DETAILED ACTION
Status
This communication is in response to the application filed on 10 December 2024 and preliminary amendment filed on 10 February 2025. Claims 1, 5-37, 47, 51, 53-55, 57, 59-60, 62-65, and 67-98 have been canceled, claims 3-4, 38-46, 48-50, 52, 56, 58, 61, and 66 have been amended, and no new claims have been added; therefore, claims 2-4, 38-46, 48-50, 52, 56, 58, 61, and 66 are pending and 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 .
Priority
Applicant’s claim for the benefit under 35 U.S.C. 119(e) to U.S. Provisional Application No. 63/608,693, filed on 11 December 2023, is acknowledged.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10 February 2025 was filed after the mailing date of the application on 10 December 2024. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claim 40 is objected to because of the following informalities: claim 40 recites “copy number amplification_ occurring”, where the underscore following “amplification” should be deleted. Appropriate correction is required.
Claim Interpretation
The Examiner notes the use of the term “enrichment score” at the claims, where this term is apparently not “usual” or easily understood. Applicant ¶ 0025 indicates this term to be an indication of the odds (e.g., odds ratio), probability, likelihood, or confidence value that a disease label is appropriate or inappropriate – i.e., that the label “can be correctly confirmed or rejected”.
The Examiner notes that at least claims 46 and 48 recite a “dominant mutational signature scoring value” and the specification uses the term “dominant mutational signature” (see, e.g., Applicant ¶¶ 0023, 0092, 0318, 0319, and 0320 as published – the only examples the Examiner has found); however, there does not appear to be a definition of the terms as used. Apparently, a “mutational signature” is any mutation (e.g., abnormality or variation) that can be detected – the detection, or capability of detection, being the signature. The term “dominant” could refer, within the context of use, to the single most frequent or evident mutation detected, or it could be the most influential mutation, or perhaps other forms of dominating the analysis. The term “dominant mutational signature” does not appear to be term of art, but appears to more be used as a descriptor associated with mutation, i.e., genetic or genomic mutation. This appears to be a breadth issue, so the Examiner is not rejecting the term under 35 USC 112(b) for indefiniteness, but is interpreting the term “dominant mutational signature” as any mutation that appears to influence the analysis performed – that is to say it dominates the analysis in some way (it does not necessarily indicate the ultimate, definitive influence, but only that it influences). This appears consistent with at least, e.g., Applicant ¶¶ 0023, 0092, and 0319, where a/the “dominant mutational signature can be associated with exposure to an alkylating agent, tobacco, or ultraviolet light, or an altered activity of APOBEC, a mutation in one or more mismatch repair pathway genes”, etc. (emphasis added, quoting 0023, but apparently the same at 0092 and 0319).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 58, 61, and 66 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claim 58 depends from independent claim 2 and recites the limitation "the confidence value" in line 5. There is insufficient antecedent basis for this limitation in the claim.
Claim 61 depends from dependent claim 4, which in turn depends from independent claim 2 and claim 61 recites the limitation "the confidence value" in line 5. There is insufficient antecedent basis for this limitation in the claim.
Claim 66 recites “selecting an anti-cancer therapy effective in treating a cancer”. First, the term “effective” appears to be a subjective term – some may consider treatment effective if or when symptoms or tumor size are eased or reduced, others may consider treatment effective only if there is no recurrence, still others may consider treatment effective if metastasis is prevented, and almost anyone may adopt their own definition of what they consider to be criteria for defining effectiveness – see Applicant ¶ 0068 as submitted and published. Further, whether any therapy is “effective” (regardless of a definition used to determine whether it was effective) is apparently time-based – a metastasis or recurrence may not occur in one day or one week, but it may occur after several weeks, or a month or two, or several years. So the term “effective” is also relative to time, and some persons may only consider permanence as “effective”, where others may consider a temporary result to be “effective”. Second, whether the therapy is, or may be, “effective” appears to be the intended use – it is intended that the selected treatment would be “effective”, but the treatment itself may or may not actually be effective (based on the subjective definition of “effective”) and whether a therapy is effective or not can only be determined after the therapy is selected and administered. Further to this issue is that the therapy may be administered, but depending on how “effective[ness]” is defined, the therapy itself may not be the cause for, e.g., lack of metastasis or recurrence (i.e., the cancer may not have ever metastasized or recurred regardless of the treatment that was administered.
Therefore, there are at least two issues: whether something is “effective” is a subjective or relative term, and the therapy cannot be selected based on whether it is effective for that patient or that disease (including cancer) since whether it is/was effective (regardless of a definition used to determine whether it was effective) can only be determined after administration of the therapy.
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 2-4, 38-46, 48-50, 52, 56, 58, 61, and 66 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Please see the following Subject Matter Eligibility (“SME”) analysis:
For analysis under SME Step 1, the claims herein are directed to a method, which would be classified under one of the listed statutory classifications (SME Step 1=Yes).
For analysis under revised SME Step 2A, Prong 1, independent claim 2 recites a method for detecting a disease type comprising: receiving, at one or more processors, test sample characterization data comprising genomic alteration statuses for a test sample having a predetermined disease label; receiving, at the one or more processors, for database samples, database characterization data, wherein at least one database sample in the database samples has a predetermined disease label; determining, using the one or more processors, similarity scores for the database samples, wherein the similarity scores indicate similarities between the test sample characterization data and the database characterization data; ranking, using the one or more processors, the database samples based on the similarity scores to generate ranked database samples; selecting, using the one or more processors, from the ranked database samples, a subset of database samples most similar to the test sample; determining, using the one or more processors, an enrichment score for the predetermined disease label based on the subset of database samples and the database samples; and confirming or rejecting, using the one or more processors, the predetermined disease label for the test sample based on the enrichment score.
The dependent claims (claims 3-4, 38-46, 48-50, 52, 56, 58, 61, and 66) appear to be encompassed by the abstract idea of the independent claims since they merely indicate reducing test and database data by excluding genomic alteration and characteristic and ranking and selecting based on the reduced data (claim 3), assigning an alternate disease label based on a score for that label (claim 4), determining a score increase when certain conditions exist (e.g., shared pathogenic short variant, pathogenic copy number amplification or deletion, etc.) (claims 38-46 and 48-50), using a predetermined number of most similar database samples (claim 52), using a confidence value for confirmation or rejection (claim 56), using a threshold and/or confidence value for rejection (claim 58) or for acceptance of the alternate disease label (claim 61), and/or selecting an anti-cancer therapy effective in treating the cancer (claim 66).
The underlined portions of the claims are an indication of elements additional to the abstract idea (to be considered below).
The claim elements may be summarized as the idea of comparing sample data to other examples, determining and ranking similarity so as to confirm or reject a diagnosis; however, the Examiner notes that although this summary of the claims is provided, the analysis regarding subject matter eligibility considers the entirety of the claim elements, both individually and as a whole (or ordered combination). This idea is within the following grouping(s) of subject matter:
Certain methods of organizing human activity (e.g. … commercial or legal interactions such as … legal obligations, … and/or managing personal behavior or relationships between people such as social activities, teaching, and following rules or instructions) since it appears that comparing data for confirming or rejecting diagnoses is what doctors and medical professionals do regularly; and
Mental processes (e.g., concepts performed in the human mind such as observation, evaluation, judgment, and/or opinion) based on the observations, evaluations, judgments, and/or opinions doctors and medical professionals use and provide.
Therefore, the claims are found to be directed to an abstract idea.
For analysis under revised SME Step 2A, Prong 2, the above judicial exception is not integrated into a practical application because the additional elements do not impose a meaningful limit on the judicial exception when evaluated individually and as a combination. The additional elements are performing the activities at or using the one or more processors. These additional elements do not reflect an improvement in the functioning of a computer or an improvement to other technology or technical field, effect a particular treatment or prophylaxis for a disease or medical condition (there is no medical disease or condition, much less a treatment or prophylaxis for one), implement the judicial exception with, or by using in conjunction with, a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing (there is no transformation/reduction of a physical article), and/or apply or use the judicial exception in some other meaningful way beyond generically linking use of the judicial exception to a particular technological environment.
The claims appear to merely apply the judicial exception, include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform the abstract idea. The additional elements appear to merely add insignificant extra-solution activity to the judicial exception and/or generally link the use of the judicial exception to a particular technological environment or field of use.
For analysis under SME Step 2B, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as indicated above, are merely “[a]dding the words ‘apply it’ (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp.” that MPEP § 2106.05(I)(A) indicates to be insignificant activity
There is no indication the Examiner can find in the record regarding any specialized computer hardware or other “inventive” components, but rather, the claims merely indicate computer components which appear to be generic components and therefore do not satisfy an inventive concept that would constitute “significantly more” with respect to eligibility. Applicant ¶ 0229 indicates “FIG. 4 illustrates an example of a computing device or system in accordance with one embodiment. Device 400 can be a host computer connected to a network. Device 900 can be a client computer or a server. As shown in FIG. 4, device 400 can be any suitable type of microprocessor-based device, such as a personal computer, workstation, server or handheld computing device (portable electronic device) such as a phone or tablet.”
The individual elements therefore do not appear to offer any significance beyond the application of the abstract idea itself, and there does not appear to be any additional benefit or significance indicated by the ordered combination, i.e., there does not appear to be any synergy or special import to the claim as a whole other than the application of the idea itself.
The dependent claims, as indicated above, appear encompassed by the abstract idea since they merely limit the idea itself; therefore the dependent claims do not add significantly more than the idea.
Therefore, SME Step 2B=No, any additional elements, whether taken individually or as an ordered whole in combination, do not amount to significantly more than the abstract idea, including analysis of the dependent claims.
Please see the Subject Matter Eligibility (SME) guidance and instruction materials at https://www.uspto.gov/patent/laws-and-regulations/examination-policy/subject-matter-eligibility, which includes the latest guidance, memoranda, and update(s) for further information.
NOTICE
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 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.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 2-4, 38-46, 48-50, 52, 56, 58, 61, and 66 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Lefkofski (U.S. Patent Application Publication No. 2021/0118559).
Claim 2: Lefkofsky discloses a method for detecting a disease type comprising:
receiving, at one or more processors, test sample characterization data comprising genomic alteration statuses for a test sample having a predetermined disease label (see Lefkofsky at least at, e.g., ¶ 0052, “Clinical information may also combine a variety of features together …. For example, clinical information in the area of cancer may include … diagnosis (Site (Tissue of Origin), Initial Diagnosis Date, Initial Diagnosis, Histology, Histologic Grade, Metastatic Diagnosis, Metastatic Diagnosis Date”, etc ; citation hereafter by number only) ;
receiving, at the one or more processors, for database samples, database characterization data, wherein at least one database sample in the database samples has a predetermined disease label (0276, “if subject 102 always returns a higher A1C with no other indications of diabetes, the model may generate a coefficient factor which compensates for the subject's natural propensity towards a higher A1C diagnostic testing result or may similarly increase the thresholds with which a physician should compare the diagnostic testing results. In another example, analysis module 180i may request a number of similar subjects based upon the subject's clinomic profile to form a cohort of similar subjects from which diagnosis and treatment comparisons may be performed. Comparisons between the diagnostics results, diagnosis, treatments, and treatment outcomes may be provided in a report to the subject's physician or visually displayed in a radial plot with informative callouts which allow the physician to explore analytics associated with the cohort in real time via an online portal”);
determining, using the one or more processors, similarity scores for the database samples, wherein the similarity scores indicate similarities between the test sample characterization data and the database characterization data (0017, “the test result may be compared against one or more sets of data reflective of the subject's history of similar test results. The comparison may be performed, for instance, to develop a pattern unique to that subject. Test results may be processed by identifying personalized subject “diagnostic result” thresholds or scaling coefficients for each subject that compensates for the subject's deviation from normal expected results due to a characteristic unique to that subject” – the thresholds and scaling coefficients requiring a score on which to base similarity, 0223, “the system 101 may reference a subject similarity metric that quantifies a degree to which subjects are similar”);
ranking, using the one or more processors, the database samples based on the similarity scores to generate ranked database samples (0023, “Plot 510 represents similarity metrics for each of the similar subjects as solid black dots. subjects (e.g., dots) which appear radially closer to subject 520 are more similar to subject 520 than subjects which appear radially further from subject 520 and subjects which appear closer to other subjects are more similar to the closer subjects than they are to other, further away subjects. Therefore similarities between all subjects to all other subjects of the cohort of similar subjects are visually represented” – the plot distances being based on, and representing, the ranking);
selecting, using the one or more processors, from the ranked database samples, a subset of database samples most similar to the test sample (0223, “Subject sub-cohorts 530 may represent clusters of subjects including subjects that are more similar to each other than to other subjects in the larger represented cohort. Sub-cohorts may represent biomarkers amongst the cohort of similar subjects which are shared with the subject 102. A user may select a sub-cohort, such as one of sub-cohorts 530a-c, and view the biomarker identified in the sub-cohort, as well as the diagnostic test result considerations associated with that biomarker”, Fig. 5, items 530a, 530b, 530c);
determining, using the one or more processors, an enrichment score for the predetermined disease label based on the subset of database samples and the database samples (0145, “Thresholds may be set at a percentage of the normal values. For examples, MSI-H may be greater than 70%, MSE may be less than 30%, and MSS may be the values between. In another example, a vector containing the mean and variance data may be put into a support vector machine classification algorithm for artificial intelligence assisted classification. Both algorithms may return the probability of the subject being MSI-H as an output or a characterization as MSI-H, MSS, or MSE”, 0209, “A prediction may be a binary representation, such as a “Yes—Target predicted to occur” or “No—Target not predicted to occur.” Predictions may be a likelihood representation such as “target predicted to occur with 83% probability/likelihood.” Predictions may be a raw adjustment score such as by a value from which the original report may be adjusted or a raw threshold adjustment score such as by a value from which the original threshold may be adjusted”); and
confirming or rejecting, using the one or more processors, the predetermined disease label for the test sample based on the enrichment score (0016, “the test result may be compared against one or more sets of data reflective of health information of the subject, individuals medically similar to the subject, other individuals, or other data sets …. [where] Comparison information may include morphologic data collected for the subject (such as pathology slides, radiology scans, etc.) that might alter how test results should be interpreted. Comparison information may include information of other subjects, like the subject who was tested, that have had similar results for whom outcome and response data has been collected in order to provide insights into which therapies or trials might be best for the subject, or how likely the disease is to occur, progress, etc.”, 0017, “Test results may be processed by identifying personalized subject “diagnostic result” thresholds or scaling coefficients for each subject that compensates for the subject's deviation from normal expected results due to a characteristic unique to that subject” and listing various examples of personalized results, such as “In another example, unique characteristics may be derived from morphologic data collected associated with a subject such as pathology slides, radiology scans, other imaging results, and so on that suggest the subject's test results should be interpreted according to a personalized scale based on the subject diagnostic results threshold rather than established normal levels. Each of these unique characteristics may be taken into account alone or in combination to determine the best personalized diagnostic result thresholds or adjustment coefficients”. This indicates that the clinical information of a diagnosis would be, or may be, confirmed or rejected based on whether the results are applicable to the specific, personalized individual being considered).
Claim 3: Lefkofsky discloses the method of claim 2, further comprising:
excluding, using the one or more processors, for the test sample, a genomic alteration status from the test sample characterization data to generate reduced test sample characterization data (0017, “Comparison information may include morphologic data collected for the subject (such as pathology slides, radiology scans, etc.) that might alter how test results should be interpreted” – indicating that other data would be excluded, 0017, “Test results may be processed by identifying personalized subject “diagnostic result” thresholds or scaling coefficients for each subject that compensates for the subject's deviation from normal expected results due to a characteristic unique to that subject” – indicating that test data below the threshold is excluded, 0113, “FASTQ data from each isolate may be filtered. Such filtering may include correcting or masking sequencer errors and removing (trimming) low quality sequences or bases, adapter sequences, contaminations, chimeric reads, overrepresented sequences, biases caused by library preparation, amplification, or capture, and other errors”));
excluding, using the one or more processors, for the database samples, the genomic alteration status from the database characterization data to generate reduced database characterization data (0016, “the test result may be compared against one or more sets of data reflective of health information of the subject, individuals medically similar to the subject, other individuals, or other data sets” – indicating the exclusion of data that is not “similar to the subject, other individuals, or other data sets, 0174. “sequenced data stage 755 may receive sequencing results in a raw format and perform filtering and/or alignment to generate an aligned format. Filtering may include detecting spurious or incorrect reads and removing them from the dataset. The bioinformatics pipeline 706 may access resource files such as one or more pool files containing reads from one or many normal samples”);
determining, using the one or more processors, second similarity scores, wherein the second similarity scores indicate similarities between the reduced test sample characterization data and, for a corresponding database sample, the reduced database characterization data (0016, “the test result may be compared against one or more sets of data reflective of health information of the subject, individuals medically similar to the subject, other individuals, or other data sets …. [where] Comparison information may include morphologic data collected for the subject (such as pathology slides, radiology scans, etc.) that might alter how test results should be interpreted. Comparison information may include information of other subjects, like the subject who was tested, that have had similar results for whom outcome and response data has been collected in order to provide insights into which therapies or trials might be best for the subject, or how likely the disease is to occur, progress, etc.”, 0017, “Test results may be processed by identifying personalized subject “diagnostic result” thresholds or scaling coefficients for each subject that compensates for the subject's deviation from normal expected results due to a characteristic unique to that subject” and listing various examples of personalized results, such as “In another example, unique characteristics may be derived from morphologic data collected associated with a subject such as pathology slides, radiology scans, other imaging results, and so on that suggest the subject's test results should be interpreted according to a personalized scale based on the subject diagnostic results threshold rather than established normal levels. Each of these unique characteristics may be taken into account alone or in combination to determine the best personalized diagnostic result thresholds or adjustment coefficients”. This indicates that the clinical information of a diagnosis would be, or may be, confirmed or rejected based on whether the results are applicable to the specific, personalized individual being considered);
ranking, using the one or more processors, the database samples based on the second similarity scores to generate second ranked database samples (0016-0017 as above);
selecting, using the one or more processors, from the second ranked database samples, a second subset of database samples most similar to the test sample (0016, “the test result may be compared against one or more sets of data reflective of health information of the subject, individuals medically similar to the subject”);
determining, using the one or more processors, a second enrichment score for the predetermined disease label based on the second subset of database samples and the database samples (0145 and 0209, as above); and
confirming or rejecting, using the one or more processors, the predetermined disease label for the test sample based on the enrichment score and the second enrichment score (0016-0017 as above).
Claim 4: Lefkofsky discloses the method of claim 2, further comprising: determining, using the one or more processors, one or more alternate enrichment scores for one or more alternate disease labels based on the subset of database samples and the database samples; rejecting, using the one or more processors, the predetermined disease label; and assigning, using the one or more processors, an alternate disease label from the one or more alternate disease labels for the test sample based on the enrichment score (0016-0017, as above – the “alternate” label as replacing or correcting the original diagnosis, 0113, “FASTQ data from each isolate may be filtered. Such filtering may include correcting or masking sequencer errors and removing (trimming) low quality sequences or bases, adapter sequences, contaminations, chimeric reads, overrepresented sequences, biases caused by library preparation, amplification, or capture, and other errors”).
Claim 38: Lefkofsky discloses the method of claim 2, wherein one or more of the determined similarity scores or one or more of the determined second similarity scores increase by a predetermined pathogenic short variant scoring value, when the test sample and the corresponding database sample share a pathogenic short variant affecting a same gene (0017, “the test result may be compared against one or more sets of data reflective of the subject's history of similar test results. The comparison may be performed, for instance, to develop a pattern unique to that subject. Test results may be processed by identifying personalized subject “diagnostic result” thresholds or scaling coefficients for each subject that compensates for the subject's deviation from normal expected results due to a characteristic unique to that subject” – the thresholds and scaling coefficients requiring a score on which to base similarity, 0052, “Features derived from DNA and RNA sequencing may include genetic variants which are present in the sequenced specime” and 0053, “analysis of the genetic variants may include additional steps such as identifying single or multiple nucleotide polymorphisms,” etc. – the variants including pathogenic short variants, 0223, “the system 101 may reference a subject similarity metric that quantifies a degree to which subjects are similar”);.
Claim 39: Lefkofsky discloses the method of claim 2, wherein one or more of the determined similarity scores or one or more of the determined second similarity scores increase by a predetermined same pathogenic effect scoring value, when the test sample and the corresponding database sample share a pathogenic short variant affecting a same gene with identical protein effects (0017, “the test result may be compared against one or more sets of data reflective of the subject's history of similar test results. The comparison may be performed, for instance, to develop a pattern unique to that subject. Test results may be processed by identifying personalized subject “diagnostic result” thresholds or scaling coefficients for each subject that compensates for the subject's deviation from normal expected results due to a characteristic unique to that subject” – the thresholds and scaling coefficients requiring a score on which to base similarity, 0052, “Features derived from DNA and RNA sequencing may include genetic variants which are present in the sequenced specime” and 0053, “analysis of the genetic variants may include additional steps such as identifying single or multiple nucleotide polymorphisms,” etc. – the variants including pathogenic short variants, 0223, “the system 101 may reference a subject similarity metric that quantifies a degree to which subjects are similar”);.
Claim 40: Lefkofsky discloses the method of claim 2, wherein one or more of the determined similarity scores or one or more of the determined second similarity scores increase by a predetermined pathogenic copy number amplification scoring value, when the test sample and the corresponding database sample share a pathogenic copy number amplification occurring on a same amplicon segment (0017, “the test result may be compared against one or more sets of data reflective of the subject's history of similar test results. The comparison may be performed, for instance, to develop a pattern unique to that subject. Test results may be processed by identifying personalized subject “diagnostic result” thresholds or scaling coefficients for each subject that compensates for the subject's deviation from normal expected results due to a characteristic unique to that subject” – the thresholds and scaling coefficients requiring a score on which to base similarity, 0053, “calculating copy number variation”, 0135, “the sequences are analyzed to identify genomic alterations (e.g., … copy number variations”), 0223, “the system 101 may reference a subject similarity metric that quantifies a degree to which subjects are similar”);.
Claim 41: Lefkofsky discloses the method of claim 2, wherein one or more of the determined similarity scores or one or more of the determined second similarity scores increases by a predetermined pathogenic copy number deletion scoring value, when the test sample and the corresponding database sample share a pathogenic copy number deletion occurring on a same commonly deleted segment (0017, “the test result may be compared against one or more sets of data reflective of the subject's history of similar test results. The comparison may be performed, for instance, to develop a pattern unique to that subject. Test results may be processed by identifying personalized subject “diagnostic result” thresholds or scaling coefficients for each subject that compensates for the subject's deviation from normal expected results due to a characteristic unique to that subject” – the thresholds and scaling coefficients requiring a score on which to base similarity, 0053, “calculating copy number variation”, 0135, “the sequences are analyzed to identify genomic alterations (e.g., … copy number variations”), 0223, “the system 101 may reference a subject similarity metric that quantifies a degree to which subjects are similar”);.
Claim 42: Lefkofsky discloses the method of claim 2, wherein one or more of the determined similarity scores or one or more of the determined second similarity scores increase by a predetermined pathogenic rearrangement scoring value, when the test sample and the corresponding database sample share a same two gene partners in a pathogenic rearrangement (0017, “the test result may be compared against one or more sets of data reflective of the subject's history of similar test results. The comparison may be performed, for instance, to develop a pattern unique to that subject. Test results may be processed by identifying personalized subject “diagnostic result” thresholds or scaling coefficients for each subject that compensates for the subject's deviation from normal expected results due to a characteristic unique to that subject” – the thresholds and scaling coefficients requiring a score on which to base similarity, 0124, “variant analysis of aligned sequence reads … includes identification of … genomic rearrangements (e.g., inversions, translocations, and gene fusions)”, 0223, “the system 101 may reference a subject similarity metric that quantifies a degree to which subjects are similar”);.
Claim 43: Lefkofsky discloses the method of claim 2, wherein one or more of the determined similarity scores or one or more of the determined second similarity scores increase by a predetermined same gene partner pathogenic rearrangement scoring value, when the test sample and the corresponding database sample share a same one gene partner in a pathogenic rearrangement (0017, “the test result may be compared against one or more sets of data reflective of the subject's history of similar test results. The comparison may be performed, for instance, to develop a pattern unique to that subject. Test results may be processed by identifying personalized subject “diagnostic result” thresholds or scaling coefficients for each subject that compensates for the subject's deviation from normal expected results due to a characteristic unique to that subject” – the thresholds and scaling coefficients requiring a score on which to base similarity, 0124, “variant analysis of aligned sequence reads … includes identification of … genomic rearrangements (e.g., inversions, translocations, and gene fusions)”, 0223, “the system 101 may reference a subject similarity metric that quantifies a degree to which subjects are similar”);.
Claim 44: Lefkofsky discloses the method of claim 2, wherein one or more of the determined similarity scores or one or more of the determined second similarity scores decrease by a predetermined non-common genomic alteration status scoring value, when the test sample and the corresponding database sample do not share a same genomic alteration status from the genomic alteration statuses (0017, “the test result may be compared against one or more sets of data reflective of the subject's history of similar test results. The comparison may be performed, for instance, to develop a pattern unique to that subject. Test results may be processed by identifying personalized subject “diagnostic result” thresholds or scaling coefficients for each subject that compensates for the subject's deviation from normal expected results due to a characteristic unique to that subject” – the thresholds and scaling coefficients requiring a score on which to base similarity, 0223, “the system 101 may reference a subject similarity metric that quantifies a degree to which subjects are similar”);.
Claim 45: Lefkofsky discloses the method of claim 2, wherein one or more of the determined similarity scores or one or more of the determined second similarity scores increase by a predetermined tumor mutational burden (TMB) scoring value, when the test sample and the corresponding database sample each have a TMB score above a predetermined TMB score threshold (0017, “the test result may be compared against one or more sets of data reflective of the subject's history of similar test results. The comparison may be performed, for instance, to develop a pattern unique to that subject. Test results may be processed by identifying personalized subject “diagnostic result” thresholds or scaling coefficients for each subject that compensates for the subject's deviation from normal expected results due to a characteristic unique to that subject” – the thresholds and scaling coefficients requiring a score on which to base similarity, 0138, “A bioinformatics pipeline may implement variant characterization via processes including a variant characterization DNA process and a variant characterization RNA process. Exemplary characterization subprocesses of the DNA process and RNA process includes … a TMB process to calculate tumor mutational burden”, 0223, “the system 101 may reference a subject similarity metric that quantifies a degree to which subjects are similar”).
Claim 46: Lefkofsky discloses the method of claim 2, wherein one or more of the determined similarity scores or one or more of the determined second similarity scores increase by a predetermined dominant mutational signature scoring value, when the test sample and the one database sample share a dominant mutational signature (0276, “For example, if subject 102 always returns a higher A1C with no other indications of diabetes, the model may generate a coefficient factor which compensates for the subject's natural propensity towards a higher A1C diagnostic testing result or may similarly increase the thresholds with which a physician should compare the diagnostic testing results. In another example, analysis module 180i may request a number of similar subjects based upon the subject's clinomic profile to form a cohort of similar subjects from which diagnosis and treatment comparisons may be performed. Comparisons between the diagnostics results, diagnosis, treatments, and treatment outcomes may be provided in a report to the subject's physician or visually displayed in a radial plot with informative callouts which allow the physician to explore analytics associated with the cohort in real time via an online portal. In yet another example, analysis module 180i may request diagnostic results from subjects who also received diagnostic test results from the same laboratory and, if available, results for the same diagnostic tests from other laboratories to identify any bias that may be introduced from that laboratory's specific processing of subject specimens. For example, a laboratory's specific testing procedures may naturally deliver results which are naturally higher than the results of another laboratory. In this manner, analysis module 180i may deliver one or more smart outputs to smart output 190. For example, a normal range of A1C level may be between 4% and 5.6% with higher levels suggesting a likelihood of being diabetic and levels exceeding 6.5% suggesting an active diagnosis of diabetes. A subject's ethnicity may inform A1C measurements such that mean A1C levels may actually hover around 5.78% for Caucasians, 5.93% for Hispanics, 6.00% for Asians, 6.12% for American Indians, and 6.18% for Africans. Therefore, analysis module 180i may apply a correction factor to generate a smart output which accounts for racial differences in A1C levels as diagnostic testing is performed on a subject”).
Claim 48: Lefkofsky discloses the method of claim 2, wherein one or more of the determined similarity scores or one or more of the determined second similarity scores increase by a predetermined high TMB and dominant mutational signature scoring value, when the test sample and the corresponding database sample share both the high TMB score and the dominant mutational signature (0017, “the test result may be compared against one or more sets of data reflective of the subject's history of similar test results. The comparison may be performed, for instance, to develop a pattern unique to that subject. Test results may be processed by identifying personalized subject “diagnostic result” thresholds or scaling coefficients for each subject that compensates for the subject's deviation from normal expected results due to a characteristic unique to that subject” – the thresholds and scaling coefficients requiring a score on which to base similarity, 0138, “A bioinformatics pipeline may implement variant characterization via processes including a variant characterization DNA process and a variant characterization RNA process. Exemplary characterization subprocesses of the DNA process and RNA process includes … a TMB process to calculate tumor mutational burden”, 0223, “the system 101 may reference a subject similarity metric that quantifies a degree to which subjects are similar”, 0276, “For example, if subject 102 always returns a higher A1C with no other indications of diabetes, the model may generate a coefficient factor which compensates for the subject's natural propensity towards a higher A1C diagnostic testing result or may similarly increase the thresholds with which a physician should compare the diagnostic testing results. In another example, analysis module 180i may request a number of similar subjects based upon the subject's clinomic profile to form a cohort of similar subjects from which diagnosis and treatment comparisons may be performed. Comparisons between the diagnostics results, diagnosis, treatments, and treatment outcomes may be provided in a report to the subject's physician or visually displayed in a radial plot with informative callouts which allow the physician to explore analytics associated with the cohort in real time via an online portal. In yet another example, analysis module 180i may request diagnostic results from subjects who also received diagnostic test results from the same laboratory and, if available, results for the same diagnostic tests from other laboratories to identify any bias that may be introduced from that laboratory's specific processing of subject specimens. For example, a laboratory's specific testing procedures may naturally deliver results which are naturally higher than the results of another laboratory. In this manner, analysis module 180i may deliver one or more smart outputs to smart output 190. For example, a normal range of A1C level may be between 4% and 5.6% with higher levels suggesting a likelihood of being diabetic and levels exceeding 6.5% suggesting an active diagnosis of diabetes. A subject's ethnicity may inform A1C measurements such that mean A1C levels may actually hover around 5.78% for Caucasians, 5.93% for Hispanics, 6.00% for Asians, 6.12% for American Indians, and 6.18% for Africans. Therefore, analysis module 180i may apply a correction factor to generate a smart output which accounts for racial differences in A1C levels as diagnostic testing is performed on a subject”).
Claim 49: Lefkofsky discloses the method of claim 2, wherein the one or more determined similarity scores or the one or more determined second similarity scores increase by a predetermined copy number signature scoring value, when the test sample and the corresponding database sample share a copy number signature (0053, “analysis of the genetic variants may include additional steps such as … calculating copy number variation).
Claim 50: Lefkofsky discloses the method of claim 2, wherein the one or more determined similarity scores or the one or more determined second similarity scores increase by a predetermined aneuploidy feature scoring value, when the test sample and the corresponding database sample share a common aneuploidy feature (0175, “[t]he variant location may include a chromosome number and a nucleotide position number to differentiate nucleotide positions that are located in the same chromosome, see also 0216 discussing chromosomal deletion).
Claim 56: Lefkofsky discloses the method of claim 2, further comprising determining a confidence value indicating whether the predetermined disease label is correctly confirmed or rejected (0223, “the system 101 may reference a subject similarity metric that quantifies a degree to which subjects are similar”).
Claim 58: Lefkofsky discloses the method of claim 2, wherein the predetermined disease label is rejected when;
the enrichment score for the predetermined disease label is less than or equal to a first predetermined enrichment score threshold (0017, “the subject's test results should be interpreted according to a personalized scale based on the subject diagnostic results threshold rather than established normal levels”), or
the confidence value is less than or equal to a first predetermined confidence value threshold (0017, “the subject's test results should be interpreted according to a personalized scale based on the subject diagnostic results threshold rather than established normal levels”); or
the enrichment score for the predetermined disease label is less than or equal to the first predetermined enrichment score threshold and the confidence value is less than or equal to the first predetermined confidence value threshold (0017, “the subject's test results should be interpreted according to a personalized scale based on the subject diagnostic results threshold rather than established normal levels”).
Claim 61: Lefkofsky discloses the method of claim 4, wherein the alternate disease label is accepted when;
the enrichment score for the alternate disease label is greater than or equal to a second predetermined enrichment score threshold (0017, “the subject's test results should be interpreted according to a personalized scale based on the subject diagnostic results threshold rather than established normal levels”); or
the confidence value is less than or equal to a second predetermined confidence value threshold (0017, “the subject's test results should be interpreted according to a personalized scale based on the subject diagnostic results threshold rather than established normal levels”); or
when the enrichment score for the predetermined disease label is greater than or equal to the second predetermined enrichment score threshold and the confidence value is less than or equal to the second predetermined confidence value threshold (0017, “the subject's test results should be interpreted according to a personalized scale based on the subject diagnostic results threshold rather than established normal levels”).
Claim 66: Lefkofsky discloses the method of selecting an anti-cancer therapy effective in treating a cancer, the method comprising:
responsive to confirming a predetermined disease label for the cancer according to the method of claim 2, selecting an anti-cancer therapy effective in treating the cancer (0217, “A Therapies module may identify differences in cancer cells (or other cells near them) that help them grow and thrive and drugs that “target” these differences. Treatment with these drugs is called targeted therapy”).
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 of this title, 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.
Claim 52 is rejected under 35 U.S.C. 103 as being unpatentable over Lefkofsky in view of Ajima (U.S. Patent Application Publication No. 2016/0242695) .
Claim 52: Lefkofsky discloses the method of claim 2, but does not appear to explicitly disclose wherein database samples corresponding to a predetermined number of most similar database samples are used for determining the enrichment score or the second enrichment score. Ajima, however, teaches that “The sample data refer to data obtained from the predetermined number of sample subjects.… Preferably, the predetermined number of sample subjects, for an improvement in accuracy of the estimation equations, is a statistically sufficient number and, simultaneously, is composed of a group having a distribution similar to a visceral fat distribution of the subjects of MS diagnosis”. Therefore, the Examiner understands and finds that to use a predetermined number of database samples is applying a known technique to a known device, method, or product ready for improvement to yield predictable results so as to provide statistically significant results.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine or modify the analysis of Lefkofsky with the sample number of Ajima in order to use a predetermined number of database samples so as to provide statistically significant results.
The rationale for combining in this manner is that to use a predetermined number of database samples is applying a known technique to a known device, method, or product ready for improvement to yield predictable results so as to provide statistically significant results as explained above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Bahado-Singh et al., . Deep Learning/Artificial Intelligence and Blood-Based DNA Epigenomic Prediction of Cerebral Palsy. Int. J. Mol. Sci. 2019, 20, 2075. https://doi.org/10.3390/ijms20092075. Downloaded 30 January 2026 from https://www.mdpi.com/1422-0067/20/9/2075, indicating “The etiology of cerebral palsy (CP) is complex and remains inadequately understood. Early detection of CP is an important clinical objective as this improves long term outcomes. We performed genome-wide DNA methylation analysis to identify epigenomic predictors of CP in newborns and to investigate disease pathogenesis” (at Abstract).
McDonald, J.H. 2014. Handbook of Biological Statistics (3rd ed.). Sparky House Publishing, Baltimore, Maryland, pp. 77-85, downloaded 3 February 2026 from https://www.biostathandbook.com/fishers.html, describing Fisher’s Exact Test and when and how to use it.
Aneuploidy, from the Cleveland Clinic, dated 25 August 2022, downloaded 25 February 2026 from https://my.clevelandclinic.org/health/diseases/24060-aneuploidy, indicating what is meant by the term “aneuploidy”.
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/SCOTT D GARTLAND/
Primary Examiner, Art Unit 3685