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 .
Status of the Claims
Claims 1-54 are pending and examined herein.
No claims are canceled.
Priority
As detailed on the date filing receipt, the application claims priority as early as 20 June 2019. At this point in examination, all claims have been interpreted as being accorded this priority date as the effective filing date.
Information Disclosure Statement
The information disclosure statements (IDS) were submitted on 20 December 2021, 13 April 2023, and 18 April 2025. The submissions are in compliance with the provisions of 37 CFR 1.97. Accordingly, the references are being considered by the examiner.
Withdrawn Objections & Rejections
The objection to the drawings is withdrawn in view of the filing to amended Figure 2.
The objection to the disclosure regarding the URLs (pg. 25, paragraph [92]); pg. 27, paragraph [111]) is withdrawn in view of persuasive argument (pg. 16).
The objection to the specification is withdrawn in view of correction of a typographical error.
The objection to claims 4, 19, 34, and 46 is withdrawn in view of amendment changing the form of “identifying.”
The objection to claim 8 is withdrawn in view of amendment to read “identifying a subset of biomarkers”.
The following objections and/or rejections constitute the complete set of objections and/or rejections for the instant claims.
Drawings
The drawings are objected to because Figures 6A and 6B have legends which are uninformative without color. While reference to color is partially removed from paragraphs [29-30]), this does not render the drawing informative. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The specification discloses color images (pg. 5-6, paragraphs [29-30]) as “saturation color” in the legend. Because the drawings are not in color, this paragraph must be amended. A petition may be filed to request color in the drawings.
Appropriate correction is required.
Claim Objections
Claim 4 is objected to because of the following informalities: the claim reads “identifying of at least one biomarker” rather than “identifying . Appropriate correction is required.
35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-54 are rejected under 35 USC § 101 because the claimed inventions are directed to an abstract idea without significantly more. "Claims directed to nothing more than abstract ideas (such as a mathematical formula or equation), natural phenomena, and laws of nature are not eligible for patent protection" (MPEP 2106.04 § I). Abstract ideas include mathematical concepts, and procedures for evaluating, analyzing or organizing information, which are a type of mental process (MPEP 2106.04(a)(2)). The claims as a whole, considering all claim elements individually and in combination, are directed to a judicial exception at Step 2A, Prong 2, and the additional elements of the claims, considered individually and in combination, do not provide significantly more at Step 2B than the abstract idea of creating synthetic biological data.
MPEP 2106 organizes JE analysis into Steps 1, 2A (Prong One & Prong Two), and 2B as analyzed below.
Step 1: Are the claims directed to a process, machine, manufacture, or composition of matter (MPEP 2106.03)?
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e., a law of
nature, a natural phenomenon, or an abstract idea (MPEP 2106.04(a-c))?
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application by an additional element (MPEP 2106.04(d))?
Step 2B: Do the claims recite a non-conventional arrangement of elements in addition to any identified judicial exception(s) (MPEP 2106.05)?
Step 1: Are the claims directed to a 101 process, machine, manufacture, or composition of matter (MPEP 2106.03)?
The claims are directed to a method (claims 1-30) and a non-transitory computer-readable medium (claims 31-54), each of which falls within one of the categories of statutory subject matter. [Step 1: Yes]
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e., a law of nature, a natural phenomenon, or an abstract idea (MPEP 2106.04(a-c))?
With respect to Step 2A, Prong One, the claims recite judicial exceptions in the form of abstract ideas. MPEP § 2106.04(a)(2) further explains that abstract ideas are defined as:
• mathematical concepts (mathematical formulas or equations, mathematical relationships
and mathematical calculations) (MPEP 2106.04(a)(2)(I));
• certain methods of organizing human activity (fundamental economic principles or practices, managing personal behavior or relationships or interactions between people) (MPEP 2106.04(a)(2)(II)); and/or
• mental processes (concepts practically performed in the human mind, including observations, evaluations, judgments, and opinions) (MPEP 2106.04(a)(2)(III)).
Claims 1 and 31 recite creating input vectors based on the data signature. Creating a vector is interpreted as generating a string of numerical values. Generating a string of number is therefore a mathematical concept. Additionally, creating a string of numbers can be practically performed by the human mind.
Claims 1 and 31 recite a machine learning platform. A machine learning platform, in a broadest reasonable interpretation in light of the specification, includes abstract ideas. The specification discloses the platform takes input vectors, which are abstract, and outputs generated data in a vector, where data is also abstract. Therefore, the broadest reasonable interpretation of the machine learning platform encompasses embodiments that are purely mathematical functions to analyze the input vector and create the output vector, or a mental process of analyzing said data.
Claims 1 and 31 recite generating a predicted biological data signature and the machine learning platform includes a model trained on data from whole blood. For the reasons explained above, the generation of the output is part of the abstract ideas as the machine learning platform may be a mathematical concept or mental process. The model being trained on certain data is additional information about the platform, which is interpreted as abstract; training the model does not change the model from being abstract under a broadest reasonable interpretation. Furthermore, the training is disclosed as using a back propagation algorithm selected by optimizing the loss function (pg. 30, paragraph [141]), which are mathematical concepts.
Claims 1 and 31 recite preparing a report. Preparation can be performed mentally and a physical product in addition to the abstract steps is not yet generated.
Claim 1 recites providing the report to the subject or a medical professional. This step does not require a computer or other interface and so, under a broadest reasonable interpretation, includes an individual disclosing the results to the subject or medical professional, and thus is interpreted as a method of organizing human activity (MPEP 2106.04(a)(2)(II)).
Dependent claims 2 and 32 recite performing one or more of the steps of the independent claims again, and the independent claim recites judicial exceptions.
Dependent claims 3 and 33 recite “comparing” signatures, “determining a difference,” and “preparing a report,” where comparing and determining a difference and preparing to report on such a difference are practically performed by the human mind as data evaluation.
Dependent claims 4-5 and 34-35 recites “identifying at least biological target,” where the human mind is practically equipped to perform such a selection.
Dependent claims 7 and 37 recite correlating data, where, under a broadest reasonable interpretation, a correlation may be a mathematical concept or mental process, as the human mind can form an opinion or evaluate the relationship between data.
Dependent claims 8 and 38 recite “performing feature importance analysis for ranking” and “identifying a subset biomarkers.” Feature importance analysis can be interpreted as a mental process of ranking, as the human mind can evaluate or form an opinion in which the items are ordered, or a mathematical concept generating values for ranking numerically. Identification of a set of biomarkers is interpreted as a mental process as the human mind can make a selection.
Dependent claims 9 and 39 recite “identifying at least one biological target,” where the human mind is practically equipped to perform identification.
Dependent claims 10 and 40 recite “correlating the predicted biological data signature with a predicted biological age,” where, under a broadest reasonable interpretation, a correlation may be a mathematical concept or mental process, as the human mind can form an opinion or evaluate the relationship between data.
Dependent claims 12 and 42 recite additional information about the signature in that it is a simulation, where the simulation is disclosed as increasing biological age (pg. 13, paragraph [49]), which is a mathematical concept.
Dependent claims 13 and 43 recite “conditioning latent codes of the input vectors” and is interpreted as applying a probability function and thus a mathematical concept.
Dependent claims 14 and 44 recite additional information about the data.
Dependent claims 15 and 45 recite additional information about the data being a simulation, where the simulation is disclosed as increasing biological age (pg. 13, paragraph [49]), which is a mathematical concept.
Dependent claims 16 and 46 recite identification in difference in a biomarker, and the human mind is practically equipped to evaluate or judge such a difference.
Dependent claims 17 and 47 recite identification of a biological target, and the human mind is practically equipped to perform such an identification.
Dependent claims 18 and 48 recite performing previously discussed steps, a comparison of outputs interpreted as a mental process, and determining a change in the data, which is interpreted as a mental process.
Dependent claims 19-20 and 49-50 recite identification of a difference, which is interpreted as a mental process because the human mind is practically equipped to determine a difference.
Dependent claims 21 and 51 recite determining a rate and tracking change, which can be interpreted as a mental process or mathematical concept as the human mind is practically equipped to determine rate change or rate of change may be interpreted as a mathematical change.
Dependent claim 22 recites repeating steps, comparing reports, determining a change and the nature of the change, and determining whether to continue, change, or stop the regimen, which are abstract ideas in the form of mental processes, where steps like comparing data, determining change in the data, and determining a course of action are mental steps.
Dependent claim 23 recites additional information about the predicted signature, which is an abstract idea as the predicted signature is the output of a mathematical concept or mental process.
Dependent claim 24 recites data comparison, which is an abstract idea in the form of a mental process where the human mind is practically equipped to compare data.
Dependent claim 25 recites a data prediction, which is a mental process because a prediction is something the human mind is equipped to do.
Dependent claims 26-27 and 52-53 recite performing “biological signal activation analysis” which, under a broadest reasonable interpretation, includes abstract ideas, and determining a health status is interpreted as a mental process.
Dependent claims 28 and 54 recite tracking aging rate, which is interpreted as a mental process as the human mind is equipped to observe data change.
Dependent claim 29 recites a prediction, which is a mental process, where the human mind can perform a prediction as a form of data evaluation.
Dependent claim 30 identifying a therapeutic protocol, which is a mental process as the human mind can identify or select data.
Hence, the claims explicitly recite numerous elements that, individually and in combination,
constitute abstract ideas. The claims must therefore be examined further to determine whether they
integrate that abstract idea into a practical application (MPEP 2106.04(d)). [Step 2A: Yes]
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application by an additional element (MPEP 2106.04(d))?
Claim 1 recites “receiving a real biological data signature” and “inputting the input vectors,” which are interpreted as elements in addition to the abstract idea. Claim 31 recites these limitations as well as a non-transitory computer readable medium.
Dependent claim 6 recites additional information about the biological signature.
Dependent claim 11 recites “obtaining the biological sample from the subject” and “obtaining the real biological data signature by performing a measurement.”
Claim 22 recites performing a therapeutic regimen.
Claims 1, 6, 11, and 31 recite data collection and inputting for the abstract idea of generating the predicted biological data signature. Therefore, these are interpreted as insignificant extra solution activities and thus do not integrate the abstract idea into a practical application (MPEP 2106.05(g)). Claim 6 recites additional information about the collected data, which does not change the data gathering step.
Similarly, providing the report is a mere data outputting, which is data output and thus also insignificant extra-solution activity (MPEP 2106.05(g)).
Claim 22 recites performing a therapeutic regimen. The therapeutic regimen is not particularly stated. Furthermore, it is interpreted as necessary data collection to perform the comparing and determining steps, which are abstract idea, and thus is also insignificant extra solution activity (MPEP 2106.05(g)).
Claim 31 recites a computer program performing the steps of the method. The claims are interpreted as stating a generic computer performs the functions that constitute the abstract idea. Hence, these are mere instructions to apply the abstract idea using a computer, and therefore the claim does not integrate that abstract idea into a practical application (see MPEP 2106.04(d) § I; and MPEP 2106.05(f)).
Thus, the judicial exceptions are not integrated into a practical application by the additional elements. [Step 2A Prong Two: No]
Step 2B: Do the claims recite a non-conventional arrangement of elements in addition to any identified judicial exception(s) (MPEP 2106.05)?
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 of 101 analysis determines whether the claims contain additional elements that amount to an inventive concept, and an inventive concept cannot be furnished by an abstract idea itself (MPEP 2106.05).
Claim 1 recites “receiving a real biological data signature” and “inputting the input vectors,” which are interpreted as elements in addition to the abstract idea. Claim 31 recites these limitations as well as a non-transitory computer readable medium. Dependent claim 6 recites additional information about the biological signature. Dependent claim 11 recites “obtaining the biological sample from the subject” and “obtaining the real biological data signature by performing a measurement.” Claim 22 recites performing a therapeutic regimen.
Receiving and inputting data is interpreted as receiving or transmitting data over a network (Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362). A review by Khan (Aging Cell 16(4): 624-633, 2017; newly cited) teaches signatures of senescence (pg. 625, col. 2, last paragraph) or hallmarks of aging (pg. 625, col. 1, first paragraph) which are input into a mathematical model (pg. 626, Fig. 2 caption) and therapies to restore decline and homeostenosis (pg. 625, col. 1, first paragraph), and outputting an age score based on a patient (Fig. 2).
Therefore, the recited additional elements, alone or in combination with the judicial exceptions, do not appear to provide an inventive concept. [Step 2B: No]
Conclusion: Claims are Directed to Non-statutory Subject Matter
For these reasons, the claims, when the limitations are considered individually and as a whole,
are directed to an abstract idea and lack an inventive concept. Hence, the claimed invention does not
constitute significantly more than the abstract idea, so the claims are rejected under 35 USC § 101 as
being directed to non-statutory subject matter.
Response to the 08 December 2025 Applicant Remarks
Applicant remarks state the receiving of a real biological data signature is not abstract at Step 2A Prong One, which is agreed to and reflected in the office action (pg. 19, third paragraph). However, Applicant remarks further state the data includes omic measurements from a real sample from the subject. The data comprising the data signature does not materially change the data signature from being data, which is abstract. Receiving of the data is the aspect of the limitation which renders it an element in addition to the abstract ideas. It is not clear how the data having been obtained from a sample links the claim to a real world implementation.
Applicant remarks state the independent claims do not recite mathematical concepts in the limitations of creating input vectors, using the machine learning platform, and outputting a predicted signature (pg. 20). Under a broadest reasonable interpretation, a creating vectors, which are interpreted as number strings, analyzing said number strings, and outputting data based on the analysis. Figures 5 and 6 are interpreted as showing the output of the analyses numerically in terms of expression and age (Fig. 5) and up- and down-expression (Fig. 6). However, even if the data are not analyzed mathematically, the steps can be interpreted as mental steps of evaluating data. Therefore, the steps are still interpreted as reciting abstract ideas.
Applicant remarks state the human mind cannot prepare a report by virtue of being omic data (pg. 21, second paragraph). It is not clear that omic data connotes complexity such that a report or output cannot be prepared in the human mind, nor is a computer required by at least claim 1. Applicant points to CyberSource, 654 F.3d at 1376, 99 USPQ2d at 1699, which is directed to detecting suspicious activity using network monitors and analyzing network packets, as evidence that the human mind cannot prepare a report on gene expression data signatures. Network monitoring and network packets are required to occur in a technical setting, whereas data analysis is not. The amendment of claim 1 to recite providing the report to the subject or a medical professional. However, as explained above, interpretation of this step encompasses transmitting information between individuals and thus a method of organizing human activity. Even if it was not interpreted as such, outputting data is insignificant extra-solution activity and is a conventional computer function, and thus would not overcome the rejection.
Applicant remarks state the analysis of the elements at Step 2A Prong One are summarily stated (pg. 21, last paragraph). However, this argument is unpersuasive as the rationale for interpretation of steps occurs in the office action. Applicant remarks state preparing a report is not a mental process which, under a broadest reasonable interpretation, includes mental steps such as data evaluation. Preparing a report is not an element in addition to the abstract ideas; preparing to produce something is not equal to actually producing it. However, preparing a report may include steps of determining what to include in the report, which considered to be evaluating the data.
At Step 2A Prong Two, Applicant remarks are understood to assert the additional elements are integrated into the abstract ideas by virtue of the clause “wherein the biological sample is a real biological
sample of a biological material from the subject, wherein the real biological data signature includes
omic data measured from the real biological sample of the subject” (pg. 22, last paragraph). The data originate from a real sample does not change that the invention receives information and, in at least the independent claim, concludes with a report. The input data being from a real sample does not provide an improvement in a technology, effect a treatment, effect a transformation, or otherwise apply the judicial exceptions in a meaningful way (MPEP 2106.04(d)(I)).
Applicant remarks state obtaining a data signature specific to the subject represents a practical application, and the trained model being trained on whole blood data shows a practical application (pg. 23, second and third paragraphs). Obtaining a data signature is interpreted as generating data, which is abstract and not an element in addition to the abstract ideas that would be analyzed for integrating into a practical application The model being trained on blood data is additional information about the model and it is unclear how this shows a practical application.
It is agreed that providing the report may be interpreted as an element in addition to the abstract ideas (pg. 23, fourth paragraph). However, providing the report does not provide an improvement in a technology, effect a treatment, effect a transformation, or otherwise apply the judicial exceptions in a meaningful way (MPEP 2106.04(d)(I)). Therefore, it is considered a data outputting element, which is insignificant extra-solution activity (MPEP 2106.05(g)), which does not integrate the abstract idea(s) into a practical application.
At Step 2B, it is determined whether the elements in addition to the abstract ideas amount to significantly more than the judicial exception(s) either alone or in combination (MPEP 2106). Applicant remarks state that at least reciting the signature is based on omic data from a real sample, using a machine learning platform which is trained on whole blood, and providing a report should be considered elements in addition to the abstract ideas and found unconventional either alone or in combination (pg. 24). However, as stated above, the signature being based on omic data does not change the step from being receiving data, which is a conventional computer function; the machine learning platform is broadly recited and includes interpretation as abstract steps, and thus is not analyzed at this step of 101 analysis; and providing a report is data outputting and a conventional computer function. Other aspects of the invention are taught in the review by Khan, and thus are not considered unconventional alone or in combination.
Therefore, the rejection 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 factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-15, 18-19, 21-28, 31-45, 48-49, and 51-54
Claims 1-15, 18-19, 21-28, 31-45, 48-49, and 51-54 is/are rejected under 35 U.S.C. 103as being unpatentable over Aliper (US 2019/0034581 A1; previously cited on the 13 April 2023 IDS form) in view of Fabris (Biogerontology 18: 171-188, 2017; newly cited).
Claim 1 recites “receiving a real biological data signature derived from a biological sample of the subject, wherein the biological sample is a real biological sample of a biological material from the subject, wherein the real biological data signature includes omic data measured from the real biological
sample of the subject.” Aliper teaches “receiving a transcriptome signature derived from a tissue or organ of the subject” (abstract), where a biological signature is interpreted as reading on a transcriptome signature, and transcriptmic data is omic data.
Claim 1 recites “creating input vectors based on the real biological data signature.” Aliper teaches “creating input vectors based on the transcriptome signature” (abstract)
Claim 1 recites “inputting the input vectors into a machine learning platform.” Aliper teaches “inputting the input vectors into a machine learning platform” (abstract).
Claim 1 recites “generating a predicted biological data signature of the subject based on the input vectors by the machine learning platform, wherein the predicted biological data signature includes synthetic biological data specific to the subject, wherein the synthetic biological data includes omic data,
wherein the machine learning platform includes a model trained on biological data from whole
blood.” This element is interpreted as outputting a predicted expression profile based on the biological aging profile.
Aliper teaches “generating a predicted biological aging clock of the tissue or organ based on the input vectors by the machine learning platform , wherein the biological aging clock is specific to the tissue or organ” (abstract), and thus a prediction based on the biological data input into the machine learning model. Aliper does not clearly teach an output comprising omic data.
Claim 1 recites “preparing a report that includes the synthetic biological data of the subject.” Aliper teaches preparing a report (abstract).
Claim 1 recites “providing the report to the subject or a medical professional.” Aliper does not teach providing the report to the subject or medical professional.
Aliper teaches a predicted biological clock for a tissue or organ (abstract), which may not constitute omic data.
Fabris teaches expression levels in certain genes changing at certain ages (pg. 180, col. 1, fourth paragraph), co-expression profiles of longevity-related proteins (pg. 181, col. 1, second paragraph), and application of machine learning to determine these patterns (abstract). Fabris also teaches methods of pattern discovery which “assist the user [in] reaching meaningful biological conclusions” (pg. 172, col. 2, third paragraph), which together with preparing the report by Aliper is interpreted as teaching reporting the output to a user, where the user and subject are interpreted as the same individual.
Claim 31 recites performing the above steps, taught by the combination of Aliper and Fabris, using a computer readable medium. Aliper teaches “a computer program product is provided on a tangible non-transitory computer readable medium” (pg. 4, col. 1, paragraph [48]).
Claims 2 and 32 recite “creating at least a second biological data signature by repeating any one or more of” the receiving, creating, inputting, and/or generating steps above, “wherein the second biological data signature is based on a second real biological data signature from the biological sample of the subject, a different biological sample of the subject, or a second biological sample of a second subject.” Aliper teaches “creating at least a second biological aging clock” by repeating steps from a different tissue (pg. 37, claim 2), which combined with the generation of predicted expression levels as taught by Fabris (pg. 180, col. 1, fourth paragraph).
Claims 3 and 33 recite “comparing the predicted biological data signature with the real biological data signature of the subject.” Aliper teaches “comparing a predicted biological age of an individual with an actual chronological age of the individual” (pg. 3, col. 2, paragraph [42]) and Fabris teaches genes which are altered in expression with age (pg. 179, col. 2, first paragraph).
The claims recite “determining a difference between the synthetic biological data of the subject with the real biological sample of the subject.” Aliper teaches determining a difference (pg. 37, col. 2, claim 4).
The claims recite “preparing the report with that identifies the difference between the synthetic
biological data with the real biological sample of the subject.” Aliper teaches “preparing a report with the comparing and with a difference” (pg. 37, col. 2, claim 4).
Claims 4 and 34 recite “identifying at least one biomarker having a difference between the synthetic biological data with the real biological sample of the subject.” Aliper teaches “to produce highly consistent sets of biologically relevant biomarkers acquired on multiple transcriptomic data sets” (pg. 13, col. 2, paragraph [136]) and Fabris teaches genes which are altered in expression with age (pg. 179, col. 2, first paragraph), which a difference between synthetic data and real data is interpreted as reading on the subject’s real expression data and expression data which is different at a different age.
Claims 5 and 35 recite “identifying at least one biological target, wherein modulation of the at least one biological target modulates the identified at least one biomarker.” Aliper teaches “modulation of the biological step either to increase the activity or decrease the activity results in a cascading series of events in response to the modulated activity” (pg. 7, col. 1, paragraph [72]).
Claims 6 and 36 recite “the real biological data signature is based on biological pathway activation signatures for genomics, transcriptomics, proteomics, metabolomics, lipidomics, glycomics, methylomics, or secretomics, and the predicted biological data corresponds with the biological activation signature.” Aliper teaches data derived from proteomics (pg. 25, col. 1, paragraph [255]) and “pathway activation signatures” (pg. 3, col. 2, paragraph [41]).
Claims 7 and 37 recite “at least one of: correlating a genomics profile with the predicted biological data signature of the subject; correlating a proteomics profile with the predicted biological data signature of the subject; correlating a transcriptomics profile with the predicted biological data signature of the subject; correlating a metabolomics profile with the predicted biological data signature of the subject; correlating a lipidomics profile with the predicted biological data signature of the subject; correlating a glycomics profile with the predicted biological data signature of the subject; correlating a secretomics profile with the predicted biological data signature of the subject; or correlating a methylomics profile with the predicted biological data signature of the subject.” Aliper teaches “correlating a gene expression level with a predicted biological age” (pg. 3, col. 2, paragraph [42]), where transcriptomic data is interpreted as reading on gene expression levels, and Fabris teaches expression profiles that are modified with age (pg. 179, col. 2, first paragraph).
Claims 8 and 38 recite “performing feature importance analysis for ranking biological data by importance in age prediction by using the real biological data signature; and identifying a subset of biomarkers of the biological pathway activation signature thereof that are selected as indicators of a condition of the subject.” Aliper teaches “performing feature importance analysis for ranking genes
or gene sets by their importance in age prediction,” “identifying a subset of a genes or gene sets or biological pathways thereof that are selected as targets the therapeutic regimen,” and correlation with a “pathway signature” (pg. 37, col. 2, claim 8), where information from genes reads on biological data and conditions are taught as “biological condition prediction” based on biomarkers (pg. 13, col. 2, paragraph [134]).
Claims 9 and 39 recite “identifying at least one biological target associated with the condition, wherein modulation of the at least one biological target modulates at least one biomarker of the identified subset of biological markers.” Aliper teaches “identifying a subset of a genes or gene sets or biological pathways thereof that are selected as targets the therapeutic regimen” (pg. 37, col. 2, claim 8) and “modulation of a the biological step either to increase the activity or decrease the activity” (pg. 7, col. 1, paragraph [72]) and where a condition’s presence is interpreted as required given a treatment regimen.
Claims 10 and 40 recite “correlating the predicted biological data signature with a predicted biological age of the subject.” Aliper teaches “correlating a biological signaling pathway signature with the predicted biological age of the subject” (pg. 37, col. 2, claim 8).
Claims 11 and 41 recite “obtaining the biological sample from the subject; and obtaining the real biological data signature by performing a measurement of the genomics, transcriptomics, proteomics, metabolomics, lipidomics, glycomics, methylomics, or secretomics.” Aliper teaches a “biopsy” followed by “RNA-seq profiles extraction” (pg. 5, col. 2, paragraph [60]), where determining RNA-seq profiles is read on by transcriptomics.
Claims 12 and 42 recite “the predicted biological data signature is based on a simulation by a computer program for biological pathway activation signatures for genomics, transcriptomics, proteomics, metabolomics, lipidomics, glycomics, methylomics, or secretomics.” Aliper teaches “simulation or other computer processing” for pathways that activate cellular senescence and age prediction (pg. 7, col. 1, paragraph [72]), and Fabris teaches identification of biomarkers that have modified with age (pg. 179, col. 2, first paragraph), where such an identification based on a regression is interpreted as a prediction.
Claims 13 and 43 recite “conditioning latent codes of the input vectors in a latent space of the machine learning platform with at least one constraint of an attribute of the subject, such that the predicted biological data signature is based on the at least one constraint.” Aliper teaches conditioning the latent layer of the machine learning algorithm (pg. 18, col. 1, paragraph [185]), and a neuron responsible for growth inhibition, which is interpreted as a constraint related to the biological data signature.
Claims 14 and 44 recite “the synthetic biological data is for a defined biological age of the subject, wherein the predicted biological data signature represents a biological data signature of the subject at the defined biological age.” Aliper teaches “to accurately predict biological age” based on biomarkers inputs into a DNN (pg. 25, col. 1, paragraph [258]), where the synthetic data is interpreted as a prediction and Fabris teaches identification of biomarkers that have modified with age based on a regression (pg. 179, col. 2, first paragraph).
Claims 15 and 45 recites “the synthetic biological data is for one of: an aging simulation to increase a biological age of the biological data signature of the subject; or a rejuvenation simulation to decrease a biological age of the biological data signature of the subject.” Aliper teaches simulation related to cellular senescence (pg. 7, col. 1, paragraph [72]), interpreted as simulating aging, while Fabris teaches identification of biomarkers that have modified with age based on a regression (pg. 179, col. 2, first paragraph), which is interpreted as a signature of an aged individual based on expression levels.
Claims 18 and 48 recite “after a defined time period,” performing the steps of claim 1 “in a second iteration; comparing the initial report with the report of the second iteration; and determining a change in the predicted biological data signature over the defined time period.” Aliper teaches determining such a change in a second iteration of performing similar steps after a time period (pg. 38, col. 1, claim 10).
Claims 21 and 51 recite “determining an aging rate over the defined time period based on the change in the predicted biological data signature; and tracking the change in the predicted biological data signature over the defined time period.” Aliper teaches biomarkers to assess aging rate (pg. 24, col. 1, paragraph [252]) and “biomarkers for tracking physiological processes related to aging” (pg. 25, col. 1, paragraph [258]). Fabris teaches biomarkers which change in expression with aging (pg. 179, col. 2, first paragraph).
Claim 22 recites “performing a therapeutic regimen over a defined time period, performing steps” comprised in claim 1 “in a second iteration; comparing the initial report with the report of the second iteration; determining a change in the predicted biological data signature over the defined
time period; and determining: whether the therapeutic regimen changed the predicted biological data signature, if the therapeutic regimen changed the predicted biological data signature, then determine whether or not to: continue therapeutic regimen, change therapeutic regimen, or stop therapeutic regimen, or if the therapeutic regimen does not change the predicted biological data signature, then determine whether or not to: continue therapeutic regimen, change therapeutic regimen, or stop therapeutic regimen.” Aliper teaches determining whether to continue, change, or stop a regimen after a period of time of use of the regimen (pg. 38, col. 1, claim 11). Fabris teaches biomarkers which change in expression with aging (pg. 179, col. 2, first paragraph).
Claim 23 recites “the predicted biological data signature is generated based on at least one attribute of the subject, wherein the attribute is selected from age, sex, tissue types, ethnicity, life expectancy, or combination thereof of the subject.” Aliper teaches “sex-adjusted… models” (pg. 28, col. 2, paragraph [307]). Fabris teaches biomarkers which change in expression with aging (pg. 179, col. 2, first paragraph) and aging markers specific to tissues (Table 1).
Claim 24 recites “received real biological data signature is compared with the generated predicted biological data signature to identify at least one biological pathway that is useful for predicting at least one of: age, sex, tissue types, cell types, ethnicity, life expectancy, and combinations thereof.” Aliper teaches “sex-adjusted… models” as part of a “an example of a biological age clock… to investigate the predictive ability of deep transcriptomic aging clocks (e.g., biological aging clock)” (pg. 28, col. 2, paragraph [307]).
Claim 25 recites “the machine learning platform predicts a biological age, sex, tissue types, cell types, ethnicity, life expectancy or combinations thereof of the synthetic biological data.” Aliper teaches a machine learning platform which generates output including a predicted biological age (pg. 3, col. 2, paragraph [38]).
Claims 26 and 52 recite “performing a biological signal activation analysis with the synthetic biological data; and determining a health status of the subject.” Aliper teaches “pathway activation analysis” (pg. 14, col. 1, paragraph [137]), determining “health status of the patient” (pg. 20, col. 1, paragraph [218]), and at least “synthetic biological age” (pg. 38, col. 2, claim 18).
Claims 27 and 53 recite “the health status of the subject is an aging rate of the subject.” Aliper teaches “to assess personal aging rates” (pg. 24, col. 1, paragraph [252]).
Claims 28 and 54 recite “tracking the aging rate of the subject over a time period.” Aliper teaches “determining a change in the predicted biological age over the defined time period” (pg. 39, col. 1, claim 21), “to assess personal aging rates” (pg. 24, col. 1, paragraph [252]), and “tracking physiological processes related to aging” (pg. 25, col. 1, paragraph [258]).
Combining Aliper and Fabris
An invention would have been obvious to one of ordinary skill in the art if some motivation in the prior art would have led that person to modify prior art reference teachings to arrive at the claimed invention prior to the effective filing date of the invention. One would have been motivated to combine the work of Fabris, which teaches machine learning applied to ageing research including correlation of protein expression and molecular pathways (abstract), with the work of Aliper, which teaches predicting biological age of a tissue or organ but not a future health condition, because both Aliper and Fabris point to changes in expression over time (Aliper: pg. 2, paragraph [19]; Fabris: pg. 179, col. 2, first paragraph) and the use of algorithmic techniques, ranging from supervised machine learning to regressions, to estimate ageing-related information from real data. Therefore, they are directed to the same field of endeavor and their combination – that is, machine learning techniques to input real age information and output expression predictions – is prima facie obvious.
Claim(s) 16, 20, 29-30, 46, and 50
Claim(s) 16-17, 20, 29-30, 46-47, and 50 is/are rejected under 35 U.S.C. 103 as being unpatentable over Aliper in view of Fabris as applied to claims 1-15, 18-19, 21-28, 31-45, 48-49, and 51-54 above in view of Cawthon (US 20110207128 A1; previously cited on the 07 August 2025 PTO-892 form).
Claims 16 and 46 recite “identification of at least one biomarker having a difference between the real biological sample of the subject with the biological data signature of the aging simulation or the rejuvenation simulation.” Aliper teaches determining biomarkers distinguishing between two groups (pg. 13, col. 1, paragraph [130]) but not using a simulation.
Cawthon teaches a model to predict mortality based on gene expression levels estimating biological age (paragraph [183]).
Claims 17 and 47 recite “identifying at least one biological target, wherein modulation of the at least one biological target modulates the identified at least one biomarker.” Aliper teaches modulating biological pathways to achieve an end goal, where the end goal is either increasing or decreasing activity in a series of events (pg. 7, col. 2, paragraph [72]).
Claims 20 and 50 recite “identifying at least one biological target, wherein modulation of the at least one biological target modulates the identified at least one biomarker.” Aliper teaches modulating biological pathways to achieve an end goal, where the end goal is either increasing or decreasing activity in a series of events (pg. 7, col. 2, paragraph [72]), where the pathway is interpreted as a target, but not the specific biomarker.
Cawthon teaches “identifying genes” – interpreted as biomarkers – “involved in modulating rates of aging” including “IQGAP1” (paragraph [263]).
Claim 29 recites “the health status is a predicted future health status of the subject.” Aliper teaches predicting biological age and assessment of health status (pg. 20, col. 1, paragraph [218]) but not future health.
Cawthon teaches models which “are better at predicting future mortality” (paragraph [10]), where health reads on mortality.
Claim 30 recites “identifying a therapeutic protocol to improve the predicted future health of the subject.” Aliper teaches obtaining a recommendation of a treatment protocol (pg. 5, col. 2, paragraph [60]), but not future health.
Cawthon teaches “to select therapies for treating a subject at risk for decreased survival” (paragraph [71]), where future health reads on risk for decreased survival.
Combining Aliper, Fabris, and Cawthon
An invention would have been obvious to one of ordinary skill in the art if some motivation in the prior art would have led that person to modify prior art reference teachings to arrive at the claimed invention prior to the effective filing date of the invention. One would have been motivated to combine the work of Cawthon, which teaches “likelihood of survival” and “modulating survival in a subject” abstract with the work of Aliper and Fabris, which together suggest using machine learning to make predictions about gene expression, because Cawthon also teaches gene expression profiles and modulating these profiles to decrease mortality risk (pg. 1, col. 1, paragraph [4]). Mortality and survival are interpreted as a future health statuses. Cawthon teaches models the superior future mortality prediction compared to competing models (pg. 1, col. 2, paragraphs [9-10]), where modulated expression of IQGAP1 is predictive of survival (pg. 7, col. 2, paragraph [69]). Given that Aliper and Fabris teach biomarkers for predicting biological age and modulating activity, and Cawthon teaches biomarkers which may modulate predicted health, and thus their combination would be prima facie obvious.
Response to the 08 December 2025 Applicant Remarks
Applicant remarks state that “Aliper is actually related to a biological aging clock. Such a biological aging clock identifies a predicted biological age. The predicted biological age is not omic data. Applicant disagrees that the subject matter of generating a predicted biological aging clock corresponds to or is related to generating predicted biological data signature” (pg. 25, third paragraph). Prior art Fabris is combined with Aliper to teach the expression of biomarkers being different at a different point in the life of a subject and prediction using machine learning techniques. The prediction of a personal, specific biological clock for an individual as taught by Aliper combined with the altered expression of proteins at different ages as taught by Fabris are together interpreted as teaching the requirements of the claims.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1-15, 18-19, 21-28, 31-45, 48-49, and 51-54 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-3, 8, and 10-11 of U.S. Patent No. 10,325,673 in view of Aliper (US 2019/0034581 A1; previously cited on the 13 April 2023 IDS form) and Fabris (Biogerontology 18: 171-188, 2017; newly cited).
Instant claim 1 is taught by reference claim 1 except for predicting an omic signature for an individual and providing a report.
Aliper teaches a physiological age of the individual (pg. 2, col. 1, paragraph [15]) and Fabris teaches prediction using regression algorithms and particular processes being altered with age (pg. 179, col. 2, first paragraph). Fabris also teaches methods of pattern discovery which “assist the user [in] reaching meaningful biological conclusions” (pg. 172, col. 2, third paragraph), which together with preparing the report by Aliper is interpreted as teaching reporting the output to a user, where the user and subject are interpreted as the same individual.
Instant claim 31 is taught by reference claim 16, where the steps are taught by Aliper and Fabris above and implemented using a non-transitory computer readable medium.
Instant claims 2 and 32 are taught by reference claim 2, except it is directed to a predicted biological signature rather than a clock. Fabris teaches prediction using regression algorithms and particular processes being altered with age (pg. 179, col. 2, first paragraph).
Claims 3 and 33 are taught by reference claim 3, except it is directed to a predicted biological signature rather than a clock. Fabris teaches prediction using regression algorithms and particular processes being altered with age (pg. 179, col. 2, first paragraph).
Regarding claims 4 and 34, Aliper teaches “to produce highly consistent sets of biologically relevant biomarkers acquired on multiple transcriptomic data sets” (pg. 13, col. 2, paragraph [136]) and Fabris teaches genes which are altered in expression with age (pg. 179, col. 2, first paragraph), which a difference between synthetic data and real data is interpreted as reading on the subject’s real expression data and expression data which is different at a different age.
Regarding claims 5 and 35, Aliper teaches “modulation of the biological step either to increase the activity or decrease the activity results in a cascading series of events in response to the modulated activity” (pg. 7, col. 1, paragraph [72]).
Regarding claims 6 and 36, Aliper teaches data derived from proteomics (pg. 25, col. 1, paragraph [255]) and “pathway activation signatures” (pg. 3, col. 2, paragraph [41]).
Regarding claims 7 and 37, reference claim 8 recites correlating gene expression level with biological age, where transcriptomics reads on gene expression.
Regarding claims 8 and 38, the limitations of performing feature importance analysis and biomarker subset identification are taught by reference claim 8.
Regarding claims 9 and 39, Aliper teaches “identifying a subset of a genes or gene sets or biological pathways thereof that are selected as targets the therapeutic regimen” (pg. 37, col. 2, claim 8) and “modulation of a the biological step either to increase the activity or decrease the activity” (pg. 7, col. 1, paragraph [72]) and where a condition’s presence is interpreted as required given a treatment regimen.
Regarding claims 10 and 40, Aliper teaches “correlating a biological signaling pathway signature with the predicted biological age of the subject” (pg. 37, col. 2, claim 8).
Regarding claims 11 and 41, Aliper teaches a “biopsy” followed by “RNA-seq profiles extraction” (pg. 5, col. 2, paragraph [60]), where determining RNA-seq profiles is read on by transcriptomics.
Regarding claims 12 and 42, Aliper teaches “simulation or other computer processing” for pathways that activate cellular senescence and age prediction (pg. 7, col. 1, paragraph [72]), and Fabris teaches identification of biomarkers that have modified with age (pg. 179, col. 2, first paragraph), where such an identification based on a regression is interpreted as a prediction.
Regarding claims 13 and 43, Aliper teaches conditioning the latent layer of the machine learning algorithm (pg. 18, col. 1, paragraph [185]), and a neuron responsible for growth inhibition, which is interpreted as a constraint related to the biological data signature.
Regarding claims 14 and 44, Aliper teaches “to accurately predict biological age” based on biomarkers inputs into a DNN (pg. 25, col. 1, paragraph [258]), where the synthetic data is interpreted as a prediction and Fabris teaches identification of biomarkers that have modified with age based on a regression (pg. 179, col. 2, first paragraph).
Regarding claims 15 and 45, Aliper teaches simulation related to cellular senescence (pg. 7, col. 1, paragraph [72]), interpreted as simulating aging, while Fabris teaches identification of biomarkers that have modified with age based on a regression (pg. 179, col. 2, first paragraph), which is interpreted as a signature of an aged individual based on expression levels.
Regarding claims 18 and 48, the limitations are taught by reference claim 10.
Regarding claims 21 and 51, Aliper teaches biomarkers to assess aging rate (pg. 24, col. 1, paragraph [252]) and “biomarkers for tracking physiological processes related to aging” (pg. 25, col. 1, paragraph [258]). Fabris teaches biomarkers which change in expression with aging (pg. 179, col. 2, first paragraph).
Regarding claim 22, the limitations are taught by reference claim 11.
Regarding claim 23, Aliper teaches “sex-adjusted… models” (pg. 28, col. 2, paragraph [307]). Fabris teaches biomarkers which change in expression with aging (pg. 179, col. 2, first paragraph) and aging markers specific to tissues (Table 1).
Regarding claim 24, Aliper teaches “sex-adjusted… models” as part of a “an example of a biological age clock… to investigate the predictive ability of deep transcriptomic aging clocks (e.g., biological aging clock)” (pg. 28, col. 2, paragraph [307]).
Regarding claim 25, Aliper teaches a machine learning platform which generates output including a predicted biological age (pg. 3, col. 2, paragraph [38]).
Regarding claims 26 and 52, Aliper teaches “pathway activation analysis” (pg. 14, col. 1, paragraph [137]), determining “health status of the patient” (pg. 20, col. 1, paragraph [218]), and at least “synthetic biological age” (pg. 38, col. 2, claim 18).
Regarding claims 27 and 53, Aliper teaches “to assess personal aging rates” (pg. 24, col. 1, paragraph [252]).
Regarding claims 28 and 54, Aliper teaches “determining a change in the predicted biological age over the defined time period” (pg. 39, col. 1, claim 21), “to assess personal aging rates” (pg. 24, col. 1, paragraph [252]), and “tracking physiological processes related to aging” (pg. 25, col. 1, paragraph [258]).
An invention would have been obvious to one of ordinary skill in the art if some motivation in the prior art would have led that person to modify prior art reference teachings to arrive at the claimed invention prior to the effective filing date of the invention. One would have been motivated to combine the recitations of the reference patent with the work of Fabris, which teaches machine learning applied to ageing research including correlation of protein expression and molecular pathways (abstract), with the work of Aliper, which teaches predicting biological age of a tissue or organ but not a future health condition, and the reference claims which are directed to predicting age because the reference patent, Aliper and Fabris point to changes in expression over time (Aliper: pg. 2, paragraph [19]; Fabris: pg. 179, col. 2, first paragraph) and the use of algorithmic techniques, ranging from supervised machine learning to regressions, to estimate ageing-related information from real data. Therefore, they are directed to the same field of endeavor and their combination – that is, machine learning techniques to input real age information and output expression predictions – is prima facie obvious.
Claims 16-17, 20, 29-30, 46-47, and 50 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-3, 8, and 10-11 of U.S. Patent No. 10,325,673 in view of Aliper and Fabris as applied to claims 1-15, 18-19, 21-28, 31-45, 48-49, and 51-54 above and further in view of Cawthon.
Regarding claims 16 and 46, Aliper teaches determining biomarkers distinguishing between two groups (pg. 13, col. 1, paragraph [130]) and Cawthon teaches a model to predict mortality based on gene expression levels estimating biological age (paragraph [183]), where a simulation is interpreted as reading on a model.
Regarding claims 17 and 47, Aliper teaches modulating biological pathways to achieve an end goal, where the end goal is either increasing or decreasing activity in a series of events (pg. 7, col. 2, paragraph [72]).
Regarding claims 20 and 50, Aliper teaches modulating biological pathways to achieve an end goal, where the end goal is either increasing or decreasing activity in a series of events (pg. 7, col. 2, paragraph [72]), where the pathway is interpreted as a target, and Cawthon teaches “identifying genes” – interpreted as biomarkers – “involved in modulating rates of aging” including “IQGAP1” (paragraph [263]).
Regarding claim 29, Aliper teaches predicting biological age and assessment of health status (pg. 20, col. 1, paragraph [218]) but not future health. Cawthon teaches models which “are better at predicting future mortality” (paragraph [10]), where health reads on mortality.
Regarding claim 30, Aliper teaches obtaining a recommendation of a treatment protocol (pg. 5, col. 2, paragraph [60]), but not future health. Cawthon teaches “to select therapies for treating a subject at risk for decreased survival” (paragraph [71]), where future health reads on risk for decreased survival.
An invention would have been obvious to one of ordinary skill in the art if some motivation in the prior art would have led that person to modify prior art reference teachings to arrive at the claimed invention prior to the effective filing date of the invention. One would have been motivated to combine the work of Cawthon, which teaches “likelihood of survival” and “modulating survival in a subject” abstract with the recitations of the reference claims and work of Aliper and Fabris, which together suggest using machine learning to make predictions about gene expression, because Cawthon also teaches gene expression profiles and modulating these profiles to decrease mortality risk (pg. 1, col. 1, paragraph [4]). Mortality and survival are interpreted as a future health statuses. Cawthon teaches models the superior future mortality prediction compared to competing models (pg. 1, col. 2, paragraphs [9-10]), where modulated expression of IQGAP1 is predictive of survival (pg. 7, col. 2, paragraph [69]). Given that the reference claims, Aliper and Fabris teach biomarkers for predicting biological age and modulating activity, and Cawthon teaches biomarkers which may modulate predicted health, and thus their combination would be prima facie obvious.
Response to the 08 December 2025 Applicant Remarks
Applicant remarks state a biological clock is not synthetic biological data that includes omic data. Therefore, the subject matter of the present claims is not obvious from the claims of U.S. Patent No. 10,325,673 (pg. 16, second paragraph). It is appreciated that a biological clock is not omic data as amended. In view of the amendment, prior art Fabris is incorporated into the combination to teach different expression at different ages, which together with the machine-learning based prediction of tissue age taught by the reference claims and Aliper, is interpreted as teaching the required limitations as the biological clock is correlated with expression level (Aliper: claim 20).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/R.J.K./Examiner, Art Unit 1685
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685