DETAILED ACTION
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 .
Applicant's response filed 6/1/2026 has been fully considered. The following rejections
and/or objections are either reiterated or newly applied.
Status of Claims
Claims 1-2, 6-14, 18-24, 28-30, and 34-35 pending and examined on the merits.
Claims 3-5, 15-17, 25-27, and 31-33 canceled.
Priority
The instant application filed on 7/13/2022 is a 371 national stage entry of PCT/GB2021/050087 having an international filing date of 1/15/2021, and claims the benefit of foreign priority to Application Nos. GB2000649.0 filed on 1/16/2020, GB2013386.4 filed 8/26/2020, and GB2013387.2 filed 8/26/2020. Thus, the effective filing date of the claims is 1/16/2020.
The applicant is reminded that amendments to the claims and specification must comply with 35 U.S.C. § 120 and 37 C.F.R. § 1.121 to maintain priority to an earlier-filed application. Claim amendments may impact the effective filing date if new subject matter is introduced that lacks support in the originally filed disclosure. If an amendment adds limitations that were not adequately described in the parent application, the claim may no longer be entitled to the priority date of the earlier filing.
Information Disclosure Statement
The IDS filed on 5/28/2026 has been entered and considered. A signed copy of the corresponding 1449 form has been included with this Office action.
Specification
The objections to the specification and drawings withdrawn in view of Applicant's claim amendments filed on 6/1/2026.
Claim Objections
The objection to claims 26, 30, and 32 withdrawn in view of Applicant's claim amendments and remarks filed on 6/1/2026.
Withdrawn Rejections
35 USC § 112(b)
The rejection of claims 1-6, 8-10, 13-21, and 24-35 under 35 USC 112(b) withdrawn in view of Applicant's claim amendments and remarks (or cancellation of claims 3-5, 15-17, 25-27, and 31-33) filed on 7/17/2026. The rejection of claims 11-12 and 22-23 under 35 USC 112(b) from the Office Action filed 3/10/2026 withdrawn in view of Applicant's claim amendments and remarks filed on 7/17/2026, however, these claims are newly rejected under 35 USC 112(b); see respective section below for details.
35 USC § 101
The rejection of claims 3-5, 15-17, 25-27, and 31-33 under 35 USC 101 withdrawn in view of Applicant's claim cancellations filed on 7/17/2026.
35 USC § 103
The rejection of claims 3-5, 15-17, 25-27, and 31-33 under 35 USC 103 withdrawn in view of Applicant's claim cancellations filed on 7/17/2026.
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 2, 7, 11-12, 14, and 22-23 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 2 and 14 recite "the screening system is operable to generate a graphical representation of the one or more phenotype-gene variant relationships for user-editing and adjustment on a graphical user interface", which is indefinite in view of the amendments to independent claims 1 and 13. The independent claims, as amended, recite “one or more phenotype-gene variant relationships” in the last two limitations (executing and generating). So it is not clear which one or more relationships is being referenced. To further prosecution, the limitation is interpreted as "the screening system is operable to generate a graphical representation of: the one or more phenotype-gene variant relationships based on the generated multi-dimensional data structure to specific historical data samples; or the one or more phenotype-gene variant relationships that have a probability that exceeds one or more threshold criteria to specific historical data samples".
Claim 7 recites "the one or more gene variants present in the compiled genome representative of the subject relative to the reference genome based to reduce stochastic errors due to at least one of: indels, call number variations (CNV's), substantial palindromes, incorrectly identified or mis- classified phenotypes". The language used is not clear regarding what the list of variants of referring to. To further prosecution, the claim is interpreted as "the one or more gene variants present in the compiled genome representative of the subject relative to the reference genome comprise at least one of: indels, call number variations (CNV's), substantial palindromes, or incorrectly identified or mis- classified phenotypes".
Claims 11 and 22 recite "the screening system includes a functionality for user-selection of a subset of the historical data samples of other subjects to test for a sensitivity or convergence of the one or more phenotype-gene variant relationships to specific historical data samples", which is indefinite in view of the amendments to independent claims 1 and 13. The independent claims, as amended, recite “one or more phenotype-gene variant relationships” in the last two limitations (executing and generating). So it is not clear which one or more relationships is being referenced. To further prosecution, the limitation is interpreted as "the screening system includes a functionality for user-selection of a subset of the historical data samples of other subjects to test for a sensitivity or convergence of: the one or more phenotype-gene variant relationships based on the generated multi-dimensional data structure to specific historical data samples; or the one or more phenotype-gene variant relationships that have a probability that exceeds one or more threshold criteria to specific historical data samples".
Claims 12 and 23 recite "the screening system, when in operation, determines a convergence of the one or more phenotype-gene variant relationships as a function of selection of the subset to determine an asymptotic trend of convergence in generation of the one or more phenotype-gene variant relationships", which is indefinite in view of the amendments to independent claims 1 and 13. The independent claims, as amended, recite “one or more phenotype-gene variant relationships” in the last two limitations (executing and generating). So it is not clear which one or more relationships is being referenced. To further prosecution, the limitation is interpreted as "the screening system, when in operation, determines a convergence of: the one or more phenotype-gene variant relationships based on the generated multi-dimensional data structure as a function of selection of the subset to determine an asymptotic trend of convergence in generation of the one or more phenotype-gene variant relationships based on the generated multi-dimensional data structure; or the one or more phenotype-gene variant relationships that have a probability that exceeds one or more threshold criteria as a function of selection of the subset to determine an asymptotic trend of convergence in generation of the one or more phenotype-gene variant relationships that have a probability that exceeds one or more threshold criteria".
Response to Arguments under 35 USC § 112
The rejection of claims 11-12, and 22-23 under 35 USC 112(b) are newly recited necessitated by claim amendments, and the rejection of claim 7 was previously recited.
The rejection of claim 7 under 35 USC 112(b) is maintained. Although Applicant has confirmed that "the examiner's interpretation of the claim as recited in the Office Action is accurate" (Remarks 6/1/2026 page 4), the limitation remains unamended and still does not clearly require the gene variants comprise the elements of "at least one of: indels, call number variations (CNV's), substantial palindromes, incorrectly identified or mis- classified phenotypes".
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.
The rejection of claims 1-2, 13-14, and 24 under 35 USC 101 contains newly recited portions necessitated by claim amendments, and the rest are previously recited.
Claims 1-2, 6-14, 18-24, 28-30, and 34-35 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of a mental process, a mathematical concept, organizing human activity, or a law of nature or natural phenomenon without significantly more. In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong 1). In the instant application, the claims recite the following limitations that equate to an abstract idea:
Claim 1, 13, and 24: “determines one or more gene variants present in the compiled genome representative of the subject relative to the reference genome based on a difference between the reference genome and the compiled genome representative of the subject” provides an evaluation (determining a difference between subject and reference sequence) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
“gene variant interpretation using a correlation function to identify one or more phenotype-gene variant relationships based on the generated multi-dimensional data structure” provides a mathematical relationship (identifying phenotype-gene variant relationships using a correlation function) that is considered a mathematical concept, which is an abstract idea.
“generates one or more Bayesian mappings, describing one or more phenotype-gene variant relationships that have a probability that exceeds one or more threshold criteria, employing an adaptive artificial intelligence or machine learning arrangement that comprises one or more models configured to receive the multi-dimensional data structure as input, wherein the multi-dimensional data structure comprises new patient data and/or new scientific information that are used to incrementally update said one or more Bayesian mappings” provides a mathematical calculation (generating Bayesian mappings as described in the spec page 20 requires calculation of statistical correlations) that is considered a mathematical concept, which is an abstract idea. The limitation also provides an evaluation (comparing a probability value to a threshold) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea. The limitation also provides a mathematical calculation (encompasses utilizing mathematical algorithms such as those listed in instant specification pages 22 and 26) that is considered a mathematical concept, which is an abstract idea.
Claim 2 and 14: “associate the one or more generated Bayesian mappings describing one or more phenotype-gene variant relationships with a secondary database of historical medical reports to identify one or more historical medical reports that are related in subject matter to the one or more generated Bayesian mappings” provides an evaluation (determining a difference between subject and reference sequence) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
Claim 12 and 23: “determines an asymptotic trend of the correlations of the one or more phenotype-gene variant relationships of the subject to the selected subset of the historical data samples” (as interpreted above) provides a mathematical relationship (determining a correlation involves identifying a mathematical relationship) that is considered a mathematical concept, which is an abstract idea.
These recitations are similar to the concepts of collecting information, analyzing it, and displaying certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), organizing and manipulating information through mathematical correlations in Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)) and comparing information regarding a sample or test to a control or target data in Univ. of Utah Research Found. v. Ambry Genetics Corp. (774 F.3d 755, 113 U.S.P.Q.2d 1241 (Fed. Cir. 2014)) and Association for Molecular Pathology v. USPTO (689 F.3d 1303, 103 U.S.P.Q.2d 1681 (Fed. Cir. 2012)) that the courts have identified as concepts that can be practically performed in the human mind or are mathematical relationships. Therefore, these limitations fall under the “Mental process” and “Mathematical concepts” groupings of abstract ideas. Additionally, while claims 1 and 24 recite performing some aspects of the analysis on “A screening system comprising a control circuitry” and “A computer program product comprising a non-transitory computer-readable storage medium having computer-readable instructions”, there are no additional limitations that indicate that this requires anything other than carrying out the recited mental processes or mathematical concepts in a generic computer environment. Merely reciting that a mental process is being performed in a generic computer environment does not preclude the steps from being performed practically in the human mind or with pen and paper as claimed. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental processes” grouping of abstract ideas. As such, claims 1-2, 6-14, 18-24, 28-30, and 34-35 recite an abstract idea (Step 2A, Prong 1: YES).
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). The judicial exceptions listed above are not integrated into a practical application because the claims do not recite an additional element or elements that reflects an improvement to technology. Specifically, the claims recite the following additional elements:
Claim 1: “A screening system comprising a control circuitry” provides insignificant extra-solution activities (running a system on generic computer components) that do not serve to integrate the judicial exceptions into a practical application.
Claim 1, 13, and 24: “receives a plurality of genomic sequences of a plurality of genomic fragments of at least one biological sample from a subject that has been sequenced in a sequencing apparatus” provides insignificant extra-solution activities (receiving genomic sequence data is a pre-solution activity involving data gathering steps) that do not serve to integrate the judicial exceptions into a practical application.
“aligns the plurality of genomic sequences to a reference genome to generate from the aligned genomic sequences a compiled genome representative of the subject” provides insignificant extra-solution activities (aligning or mapping genomic sequence data to a reference sequence is a pre-solution activity involving data manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
“receiving phenotype information of the subject, and historical data samples of other subjects including their one or more gene variants and their corresponding phenotype information” (as interpreted above) provides insignificant extra-solution activities (receiving phenotype and historical variant and phenotype data is a pre-solution activity involving data gathering steps) that do not serve to integrate the judicial exceptions into a practical application.
“generates a multi-dimensional data structure” provides insignificant extra-solution activities (structuring data is a post-solution activity involving data manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 2 and 14: “generate a graphical representation of the one or more phenotype-gene variant relationships for user-editing and adjustment on a graphical user interface, wherein the graphical representation also provides a visual indication of strengths of correlation” provides insignificant extra-solution activities (outputting and displaying data generated by the abstract idea are post-solution activities) that do not serve to integrate the judicial exceptions into a practical application.
Claim 8 and 19: “adds a copy of the one or more gene variants and the phenotype information of the subject to augment the historical data samples of other subjects including their corresponding phenotype information of the other subjects and their one or more gene variants” provides insignificant extra-solution activities (augmenting or padding data is a pre-solution activity involving data manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 9 and 20: “process the historical data samples of other subjects including their corresponding phenotype information of the other subjects and their one or more gene variants to enable the historic data samples to be communicated and shared with other screening systems, to allow for data to be shared to increase a total size of the historical data samples of other subjects” provides insignificant extra-solution activities (enabling data for sharing is a pre-solution activity involving data manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 10 and 21: “obfuscates the historical data samples of other subjects so that an identity of the other subjects is not discernible, wherein obfuscation is performed using at least one of: data extrapolation to generate additional synthetic subject data, or data blurring” provides insignificant extra-solution activities (anonymizing data is a pre-solution activity involving data manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 11 and 22: “a functionality for user-selection of a subset of the historical data samples” provides insignificant extra-solution activities (selecting data subsets for testing is a pre-solution activity involving data manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 24: “A computer program product comprising a non-transitory computer-readable storage medium having computer-readable instructions” provides insignificant extra-solution activities (running instructions on generic computer components) that do not serve to integrate the judicial exceptions into a practical application.
Claim 29 and 35: “the decision support information associated with the one or more gene variant-phenotype relationships for generating the Bayesian mappings are employed to train the adaptive artificial intelligence or machine or machine learning arrangement to update the Bayesian mappings” is generally linking the abstract idea to the technological environment of AI/ML AI/ML models.
The steps for receiving, aligning, structuring, augmenting, obfuscating, selecting, inputting, outputting, and visualizing data; and employing, iterating, and optimizing AI/ML models are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application because they are pre- and post-solution activities involving data gathering and manipulation steps (see MPEP 2106.04(d)(2)). Furthermore, the limitations regarding implementing program instructions do not indicate that they require anything other than mere instructions to implement the abstract idea in a generic way or in a generic computing environment. As such, this limitation equates to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. Finally, the "adaptive artificial intelligence or machine learning arrangement" (referred to here as a neural network (NN) model) of independent claims 1, 13, and 24 is used to generally apply the abstract idea (i.e., perform the Bayesian mappings) without placing any limitation on how the "the multi-dimensional data structure for generating said one or more Bayesian mappings reduces a susceptibility of the gene variant interpretation to be affected by the stochastic errors and stochastic distortion". The claim omits any details as to how the NN solves a technical problem and instead recites only the idea of a solution or outcome. See MPEP 2106.05(f). Therefore, the limitation represents no more than mere instructions to implement the abstract idea, which is equivalent to adding the words “apply it” to the recited judicial exception. In addition, the claim confines the use of the recited judicial exception recited in the independent claims to the technological environment of a NN by generally linking the use of the judicial exception to the recited NN. Therefore, this general NN recitation does not integrate the judicial exception into a practical application. See MPEP 2106.05(h). Therefore, it can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to a particular field of use or a technological environment. Therefore, claims 1-2, 6-14, 18-24, 28-30, and 34-35 are directed to an abstract idea (Step 2A, Prong 2: NO).
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application, or equate to mere instructions to apply the recited exception in a generic way or in a generic computing environment.
As discussed above, there are no additional elements to indicate that the claimed “A screening system comprising a control circuitry” and “A computer program product comprising a non-transitory computer-readable storage medium having computer-readable instructions” requires anything other than generic computer components in order to carry out the recited abstract idea in the claims. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. MPEP 2106.05(f) discloses that mere instructions to apply the judicial exception cannot provide an inventive concept to the claims. Additionally, the limitations for receiving, aligning, structuring, augmenting, obfuscating, selecting, inputting, outputting, and visualizing data; and employing, iterating, and optimizing AI/ML models are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application. Furthermore, no inventive concept is claimed by these limitations as they are well-understood, routine, and conventional. For conventionality of neural networks and generally receiving/transmitting data, see MPEP 2106.05(f)(2): Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone) and TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit).
The additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: No). As such, claims 1-2, 6-14, 18-24, 28-30, and 34-35 are not patent eligible.
Response to Arguments under 35 USC § 101
Applicant’s arguments filed 6/1/2026 are fully considered but they are not persuasive.
Applicant asserts that as now amended, "the claimed invention is directed to an improvement in the functioning of a computer" because the "incremental training of the screening system provides a technical advantage over conventional systems which are trained independently for analysis of similar types of data from diverse sources" and "other improvements include improved accuracy and reduced risk of misinterpretation of gene variants" (Remarks 6/1/2026 page 9). Examiner notes that the improvement has to be rooted in an additional element, which these alleged improvements are not, and cannot come from the judicial exceptions themselves (MPEP 2106.05(a) II: it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology; i.e. one cannot argue for "a better algorithm").
Applicant also asserts that "[a] particular technology for identifying phenotype-gene variant relationships is not an 'idea' at all", that "[i]t is a quintessential technological process that provides multiple technical improvements", and that "[c]laims directed to such quintessentially technological processes should be patent eligible subject matter" (Remarks 6/1/2026 pages 9-10). Examiner notes that the quintessence of a claim has nothing to do with its patent eligibility, and an Applicant's assertion that it "should be" is not persuasive. Examiner refers back to the immediately preceding paragraph regarding an improvement to technology.
Therefore, the rejection of claims 1-2, 6-14, 18-24, 28-30, and 34-35 under 35 USC 101 is maintained.
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 rejection of claims 1-2, 13, and 24 under 35 USC 103 contains newly recited portions necessitated by claim amendments, and the rest are previously recited.
Claims 1-2, 6-9, 11-14, 18-20, 22-24, 28-30, and 34-35 rejected under 35 U.S.C. 103 as being unpatentable over Deciu et al. (JP-2014534507) in view of Azab et al. (WO-2018136888) and Liang et al. (Journal of the American Statistical Association 113.523 (2018): 955-972).
Regarding independent claims 1, 13, and 24, Deciu teaches receiving a plurality of genomic sequences of a plurality of genomic fragments of at least one biological sample from a subject that has been sequenced in a sequencing apparatus, wherein the plurality of genomic sequences includes stochastic errors and stochastic distortion (Page 1 Abstract "obtaining a sequence read from the cell-free sample nucleic acid" and pages 18-19 list many nucleic acid sequencers that will produce sequence data having stochastic errors and/or distortion).
Deciu also teaches aligning the plurality of genomic sequences to a reference genome to generate from the aligned genomic sequences a compiled genome representative of the subject (Page 20 last paragraph "Nucleotide sequence reads (ie, sequence information from fragments whose physical genomic location is unknown) can be mapped in a number of ways, often with the resulting sequence reads and reference genome (Eg, Li et al., “Mapping short DNA sequencing reads and calling variants mapping quality score,” Genome Res., 2008 Aug 19.)").
Deciu also teaches determining one or more gene variants present in the compiled genome representative of the subject relative to the reference genome based on a difference between the reference genome and the compiled genome representative of the subject (Page 34 paragraph 3 "Classification module Copy number variation (eg, maternal and / or fetal copy number variation, fetal copy number variation, duplication, insertion, deletion) is classified by the classification module or by the device containing the classification module [] An increase (eg, a first increase) determined to be significantly different from another increase (eg, a second increase) may be identified by the classification module as representing a copy number polymorphism").
Deciu also teaches receiving phenotype information of the subject, and historical data samples of other subjects including their one or more gene variants and their corresponding phenotype information (as interpreted above) (Page 50 paragraph 11 "There are functional data to confirm the effects of multigene administration, there are confirmed or strong candidate genes, clinical management related items are defined, and the cancer risk rate is known, including the meaning of monitoring, There are multiple sources (OMIM, GeneReviews, Orpha net, Unique, Wikipedia) and/ or available for diagnostic use (pregnancy counseling)").
Deciu also teaches generating a multi-dimensional data structure that includes the one or more gene variants in respect of a first dimension and the phenotype information in respect of a second dimension (Page 45 paragraph 6 "the data or data set can be organized into a matrix having two or more dimensions based on one or more features or variables. Data organized in a matrix can be organized using any suitable feature or variable. Non-limiting examples of matrix data include data organized by maternal age, maternal ploidy, and fetal contributions").
Deciu also teaches executing a gene variant interpretation using a correlation function to identify one or more phenotype-gene variant relationships based on the generated multi-dimensional data structure, wherein using the multi-dimensional data structure reduces a susceptibility of the gene variant interpretation to be affected by the stochastic errors and stochastic distortion (Page 29 paragraph 9 "Compare profiles created in a test subject with profiles created in one or more reference subjects to facilitate interpretation and / or provide results for mathematical and / or statistical manipulation of datasets" and Page 37 paragraph 14 "a regulated increase in profile is compared. []. An anomaly or error can be a profile or rising peak or dip, where the cause of the peak or dip is known or unknown. In some examples, adjusted elevations are compared and an anomaly or error is identified if the anomaly or error is due to a stochastic, systematic, random or user error. The adjusted rise may be compared and anomalies or errors may be removed from the profile. In some examples, adjusted rises are compared and abnormalities or errors are adjusted").
Deciu does not explicitly teach: a set of data samples including one or more gene variants representative of the subject and their corresponding phenotype information, and corresponding historical data samples of other subjects including their one or more gene variants and their corresponding phenotype information (as interpreted above); generating one or more Bayesian mappings describing one or more phenotype-gene variant relationships that have a probability that exceeds one or more threshold criteria, employing an adaptive artificial intelligence or machine learning arrangement to generate the one or more Bayesian mappings; and the multi-dimensional data structure serves as input to one or more models configured to generate the one or more Bayesian mappings; nor the multi-dimensional data structure comprises new patient data and/or new scientific information that may be used to incrementally update said one or more Bayesian mappings.
However, Azab teaches comparing subject variant profiles to one or more reference subjects (historical data) and applying a variant calling algorithm to historical data (Page 32 paragraph 3 "A profile generated for a test subject sometimes is compared to a profile generated for one or more reference subjects, to facilitate interpretation of mathematical and/or statistical manipulations of a data set and/or to provide an outcome" and page 192 line 23 "a position specific variant calling algorithm is applied to position specific data generated as described above. Generally, the input to the algorithm is a list of loci (or filtered list of loci) along with historical data pertaining to the loci").
Azab also suggests the limitation of the multi-dimensional data structure comprises new patient data and/or new scientific information that may be used to incrementally update said one or more Bayesian mappings (page 41 paragraph 3 "In certain embodiments, several algorithms may be implemented for use in software. These algorithms can be trained with raw data in some embodiments. For each new raw data sample, the trained algorithms may produce a representative processed data set or outcome. A processed data set sometimes is of reduced complexity compared to the parent data set that was processed. Based on a processed set, the performance of a trained algorithm may be assessed based on sensitivity and specificity, in some embodiments. An algorithm with the highest sensitivity and/or specificity may be identified and utilized, in certain embodiments"), and it would have been obvious to one of ordinary skill in the art to perform the claimed analysis on a new data set of the same kind, as such a modification represents a predictable variation of known techniques.
However, Liang teaches generating one or more Bayesian mappings describing one or more phenotype-gene variant relationships that have a probability that exceeds one or more threshold criteria, employing an adaptive artificial intelligence or machine learning arrangement to generate the one or more Bayesian mappings; and the multi-dimensional data structure serves as input to one or more models configured to generate the one or more Bayesian mappings (Page 1 abstract "The proposed method is successfully applied to identification of the genes that are associated with anticancerdrug sensitivities based on the data collected in the cancer cell line encyclopedia (CCLE) study"; Page 11 paragraph 1 "The proposed approach is to choose the variables for which the marginal inclusion probability is greater than a threshold value"; and Page 19 paragraph 2 "For comparison, we have also applied the generalized additive model (GAM), random forest (RF), Bayesian adaptive regression trees (BART), and Bayesian regularized neural network (BRNN) to this example").
Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the methods of Deciu as taught by Azab in order to distinguish signal from background noise by using data from many samples (page 192 line 27 "a position specific classification model which can distinguish signal from background noise by utilizing information residing in a cohort of samples"). One skilled in the art would have a reasonable expectation of success because both methods are using patient population data to predict gene variant phenotypes for medical decision support.
Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the methods of Deciu as taught by Liang in order to approximate any continuous functions on compact subsets to any desired degree of accuracy (page 2 last paragraph "The proposed method is motivated by the universal approximation ability of the feed-forward neural networks (FNNs)(Cybenko, 1989; Funahashi, 1989; Hornik et al., 1989), i.e., an FNN is capable of approximating any continuous functions on compact subsets to any desired degree of accuracy"). One skilled in the art would have a reasonable expectation of success because both methods are concerned with identifying genotype-phenotype relationships (biomarker discovery in Liang's case).
Regarding claims 2 and 14, Deciu in view of Azab and Liang teach the methods of Claims 1 and 13 on which this claim depends/these claims depend, respectively. Azab also teaches generating a graphical representation of the one or more phenotype-gene variant relationships for user-editing and adjustment on a graphical user interface, wherein the graphical representation also provides a visual indication of strengths of correlation (Page 144 line 17 "Machines, software and interfaces may be used to conduct methods described herein. Using machines, software and interfaces, a user may enter, request query or determine options for using particular information, programs or processes (e.g., mapping sequence reads, processing mapped data and/or providing an outcome), which can involve implementing statistical analysis algorithms, statistical significance algorithms, statistical algorithms, iterative steps, validation algorithms, and graphical representations" and page 145 line 27 "Systems addressed herein may comprise general components of computer systems, such as, for example, [], or other output useful for providing visual, auditory and/or hardcopy output of information (e.g., outcome and/or report)").
Azab also teaches associating the one or more generated Bayesian mappings describing one or more phenotype-gene variant relationships with a secondary database of historical medical reports containing data samples of other subjects including their one or more gene variants and their corresponding phenotype information to identify one or more historical medical reports that are related in subject matter to the one or more generated Bayesian mappings (as interpreted above) (Page 84 line 15 "Portions can be filtered and/or selected according to any suitable feature or parameter that correlates with a feature or parameter listed or described herein. Portions can be filtered and/or selected according to features or parameters that are specific to a portion (e.g., as determined for a single portion according to multiple samples) and/or features or parameters that are specific to a sample (e.g., as determined for multiple portions within a sample").
Regarding claims 6 and 18, Deciu in view of Azab and Liang teach the methods of Claims 1 and 13 on which this claim depends/these claims depend, respectively. Azab also teaches using the identified one or more generated Bayesian mappings and the identified one or more historical medical reports to provide decision support information in respect of the subject (Page 123 "Decision Analysis" section line 21 "For example, a decision analysis sometimes comprises applying one or more methods that produce one or more results, an evaluation of the results, and a series of decisions based on the results, evaluations and/or the possible consequences of the decisions and terminating at some juncture of the process where a final decision is made").
Regarding claims 7 and 30, Deciu in view of Azab and Liang teach the methods of Claim 1 on which this claim depends/these claims depend, respectively. Azab also teaches the one or more gene variants present in the compiled genome representative of the subject relative to the reference genome comprise at least one of: indels, call number variations (CNV's), substantial palindromes, or incorrectly identified or mis- classified phenotypes (as interpreted above) (Page 195 line 4 "Certain terms used in the methods below include: CNV (copy number variant); GVCF (genomic variant call format); INDEL (short insertion and deletion, e.g. < 100 bp); SNV (single nucleotide variant); VCF (variant call format); and variant classification (function annotations specified by HGVS (Human Genome Variation Society) implemented in snpEff (genetic variant annotation and effect prediction toolbox)", as snpEff contains output annotations for input variants).
Regarding claims 8 and 19, Deciu in view of Azab and Liang teach the methods of Claims 1 and 13 on which this claim depends/these claims depend, respectively. Azab also teaches adds a copy of the one or more gene variants and the phenotype information of the subject to augment the historical data samples of other subjects including their corresponding phenotype information of the other subjects and their one or more gene variants (padding data) (Page 115 line 5 "A profile comprising one or more levels is sometimes padded (e.g., hole padding). Padding (e.g., hole padding) refers to a process of identifying and adjusting levels in a profile that are due to copy number alterations (e.g., microduplications or microdeletions in a patient's genome, maternal microduplications or microdeletions)").
Regarding claims 9 and 20, Deciu in view of Azab and Liang teach the methods of Claims 1 and 13 on which this claim depends/these claims depend, respectively. Azab also teaches processing the historical data samples of other subjects including their corresponding phenotype information and their one or more gene variants to enable the historic data samples to be communicated and shared with other screening systems (Page 137 line 4 "report may be generated by a computer and/or by human data entry, and can be transmitted and communicated using a suitable electronic medium (e.g., via the internet, via computer, via facsimile, from one network location to another location at the same or different physical sites), or by another method of sending or receiving data (e.g., mail service, courier service and the like)").
Regarding claims 11 and 22, Deciu in view of Azab and Liang teach the methods of Claims 1 and 13 on which this claim depends/these claims depend, respectively. Azab also teaches a functionality for user-selection of a subset of the historical data samples of other subjects to test for a correlation of the one or more phenotype-gene variant relationships to specific historical data samples (as interpreted above) (Page 53 line 4 "In some embodiments, an outcome comprises factoring the minority species fraction in the sample nucleic acid (e.g., adjusting counts, removing samples, making a call or not making a call)").
Regarding claims 12 and 23, Deciu in view of Azab and Liang teach the methods of Claims 1 and 13 on which this claim depends/these claims depend, respectively. Azab also teaches determining an asymptotic trend of the correlations of the one or more phenotype-gene variant relationships of the subject to the selected subset of the historical data samples (as interpreted above) (Page 103 line 18 "Non-limiting examples of a relationship include a mathematical and/or graphical representation of a function, a correlation, a distribution, a linear or non-linear equation, a line, a regression, a fitted regression, the tike or a combination thereof. Sometimes a relationship comprises a fitted relationship. In some embodiments a fitted relationship comprises a fitted regression").
Regarding claims 29 and 35, Deciu in view of Azab and Liang teach the methods of Claims 1 and 13 on which this claim depends/these claims depend, respectively. The following claims are either still simply receiving data just from different subjects, or are routine, iterative optimization of models which both are obvious in view of either Deciu or Azab: the decision support information associated with the one or more gene variant-phenotype relationships for generating the Bayesian mappings are employed to train the adaptive artificial intelligence or machine or machine learning arrangement to update the Bayesian mappings (training a second model on the mappings). It would have been obvious to one of ordinary skill in the art to perform the claimed analysis on a new data set of the same kind, as such a modification represents a predictable variation of known techniques.
Regarding claims 28 and 34, Deciu in view of Azab and Liang teach the methods of Claims 1 and 13 on which this claim depends/these claims depend, respectively. Azab also teaches the decision support information is selected from a group comprising: patient name, date of birth, Lab ID, phenotype summary, Year of birth, family, clinical presentation, comments, data type, HPO terms, primary findings for decision support, and secondary findings for decision support (Page 139 line 18 "Non-limiting examples of recommendations that can be provided based on an outcome or classification in a laboratory report includes, without limitation, surgery, radiation therapy, chemotherapy, genetic counseling, after-birth treatment solutions (e.g., life planning, long term assisted care, medicaments, symptomatic treatments), pregnancy termination, organ transplant, blood transfusion, further testing described in the previous paragraph, the like or combinations of the foregoing. Thus, methods for treating a subject and methods for providing health care to a subject sometimes include generating a classification for presence or absence of a genotype, phenotype, a genetic variation and/or a medical condition for a test sample by a method described herein, and optionally generating and transmitting a laboratory report that includes a classification of presence or absence of a genotype, phenotype, genetic variation and/or medical condition for the test sample").
Claims 10 and 21 rejected under 35 U.S.C. 103 as being unpatentable over Deciu et al. (JP-2014534507) in view of Azab et al. (WO-2018136888) and Liang et al. (Journal of the American Statistical Association 113.523 (2018): 955-972) as applied to claims 1-2, 6-9, 11-14, 18-20, 22-24, 28-30, and 34-35 above, and further in view of Al-Zubaidie et al. (Al-Zubaidie et al. International Journal of Environmental Research and Public Health 16.9 (2019): 1490).
Deciu et al. in view of Azab et al. and Liang et al. are applied to claims 1-2, 6-9, 11-14, 18-20, 22-24, 28-30, and 34-35.
Regarding claim 10 and 21, Deciu in view of Azab and Liang teach the method of Claims 1 and 13 on which this claim depends/these claims depend.
Deciu nor Azab explicitly teach anonymizing the historical data samples of other subjects.
However, Al-Zubaidie teaches an anonymization system used for electronic health records, of which variant data and medical records may be a part of (Page 3 first bullet "we integrate two existing models (ABAC and RBAC) to develop a system that provides handling of patients’ information at the coarse-grained and fine-grained levels" and page 8 paragraph 2 "As shown in Figure 4, Pseudonymization and Anonymization with the XACML (PAX) is an authorisation system that works with HER").
Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the methods of Deciu and Azab as taught by Al-Zubaidie in order to address security and privacy problems associated with users (page 1 Abstract "To address the security and privacy problems associated with specific users, we develop the Pseudonymization and Anonymization with the XACML (PAX) modular system, which depends on client and server applications. It provides a security solution to the privacy issues and the problem of safe-access decisions for patients’ data in the EHR. The results of theoretical and experimental security analysis prove that PAX provides security features in preserving the privacy of healthcare users and is safe against known attacks"). One skilled in the art would have a reasonable expectation of success because both methods use patient data for research or decision making.
Response to Arguments under 35 USC § 103
Applicant’s arguments filed 6/1/2026 are fully considered but they are not persuasive.
Applicant asserts that, regarding independent claim 1, "Azab fails to teach generating one or more Bayesian mappings, where said Bayesian mappings are generated using a multiple-dimensional data structure including the specific data elements recited in the claims" (Remarks 6/1/2026 page 11). Examiner notes that the instant specification on page 20 characterizes the Bayesian mappings as resulting from "the execution of the correlation function in relation to the latent variables". And as previously recited for the rejections of canceled claims 3 and 5 (which have been rolled up into the independent claims), Azab teaches executing correlations and thresholding using a Bayesian confidence interval (see citations above).
Applicant also asserts that, regarding independent claim 1, while Azab includes multiple references to training models and algorithms, it "does not disclose incrementally updating/training Bayesian mappings or any other models or algorithms using new patient data and/or new scientific information" (Remarks 6/1/2026 page 11). Examiner finds this argument persuasive, however a new rejection necessitated by amendment has been added above, in which Liang does in fact teach or suggest this limitation (Page 1 abstract "The proposed method is successfully applied to identification of the genes that are associated with anticancerdrug sensitivities based on the data collected in the cancer cell line encyclopedia (CCLE) study"; Page 11 paragraph 1 "The proposed approach is to choose the variables for which the marginal inclusion probability is greater than a threshold value"; and Page 19 paragraph 2 "For comparison, we have also applied the generalized additive model (GAM), random forest (RF), Bayesian adaptive regression trees (BART), and Bayesian regularized neural network (BRNN) to this example"), and it would have been obvious to one of ordinary skill in the art to perform the claimed analysis on a new data set of the same kind, as such a modification represents a predictable variation of known techniques.
Therefore, the rejection of independent claims 1, 13, and 24 under 35 USC 103 is maintained. All other claims depend from these independent claims; therefore, their rejection is likewise maintained.
Conclusion
No claims are allowed.
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 TH REE-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 finaI action.
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/R.A.P./Examiner, Art Unit 1686
/KAITLYN L MINCHELLA/Primary Examiner, Art Unit 1685
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