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 3/28/2023, 6/02/2023, 11/01/2023, 12/04/2023, 12/12/2023, 3/8/2024, 10/07/2024, 11/21/2024, 12/20/2024, 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 Objections
Claim 14 is objected to because of the following informalities: claim 14 includes a typographical error “[Does the first network really have the first two parts as well as a full network” which appears to be residual notation created before filing the claim set. Appropriate correction is required.
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 1-16 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 1-16 are drawn to a method, so a process.
Step 2A Prong One: Identification of an Abstract Idea
The claim(s) recite(s):
1. generating, based on the first inputs, a first set of networks, wherein each network comprises a plurality of nodes, each node representing a baseline level of a molecule, and a plurality of edges between pairs of nodes, each edge representing a molecule-molecule interaction.
This step is drawn to determining relationships between molecules and organizing the information about relationships into a network. The step can be achieved by the human mind and is therefore an abstract idea.
2. determining, from the second inputs, a disease-associated molecule level for
nodes representing molecules in the first network.
This step is drawn to analyzing information to determine a molecule level to be used as information in the network of nodes. The step can be achieved by the human mind and is therefore an abstract idea.
3. generating, based on the first network and the disease-associated molecule levels, a second set of networks, wherein the second set of networks, updated using the second inputs, represent a calibrated in silico systems biology model of the atherosclerotic cardiovascular disease and includes the disease-associated molecule levels for nodes representing proteins in the second set of networks.
This step is drawn to organizing information into a second set of networks based on information of the second inputs wherein the nodes include information about the disease-associated molecule levels. Organizing information into such a network can be achieved by the human mind and is therefore an abstract idea. The limitation that the second set of networks “represent a calibrated in silico systems biology model of the atherosclerotic cardiovascular disease,” is drawn to an intended use. The recitation of an in silico system means that the network model is to be applied for simulation on a tangentially recited computer. However, such recitation of a computer is deemed tangential, as described in MPEP 2106.05(f).
Claims 2-15 further characterize the network by describing the information that the network represents. The claims are also part of the abstract idea and are therefore also judicial exceptions.
Step 2A Prong Two: Consideration of Practical Application
The claims result in the generation of a network to be used as an in silico (computerized) model of atherosclerotic cardiovascular disease. This step is drawn to organizing information into a model and is an abstract idea. The claims do not recite any additional elements that integrate the abstract idea 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. obtaining multiple first inputs representing biological pathways associated with the atherosclerotic cardiovascular disease, as in claim 1.
This step is drawn to data gathering which is an extra-solution activity as described in MPEP 2106.05(g).
2. obtaining second inputs indicative of calibration data from multiple test subjects who have been diagnosed with the atherosclerotic cardiovascular disease, as in claim 1.
This step is drawn to data gathering which is an extra-solution activity as described in MPEP 2106.05(g).
3. non-invasively obtained imaging data by CT or other methods as recited in claims 4 and 5.
Collecting physiological images by CT, MRI, PET, US, NIRS and the other methods listed in claim 5 are all well known, routine and conventional. The step is also drawn to necessary data gathering which is an extra-solution activity, as in MPEP 2106.05(g).
4. transcriptomic data obtained by microarray or other methods listed in claim 16.
Collecting transcriptomic data by microarray, RNA sequences and the method so claim 16 are all well known, routine and conventional. The step is also drawn to necessary data gathering which is an extra-solution activity, as 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 the recited additional elements are drawn to routine data transmission and data gathering by well known, routine and conventional techniques.
Other elements of the method include recitation of a “computer-implemented method” and that the generated model is represented “in silico” which suggest a computer and is a recitation of generic computer structure that serves to perform generic computer functions that are well-understood, routine, and conventional activities previously known to the pertinent industry. 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 § 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 1-16 are rejected under 35 U.S.C. 103(a) as being unpatentable over Tommaso et al. (2014/0012558) in view of Nayor et al. (Cardiovascular research, vol. 128 (21 January 2021) pgs. 287-303).
Tommaso et al. teach (Abstract and par. 0007) an integrated analysis of imaging data, molecular data and clinical data by a multi-scale model, molecular model and linking component. Tommaso et al. teach (par. 0003) their method as applied to cardiovascular disease. Tommaso et al. teach (par. 0004) obtaining biomarkers associated with molecular pathways.
Tommaso et al. teach measurements from various components of a biological system that serve as input to models (par. 0037); inputs can be obtained from a molecular model (par. 0041) or a patient specific heart model is used to generate input for the molecular model (par. 0056)(i.e. obtain multiple first inputs representing biological pathways), as in claim 1.
Tommaso et al. teach (par. 0053) a molecular interaction network that represents activities in a cell where every cell has a molecule-molecule “wiring diagram” wherein it is known to one of ordinary skill in the art that an molecule interactome would be represented by nodes connected by links/edges as suggested by Tommaso et al.’s “wiring diagram.” (i.e. generating a first set of networks wherein each network comprises a plurality of nodes representing a baseline level of a molecule and a plurality of edges between pairs of nodes, each edge representing molecule-molecule interaction), as in claim 1.
Tommaso et al. teach (par. 0055) that once the subnetworks have been identified, they may be quantified for the level of dysregulation in each patient based on whole-genome sequencing data and/or gene expression data (i.e. obtaining second inputs indicative of calibration data from multiple test subjects); Tommaso et al. teach that for whole-genome sequencing data, a subnetwork dysregulation can be defined as the normalized count of genetic variants for genes in each subnetwork (i.e. determining from the second inputs a disease associated molecule level for nodes representing molecules in the first network), as in claim 1.
Tommaso et al. teach (ap. 0055) that a new subnetwork expression matrix can then be generated, where each row represents a subnetwork (instead of gene) and each column represents a patient sample (i.e. generating, based on the first network and disease associated molecule levels, a second set of networks wherein the second set of networks are updated using the second inputs), as in claim 1.
Tommaso et al. teach (par. 0063) the patient-specific cell model includes a set of standardized parameters established based on previously generated experimental results available, for example, in literature, commercial databases, or academic database known in the art. Tommaso et al. teach standardized parameters are modified based on the molecular pathways and biological processes of the patient. Tommaso et al. also teach that their model is particularly useful for predicting cardiovascular disease (par. 0003 and 0006) with a molecular network model that includes genes related to cardiac functions (par. 0013).
Tommaso et al. teach analysis of molecular data applicable to cardiac disease (par. 0006) and a cellular model based on molecular findings (par. 0008) but do not teach that the inputs representing biological pathways are specifically associated with atherosclerotic cardiovascular disease, as in claim 1.
Tommaso et al. do not teach second inputs from test subjects specifically diagnosed with atherosclerotic disease, as in claim 1.
Tommaso et al. do not teach that the second network represents a biological model of atherosclerotic cardiovascular disease, as in claim 1.
Tommaso et al. do not teach that the nodes specifically represent molecules that are affected by the atherosclerotic cardiovascular disease, as in claim 2.
Nayor et al. teach the molecular basis of Atherosclerotic Cardiovascular Disease (Abstract) and the genes that express proteins associated with Atherosclerotic Cardiovascular Disease (page 288, col. 2, par. 3 and page 290) including proteins related to plaque formation (page 292, col. 2, par. 1)(i.e. first inputs representing pathways associated with atherosclerotic cardiovascular disease), as in claim 1.
Nayor et al. teach specific levels of lipids and proteins (page 290, col. 1, par. 1) including novel protein markers related to subjects with Atherosclerotic Cardiovascular Disease (page 291, col. 1, section “Proteomics”)(i.e. calibration data from subjects who have been diagnosed with Atherosclerotic Cardiovascular Disease and molecules whose levels are affected by atherosclerotic cardiovascular disease), as in claims 1 and 2.
Nayor et al. teach multidimensional data for risk prediction (page 295, col. 2) including risk markers (Table 1) which represent pathways associated with atherosclerotic cardiovascular disease; Nayor et al. teach developing models specific for atherosclerotic cardiovascular disease (page 296, col. 2, par. 1)(i.e. a calibrated systems biology model of atherosclerotic cardiovascular disease), as in claim 1.
It would have been obvious to one of ordinary skill in the art at the time the invention was made to have to have combined the teachings of Tommaso et al. for molecular network modeling as applied to cardiovascular disease with the teachings of Nayor et al. who teach specific pathways, proteins and molecular levels associated with atherosclerotic cardiovascular disease. Nayor et al. provide motivation by teaching (Abstract) that integrated knowledge the biology of molecular signature of atherosclerotic cardiovascular disease, snapshots of atherosclerotic cardiovascular disease development can be captured. One of skill in the art would have had a reasonable expectation of success at combining the teachings of Tommaso et al. and Nayor et al. because both are concerned with analyzing cardiovascular disease progression. The combination of Tommaso et al.’s modeling with atherosclerotic cardiovascular specific input taught by Nayor et al. would predictable result in a systems biology model of atherosclerotic cardiovascular disease that includes disease-associated molecule levels for nodes presenting proteins in the second network. Such is combination of known elements to achieve a predictable result.
Regarding dependent claims 2-16:
Tommaso et al. teach that the model receives findings related to a disease from a molecule model (par. 0009) and providing disease-specific biomarkers (par. 0033)(i.e. the networks include disease associated molecule level, as in claim 3.
Tommaso et al. teach CT, MRI, and ultrasound imaging to capture physical quantities in a biological system (par. 0034), as in claims 4-5.
Tommaso et al. teach sample data includes protein profiles, metabolites (par. 0034 and par. 0038) and gene data (par. 0055) that are used to build the models, as in claim 6.
Tommaso et al. teach a protein-protein interaction network (par. 0055) and relationships between genes (par. 0053 and 0055), as in claims 7.
Tommaso et al. teach determining direct influence between two candidates (i.e. nodes) in the molecular interaction network (par. 0053)(i.e. activation, inhibition and indirect effect), as in claim 8.
Tommaso et al. teach a molecular model that captures molecular pathways from gene to cell (par. 0033) and that the model receives disease-specific pathway data from an external database, as in claim 9.
Regarding claim 10, Tommaso et al. do not teach the KEGG data base as in claim 10, however the KEGG database is well known for carrying information of gene functions and linking genomic information with biological function. It would be obvious for one of skill in the art to use information from the KEGG in the network models of Tommaso et al. Such is a combination of known elements that would achieve a predictable result.
Tommaso et al. teach (par. 0054) that nodes with a high rank (i.e. weight) have paths to the reference node (i.e. edge is directed with weight indicating a direction of the molecule-molecule interaction), as in claim 11.
Tommaso et al. teach that the molecular model captures molecular pathways from gene to cell (par. 0033) and that every cell has a unique molecule-molecule “wiring diagram,” (par. 0053) suggesting a network of subnetworks representing different cells, as in claim 12.
Tommaso et al. teach a model capturing mechanisms of the sarcoplasmic reticulum pathway (i.e. vascular smooth muscle) and actic/myosin pathway (par. 0064), as in claim 13.
Tommaso et al. teach that the molecular model captures molecular pathways from gene to cell (par. 0033) and that every cell has a unique molecule-molecule “wiring diagram,” (par. 0053) and Tommaso et al. teach modeling relationships between proteins (par. 0053) including a PPI network, which suggests that protein-protein interaction networks for individual and multiple cell types are known. Tommaso et al. therefore make obvious a PPI network for “all” cell types as needed for a biological model, as in claim 14.
Tommaso et al. teach a model which includes relationships between transcripts and proteins (par. 0053); and a PPI and transcriptional network (par. 0055), as in claim 15.
Tommaso et al. teach whole genome sequencing (par. 0055) and obtaining micro-RNA profiles (par. 0034 and 0038) which suggests RNA sequencing, as in claims 16.
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.
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/Anna Skibinsky/
Primary Examiner, AU 1635
Prosecution Insights