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 .
Information Disclosure Statement
The IDS filed 7/26/2022, 9/22/2022, 5/25/2023, 10/17/2023, 11/16/2023, 1/31/2024, 3/18/2024, 7/08/2024, 10/25/2024, 12/20/2024, 4/3/2025, and 2/26/2026 have been considered by the Examiner.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, or 365(c) is acknowledged. Priority of US application 63/209164 filed 6/10/2021 is acknowledged.
Claim Election/Restriction
Election without Traverse
Applicant’s election without traverse of Group IV (claims 2-17 and 43-44) in the reply filed on 6/23/2026 is acknowledged.
Claims 18-42 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected Groups, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 6/23/2026.
Status of Claims
Claims 2-17 and 43-44 are under examination.
Claim 1 is cancelled.
Claims 18-42 are withdrawn.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 2-17 and 43-44 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Step 1: Process, Machine, Manufacture or Composition
Claims 2-17 and 43-44 are drawn to a method so a process.
Step 2A Prong One: Identification of an Abstract Idea
The claim(s) recite(s):
1. applying an estimation model to the non-invasively obtained vessel wall data to generate virtual 'omics data that include estimated levels of molecules or pathways, or both, for the patient.
This step reads on a mental process or mathematics of evaluating obtained vessel wall data with rules, criteria or instructions. The step is therefore an abstract idea.
2. updating a systems biology model using the generated virtual 'omics data to generate a patient-specific systems biology model wherein (i) the systems biology model comprises a set of networks, wherein each network comprises a plurality of nodes representing level of a molecule or pathway and a plurality of edges representing an interaction between molecules or between pathways.
This step reads on organizing an network graph of “nodes” representing molecular information and their connectivity through “edges.” A network is a mathematical concept based on graph theory. Updating a network can be performed by the human mind or with math and is therefore an abstract idea.
3. wherein, (ii) at least two of the nodes of the plurality of nodes in each network represent molecules or pathways whose levels are affected by an atherosclerotic cardiovascular disease.
This limitation is drawn to describing the network which is an abstract idea. Organizing specific information into a network graph is an abstract idea.
4. (iii) one or more nodes of the plurality of nodes in the systems biology model is
updated with a disease-associated level for each molecule or pathway, or both, from the
virtual 'omics data.
This limitation is drawn to further describing the network which is an abstract idea. Organizing specific information into a network graph is an abstract idea.
5. obtaining information relating to an intended effect of one or more potential therapies for the patient.
This limitation is drawn to determining information about the effect of a therapy on a patient, which reads on a mental process and is therefore an abstract idea.
6. perturbing the patient-specific systems biology model with the information to simulate a therapeutic response for each potential therapy in the patient-specific systems biology model to obtain a simulated therapeutic response for each potential therapy.
This step reads on inputting determined information into the patient-specific systems biology model which is the network model and performing an iterative calculation. The limitation of “to simulate a therapeutic response for each potential therapy in the patient-specific systems biology model to obtain a simulated therapeutic response for each potential therapy” reads on an intended use of calculating a therapeutic response based on the model. The step of using a network model to determine information is an abstract idea.
Dependent claims 2-17 and 44 are drawn to describing the information input to and organization of the model network model and are therefore also abstract ideas.
Step 2A Prong Two: Consideration of Practical Application
The claims result in a step of perturbing a patient-specific systems biology model with information about an effect of a therapy. The step reads on an abstract idea of calculating information. The claims do not recite any additional elements that integrate the judicial exception into a practical application.
This judicial exception is not integrated into a practical application because the claims do not meet any of the following criteria:
An additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than
a drafting effort designed to monopolize the exception.
Step 2B: Consideration of Additional Elements and Significantly More
The claimed method also recites "additional elements" that are not limitations drawn to an abstract idea. The recited additional elements are drawn to:
1. receiving non-invasively obtained data of one or more vessel walls of the patient, where in the data is radiological imaging data as listed in claim 5, as in claims 43 and 4-5.
This step reads on extra solution activity of data gathering as described in MPEP 2106.05(g). Image data is routinely gathered and used as a source of information for further analysis.
2. providing a report recommending a preferred therapy, as in claim 44.
This step reads on extra solution activity of outputting data as described in MPEP 2106.05(g).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because receiving data for analysis by math and mental process steps and outputting the results of the analysis to a report is extra solution activity.
Viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea recited in the instantly presented claims into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 112-2nd paragraph
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 16 and 17 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claim 16 recites comparing “the defined therapeutic effect.” There is lack of antecedent basis support for this limitation. Claim 16 dependents from claim 13 however the therapeutic effect is defined in claim 15. Correction of claim dependency is needed.
Claim 17 recites pathways listed in Tables 5 and 6. USPTO’s guidance requires content from tables to be directly recited in the claims when such incorporation is possible, as set forth in MPEP 2173.05(s):
Where possible, claims are to be complete in themselves. Incorporation by reference to a specific figure or table "is permitted only in exceptional circumstances where there is no practical way to define the invention in words and where it is more concise to incorporate by reference than duplicating a drawing or table into the claim. Incorporation by reference is a necessity doctrine, not for applicant’s convenience." Ex parte Fressola, 27 USPQ2d 1608, 1609 (Bd. Pat. App. & Inter. 1993).
Instantly it is possible to incorporate Tables 5 and 6 into claim17. Otherwise, cancelation of the claim is required.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims under 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of 35 U.S.C. 103(c) and potential 35 U.S.C. 102(e), (f) or (g) prior art under 35 U.S.C. 103(a).
Claims 2-17 and 43-44 are rejected under 35 U.S.C. 103(a) as being unpatentable over Selevsek et al. (Communications Biology, vol. 3 (2020) pgs. 1-15) in view of Sanz et al. (Nature, vol. 451 (2008) pgs. 953-957).
Selevsek et al. teach an integrating omics data with a molecule network to analyze response to therapeutics.
Selevsek et al. teach gathering proteomic data from mass spectrometry (page 11, col. 2, par. 6) and sequence data from libraries and at least 19 different data sources for PPI (page 12, col. 2, par. 2-4)(i.e. receiving non-invasively obtained data); the data is derived from endomyocardial biopsies (page 11, col. 2, par. 3) of one or more vessel walls of a patient). It is noted that the instant claims are not specific as to how the data is received or what the initial data is. Therefore the Selevsek et al. teaching of data generated from mass spectrometry and sequencing reads on the limitation of claim 43.
Selevsek et al. first teach a quality assessment of the data using supervised and unsupervised methods (i.e. applying an estimation model to the non-invasively obtained data to generate virtual ‘omics data that include estimated levels of molecules or pathways for the patient), as in claim 43.
Selevsek et al. teach computational modeling of mitochondrial functions (page 12-13, connecting par.) to model solutions seek to emulate the functional behavior of the mitochondrion subject to drug exposure by modifying the kinetic parameters of the baseline model (i.e. update a systems biology model using the ‘omics data to generate a patient-specific model), as in claim 43.
Selevsek et al. teach (page 4, col. 1, par. 4) a network model of pathways related to sarcomere and mitochondrial function. Selevsek et al. teach a network of genes, metabolites and proteins complexes represented by “nodes” and connected by links or “edges” (Figures 2-3 and Figure 4)(i.e. (i) systems biology model of a set of network presented by notes and edges between the pairs of nodes, each edge representing interaction between molecules or pathways), as in claim 43.
Selevsek et al. teach network response to anthracycline (AC) treatment (page 2, col. 1, par. 3-col. 2); changes in the proteome and transcriptome are taught in response to AC treatment (page 4, col. 1, par. 3) and a ACT toxicity network response model is taught including notes and edges (Figure 4)(i.e. obtaining information relating to an intended effect of potential therapies for the patient), as in claim 43.
Selevsek et al. teach node scoring by using scores that reflect dynamic changes of the proteins after AC treatment in order to weight the proteins/genes according to their information content with respect to time-sensitive AC treatment responses (page 12, col. 2, section “Network propagation” second par.)(i.e. (iii) nodes are updated with disease-associated level for each molecule or pathway from the ‘omics data), as in claim 43, step (iii).
Selevsek et al. teach response to four different drugs represented by DAU, DOX, EPI and IDA (page 7, Figure 4); Selevsek teach integrating changes in the proteome and transcriptome caused by the AC treatments into a large protein-protein interaction (PPI) network (page 2, col. 2, par. 2)and that “we were mainly interested in the common AC responses at clinical conditions we continued with the four AC subnetworks computed from the integrated data at therapeutic doses and combined these to an ACT response network that consists of 175 proteins.” (page 7, col. 1, par. 2)(i.e. perturbing the model with information to stimulate a therapeutic response for each potential therapy in the model to obtain therapeutic response for each potential therapy), as in claim 43.
Selevsek et al. teach (page 2, col. 2, par. 1) the effect of the four drugs DAU, DOX, EPI and IDA on the network and identified 175 protein in the network representing a signature of AC toxicity (ACT)(i.e. comparing simulated therapeutic response for each therapy), as in claim 44.
Selevsek et al. teach evaluating the ACT response network in the context of cardiomyopathy patients as well as with a computational model (page 2, col. 2, par. 3); the therapeutic and toxic effect on cardiac transcription factors YYI, SRF, ETS1 is studied which shows gene regulation of these factors is impaired by AC causing disturbance of regulatory pathways crucial for cardiac physiology (page 4, col. 1, par. 2 and Figure 1c); Selevsek teach (page 10, c0ol. 1, par. 1) comparing ACT network proteins in figure 4 and determining significant ATP concentration impact after DOX compared to the other three treatments and ranking the toxicity as DOX>EPI and IA>DAU (page 10, col. 2, par. 1)(i.e. selecting potential therapy as a preferred therapy based on comparison and providing a report), as in claim 44.
Selevsek et al. teach molecular analysis derived from cardiomyopathy patients (Abstract)(i.e. disease transcript, protein or metabolite), as in claim 3.
Selevsek et al. teach sarcomere (muscle cell) and mitochondrial networks (Figures 2-3)(i.e. cell specific), as in claims 9-10.
Selevsek et al. teach (Abstract) doxorubicin, epirubicin, idarubicin and daunorubicin, which are known to those of ordinary skill to trigger inflammatory cascades and are also anthracycline antibiotics, as in claims 11-12.
Selevsek et al. teach (Abstract) identifying toxicity and adverse drug response resulting from therapy, as in claims 13-14.
Selevsek et al. teach (Figures 3 and Figure 4) determining know molecules and pathways, defining therapeutic effect including toxicity (Figure 2a) and estimating a toxicity response network from proteome and transcriptome data (Figure 4), as in claim 15.
Selevsek et al. teach analyzing AC induced dynamic (time period) response in proteome and transcriptome in protein interaction networks (page 4, col. 2, par. 3) and show dynamic proteome changes upon AC treatment (page 6, Figure 2 caption)(i.e. comparing estimated therapeutic effect levels before and after simulated therapeutic response is obtained), as in claim 16.
Selevsek et al. teach a network representing sarcomere (muscle cell)(i.e. smooth cell, Table 5), as in claim 17.
Selevsek et al. do not specifically teach that the nodes represent molecules or pathways whose levels are affected by an atherosclerotic cardiovascular disease, as in claim 43, step (ii).
Selevsek et al. also do not teach claims 2 and 4-8.
Sanz et al. however teach imaging of atherosclerotic cardiovascular disease (Title) in which cholesterol deposition, inflammation, extracellular-matrix formation and thrombosis have important roles (page 953, col. 1, par. 1). Sanz et al. teach that imaging allows assessment of morphology as well as composition of vessel walls (page 953, col. 1, par. 2); the molecules associated with the physiology of atherosclerotic development are taught (page 945, Figure 1); Sanz et al. teach (page 957, col. 1, lines 7-8) that imaging might be able to provide a direct read-out of the agent’s local concentration and activity which would aid in drug development.
Sanz et al. teach Computed Tomography (CT) to non-invasively image coronary arteries (page 954, col. 2, par. 2) and determining a “calcium score” by non-invasive detection of coronary calcium deposits (page 954, col. 2, par. 2)(i.e. setting decreased levels of molecules related to plaque development and increased levels of molecules related to plaque stability), as in claim 2.
Sanz et al. teach molecular imaging including CT, ultrasound , PET, MRI, and SPECT to image cardiovascular anatomy and target specific cells or molecular pathways of relevance; imaging probes with high affinity for desired target molecules are taught (page 955-956, connecting par.). Sanz et al. teach measuring atherosclerosis by fluorescent probe activated enzymatic degradation (page 956, col. 2, par. 3)(i.e. non-invasive data is radiological imaging data), as in claims 4-5.
Sanz et al. teach radiological imaging to evaluate morphology of plaques of the vessel wall and other features of the vessel wall (page 954, col. 2, par. 1)(i.e. structural anatomy data and tissue composition data, which relates to thickening and plaque burden), as in claim 6-7.
Sanz et al. teach CT imaging to detect calcium deposits (page 954, col. 2, par. 2)(i.e. calcification), as in claim 8.
It would have been obvious to one of ordinary skill in the art at the time the invention was made to have combined the network modeling of cardiovascular disease with genomic and proteomic (‘omic) data as taught by Selevsek et al. with the atherosclerotic data derived by imaging as taught in Sanz et al. Sanz et al. specifically teach target molecules in atherosclerotic cardiovascular disease development (page 955, Table 1) and the type of imaging for measuring the molecular pathways (page 955, col. 2, par. 2). Selevsek et al. teach a theoretical network model comprising proteomic and genomic data that can be used to model pathways in cardiovascular disease. Sanz et al. provide motivation by teaching that their imaging techniques and data collected therefrom would be useful in aiding in drug development by providing a direct read of the agent’s activity (page 957, col. 1, par. 1). One of skill in the art would have had a reasonable expectation of success at combing Selevsek et al. and Sanz et al. because both teach assessing changes in molecular levels related to cardiovascular disease.
E-mail communication Authorization
Per updated USPTO Internet usage policies, Applicant and/or applicant’s representative is encouraged to authorize the USPTO examiner to discuss any subject matter concerning the above application via Internet e-mail communications. See MPEP 502.03. To approve such communications, Applicant must provide written authorization for e-mail communication by submitting the following statement via EFS Web (using PTO/SB/439) or Central Fax (571-273-8300):
Recognizing that Internet communications are not secure, I hereby authorize the USPTO to communicate with the undersigned and practitioners in accordance with 37 CFR 1.33 and 37 CFR 1.34 concerning any subject matter of this application by video conferencing, instant messaging, or electronic mail. I understand that a copy of these communications will be made of record in the application file.
Written authorizations submitted to the Examiner via e-mail are NOT proper. Written authorizations must be submitted via EFS-Web (using PTO/SB/439) or Central Fax (571-273-8300). A paper copy of e-mail correspondence will be placed in the patent application when appropriate. E-mails from the USPTO are for the sole use of the intended recipient, and may contain information subject to the confidentiality requirement set forth in 35 USC § 122. See also MPEP 502.03.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Anna Skibinsky whose telephone number is (571) 272-4373. The examiner can normally be reached on 12 pm - 8:30 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Ram Shukla can be reached on (571) 272-7035. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Anna Skibinsky/
Primary Examiner, AU 1635
Prosecution Insights