elements that integrate the exception into a practical application of the exception. NO
The claim does recite additional elements; however they don’t integrate the exception into a practical application of the exception.
computing system (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f))
processors(Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f))
memory (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f))
electronic health records (EHR) system (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f))
natural language processing (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f))
displaying (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f))
display screen(Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f))
server(Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f))
transmitting the report to a computing device (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g) )
storing the report in a non-volatile computer-readable storage medium(Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g) )
Step 2B: evaluate whether the claim recites additional elements that amount to an inventive concept (aka “significantly more”) than the recited judicial exception? NO
As discussed previously with respect to Step 2A Prong Two, the additional element in the claim amounts to no more than mere instructions to apply the exception using a generic computer component.
Regarding the claim limitations:
transmitting the report to a computing device” the courts have recognized the computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (“i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information”); See, MPEP 2106.05 (d)(II)
storing the report in a non-volatile computer-readable storage medium the courts have recognized the computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; See, MPEP 2106.05 (d)(II)
The same analysis applies here in 2B, i.e., mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Dependent claims 2-12 and 18 further narrows the abstract idea and add the additional elements of “parsing”, “operators”, “tab-delimited tables”, “unmerged nested cells”.
Under step 2A, prong two, the additional elements don’t integrate the exception into a practical application of the exception as merely adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).
As discussed previously with respect to Step 2A Prong Two, the additional elements in the claim amounts to no more than mere instructions to apply the exception using a generic computer component.
The same analysis applies here in 2B, i.e., mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Regarding claim 13, it is rejected under the same rationale as claim 1. In addition it adds the additional element of “computing system”.
Under step 2A, prong two, the additional elements don’t integrate the exception into a practical application of the exception as merely adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).
As discussed previously with respect to Step 2A Prong Two, the additional elements in the claim amounts to no more than mere instructions to apply the exception using a generic computer component.
The same analysis applies here in 2B, i.e., mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Dependent claims 14-17 and 19-20 further narrows the abstract idea defined in claim 13. In addition the additional elements in these claims are “processor”, “operators”, “tab-delimited tables”
Under step 2A, prong two, the additional elements don’t integrate the exception into a practical application of the exception as merely adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).
As discussed previously with respect to Step 2A Prong Two, the additional elements in the claim amounts to no more than mere instructions to apply the exception using a generic computer component.
The same analysis applies here in 2B, i.e., mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-2, 5-9, 12-13, and 15-18 are rejected under 35 U.S.C. 102a(1) as anticipated by Ozeran et al. ( US 20200381087 A1)
Regarding claim 1, Ozeran teaches A method comprising:
retrieving, by a computing system comprising one or more processors and a memory with instructions executable by the one or more processors(see para 0029),
from an electronic health records (EHR) system, for a patient with a medical condition, a structured dataset and an unstructured dataset (para, “[0181] ….In some aspects, the clinical data can come from a structured clinical data source (e.g., an EMR, a clinical lab record, an electronic data warehouse, a health information exchange, etc.). System 100 can prepopulate the attributes 1150 based on the structured clinical data.” Note: Also, see para 0156, 0238
Also, para “[0321] At 4904, the process 4900 can receive patient health information. In some embodiments, the patient health information can include information from an electronic medical record. In some embodiments, the patient health information can include at least a portion of the features in the patient data store 3202 in FIG. 32. In some embodiments, the patient health information can be unstructured.”
Also, para “[0227] In some aspects, records and documentation 2783 can include source document types, record storage methods, and/or EHR/EMR systems.”)
the structured dataset comprising demographic and clinical data for the patient (para, “[0131] ….Alternatively, electronic records can be automatically transferred to server 120 from various facilities, practitioners, or third party applications, where appropriate. As shown in FIG. 1, patient data communicated to server 120 can include, but is not limited to, treatment data (such as current treatment information and resulting data), genetic data (such as RNA, DNA data), brain scans (such as PET scans, CT, MRI, etc.), and/or clinical records (such as biographical information, patient history, patient demographics, family history, comorbidity conditions, etc.).),
and the unstructured dataset comprising a report with free-form text of a clinician with respect to a medical procedure (para“[0143] FIGS. 2-9 generally provide graphical user interfaces (GUIs) that can be implemented in system 100 to structure data (e.g., clinical trial data). In some aspects, reports that flow for clinical patients can rely on recommendations and suggestions on which clinical trials the patient is eligible for, as well as clinical and molecular insights. In order to do that effectively, unstructured clinical trial data can be structured using free-text (unstructured data) sourced from clinical trial databases and/or websites (e.g., clinicaltrials.gov)….”
Also, para “[0116] In some embodiments of the present disclosure, the system can create structure around clinical trial data. This can include reviewing free text (i.e., unstructured data), determining relevant information, and populating corresponding structured data field with the information. As an example, a clinical trial description may specify that only patients diagnosed with stage I breast cancer may enroll….”);
analyzing, by the computing system, the structured dataset and the unstructured dataset to generate a plurality of health indicators for the patient (para, “[0246] In some embodiments, the flow 3200 can include generating and/or receiving a number of stored alteration features 3250 associated with the patient. The patient data store 3202 can include the stored alteration features 3250. In some embodiments, the stored alteration features 3250 can be generated using a machine learning technique one or more features, such as at least one of the features described above. For example, a machine learning model may generate a data science prediction, such as data science predictions 3254, of a patient's future probability of metastasis, origin of a metastasized tumor, and/or a progression-free survival probability based on a patient's state (collection of features) at any time during their treatment. In some embodiments, the stored alteration features 3250 can include features associated with Isoforms, single-nucleotide polymorphisms (SNPs), and/or Fusions.”
Also, para, “[0284] For example, a document may have patient information from a next generation sequencing report containing molecular marker results or laboratory testing results from testing performed on a patient's blood. Standard information, such as the patient's name, date of birth, address may be found in a document. Other information such as the laboratory name, address, CAP/CLIA number, testing procedure performed may also be present. Clinical information such as the results of the next generation sequencing test, such as specific single nucleotide variants, copy number alterations, fusions, or other genomic alterations may be reported.”
Also, para, “[0239]In some embodiments, the clinical features 3212 can include features such as diagnosis, symptoms, therapies, outcomes, patient demographics such as patient name, date of birth, gender, ethnicity, date of death, address, smoking status, diagnosis dates for cancer, illness, disease, diabetes, depression, and/or other physical or mental maladies, personal medical history, or family medical history, clinical diagnoses such as date of initial diagnosis, date of metastatic diagnosis, cancer staging, tumor characterization, tissue of origin, treatments and outcomes such as line of therapy, therapy groups, clinical trials, medications prescribed or taken, surgeries, radiotherapy, imaging, adverse effects, associated outcomes, and/or corresponding dates, and genetic testing and laboratory information such as genetic testing, performance scores, lab tests, pathology results, prognostic indicators, or corresponding dates, and/or more detailed information including date of genetic testing, testing provider used……. testing method used, such as genetic sequencing method and/or gene panel, gene results, such as included genes, variants, and/or expression levels/statuses. In some embodiments, the clinical features 3212 can include a unified record database 3220. The unified record database 3220 can include copies of any of the above clinical features structured in a unified format….”),
wherein analyzing the structured dataset and the unstructured dataset comprises applying natural language processing to the free-form text in the report to extract one or more of the plurality of indicators (para, “[0134]A diagnosis indication may be based on any portion of individual patient data 122 or aggregated data from multiple patients, including clinical data and molecular data. In one example, individual patient data 122 is normalized, de-identified, and stored collectively in database 134 to facilitate easy query access to the dataset in aggregate to enable a medical provider to use system 100 to compare patients' data. Clinical data may include physician notes and imaging data, and may be generated from clinical records, hospital EMR systems, researchers, patients, and community physician practices. To generate standardized data to support internal precision medicine initiatives, clinical data, including free form text, scanned documents, and/or handwritten notes, may be processed and structured into phenotypic, therapeutic, and outcomes or patient response data by methods including optical character recognition (OCR), natural language processing (NLP), and manual curation methods that may check for completeness of data, interpolate missing information, use manual and/or automated quality assurance protocols, and store data in FHIR compliant data structures using industry standard vocabularies for medical providers to access through the system 100. Molecular data may include variants or other genetic alterations, DNA sequences, RNA sequences and expression levels, miRNA sequences, epigenetic data, protein levels, metabolite levels, etc. Molecular markers specific variants or other genetic alterations, DNA sequences, RNA sequences and expression levels, miRNA sequences, epigenetic data, protein levels, metabolite levels, etc. that can indicate disruption in a patient.”
Also, para, “[0035] Yet another implementation of the present disclosure is a method of matching a patient to a clinical trial. The method includes receiving health information from an electronic medical record corresponding to the patient, determining data elements within the health information using at least one of an optical character recognition (OCR) method and a natural language processing (NLP) method, comparing the data elements to pre-determined trial criteria, including trial inclusion criteria and trial exclusion criteria, determining at least one matching clinical trial, based on the comparing of the data elements to the predetermined trial criteria, and notifying a practitioner associated with the patient of the at least one matching clinical trial.”
Also, para, “[0303] In some embodiments, a natural language processor (NLP) can pre-populate the data field portion 3602 with a number of data fields and/or filters based on clinical trial source information. The GUI 3600 can include a clinical trial source portion 3610. The NLP can ingest at least a portion of the clinical trial source portion 3610 and populate the data field portion 3602 with a number of suitable data fields and/or filters.”);
generating, by the computing system, based on the plurality of health indicators, one or more categorizations corresponding to the medical condition (para, “[0038] In some aspects, the data elements can include at least one of a clinical feature, a molecular feature, an epigenome feature, a microbiome feature, an organoid feature, or an imaging feature.”
Also, (para “[0134] A diagnosis indication may be based on any portion of individual patient data 122 or aggregated data from multiple patients, including clinical data and molecular data. In one example, individual patient data 122 is normalized, de-identified, and stored collectively in database 134 to facilitate easy query access to the dataset in aggregate to enable a medical provider to use system 100 to compare patients' data. Clinical data may include physician notes and imaging data, and may be generated from clinical records, hospital EMR systems, researchers, patients, and community physician practices. To generate standardized data to support internal precision medicine initiatives, clinical data, including free form text, scanned documents, and/or handwritten notes, may be processed and structured into phenotypic, therapeutic, and outcomes or patient response data by methods including optical character recognition (OCR), natural language processing (NLP), and manual curation methods that may check for completeness of data, interpolate missing information, use manual and/or automated quality assurance protocols, and store data in FHIR compliant data structures using industry standard vocabularies for medical providers to access through the system 100. Molecular data may include variants or other genetic alterations, DNA sequences, RNA sequences and expression levels, miRNA sequences, epigenetic data, protein levels, metabolite levels, etc. Molecular markers specific variants or other genetic alterations, DNA sequences, RNA sequences and expression levels, miRNA sequences, epigenetic data, protein levels, metabolite levels, etc. that can indicate disruption in a patient” Note: Also, see para 0236, 0333 for classification)
determining, by the computing system, a treatment regimen based on drug orders in the structured dataset (para, “[0272] Other relationships between concepts may also be represented. For example, treatments in a treatment dictionary may be related to other treatments of a separate treatment database through relationships describing the drugs administered or the illness treated. Entities (such as MMSL#3826, C0711228, RXNORM#. . . , etc.) are each linked to their respective synonyms, (such as Tylenol 50 mg, Acetaminophen, Mapap, Ofirmev, etc.). Links between concepts (synonyms), may be explored to effectively normalize any matched candidate concept to an RXNORM entity.”
Also, para “[0312] FIG. 43 is shown to include a graphical user interface (GUI) 4300. In some embodiments, GUI 4300 can be implemented by the system 100 in FIG. 1. In some embodiments, the GUI 4300 can include a patient report 4302 corresponding to a patient selected and/or applying for a clinical trial. The patient report 4302 can include a number of therapies 4304 (e.g., drug therapies) that have been matched to the patient based on DNA and/or RNA data. The patient report 4302 can include a number of clinical trials 4306 that have been matched to the patient”;)
performing, by the computing system, survival modeling to generate, by the computing system, based on (i) the plurality of health indicators, (ii) the one or more categorizations, and (iii) the treatment regimen, a prediction corresponding to a survival of the patient following administration of a treatment to the patient for the medical condition (para, “[0246] In some embodiments, the flow 3200 can include generating and/or receiving a number of stored alteration features 3250 associated with the patient. The patient data store 3202 can include the stored alteration features 3250. In some embodiments, the stored alteration features 3250 can be generated using a machine learning technique one or more features, such as at least one of the features described above. For example, a machine learning model may generate a data science prediction, such as data science predictions 3254, of a patient's future probability of metastasis, origin of a metastasized tumor, and/or a progression-free survival probability based on a patient's state (collection of features) at any time during their treatment. In some embodiments, the stored alteration features 3250 can include features associated with Isoforms, single-nucleotide polymorphisms (SNPs), and/or Fusions.” Also, see para 0132 );
and providing, by the computing system, a report comprising the prediction to one or more users for determining whether to administer the treatment to the patient for the medical condition (para,” [0132] Still referring to FIG. 1, server 120 is shown to include analytics module 136, which can analyze data from database 134 (empirical patient outcomes), and individual patient data 122. Database 34 can store empirical patient outcomes for a large number of patients suffering from the same or similar conditions or diseases as patient 114. For example, “individual patient data” for numerous patients can be associated with each respective treatment and treatment outcomes, and subsequently stored in database 134. As new patient data and/or treatment data becomes available, database 134 can be updated. As one example, provider 112 may suggest a specific treatment (e.g., a clinical trial) for patient 114, and individual patient data 122 may then be included in database 134.”
Also, para “[0143] FIGS. 2-9 generally provide graphical user interfaces (GUIs) that can be implemented in system 100 to structure data (e.g., clinical trial data). In some aspects, reports that flow for clinical patients can rely on recommendations and suggestions on which clinical trials the patient is eligible for, as well as clinical and molecular insights. In order to do that effectively, unstructured clinical trial data can be structured using free-text (unstructured data) sourced from clinical trial databases and/or websites (e.g., clinicaltrials.gov). Notably, many clinical trial databases and websites contain clinical trials that are available to the public. Some clinical trials and/or clinical trial information remain private, and can be protocol-specific from various sponsors (e.g., pharma sponsors). Regardless of public or private status, structured clinical trial data can be used in a variety of ways, including to match patients to appropriate clinical trials.”)
wherein providing the report comprises at least one of (1) transmitting the report to a computing device, (2) displaying the report on a display screen, or (3) storing the report in a non-volatile computer-readable storage medium for access via a server(para, “[0135] As shown, outputs from analytics module 136 can be provided to display device 116 via communication network 118. Further, provider 112 can input additional data via display device 116, and the data can be transmitted to server 120. In some embodiments, provider 112 can input clinical trial information via display device 116, and the data can be transmitted to server 120. The clinical trial information can include inclusion and exclusion criteria, site information, trial status (e.g., recruiting, active, closed, etc.), among other things.”)
Regarding claim 2, Ozeran teaches the method of claim 1.
Ozeran further teaches further comprising administering the treatment to the patient (para, “[0254] In some embodiments, the clinical trial information can include a study type (e.g., interventional or observational), study results, a recruitment stage (e.g., not yet recruiting, recruiting, enrollment by invitation, suspended, unknown, etc.), a title, a planned measurement such as one described in the protocol that is used to determine the effect of an intervention/treatment on participants, interventions including drugs, medical devices, procedures, vaccines, and/or other products that are either investigational or already available, interventions including noninvasive approaches of education or modifying diet and exercise, sponsors and/or funding sources, a geographic location (e.g., country, state, city, facility), a trial stage such as those based on definitions developed by the FDA for the study's objective, a number of participants, notable dates (e.g., a start date and/or an end date), and/or other characteristics (e.g., Early Phase 1, Phase 1, Phase 2, Phase 3, and Phase 4).”)
Regarding claim 5, Ozeran teaches the method of claim 1.
Ozeran further teaches wherein applying natural language processing to the free-form text comprises parsing the report using a plurality of expression patterns, each expression pattern comprising one or more operators (para, “[0331] In some embodiments, the flow 5000 can include preprocessing the text of each page of a document by removing any duplicate consecutive characters and breaking any wrongly combined words into single words, which may be caused by an OCR technique. The flow 5000 can also include removing any short tokens, stop words, digits, punctuation tokens, and other tokens that look like numbers (e.g., ten, 3.9, etc.). In some embodiments, the preprocessing can inlcude using a spaCy/ScispaCy parser to parse text. After preprocessing, the flow 5000 can include extracting features 5008 such as emails, phone numbers, URLs, noun chunks, and unigrams from the preprocessed document's texts.”
Para, “[0287] In a template for mapping bilirubin count to an inclusion criteria, a phrase “Total bilirubin >=1.5×institutional upper limit of normal (ULN)” may be parsed from a clinical trial inclusion/exclusion criteria document into a series of data elements that must be present, and then an expression may be generated which represents the criteria in a computer calculable algorithm which maps the requisite data elements top to their respective values along with the expected mathematical expressions used to generate the result….”)
Regarding claim 6, Ozeran teaches the method of claim 5.
Ozeran further teaches wherein one or more of the plurality of health indicators requires one or more of the expression patterns to be triggered (Para, “[0287] In a template for mapping bilirubin count to an inclusion criteria, a phrase “Total bilirubin >=1.5×institutional upper limit of normal (ULN)” may be parsed from a clinical trial inclusion/exclusion criteria document into a series of data elements that must be present, and then an expression may be generated which represents the criteria in a computer calculable algorithm which maps the requisite data elements top to their respective values along with the expected mathematical expressions used to generate the result….”)
Regarding claim 7, Ozeran teaches the method of claim 1.
Ozeran further teaches wherein the one or more categorizations comprise at least one of a cytogenetic category, a radiographic category, a molecular category, or a histological category (para, “[0324] At 4916, the process 4900 can compare the data elements clinical trial information. In some embodiments, the process 4900 can compare at least a portion of the data elements to the inclusion criteria and/or at least a portion of the data elements to the exclusion criteria for each clinical trial. In some embodiments, the process 4900 can compare a molecular marker of the patient to the inclusion criteria and/or the exclusion criteria.”
Para, “[0328] Referring now to FIG. 50, an exemplary flow 5000 for determining whether or not a next-generation sequencing (NGS) report is included in a medical report associated with a patient. In some embodiments, the flow 5000 can be implemented as one or more processes and/or executed by the system 100 in FIG. 1. In some embodiments, to predict the presence of molecular reports in a patient's case, the flow 5000 can generate a most probable label (e.g., a preparing organization name and/a or negative for the cases where no reports were predicted) based on the text of each document in the case.”
Para, “[0023] One implementation of the present disclosure is a method of matching a patient to a clinical trial. The method includes receiving text-based criteria for the clinical trial, including a molecular marker, associating at least a portion of the text-based criteria to one or more pre-defined data fields containing molecular marker information, comparing a molecular marker of the patient to the one or more pre-defined data fields, and generating a report for a provider, the report based on the comparison and including a match indication of the patient to the clinical trial.”)
Regarding claim 8, Ozeran teaches the method of claim 1.
Ozeran further teaches wherein the demographic and clinical data identifies a plurality of patient age, patient gender, the medical condition, or drugs administered to the patient (para, “[0176] In some aspects, GUI 1000 can be configured for a physician or other provider for identifying trials that are the most appropriate for their patients. As an example, GUI 1000 shows information for a patient, Melissa Frank. The patient identifier 1041 can include the patient's name, an ID number, etc. The trial matching 1040 can include the patent demographics 1042, such as disease status, disease type, etc. The combination of attributes shown for the patient can be provided using similar methods as the above-described “trial metadata” data abstraction. Accordingly, a user can view and/or enter all of the relevant information corresponding to the patients and diseases. This can enable system 100 to correctly match clinical trial elements with patient data (e.g., histology, stage/grade, disease type, etc.).”
Para, “[0239] In some embodiments, the clinical features 3212 can include features such as diagnosis, symptoms, therapies, outcomes, patient demographics such as patient name, date of birth, gender, ethnicity, date of death, address, smoking status, diagnosis dates for cancer, illness, disease, diabetes, depression, and/or other physical or mental maladies, personal medical history…”)
Regarding claim 9, Ozeran teaches the method of claim 1.
Ozeran further teaches wherein the one or more health indicators corresponds to results of flow cytometry, cytogenetic assessment, fluorescence in-situ hybridization (FISH), a single nucleotide polymorphism (SNIP) array, next generation sequencing (NGS) testing for gene mutations and/or rearrangements, and/or targeted molecular assays (para, “[0328] Referring now to FIG. 50, an exemplary flow 5000 for determining whether or not a next-generation sequencing (NGS) report is included in a medical report associated with a patient. In some embodiments, the flow 5000 can be implemented as one or more processes and/or executed by the system 100 in FIG. 1. In some embodiments, to predict the presence of molecular reports in a patient's case, the flow 5000 can generate a most probable label (e.g., a preparing organization name and/a or negative for the cases where no reports were predicted) based on the text of each document in the case.”
Para, “[0263] In some embodiments, inclusion and exclusion criteria may be mapped according to the same classification conventions above, however, nested criteria or more complicated criteria may be converted to another format, such as JavaScript Object Notation (JSON) to preserve the inclusion or exclusion criteria in the proper format without any information loss. For example, an inclusion criteria “Histologically or cytologically confirmed diagnosis of locally advanced or metastatic solid tumor that harbors an NTRK1/2/3, ROS1, or ALK gene rearranement” may touch Limn the following classification codes in Table 5 below.”)
Regarding claim 12, Ozeran teaches the method of claim 1.
Ozeran further teaches wherein the medical condition is a cancer, and wherein the treatment is a cancer treatment (para, “[0181] As shown by FIGS. 10-11, the clinical data corresponding to the patient Melissa Frank is already prepopulated via the attributes 1150. By “disease type” for example, a user can see that Melissa has solid cancer (ovarian), histology is a serous carcinoma, the cancer is in an advanced stage, and Melissa has certain mutations, amplifications, and rearrangements. In some aspects, the clinical data can come from a structured clinical data source (e.g., an EMR, a clinical lab record, an electronic data warehouse, a health information exchange, etc.). System 100 can prepopulate the attributes 1150 based on the structured clinical data.”
Para, “[0239] In some embodiments, the clinical features 3212 can include features such as diagnosis, symptoms, therapies, outcomes, patient demographics such as patient name, date of birth, gender, ethnicity, date of death, address, smoking status, diagnosis dates for cancer, illness, disease, diabetes, depression, and/or other physical or mental maladies, personal medical history, or family medical history, clinical diagnoses such as date of initial diagnosis, date of metastatic diagnosis, cancer staging, tumor characterization, tissue of origin, treatments and outcomes such as line of therapy, therapy groups, clinical trials, medications prescribed or taken, surgeries, radiotherapy, imaging, adverse effects, associated outcomes, and/or corresponding dates, and genetic testing and laboratory information such as genetic testing, performance scores, lab tests, pathology results, prognostic indicators, or corresponding dates, and/or more detailed information including date of genetic testing, testing provider used, testing method used, such as genetic sequencing method and/or gene panel, gene results, such as included genes, variants, and/or expression levels/statuses. In some embodiments, the clinical features 3212 can include a unified record database 3220. The unified record database 3220 can include copies of any of the above clinical features structured in a unified format. The unified format can allow the flow 3200 to disseminate patient features regardless of the original format the patient features were stored in, which may be helpful when matching patients from different medical systems with clinical trials.”)
Regarding claim 13, Ozeran teaches a computing system comprising one or more processors and a memory with instructions configured to be executable by the one or more processors to cause the one or more processors to(see para 0029):
retrieve, from an electronic health records (EHR) system, for a patient with a medical condition, a structured dataset and an unstructured dataset((para, “[0181] ….In some aspects, the clinical data can come from a structured clinical data source (e.g., an EMR, a clinical lab record, an electronic data warehouse, a health information exchange, etc.). System 100 can prepopulate the attributes 1150 based on the structured clinical data.” Note: Also, see para 0156, 0238
Also, para “[0321] At 4904, the process 4900 can receive patient health information. In some embodiments, the patient health information can include information from an electronic medical record. In some embodiments, the patient health information can include at least a portion of the features in the patient data store 3202 in FIG. 32. In some embodiments, the patient health information can be unstructured.”
Also, para “[0227] In some aspects, records and documentation 2783 can include source document types, record storage methods, and/or EHR/EMR systems.”),
the structured dataset comprising demographic and clinical data for the patient((para, “[0131] ….Alternatively, electronic records can be automatically transferred to server 120 from various facilities, practitioners, or third party applications, where appropriate. As shown in FIG. 1, patient data communicated to server 120 can include, but is not limited to, treatment data (such as current treatment information and resulting data), genetic data (such as RNA, DNA data), brain scans (such as PET scans, CT, MRI, etc.), and/or clinical records (such as biographical information, patient history, patient demographics, family history, comorbidity conditions, etc.).)
and the unstructured dataset comprising a report with free-form text of a clinician with respect to a medical procedure(para“[0143] FIGS. 2-9 generally provide graphical user interfaces (GUIs) that can be implemented in system 100 to structure data (e.g., clinical trial data). In some aspects, reports that flow for clinical patients can rely on recommendations and suggestions on which clinical trials the patient is eligible for, as well as clinical and molecular insights. In order to do that effectively, unstructured clinical trial data can be structured using free-text (unstructured data) sourced from clinical trial databases and/or websites (e.g., clinicaltrials.gov)….”
Also, para “[0116] In some embodiments of the present disclosure, the system can create structure around clinical trial data. This can include reviewing free text (i.e., unstructured data), determining relevant information, and populating corresponding structured data field with the information. As an example, a clinical trial description may specify that only patients diagnosed with stage I breast cancer may enroll….”);
analyze the structured dataset and the unstructured dataset to generate a plurality of health indicators for the patient, wherein analyzing the structured dataset and the unstructured dataset comprises applying natural language processing to the free-form text in the report to extract one or more of the plurality of indicators((para, “[0246] In some embodiments, the flow 3200 can include generating and/or receiving a number of stored alteration features 3250 associated with the patient. The patient data store 3202 can include the stored alteration features 3250. In some embodiments, the stored alteration features 3250 can be generated using a machine learning technique one or more features, such as at least one of the features described above. For example, a machine learning model may generate a data science prediction, such as data science predictions 3254, of a patient's future probability of metastasis, origin of a metastasized tumor, and/or a progression-free survival probability based on a patient's state (collection of features) at any time during their treatment. In some embodiments, the stored alteration features 3250 can include features associated with Isoforms, single-nucleotide polymorphisms (SNPs), and/or Fusions.”
Also, para, “[0284] For example, a document may have patient information from a next generation sequencing report containing molecular marker results or laboratory testing results from testing performed on a patient's blood. Standard information, such as the patient's name, date of birth, address may be found in a document. Other information such as the laboratory name, address, CAP/CLIA number, testing procedure performed may also be present. Clinical information such as the results of the next generation sequencing test, such as specific single nucleotide variants, copy number alterations, fusions, or other genomic alterations may be reported.”
Also, para, “[0239]In some embodiments, the clinical features 3212 can include features such as diagnosis, symptoms, therapies, outcomes, patient demographics such as patient name, date of birth, gender, ethnicity, date of death, address, smoking status, diagnosis dates for cancer, illness, disease, diabetes, depression, and/or other physical or mental maladies, personal medical history, or family medical history, clinical diagnoses such as date of initial diagnosis, date of metastatic diagnosis, cancer staging, tumor characterization, tissue of origin, treatments and outcomes such as line of therapy, therapy groups, clinical trials, medications prescribed or taken, surgeries, radiotherapy, imaging, adverse effects, associated outcomes, and/or corresponding dates, and genetic testing and laboratory information such as genetic testing, performance scores, lab tests, pathology results, prognostic indicators, or corresponding dates, and/or more detailed information including date of genetic testing, testing provider used……. testing method used, such as genetic sequencing method and/or gene panel, gene results, such as included genes, variants, and/or expression levels/statuses. In some embodiments, the clinical features 3212 can include a unified record database 3220. The unified record database 3220 can include copies of any of the above clinical features structured in a unified format….”),
generate, based on the plurality of health indicators, one or more categorizations corresponding to the medical condition((para, “[0038] In some aspects, the data elements can include at least one of a clinical feature, a molecular feature, an epigenome feature, a microbiome feature, an organoid feature, or an imaging feature.”
Also, (para “[0134] A diagnosis indication may be based on any portion of individual patient data 122 or aggregated data from multiple patients, including clinical data and molecular data. In one example, individual patient data 122 is normalized, de-identified, and stored collectively in database 134 to facilitate easy query access to the dataset in aggregate to enable a medical provider to use system 100 to compare patients' data. Clinical data may include physician notes and imaging data, and may be generated from clinical records, hospital EMR systems, researchers, patients, and community physician practices. To generate standardized data to support internal precision medicine initiatives, clinical data, including free form text, scanned documents, and/or handwritten notes, may be processed and structured into phenotypic, therapeutic, and outcomes or patient response data by methods including optical character recognition (OCR), natural language processing (NLP), and manual curation methods that may check for completeness of data, interpolate missing information, use manual and/or automated quality assurance protocols, and store data in FHIR compliant data structures using industry standard vocabularies for medical providers to access through the system 100. Molecular data may include variants or other genetic alterations, DNA sequences, RNA sequences and expression levels, miRNA sequences, epigenetic data, protein levels, metabolite levels, etc. Molecular markers specific variants or other genetic alterations, DNA sequences, RNA sequences and expression levels, miRNA sequences, epigenetic data, protein levels, metabolite levels, etc. that can indicate disruption in a patient” Note: Also, see para 0236, 0333 for classification) ;
perform survival modeling to generate, based on the plurality of health indicators and the one or more categorizations, a prediction corresponding to a survival of the patient following administration of a treatment to the patient for the medical condition((para, “[0246] In some embodiments, the flow 3200 can include generating and/or receiving a number of stored alteration features 3250 associated with the patient. The patient data store 3202 can include the stored alteration features 3250. In some embodiments, the stored alteration features 3250 can be generated using a machine learning technique one or more features, such as at least one of the features described above. For example, a machine learning model may generate a data science prediction, such as data science predictions 3254, of a patient's future probability of metastasis, origin of a metastasized tumor, and/or a progression-free survival probability based on a patient's state (collection of features) at any time during their treatment. In some embodiments, the stored alteration features 3250 can include features associated with Isoforms, single-nucleotide polymorphisms (SNPs), and/or Fusions.” Also, see para 0132 );
and provide a report comprising one or more categorizations and/or the prediction to one or more users for determining whether to administer the treatment to the patient for the medical condition((para,” [0132] Still referring to FIG. 1, server 120 is shown to include analytics module 136, which can analyze data from database 134 (empirical patient outcomes), and individual patient data 122. Database 34 can store empirical patient outcomes for a large number of patients suffering from the same or similar conditions or diseases as patient 114. For example, “individual patient data” for numerous patients can be associated with each respective treatment and treatment outcomes, and subsequently stored in database 134. As new patient data and/or treatment data becomes available, database 134 can be updated. As one example, provider 112 may suggest a specific treatment (e.g., a clinical trial) for patient 114, and individual patient data 122 may then be included in database 134.”
Also, para “[0143] FIGS. 2-9 generally provide graphical user interfaces (GUIs) that can be implemented in system 100 to structure data (e.g., clinical trial data). In some aspects, reports that flow for clinical patients can rely on recommendations and suggestions on which clinical trials the patient is eligible for, as well as clinical and molecular insights. In order to do that effectively, unstructured clinical trial data can be structured using free-text (unstructured data) sourced from clinical trial databases and/or websites (e.g., clinicaltrials.gov). Notably, many clinical trial databases and websites contain clinical trials that are available to the public. Some clinical trials and/or clinical trial information remain private, and can be protocol-specific from various sponsors (e.g., pharma sponsors). Regardless of public or private status, structured clinical trial data can be used in a variety of ways, including to match patients to appropriate clinical trials.”),
wherein providing the report comprises at least one of (1) transmitting the report to a computing device, (2) displaying the report on a display screen, or (3) storing the report in a non-volatile computer-readable storage medium for access via a server((para, “[0135] As shown, outputs from analytics module 136 can be provided to display device 116 via communication network 118. Further, provider 112 can input additional data via display device 116, and the data can be transmitted to server 120. In some embodiments, provider 112 can input clinical trial information via display device 116, and the data can be transmitted to server 120. The clinical trial information can include inclusion and exclusion criteria, site information, trial status (e.g., recruiting, active, closed, etc.), among other things.”)
Regarding claim 15, Ozeran teaches the system of claim 13.
Ozeran further teaches wherein applying natural language processing to the free- form text comprises parsing the report using a plurality of expression patterns, each expression pattern comprising one or more operators, wherein one or more of the plurality of health indicators requires one or more of the expression patterns to be triggered (para, “[0331] In some embodiments, the flow 5000 can include preprocessing the text of each page of a document by removing any duplicate consecutive characters and breaking any wrongly combined words into single words, which may be caused by an OCR technique. The flow 5000 can also include removing any short tokens, stop words, digits, punctuation tokens, and other tokens that look like numbers (e.g., ten, 3.9, etc.). In some embodiments, the preprocessing can inlcude using a spaCy/ScispaCy parser to parse text. After preprocessing, the flow 5000 can include extracting features 5008 such as emails, phone numbers, URLs, noun chunks, and unigrams from the preprocessed document's texts.”
Para, “[0287] In a template for mapping bilirubin count to an inclusion criteria, a phrase “Total bilirubin >=1.5×institutional upper limit of normal (ULN)” may be parsed from a clinical trial inclusion/exclusion criteria document into a series of data elements that must be present, and then an expression may be generated which represents the criteria in a computer calculable algorithm which maps the requisite data elements top to their respective values along with the expected mathematical expressions used to generate the result….”
Also, para, (“[0287] In a template for mapping bilirubin count to an inclusion criteria, a phrase “Total bilirubin >=1.5×institutional upper limit of normal (ULN)” may be parsed from a clinical trial inclusion/exclusion criteria document into a series of data elements that must be present, and then an expression may be generated which represents the criteria in a computer calculable algorithm which maps the requisite data elements top to their respective values along with the expected mathematical expressions used to generate the result….”)
Regarding claim 16, Ozeran teaches the system of claim 13.
Ozeran further teaches wherein the one or more categorizations comprise at least one of a cytogenetic category, a radiographic category, a molecular category, or a histological category((para, “[0324] At 4916, the process 4900 can compare the data elements clinical trial information. In some embodiments, the process 4900 can compare at least a portion of the data elements to the inclusion criteria and/or at least a portion of the data elements to the exclusion criteria for each clinical trial. In some embodiments, the process 4900 can compare a molecular marker of the patient to the inclusion criteria and/or the exclusion criteria.”
Para, “[0328] Referring now to FIG. 50, an exemplary flow 5000 for determining whether or not a next-generation sequencing (NGS) report is included in a medical report associated with a patient. In some embodiments, the flow 5000 can be implemented as one or more processes and/or executed by the system 100 in FIG. 1. In some embodiments, to predict the presence of molecular reports in a patient's case, the flow 5000 can generate a most probable label (e.g., a preparing organization name and/a or negative for the cases where no reports were predicted) based on the text of each document in the case.”
Para, “[0023] One implementation of the present disclosure is a method of matching a patient to a clinical trial. The method includes receiving text-based criteria for the clinical trial, including a molecular marker, associating at least a portion of the text-based criteria to one or more pre-defined data fields containing molecular marker information, comparing a molecular marker of the patient to the one or more pre-defined data fields, and generating a report for a provider, the report based on the comparison and including a match indication of the patient to the clinical trial.”)
Regarding claim 17, Ozeran teaches the system of claim 13.
Ozeran further teaches wherein the demographic and clinical data identifies a plurality of patient age, patient gender, the medical condition, or drugs administered to the patient((para, “[0176] In some aspects, GUI 1000 can be configured for a physician or other provider for identifying trials that are the most appropriate for their patients. As an example, GUI 1000 shows information for a patient, Melissa Frank. The patient identifier 1041 can include the patient's name, an ID number, etc. The trial matching 1040 can include the patent demographics 1042, such as disease status, disease type, etc. The combination of attributes shown for the patient can be provided using similar methods as the above-described “trial metadata” data abstraction. Accordingly, a user can view and/or enter all of the relevant information corresponding to the patients and diseases. This can enable system 100 to correctly match clinical trial elements with patient data (e.g., histology, stage/grade, disease type, etc.).”
Also, Para, “[0239] In some embodiments, the clinical features 3212 can include features such as diagnosis, symptoms, therapies, outcomes, patient demographics such as patient name, date of birth, gender, ethnicity, date of death, address, smoking status, diagnosis dates for cancer, illness, disease, diabetes, depression, and/or other physical or mental maladies, personal medical history…”)
Regarding claim 18, Ozeran teaches the system of claim 1.
Ozeran further teaches, wherein the one or more health indicators corresponds to results of flow cytometry, cytogenetic assessment, fluorescence in-situ hybridization (FISH), a single nucleotide polymorphism (SNP) array, next generation sequencing (NGS) testing for gene mutations and/or rearrangements, and/or targeted molecular assays para, “[0328] Referring now to FIG. 50, an exemplary flow 5000 for determining whether or not a next-generation sequencing (NGS) report is included in a medical report associated with a patient. In some embodiments, the flow 5000 can be implemented as one or more processes and/or executed by the system 100 in FIG. 1. In some embodiments, to predict the presence of molecular reports in a patient's case, the flow 5000 can generate a most probable label (e.g., a preparing organization name and/a or negative for the cases where no reports were predicted) based on the text of each document in the case.”
Para, “[0263] In some embodiments, inclusion and exclusion criteria may be mapped according to the same classification conventions above, however, nested criteria or more complicated criteria may be converted to another format, such as JavaScript Object Notation (JSON) to preserve the inclusion or exclusion criteria in the proper format without any information loss. For example, an inclusion criteria “Histologically or cytologically confirmed diagnosis of locally advanced or metastatic solid tumor that harbors an NTRK1/2/3, ROS1, or ALK gene rearranement” may touch Limn the following classification codes in Table 5 below.”)
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 3-4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Ozeran in view of Schmidt et al. (US 20240426826 A1)
Regarding claim 3, Ozeran teaches the method of claim 2.
Ozeran does not explicitly teach wherein the treatment is administered only if the prediction indicates a likelihood of survival exceeding a threshold.
Schmidt teaches wherein the treatment is administered only if the prediction indicates a likelihood of survival exceeding a threshold (para, “[0068] The present invention provides a novel method for computing a score indicative of how a cancer patient will respond to a therapy that uses an anti-HER2 antibody-drug conjugate. Another aspect of the invention relates to a method for computing a score for a cancer patient indicative of a survival probability of a cancer patient treated with the ADC. Another aspect of the invention relates to a method for predicting a response of a cancer patient to the ADC. Another aspect of the invention relates to identifying a cancer patient who will exhibit a predetermined response to the ADC. Yet another aspect of the invention relates to a method of treating a cancer patient by administering a therapy involving the ADC if a treatment score exceeds a predetermined threshold.”)
It would have been obvious for a person of ordinary skill in the art to apply threshold teaching of Schmidt into the teachings of Ozeran at the time the application was filed in order to administer treatment based on patient response. (Abstract, “….The response of the cancer patient to the ADC therapy is predicted by aggregating all single-cell ADC scores of the tissue sample using a statistical operation.”)
Regarding claim 4, Ozeran teaches the method of claim 1.
Ozeran does not explicitly teach further comprising determining that the prediction indicates a likelihood of survival exceeding a threshold, wherein the report comprises an indication of the likelihood of survival.
Schmidt teaches further comprising determining that the prediction indicates a likelihood of survival exceeding a threshold, wherein the report comprises an indication of the likelihood of survival (para, “[0007] A method for predicting how a cancer patient will respond to a therapy involving an antibody drug conjugate (ADC) involves computing a response score based on single-cell ADC scores for each cancer cell. The ADC includes an ADC payload and an ADC antibody that targets a protein on each cancer cell. A tissue sample is immunohistochemically stained using a dye linked to a diagnostic antibody that binds to the protein on the cancer cells in the tissue sample. A digital image of the tissue sample is acquired. Image analysis is performed on the digital image to detect the cancer cells using a convolutional neural network. For each cancer cell, a single-cell ADC score is computed based on the staining intensities of the dye in the membrane and/or cytoplasm of the cancer cell and/or in the membranes and cytoplasms of other cancer cells that are closer than a predefined distance to the cancer cell. A response score is generated that predicts the response of the cancer patient to the ADC therapy by aggregating all single-cell ADC scores of the tissue sample using a statistical operation. Patients having a response score higher than a predetermined threshold are recommended for a therapy involving the ADC.” Para, “[0158] In step 6 of the method, the anti-HER2 ADC therapy is recommended to the cancer patient if the response score is larger than a predetermined threshold. The predetermined threshold in step 6 and the quantile in step 5 are determined by optimizing the positive predictive value, the negative predictive value, and the prevalence of a positive recommendation using a cohort of patients with known single-cell ADC scores and therapy response parameters.”)
It would have been obvious for a person of ordinary skill in the art to apply threshold teaching of Schmidt into the teachings of Ozeran at the time the application was filed in order to administer treatment based on patient response. (Abstract, “….The response of the cancer patient to the ADC therapy is predicted by aggregating all single-cell ADC scores of the tissue sample using a statistical operation.”)
Regarding claim 14, Ozeran teaches the system of claim 13.
Ozeran does not explicitly teach wherein the instructions further cause the one or more processors to determine that the prediction indicates a likelihood of survival exceeding a threshold, wherein the report further includes an indication of the likelihood of survival.
Schmidt teaches determine that the prediction indicates a likelihood of survival exceeding a threshold, wherein the report further includes an indication of the likelihood of survival(para, “[0007] A method for predicting how a cancer patient will respond to a therapy involving an antibody drug conjugate (ADC) involves computing a response score based on single-cell ADC scores for each cancer cell. The ADC includes an ADC payload and an ADC antibody that targets a protein on each cancer cell. A tissue sample is immunohistochemically stained using a dye linked to a diagnostic antibody that binds to the protein on the cancer cells in the tissue sample. A digital image of the tissue sample is acquired. Image analysis is performed on the digital image to detect the cancer cells using a convolutional neural network. For each cancer cell, a single-cell ADC score is computed based on the staining intensities of the dye in the membrane and/or cytoplasm of the cancer cell and/or in the membranes and cytoplasms of other cancer cells that are closer than a predefined distance to the cancer cell. A response score is generated that predicts the response of the cancer patient to the ADC therapy by aggregating all single-cell ADC scores of the tissue sample using a statistical operation. Patients having a response score higher than a predetermined threshold are recommended for a therapy involving the ADC.” Para, “[0158] In step 6 of the method, the anti-HER2 ADC therapy is recommended to the cancer patient if the response score is larger than a predetermined threshold. The predetermined threshold in step 6 and the quantile in step 5 are determined by optimizing the positive predictive value, the negative predictive value, and the prevalence of a positive recommendation using a cohort of patients with known single-cell ADC scores and therapy response parameters.”)
It would have been obvious for a person of ordinary skill in the art to apply threshold teaching of Schmidt into the teachings of Ozeran at the time the application was filed in order to administer treatment based on patient response. (Abstract, “….The response of the cancer patient to the ADC therapy is predicted by aggregating all single-cell ADC scores of the tissue sample using a statistical operation.”)
Claims 10-11, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Ozeran in view of NOURBAKHSH et al. (US 20220365916 A1)
Regarding claim 10, Ozeran teaches the method of claim 1.
Ozeran does not explicitly teaches wherein analyzing the structured dataset and the unstructured dataset further comprises generating tab-delimited tables based on the structured dataset.
NOURBAKHSH teaches wherein analyzing the structured dataset and the unstructured dataset further comprises generating tab-delimited tables based on the structured dataset (para,” [0080] At step S410, the automated data extraction and format standardization module 302 determines one or more row headers and one or more column headers for each content cell. In an exemplary embodiment, the automated data extraction and format standardization module 302 may also determine a hierarchy of metrics for each content cell, i.e., an ordered sequence of metrics that describes the content of the respective cell. Then, at step S412, the automated data extraction and format standardization module 302 generates an output report based on the determined arrangement of cells. In an exemplary embodiment, the output table may include a JavaScript Object Notation (JSON) object and/or a tab-delimited file.”)
It would have been obvious for a person of ordinary skill in the art to apply extracting and converting teaching of NOURBAKHSH into the teachings of Ozeran at the time the application was filed in order to convert data into standard format. (Para, “[0001] This technology generally relates to methods and systems for retrieving financial data from a table, and more particularly to methods and systems for automated extraction of financial time-series data from a semi-structured tabular input and conversion of the data into a unified standard format.”)
Regarding claim 11, Ozeran teaches the method of claim 10.
NOURBAKHSH further teaches wherein generating the tab-delimited tables comprises extracting data from unmerged nested cells and reformatting tabs into the tab-delimited tables (para,” [0080] At step S410, the automated data extraction and format standardization module 302 determines one or more row headers and one or more column headers for each content cell. In an exemplary embodiment, the automated data extraction and format standardization module 302 may also determine a hierarchy of metrics for each content cell, i.e., an ordered sequence of metrics that describes the content of the respective cell. Then, at step S412, the automated data extraction and format standardization module 302 generates an output report based on the determined arrangement of cells. In an exemplary embodiment, the output table may include a JavaScript Object Notation (JSON) object and/or a tab-delimited file.”)
It would have been obvious for a person of ordinary skill in the art to apply extracting and converting teaching of NOURBAKHSH into the teachings of Ozeran at the time the application was filed in order to convert data into standard format. (Para, “[0001] This technology generally relates to methods and systems for retrieving financial data from a table, and more particularly to methods and systems for automated extraction of financial time-series data from a semi-structured tabular input and conversion of the data into a unified standard format.”)
Regarding claim 19, Ozeran teaches the system of claim 13.
Ozeran does not explicitly teach wherein analyzing the structured dataset and the unstructured dataset further comprises generating tab-delimited tables based on the structured dataset.
NOURBAKHSH teaches wherein analyzing the structured dataset and the unstructured dataset further comprises generating tab-delimited tables based on the structured dataset (para,” [0080] At step S410, the automated data extraction and format standardization module 302 determines one or more row headers and one or more column headers for each content cell. In an exemplary embodiment, the automated data extraction and format standardization module 302 may also determine a hierarchy of metrics for each content cell, i.e., an ordered sequence of metrics that describes the content of the respective cell. Then, at step S412, the automated data extraction and format standardization module 302 generates an output report based on the determined arrangement of cells. In an exemplary embodiment, the output table may include a JavaScript Object Notation (JSON) object and/or a tab-delimited file.”)
It would have been obvious for a person of ordinary skill in the art to apply extracting and converting teaching of NOURBAKHSH into the teachings of Ozeran at the time the application was filed in order to convert data into standard format. (Para, “[0001] This technology generally relates to methods and systems for retrieving financial data from a table, and more particularly to methods and systems for automated extraction of financial time-series data from a semi-structured tabular input and conversion of the data into a unified standard format.”)
Regarding claim 20, Ozeran teaches the system of claim 19.
NOURBAKHSH further teaches wherein generating the tab-delimited tables comprises extracting data from unmerged nested cells and reformatting tabs into the tab-delimited tables (para,” [0080] At step S410, the automated data extraction and format standardization module 302 determines one or more row headers and one or more column headers for each content cell. In an exemplary embodiment, the automated data extraction and format standardization module 302 may also determine a hierarchy of metrics for each content cell, i.e., an ordered sequence of metrics that describes the content of the respective cell. Then, at step S412, the automated data extraction and format standardization module 302 generates an output report based on the determined arrangement of cells. In an exemplary embodiment, the output table may include a JavaScript Object Notation (JSON) object and/or a tab-delimited file.”)
It would have been obvious for a person of ordinary skill in the art to apply extracting and converting teaching of NOURBAKHSH into the teachings of Ozeran at the time the application was filed in order to convert data into standard format. (Para, “[0001] This technology generally relates to methods and systems for retrieving financial data from a table, and more particularly to methods and systems for automated extraction of financial time-series data from a semi-structured tabular input and conversion of the data into a unified standard format.”)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20200411199 A1 “The present disclosure provides platforms, systems, media, and methods for capturing clinical cases and expert-derived treatment rationales to facilitate biomedical decision making, which can include virtual clinical trials that continuously learn from the experiences of all patients, on all treatments, and all the time. Algorithms such as Bayesian machine learning methods can be applied to coordinate such virtual trials.”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HUMA WASEEM whose telephone number is (571)272-1316. The examiner can normally be reached Monday-Friday(9:00 am - 5 pm) EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jason B. Dunham can be reached on (571) 272-8109. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/HUMA WASEEM/Examiner, Art Unit 3686
/JASON B DUNHAM/Supervisory Patent Examiner, Art Unit 3686
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