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 .
Status of Claims
This office action for the 19/362140 application is in response to the communications filed July 23, 2026.
Claims 23, 41 and 42 were amended July 23, 2026.
Claims 23-42 are currently pending and considered below.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 23-42 are rejected under 35 U.S.C. 103 as being unpatentable over Bagaev et al. (US 2018/0358118; herein referred to as Bagaev) in view of Elton et al. (US 2012/0231959; herein referred to as Elton).
As per claim 23,
Bagaev teaches a method for subject report delivery:
(Paragraphs [0187] and [0188] of Bagaev. The teaching describes a process by which therapy scores for each particular one of the multiple therapies are determined based on normalized biomarker scores for the biomarkers associated with the each particular one therapy. A therapy score may be calculated using multiple normalized biomarker scores as a sum, as a weighted sum, using a linear or generalized linear model, using a statistical model, or combinations thereof. The therapy score may be calculated using any suitable number of normalized biomarker scores, e.g., 2, 10, 50, or 100 normalized biomarker scores. Further aspects relating to determining therapy scores are provided in section “Predicting Therapy Response”. Therapy scores for any number of therapies may be output to a user, in some embodiments, by displaying the information to the user in a graphical user interface (GUI), including the information in a report, sending an email to the user, and/or in any other suitable way.)
Bagaev further teaches accessing a set of programs stored in a cloud service platform:
(Paragraph [0153] of Bagaev. The teaching describes a software program may provide a user with a visual representation presenting information related to a patient's biomarkers scores (e.g., a biomarker score, and/or a therapy score, and/or an impact score), and predicted efficacy of a therapy. Such a software program may execute in any suitable computing environment including, but not limited to, a cloud-computing environment, a device co-located with a user (e.g., the user's laptop, desktop, smartphone, etc.), one or more devices remote from the user (e.g., one or more servers), etc.)
Bagaev further teaches obtaining clinical data from a plurality of sources in a plurality of different formats, including at least some non-structured formats, the clinical data including at least one cancer state and one or more of sequencing information, pathology information, or epigenomic information, the clinical data further including, for at least one subject of a plurality of subjects, clinical data obtained from a plurality of different time points:
(Paragraphs [0111] and [0166] of Bagaev. The teaching describes that biological sample may be any type of sample including, for example, a sample of a bodily fluid, one or more cells, a piece of tissue, or some or all of an organ. In certain embodiments, one sample will be taken from a subject for analysis. In some embodiments, more than one (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, or more) samples may be taken from a subject for analysis. In some embodiments, one sample from a subject will be analyzed. In certain embodiments, more than one (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, or more) samples may be analyzed. If more than one sample from a subject is analyzed, the samples may be procured at the same time (e.g., more than one sample may be taken in the same procedure), or the samples may be taken at different times (e.g., during a different procedure including a procedure 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 days; 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 weeks; 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 months, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 years, or 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 decades after a first procedure). A second or subsequent sample may be taken or obtained from the same region (e.g., from the same tumor or area of tissue) or a different region (including, e.g., a different tumor). A second or subsequent sample may be taken or obtained from the subject after one or more treatments, and may be taken from the same region or a different region. As a non-limiting example, the second or subsequent sample may be useful in determining whether the cancer in each sample has different characteristics (e.g., in the case of samples taken from two physically separate tumors in a patient) or whether the cancer has responded to one or more treatments (e.g., in the case of two or more samples from the same tumor or different tumors prior to and subsequent to a treatment). the results of the analysis performed by server(s) 110 may be provided to doctor 114 through a computing device 114 (which may be a portable computing device, such as a laptop or smartphone, or a fixed computing device such as a desktop computer). The results may be provided in a written report, an e-mail, a graphical user interface, and/or any other suitable way. It should be appreciated that although in the embodiment of FIG. 1A, the results are provided to a doctor, in other embodiments, the results of the analysis may be provided to patient 102 or a caretaker of patient 102, a healthcare provider such as a nurse, or a person involved with a clinical trial.)
Bagaev further teaches accessing system data in a first network access storage service:
(Paragraphs [0011], [0065], [0074] and [0154] of Bagaev. The teaching describes systems and methods for determining therapy scores for multiple therapies based on normalized biomarker scores comprises, in some embodiments, accessing sequence data for a subject, accessing biomarker information indicating distribution of values for biomarkers associated with multiple therapies, determining normalized biomarker scores for the subject using sequencing data and biomarker information, and determining therapy scores for the multiple therapies based on normalized biomarker scores. Recent advances in personalized genomic sequencing and cancer genomic sequencing technologies have made it possible to obtain patient-specific information about cancer cells (e.g., tumor cells) and cancer microenvironments from one or more biological samples obtained from individual patients. The methods described are based on in part on the analysis of anthropometric, clinical, tumor, and/or cancerous cell microenvironment parameters, and tumor and/or cancerous cell parameters of a subject (e.g., a patient), along with accompanying disease information. For such analyses, sequence data such as that from transcriptome, exome, and/or genome sequencing of a patient's tumor biopsy, or from other tissues of the patient are suitable although any type of sequence data may be used. The techniques described herein may be implemented in the illustrative environment 100 shown in FIG. 1A. As shown in FIG. 1A, within illustrative environment 100, one or more biological samples of a patient 102 may be provided to a laboratory 104. Laboratory 104 may process the biological sample(s) to obtain sequencing data (e.g., transcriptome, exome, and/or genome sequencing data) and provide it, via network 108, to at least one database 106 that stores information about patient 102.)
Bagaev further teaches generating, via a program in the set of programs, new data products for a plurality of subjects, the new data products including a biomarker derived from the one or more of sequencing information, pathology information, or epigenomic information, the new data products being stored in a second network access storage service:
(Paragraphs [0098]-[0100] and [0157] of Bagaev. The teaching describes that the present disclosure provide systems and methods that normalize biomarker scores to a common scale, thereby allowing comparison of biomarker scores across different cell populations and/or among different subjects. Normalized biomarker scores may be determined for any number of biomarkers as described herein. As used herein, the term “normalized biomarker score” refers to a biomarker value that has been adjusted (e.g., normalized) to a common scale according to the techniques described herein. In some embodiments, biomarker values are normalized to create normalized biomarker scores based on a respective distribution of values for each biomarker in a reference subset of biomarkers. In some embodiments, the reference subset of biomarkers comprises biomarker information from any number of reference subjects. In one embodiment, a “reference subset” is a subset of biomarkers from one or more reference subjects, the values of which may be used to normalize a biomarker of a subject. As shown in FIG. 1A, illustrative environment 100 includes one or more external databases 116, which may store information for patients other than patient 102. For example, external databases 116 may store expression data (of any suitable type) for one or more patients, medical history data for one or more patients, test result data (e.g., imaging results, biopsy results, blood test results) for one or more patients, demographic and/or biographic information for one or more patients, and/or any other suitable type of information.)
Bagaev further teaches retrieving, by another of the set of programs, the new data products from the second network access storage service:
(Paragraphs [0098]-[0100], [0157] and [0186]-[0188] of Bagaev. The teaching describes that the present disclosure provide systems and methods that normalize biomarker scores to a common scale, thereby allowing comparison of biomarker scores across different cell populations and/or among different subjects. Normalized biomarker scores may be determined for any number of biomarkers as described herein. As used herein, the term “normalized biomarker score” refers to a biomarker value that has been adjusted (e.g., normalized) to a common scale according to the techniques described herein. In some embodiments, biomarker values are normalized to create normalized biomarker scores based on a respective distribution of values for each biomarker in a reference subset of biomarkers. In some embodiments, the reference subset of biomarkers comprises biomarker information from any number of reference subjects. In one embodiment, a “reference subset” is a subset of biomarkers from one or more reference subjects, the values of which may be used to normalize a biomarker of a subject. As shown in FIG. 1A, illustrative environment 100 includes one or more external databases 116, which may store information for patients other than patient 102. For example, external databases 116 may store expression data (of any suitable type) for one or more patients, medical history data for one or more patients, test result data (e.g., imaging results, biopsy results, blood test results) for one or more patients, demographic and/or biographic information for one or more patients, and/or any other suitable type of information. process 200 proceeds to act 206, where normalized biomarker scores for the subject are determined using sequencing data obtained at act 202 and the biomarker information obtained at act 204. Normalized biomarker scores for the subject are determined, in some embodiments, using a reference subset of biomarkers comprising any number of biomarkers from any number of reference subjects. In that way, the subject's biomarker score is adjusted (e.g., normalized) to a common scale based on a distribution of biomarker values in a reference subset of biomarkers. Further aspects relating to determining normalized biomarker scores are provided in section “From Biomarker Values To Normalized Biomarker Scores”. Next, process 200 proceeds to act 208, where therapy scores for each particular one of the multiple therapies are determined based on normalized biomarker scores for the biomarkers associated with the each particular one therapy. A therapy score may be calculated using multiple normalized biomarker scores as a sum, as a weighted sum, using a linear or generalized linear model, using a statistical model, or combinations thereof. The therapy score may be calculated using any suitable number of normalized biomarker scores, e.g., 2, 10, 50, or 100 normalized biomarker scores. Further aspects relating to determining therapy scores are provided in section “Predicting Therapy Response”. Because the normalized biomarker scores are stored at an address on the network and then used to determine the therapy scores, the therapy scores are a generation of fulfillment indication that that information that the therapy score was based on was at its properly stored location and retrieved to generate the therapy score.)
Bagaev further teaches generating a report comprising the biomarker:
(Paragraph [0230] and [0231] and Figure 10 of Bagaev. The teaching describes that a user of the software program may interact with the GUI to log into the software program. The user may select a stored report to view a screen presenting information relating to the selected report. The user may select the create new report portion to view a screen for creating a new report. FIG. 10 is a screenshot presenting the selected patient's report including information related to the patient's sequencing data, the patient, and the patient's cancer. The therapy biomarkers portion (as shown in the left panel) presents information related to available therapies (e.g., immunotherapies and targeted therapies) and their predicted efficacy in the selected patient. Additional predictions of the efficacy of a therapy in the patient are provided in the machine predictor portion and additional portion (as shown in the left panel). The MF profile portion presents information relating to the molecular characteristics of a tumor including tumor genetics, pro-tumor microenvironment factors, and anti-tumor immune response factors (as shown in the middle panel). The clinical trials portion provides information relating to clinical trials (as shown in the right panel). The monotherapy or combinational therapy portion (as shown in the middle panel) may be selected by the user to interactively design a personalized treatment for a patient.)
Bagaev does not explicitly disclose applying a natural language processor comprising at least one machine learning component to the obtained clinical data.
However, Elton teaches applying a natural language processor comprising at least one machine learning component to obtained clinical data, the at least one machine learning component trained on a training set within a clinical domain and at least one medical ontology, the at least one machine learning component configured to identify clinical information from the clinical data based, at least in part, on: terms identified in clinical data, relationships of the identified terms to other terms in the clinical data, and the at least one medical ontology; normalizing the identified clinical information in a format defined for use with a unified dataset; storing the normalized clinical information as system data in a first network access storage service in the format defined for use with the unified dataset and generating, via a program in the set of programs, new data products for a plurality of subjects, the new data products including a biomarker reflecting a resistance mutation derived from a longitudinal analysis of the one or more of sequencing information, pathology information, or epigenomic information and additional clinical data of the at least one subject:
(Paragraphs [0067], [0091] and [0092] of Elton. The teaching describes that the system can utilize daily batch processes to match criteria from patient profiles and clinical trials profiles in the system. The system can utilize technologies incorporating such as Natural Language Processing (NLP) and Dynamic Rules-Workflow Engines to extract clinical data from free-text documents, capture protocol criteria from text documents, and match patients with protocol criteria and rank them in terms of match. Once this has occurred the information is provided to a network member (e.g., healthcare provider). An exemplary format is shown in FIG. 16. The healthcare provider portal contains a list of applicable clinical trials and lists the patients who qualify for these trials. An email alert will be sent whenever updates occur pertaining to new patients who quality for a trial or the instantiation of a new clinical trial. The system also includes the compilation of information from members of a network (“network members”). In some embodiments, “network members” include healthcare practices (referred to in this embodiment as “network member practices”). FIG. 11 shows such an embodiment of the present system 1100. A network member practice 1110 is connected to the KEW database 1120. The network member practice 1110 has electronic medical records (“EMR”) for a patient 1130. The EMR 1130 is provided to the database by the network member practice 1110 as well as any information relating to the diagnosis of the patient. The database 1120 processes the EMR 1130 and information to determine an evidence-based treatment protocol 1160. The pathway is sent to the network member practice 1110. The pathway is utilized by the network member practice 1110 and information regarding treatment is provided to the system 1100. The system 1100 monitors the compliance with the pathway according to the information provided by the network member practice 1110. The system utilizes the patterns recognized in the data stored in the operational data store 110 and/or data warehouse 120 to calculate a therapeutic pathway. The system comprises module configured to calculate a therapeutic pathway based on the pattern. Therapeutic pathways are generated based on patient data 100 and the other available information stored in the operational data store 110 and/or data warehouse 120. In certain embodiments, the therapeutic pathway is a decision tree that takes into account both the genotype and phenotype of the patient, as well as data in the database associated with the particular disease of the patient. As shown in FIG. 2, a patient diagnosed with non-small cell lung carcinoma presents with an EGFR sensitizing mutation, identified by genetic analysis of the cancer cells by the diagnostic laboratory. The system contains information relating to non-small cell lung carcinoma involving the effect on therapy of three outcomes of EGFR diagnostic analysis: an EGFR sensitizing mutation 200, absence of EGFR mutation 210, and an EGFR resistance mutation 220. Based on the information in the database for EGFR sensitizing mutations 200, the system calculates that the best treatment for this type of mutation is one of the EGFR TKI, gefitinib or erlotinib, and provides this information to the practitioner. In other words, this information allows the system to generate a range of potential treatment options. In each case, one or more pathways 230, 240, and 250 for potential treatment may be suggested depending on the information. Note that the treatment options in this embodiment involve pharmaceutical courses of treatment. The system can also suggest changes in lifestyle such as changes to exercise habits, cessation of smoking, and dietary changes to improve the quality of treatment.)
It would have been obvious to one of ordinary skill in the art before the time of filing, to add to the clinical trial assessment tools of Bagaev, the NLP-based clinical trial assessment tools of Elton. Paragraph [0091] of Elton teaches that the methods disclosed provide users with the ability to quickly match cancer patients with suitable treatment when a new trial is available in a timely manner. One of ordinary skill in the art in possession of Bagaev would have looked to Elton to achieve such an advantage. One of ordinary skill in the art would have added to the teaching of Bagaev, the teaching of Elton based on this incentive without yielding unexpected results.
As per claim 24,
The combined teaching of Bagaev and Elton teaches the limitations of claim 23.
Bagaev further teaches wherein the sequencing information comprises DNA sequencing data:
(Paragraph [0116] of Bagaev. The teaching describes that any type of sequencing data may be obtained from a biological sample of a subject. In some embodiments, the sequencing data is DNA sequencing data. In some embodiments, the sequencing data is RNA sequencing data. In some embodiments, the sequencing data is proteome sequencing data.)
As per claim 25,
The combined teaching of Bagaev and Elton teaches the limitations of claim 24.
Bagaev further teaches wherein the DNA sequencing data relates to three or more of the following genes: ABL1, ACVR1B, AKT2, AKT3, ALOX12B, ARFRP1, ASXL1, ATR, ATRX, AURKA, AURKB, AXIN1, AXL, BAP1, BARD1, BCL2, BCL2L1, BCL2L2, BCL6, BCOR, BCORL1, BRD4, BRIP1, BTG1, BTG2, BTK, CARD11, CASP8, CBFB, CBL, CCND3, CD274, CD79A, CD79B, CDC73, CDK8, CDK12, CDKN1A, CDKN1B, CDKN2B, CDKN2C, CEBPA, CHEK1, CHEK2, CIC, CREBBP, CRKL, CSF1R, CTCF, CTNNA1, CUL3, DAXX, DDR1, DNMT3A, DOT1L, EP300, EPHA3, EPHB1, ERBB3, ERBB4, ERCC4, ERG, ERRFI1, FAM46C, FANCA, FANCC, FANCG, FANCL, FAS, FGF3, FGF4, FGF6, FGF10, FGF14, FGF19, FGF23, FGFR4, FH, FLCN, FLT1, FLT3, FOXL2, FUBP1, GATA6, GID4, GNA13, GSK3B, H3F3A, HGF, IGF1R, IKBKE, IKZF1, INPP4B, IRF4, IRS2, JAK1, JUN, KDM5A, KDM5C, KDM6A, KDR, KEAP1, KLHL6, KMT2A, KMT2D, MAP2K4, MCL1, MDM2, MDM4, MED12, MEF2B, MEN1, MERTK, MITF, MSH2, MSH3, MSH6, MUTYH, MYCL, MYCN, MYD88, NBN, NF2, NFKBIA, NKX2-1, NOTCH2, NOTCH3, PALB2, PARP1, PARP2, PAX5, PBRM1, PDCD1, PDCD1LG2, PDGFRB, PDK1, PIK3C2B, PIK3CB, PIK3R1, PIM1, PMS2, POLD1, POLE, PPARG, PPP2R1A, PPP2R2A, PRDM1, PRKAR1A, PRKCI, PTCH1, RAC1, RAD21, RAD51, RAD51B, RAD51C, RAD51D, RAD52, RAD54L, RARA, RBM10, RICTOR, RNF43, ROS1, RPTOR, SDHB, SDHC, SDHD, SETD2, SF3B1, SMAD2, SMARCA4, SMARCB1, SOCS1, SOX2, SOX9, SPEN, SPOP, SRC, STAG2, STAT3, SUFU, SYK, TBX3, TEK, TET2, TGFBR2, TNFAIP3, TNFRSF14, TSC1, TSC2, TYRO3, U2AF1, VEGFA, WT1, XPO1, XRCC2, ZNF217, or ZNF703:
(Paragraph [0084] of Bagaev. The teaching describes that the genetic biomarker may be associated with genes from at least: VEGFA, FLT1, KDR, TEK, AURKA and a litany of other genes.)
As per claim 26,
The combined teaching of Bagaev and Elton teaches the limitations of claim 23.
Bagaev further teaches wherein the sequencing information comprises RNA sequencing data:
(Paragraph [0116] of Bagaev. The teaching describes that any type of sequencing data may be obtained from a biological sample of a subject. In some embodiments, the sequencing data is DNA sequencing data. In some embodiments, the sequencing data is RNA sequencing data. In some embodiments, the sequencing data is proteome sequencing data.)
As per claim 27,
The combined teaching of Bagaev and Elton teaches the limitations of claim 23.
Bagaev further teaches wherein the sequencing information comprises variant calling information relative to a germline sample or one or more sources of available variant data:
(Paragraph [0138] of Bagaev. The teaching describes that various genes recited herein are, in general, named using human gene naming conventions. The various genes, in some embodiments, are described in publicly available resources such as published journal articles. The gene names may be correlated with additional information (including sequence information) through use of, for example, the NCBI GenBank® databases available at www<dot>ncbi<dot>nlm<dot>nih<dot>gov; the HUGO (Human Genome Organization) Gene Nomination Committee (HGNC) databases available at www<dot>genenames<dot>org; the DAVID Bioinformatics Resource available at www<dot>david<dot>ncifcrf<dot>gov. It should be appreciated that a gene may encompass all variants of that gene. For organisms or subjects other than human subjects, corresponding specific-specific genes may be used. Synonyms, equivalents, and closely related genes (including genes from other organisms) may be identified using similar databases including the NCBI GenBank® databases described above.)
As per claim 28,
The combined teaching of Bagaev and Elton teaches the limitations of claim 23.
Bagaev further teaches wherein the sequencing information comprises variant characterization information:
(Paragraph [0138] of Bagaev. The teaching describes that various genes recited herein are, in general, named using human gene naming conventions. The various genes, in some embodiments, are described in publicly available resources such as published journal articles. The gene names may be correlated with additional information (including sequence information) through use of, for example, the NCBI GenBank® databases available at www<dot>ncbi<dot>nlm<dot>nih<dot>gov; the HUGO (Human Genome Organization) Gene Nomination Committee (HGNC) databases available at www<dot>genenames<dot>org; the DAVID Bioinformatics Resource available at www<dot>david<dot>ncifcrf<dot>gov. It should be appreciated that a gene may encompass all variants of that gene. For organisms or subjects other than human subjects, corresponding specific-specific genes may be used. Synonyms, equivalents, and closely related genes (including genes from other organisms) may be identified using similar databases including the NCBI GenBank® databases described above.)
As per claim 29,
The combined teaching of Bagaev and Elton teaches the limitations of claim 23.
Bagaev further teaches wherein the report includes treatment information:
(Paragraph [0230] and [0231] and Figure 10 of Bagaev. The teaching describes that a user of the software program may interact with the GUI to log into the software program. The user may select a stored report to view a screen presenting information relating to the selected report. The user may select the create new report portion to view a screen for creating a new report. FIG. 10 is a screenshot presenting the selected patient's report including information related to the patient's sequencing data, the patient, and the patient's cancer. The therapy biomarkers portion (as shown in the left panel) presents information related to available therapies (e.g., immunotherapies and targeted therapies) and their predicted efficacy in the selected patient. Additional predictions of the efficacy of a therapy in the patient are provided in the machine predictor portion and additional portion (as shown in the left panel). The MF profile portion presents information relating to the molecular characteristics of a tumor including tumor genetics, pro-tumor microenvironment factors, and anti-tumor immune response factors (as shown in the middle panel). The clinical trials portion provides information relating to clinical trials (as shown in the right panel). The monotherapy or combinational therapy portion (as shown in the middle panel) may be selected by the user to interactively design a personalized treatment for a patient.)
As per claim 30,
The combined teaching of Bagaev and Elton teaches the limitations of claim 29.
Bagaev further teaches wherein the treatment information includes a DNA-related therapy and an RNA-related therapy:
(Paragraph [0230] and [0231] and Figure 10 of Bagaev. The teaching describes that a user of the software program may interact with the GUI to log into the software program. The user may select a stored report to view a screen presenting information relating to the selected report. The user may select the create new report portion to view a screen for creating a new report. FIG. 10 is a screenshot presenting the selected patient's report including information related to the patient's sequencing data, the patient, and the patient's cancer. The therapy biomarkers portion (as shown in the left panel) presents information related to available therapies (e.g., immunotherapies and targeted therapies) and their predicted efficacy in the selected patient. Additional predictions of the efficacy of a therapy in the patient are provided in the machine predictor portion and additional portion (as shown in the left panel). The MF profile portion presents information relating to the molecular characteristics of a tumor including tumor genetics, pro-tumor microenvironment factors, and anti-tumor immune response factors (as shown in the middle panel). The clinical trials portion provides information relating to clinical trials (as shown in the right panel). The monotherapy or combinational therapy portion (as shown in the middle panel) may be selected by the user to interactively design a personalized treatment for a patient.)
As per claim 31,
The combined teaching of Bagaev and Elton teaches the limitations of claim 23.
Bagaev further teaches wherein the report includes clinical trial information:
(Paragraph [0230] and [0231] and Figure 10 of Bagaev. The teaching describes that a user of the software program may interact with the GUI to log into the software program. The user may select a stored report to view a screen presenting information relating to the selected report. The user may select the create new report portion to view a screen for creating a new report. FIG. 10 is a screenshot presenting the selected patient's report including information related to the patient's sequencing data, the patient, and the patient's cancer. The therapy biomarkers portion (as shown in the left panel) presents information related to available therapies (e.g., immunotherapies and targeted therapies) and their predicted efficacy in the selected patient. Additional predictions of the efficacy of a therapy in the patient are provided in the machine predictor portion and additional portion (as shown in the left panel). The MF profile portion presents information relating to the molecular characteristics of a tumor including tumor genetics, pro-tumor microenvironment factors, and anti-tumor immune response factors (as shown in the middle panel). The clinical trials portion provides information relating to clinical trials (as shown in the right panel). The monotherapy or combinational therapy portion (as shown in the middle panel) may be selected by the user to interactively design a personalized treatment for a patient.)
As per claim 32,
The combined teaching of Bagaev and Elton teaches the limitations of claim 23.
Bagaev further teaches wherein the report is associated with one or more of the sequencing information, pathology information, epigenomic information, or clinical information:
(Paragraph [0230] and [0231] and Figure 10 of Bagaev. The teaching describes that a user of the software program may interact with the GUI to log into the software program. The user may select a stored report to view a screen presenting information relating to the selected report. The user may select the create new report portion to view a screen for creating a new report. FIG. 10 is a screenshot presenting the selected patient's report including information related to the patient's sequencing data, the patient, and the patient's cancer. The therapy biomarkers portion (as shown in the left panel) presents information related to available therapies (e.g., immunotherapies and targeted therapies) and their predicted efficacy in the selected patient. Additional predictions of the efficacy of a therapy in the patient are provided in the machine predictor portion and additional portion (as shown in the left panel). The MF profile portion presents information relating to the molecular characteristics of a tumor including tumor genetics, pro-tumor microenvironment factors, and anti-tumor immune response factors (as shown in the middle panel). The clinical trials portion provides information relating to clinical trials (as shown in the right panel). The monotherapy or combinational therapy portion (as shown in the middle panel) may be selected by the user to interactively design a personalized treatment for a patient.)
As per claim 33,
The combined teaching of Bagaev and Elton teaches the limitations of claim 23.
Bagaev further teaches wherein the report includes information relating to a cohort of subjects who responded favorably or unfavorably to one or more treatments:
(Paragraph [0230] and [0231] and Figure 10 of Bagaev. The teaching describes that a user of the software program may interact with the GUI to log into the software program. The user may select a stored report to view a screen presenting information relating to the selected report. The user may select the create new report portion to view a screen for creating a new report. FIG. 10 is a screenshot presenting the selected patient's report including information related to the patient's sequencing data, the patient, and the patient's cancer. The therapy biomarkers portion (as shown in the left panel) presents information related to available therapies (e.g., immunotherapies and targeted therapies) and their predicted efficacy in the selected patient. Additional predictions of the efficacy of a therapy in the patient are provided in the machine predictor portion and additional portion (as shown in the left panel). The MF profile portion presents information relating to the molecular characteristics of a tumor including tumor genetics, pro-tumor microenvironment factors, and anti-tumor immune response factors (as shown in the middle panel). The clinical trials portion provides information relating to clinical trials (as shown in the right panel). The monotherapy or combinational therapy portion (as shown in the middle panel) may be selected by the user to interactively design a personalized treatment for a patient.)
As per claim 34,
The combined teaching of Bagaev and Elton teaches the limitations of claim 23.
Bagaev further teaches wherein the report includes information relating to a cohort of subjects who did not respond to one or more treatments:
(Paragraph [0230] and [0231] and Figure 10 of Bagaev. The teaching describes that a user of the software program may interact with the GUI to log into the software program. The user may select a stored report to view a screen presenting information relating to the selected report. The user may select the create new report portion to view a screen for creating a new report. FIG. 10 is a screenshot presenting the selected patient's report including information related to the patient's sequencing data, the patient, and the patient's cancer. The therapy biomarkers portion (as shown in the left panel) presents information related to available therapies (e.g., immunotherapies and targeted therapies) and their predicted efficacy in the selected patient. Additional predictions of the efficacy of a therapy in the patient are provided in the machine predictor portion and additional portion (as shown in the left panel). The MF profile portion presents information relating to the molecular characteristics of a tumor including tumor genetics, pro-tumor microenvironment factors, and anti-tumor immune response factors (as shown in the middle panel). The clinical trials portion provides information relating to clinical trials (as shown in the right panel). The monotherapy or combinational therapy portion (as shown in the middle panel) may be selected by the user to interactively design a personalized treatment for a patient.)
As per claim 35,
The combined teaching of Bagaev and Elton teaches the limitations of claim 23.
Bagaev further teaches wherein a program of the set of programs requires a plurality of dependencies to have been completed in order to perform its task, the method further comprising: broadcasting notifications to the program when each dependency is completed:
(Paragraph [0030] and Figure 2E of Bagaev. The teaching describes identifying the subject as a member of one or more cohorts based on the set of normalized biomarker scores for the subject, wherein each of the one or more cohorts is associated with a positive or negative outcome of the at least one candidate therapy; and outputting an indication of the one or more cohorts in which the subject is a member. This outputting of an indication is construed to be a broadcast of a notification as is seen in element 290 of Figure 2E. Element 290 to actualize, steps 282-288 must be completed prior. It is construed that each of these steps in Figure 2E is a set of programs with step 290 being the claimed program or a subset of programs that include the claimed program. Either interpretation is likely.)
As per claim 36,
The combined teaching of Bagaev and Elton teaches the limitations of claim 35.
Bagaev further teaches wherein the notifications are broadcast directly to the program:
(Paragraph [0030] and Figure 2E of Bagaev. The teaching describes identifying the subject as a member of one or more cohorts based on the set of normalized biomarker scores for the subject, wherein each of the one or more cohorts is associated with a positive or negative outcome of the at least one candidate therapy; and outputting an indication of the one or more cohorts in which the subject is a member. This outputting of an indication is construed to be a broadcast of a notification as is seen in element 290 of Figure 2E. Element 290 to actualize, steps 282-288 must be completed prior. It is construed that each of these steps in Figure 2E is a set of programs with step 290 being the claimed program or a subset of programs that include the claimed program. Either interpretation is likely.)
As per claim 37,
The combined teaching of Bagaev and Elton teaches the limitations of claim 35.
Bagaev further teaches wherein the notifications are broadcast indirectly to a subset of programs that include the program:
(Paragraph [0030] and Figure 2E of Bagaev. The teaching describes identifying the subject as a member of one or more cohorts based on the set of normalized biomarker scores for the subject, wherein each of the one or more cohorts is associated with a positive or negative outcome of the at least one candidate therapy; and outputting an indication of the one or more cohorts in which the subject is a member. This outputting of an indication is construed to be a broadcast of a notification as is seen in element 290 of Figure 2E. Element 290 to actualize, steps 282-288 must be completed prior. It is construed that each of these steps in Figure 2E is a set of programs with step 290 being the claimed program or a subset of programs that include the claimed program. Either interpretation is likely.)
As per claim 38,
The combined teaching of Bagaev and Elton teaches the limitations of claim 23.
Bagaev further teaches wherein a program of the set of programs requires a plurality of resources in order to perform its task, the method further comprising: adding the task to a queue until the plurality of resources are available:
(Paragraphs [0030], and [0126]-[0129] and Figure 2E of Bagaev. The teaching describes obtaining sequencing data about at least one biological sample of a subject. This step, step 282 in Figure 2E is construed as a program of a set of programs including steps 282-290. For step 282 to perform its task, sequence data from a subject must be acquired. Any of the biological samples described herein can be used for obtaining expression data using conventional assays or those described herein. Expression data, in some embodiments, includes gene expression levels. In some embodiments, gene expression levels are determined by detecting a level of a protein in a sample and/or by detecting a level of activity of a protein in a sample. Such immunoassays may involve the use of an agent (e.g., an antibody) specific to the target protein. An antibody that “specifically binds” to a target peptide or an epitope thereof may not bind to other peptides or other epitopes in the same antigen. In some embodiments, a sample may be contacted, simultaneously or sequentially, with more than one binding agent that binds different proteins (e.g., multiplexed analysis). For the assays to be conducted sequentially, there is a necessary queue in the sequence that the task must be placed in for the sequence data to be obtained.)
As per claim 39,
The combined teaching of Bagaev and Elton teaches the limitations of claim 23.
Bagaev further teaches wherein a first program of the set of programs generates a first data product usable by one or more other programs of the set of programs, the method further comprising: transmitting, by the first program, a notification once the first data product has been generated:
(Paragraph [0030] and Figure 2E of Bagaev. The teaching describes obtaining sequencing data about at least one biological sample of a subject; accessing, in the at least one database, biomarker information indicating a distribution of values for each biomarker, across a respective group of people, in at least a reference subset of the plurality of biomarkers, each of the plurality of biomarkers being associated with at least one candidate therapy; determining, using the sequencing data and the biomarker information, a normalized score for each biomarker in at least a subject subset of the plurality of biomarkers to obtain a set of normalized biomarkers for the subject; identifying the subject as a member of one or more cohorts based on the set of normalized biomarker scores for the subject, wherein each of the one or more cohorts is associated with a positive or negative outcome of the at least one candidate therapy; and outputting an indication of the one or more cohorts in which the subject is a member. Transmitting information from Step 284 to Step 286 is construed as a transmission of a notification that the first data product has been generated by first program 284. This outputting of an indication is construed to be a transmission of a second notification as is seen in element 290 of Figure 2E.)
As per claim 40,
The combined teaching of Bagaev and Elton teaches the limitations of claim 39.
Bagaev further teaches further comprising: receiving, by a second program of the set of programs, the notification; and accessing, by the second program, the first data product generated by the first program:
(Paragraph [0030] and Figure 2E of Bagaev. The teaching describes obtaining sequencing data about at least one biological sample of a subject; accessing, in the at least one database, biomarker information indicating a distribution of values for each biomarker, across a respective group of people, in at least a reference subset of the plurality of biomarkers, each of the plurality of biomarkers being associated with at least one candidate therapy; determining, using the sequencing data and the biomarker information, a normalized score for each biomarker in at least a subject subset of the plurality of biomarkers to obtain a set of normalized biomarkers for the subject; identifying the subject as a member of one or more cohorts based on the set of normalized biomarker scores for the subject, wherein each of the one or more cohorts is associated with a positive or negative outcome of the at least one candidate therapy; and outputting an indication of the one or more cohorts in which the subject is a member. Transmitting information from Step 284 to Step 286 is construed as a transmission of a notification that the first data product has been generated by first program 284. This outputting of an indication is construed to be a transmission of a second notification as is seen in element 290 of Figure 2E.)
As per claim 41,
Claim 41 is substantially similar to claim 23. Accordingly, claim 41 is rejected for the same reasons as claim 23.
Bagaev further teaches a system, comprising: a computer including a processing device:
(Paragraph [0307] of Bagaev. The teaching describes that the system comprises at least one computer hardware processor; at least one database that stores biomarker information; and at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform the disclosed functions.)
As per claim 42,
Claim 42 is substantially similar to claim 23. Accordingly, claim 42 is rejected for the same reasons as claim 23.
Bagaev further teaches a non-transitory computer-readable storage medium having stored thereon program code instructions:
(Paragraph [0307] of Bagaev. The teaching describes that the system comprises at least one computer hardware processor; at least one database that stores biomarker information; and at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform the disclosed functions.)
Response to Arguments
Applicant's arguments filed July 23, 2026 have been fully considered.
Applicant’s arguments pertaining to rejections made under 35 U.S.C. 101 are persuasive.
The Applicant argues that the pending claims provide a practical application of the previously identified abstract idea be reflecting an improvement in the technical field of medical treatment and data accuracy. Paragraphs [00153]-[00154] of the as-filed specification describes that there is a technical challenge in interpreting RNA sequencing data and isolating biomarkers for disease susceptibility. This technical challenge is rooted in a lack of structured information between the human genome and patient/clinical information. Paragraphs [002855]-[002858] of the as-filed specification describes that the claimed invention provides a structured basis for interpreting information between RNA sequencing data and biomarker data to better identify medical treatments for patients with a greater degree of accuracy. For example, although pancreatic cancer has few well-established therapeutic options, the applicant’s invention provides evidence that they were able to identify biomarker-based clinical trial options for 94% of pancreatic cancer patients. The Examiner sees this capability as an improvement in medical data analysis techniques. Accordingly, the pending claims are eligible for patent under 35 U.S.C. 101.
Applicant’s arguments pertaining to rejections made under 35 U.S.C. 102 are mostly persuasive.
The Applicant argues that Bagaev does not teach of suggest obtaining clinical data for at least one subject of a plurality of subjects from a plurality of different time points.
The Examiner respectfully disagrees. Paragraph [0111] of Bagaev clearly teaches that “biological sample may be any type of sample including, for example, a sample of a bodily fluid, one or more cells, a piece of tissue, or some or all of an organ. In certain embodiments, one sample will be taken from a subject for analysis. In some embodiments, more than one (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, or more) samples may be taken from a subject for analysis. In some embodiments, one sample from a subject will be analyzed. In certain embodiments, more than one (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, or more) samples may be analyzed. If more than one sample from a subject is analyzed, the samples may be procured at the same time (e.g., more than one sample may be taken in the same procedure), or the samples may be taken at different times (e.g., during a different procedure including a procedure 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 days; 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 weeks; 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 months, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 years, or 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 decades after a first procedure). A second or subsequent sample may be taken or obtained from the same region (e.g., from the same tumor or area of tissue) or a different region (including, e.g., a different tumor). A second or subsequent sample may be taken or obtained from the subject after one or more treatments, and may be taken from the same region or a different region. As a non-limiting example, the second or subsequent sample may be useful in determining whether the cancer in each sample has different characteristics (e.g., in the case of samples taken from two physically separate tumors in a patient) or whether the cancer has responded to one or more treatments (e.g., in the case of two or more samples from the same tumor or different tumors prior to and subsequent to a treatment).” These sample collections can happen for the plurality of patients for each of these plurality of time points.
The Applicant’s remaining arguments are persuasive and these rejections are hereby withdrawn.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHAD A NEWTON whose telephone number is (313)446-6604. The examiner can normally be reached M-F 8:00AM-4:00PM (EST).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, PETER H. CHOI can be reached at (469) 295-9171. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHAD A NEWTON/Primary Examiner, Art Unit 3681
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