DETAILED ACTION
Notice of 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 .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Withdrawal of Objections and Rejections
Applicant's response, filed 03/04/2026, has been fully considered.
In view of the amendment and remarks from 03/04/2026, the objection to the drawings, the objection to the specification and the rejection of the following claims are withdrawn:
claims 1-2, 4-6, 8-13, 16-18, 20-22, 24, and 27-34 under 35 USC § 112b;
claims 3, 10, 14-15 and 23 under 35 U.S.C. § 101 due to said claims being cancelled; and
claims 3, 10, 14-15 and 23 under 35 U.S.C. § 103 due to said claims being cancelled.
The following rejections and/or objections are either maintained or newly applied for claims 1-2, 4-6, 8-9, 11-13, 16-18, 20-22, 24 and 27-35. They constitute the complete set applied to the instant application. Herein, "the previous Office action" refers to the Non-Final Rejection of 12/04/2025.
Status of the Claims
Claims 3, 7, 10, 14-15, 19, 23 and 25-26 are canceled.
Claims 1-2, 4-6, 8-9, 11-13, 16-18, 20-22, 24 and 27-35 are pending.
Claims 1-2, 4-6, 8-9, 11-13, 16-18, 20-22, 24 and 27-35 are rejected.
Priority
This application is a 371 of PCT/EP2020/051376 01/21/2020 which claims priority from Foreign Application No. EPO 19152836.3 (01/21/2019) as reflected in the filing receipt mailed on 12/06/2021. The claims to the benefit of priority are acknowledged. The effective filing date of claims 1-2, 4-6, 8-9, 11-13, 16-18, 20-22, 24 and 27-35 is 01/21/2019.
Specification Objections
The disclosure (07/13/2021) is objected to because it contains an embedded hyperlink and/or other form of browser-executable code in pg. 12 line 4; pg. 24 lines 19-20; pg. 27 line 14 and pg. 30 lines 12-13 and 31. Applicant is required to delete the embedded hyperlink and/or other form of browser-executable code; references to websites should be limited to the top-level domain name without any prefix such as http:// or other browser-executable code. See MPEP § 608.01.
The disclosure is objected to because there is a sentence in pg. 24 line 30 in German and not English. The specification should be resubmitted with correct paragraph numbering. Appropriate correction for all objections to the specification is required
Claim objections
Claims 2 and 31-32 are objected to because of the following informalities. Appropriate correction is required.
Claim 2 recites "(SPMs)" which appears to be an abbreviation not being used anywhere else in the claims and thus it can be deleted.
Claim 31 repeats the issue above for "(LPS)."
Claim 32 recites "HMGB" which should be spelled out completely.
Claim Rejections - 35 USC § 112
112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 1-2, 4-6, 8-9, 11-13, 16-18, 20-22, 24 and 27-34 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. Dependent claims are rejected similarly, unless otherwise noted below. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Any newly recited portions are necessitated by claim amendment.
Regarding claims 1 and 13, the instant specification in pg. 11 para. 1 provides support for "biological components such as genes, proteins, protein complexes and other species are represented in the nodes associated with discrete state values (0 or 1)." However, there is not support within the specification, nor has Applicant provided such support, for "(c) … (ii) state transition functions specifying how a future state value of a node is determined from current state values of one or more regulator nodes according to regulatory relationships encoded by one or more edges of the interaction network data structure."
112(b)
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.
Claims 1-2, 4-6, 8-9, 11-13, 16-18, 20-22, 24 and 27-34 are rejected under 35 U.S.C. 112(b)as being indefinite for failing to particularly point out and distinctly claim the subject matter the invention. Dependent claims are rejected similarly, unless otherwise noted below. Any newly recited portions are necessitated by claim amendment. The following issues cause the respective claims to be rejected under 112(b) as indefinite:
In claim 1, the relationship is unclear between the recited "the nodes" in steps (I)(e) lats line and (II)(b)(ii) last line, and the previously recited nodes. It is unclear if "the nodes" instances refer to the "nodes" previously recited in step (I)(b)(i) or the "one or more regulator nodes" in step (I)(c)(ii).
Claim 13 repeats the issue above.
Claim 4 depends from claim 1 and it recites "identifies molecules suspected to be involved in inflammation"; which is indefinite because it is unclear if the recited molecules refer to the same molecules in claim 1 or not. To overcome this rejection, claim 4 may be amended to "identifies the molecules suspected to be involved in inflammation."
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 1-2, 4-6, 8-9, 11-13, 16-18, 20-22, 24 and 27-35 are rejected under 35 USC § 101 because the claimed inventions are directed to one or more Judicial Exceptions (JEs) without significantly more. Regarding JEs, "Claims directed to nothing more than abstract ideas..., 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)). Any newly recited portions are necessitated by claim amendment.
101 background
MPEP 2106 organizes JE analysis into Steps 1, 2A (Prong One & Prong Two), and 2B as analyzed below. MPEP 2106 and the following USPTO website provide further explanation and case law citations: uspto.gov/patent/laws-and-regulations/examination-policy/examination-guidance-and-training-materials.
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)?
Analysis of instant claims
Step 1: Are the claims directed to a 101 process, machine, manufacture, or composition of matter (MPEP 2106.03)?
The instant claims are directed to a system (claims 1-2, 4-6, 8-9, 11-12, 27-29, 31-32 and 35), and a method (claims 13, 16-18, 20-22, 24, 30 and 33-34); each of which falls within one of the categories of statutory subject matter.
[Step 1: claims 1-2, 4-6, 8-9, 11-13, 16-18, 20-22, 24 and 27-35: 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))?
Background
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)).
Analysis of instant claims
With respect to the instant claims, under the Step 2A, Prong One evaluation, the claims are found to recite abstract ideas that fall into the grouping of mental processes (in particular procedures for observing, analyzing and organizing information) and mathematical concepts (in particular mathematical relationships and formulas) are as follows:
• "(c) generate/generating, from the interaction network data structure, an executable dynamic model comprising: (i) a state value, selected from a finite set of state values, assigned to each of at least a subset of the nodes, and (ii) state transition functions specifying how a future state value of a node is determined from current state values of one or more regulator nodes according to regulatory relationships encoded by one or more edges of the interaction network data structure" (independent claims 1 and 13);
• "(e) execute/executing the executable dynamic model over successive … iterations to generate simulation results representing a temporal evolution of the state values for at least the subset of the nodes" (independent claims 1 and 13);
• "applies algorithmic rules for gene prioritization, determining node degree, determining betweenness centrality, motif identification, and/or determining association with inflammation" (claim 6); and
• " (d) execute the executable dynamic model over a plurality of computational iterations to generate simulation results representing a temporal evolution of the state information" (independent claim 35).
The claims identified above read on math. The abstract ideas recited in the claims are evaluated under the Broadest Reasonable Interpretation (BRI) and determined to each cover performance either in the mind and/or by mathematical operation. Without further detail as to the methodology involved in "executing a model comprising algorithmic rules for gene prioritization and represent temporal evolution of the state values to generate simulation results", under the BRI, one may simply, for example, use pen and paper to perform mathematical steps to arrive at the described steps. Further support for the mathematical techniques used in the claims is provided in the specification at pg. 1 para. 1, which discloses an algorithm implemented in the processing unit which generates a network map based on the plurality of datasets in the database and which allows for identifying nodes within network map based on predefined parameters; and at pg. 9 para. 3 which discloses the execution of logical operations. Thus, the recited terms correspond to verbal equivalents of mathematical concepts because they constitute actions executed by a group of mathematical steps in a form of a mathematical algorithm; thus mathematical concepts (MPEP 2106.04(a)(2)). A mathematical concept need not be expressed in mathematical symbols, because "words used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). MPEP 2106.04(a)(2) pertains.
The following recitations have been identified as mental processes:
• "(d) integrate/integrating molecular expression data to calibrate at least a portion of the state transition functions, wherein the molecular expression data comprise transcriptomics data obtained from the subject following administration of a drug to the subject" (independent claims 1 and 13);
• "eliminating datasets from the database if no match is found between the identifier for a molecule in the dataset and the molecule identifier in the transcriptomics data" (claim 21); and
• "(ii) dynamically arranging the nodes presented within a respective functional module based on inter-modular connections between the respective functional module and other functional modules, so as to reduce visual overlap or crossing of edges representing the inter-modular connections" (independent claim 35).
The human mind is also sufficiently capable of integrating, eliminating data and arrange data presented to reduce visual overlap.
Dependent claims 2, 4-5, 8-9, 11, 16-17, 20, 31-32 and 34 recite further steps that limit the judicial exceptions in independent claims 1 and 6 and, as such, also are directed to those abstract ideas. For example, claims 2, 5, and 17 recite further details about the simulated molecular interactions involved in inflammation; claims 4, 16, and 31-32 recite further details about the type of molecules simulated; claims 8-9, 20 and 34 recite further details about the transcriptomics data and claims 29-30 recite further details about the motif identification.
[Step 2A Prong One: claims 1-2, 4-6, 8-9, 11-13, 16-18, 20-22, 24 and 27-35: 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))?
Background
MPEP 2106.04(d).I lists the following example considerations for evaluating whether a judicial exception is integrated into a practical application:
An improvement in the functioning of a computer or an improvement to other technology or another technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a);
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2);
Implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, as discussed in MPEP § 2106.05(b);
Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP § 2106.05(c); and
Applying or using 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, as discussed in MPEP § 2106.05(e).
Analysis of instant claims
Instant claims 1, 13 and 35 recite additional elements that are not abstract ideas:
• "(a) access/accessing a database comprising a plurality of structured datasets each comprising: (i) at least an identifier for a molecule identified from a damage associated molecular pattern (DAMP) or from a pathogen associated molecular pattern (PAMP), (ii) data on molecular interactions of the molecule with one or more other molecules, and (iii) at least one data characteristic indicative of a biological function of the molecule in inflammation" (independent claims 1 and 13);
• "(a) access structured molecular interaction data describing molecules involved in inflammation and interactions among the molecules, wherein the structured molecular interaction data comprises at least one data characteristic indicative of a biological function of the molecules in inflammation" (independent claim 35);
• "a processing unit coupled to a non-transitory computer-readable storage medium comprising instructions executable by the processing unit" (independent claims 1 and 35);
• "(b) generate/generating from the structured datasets, an interaction network data structure comprising: (i) nodes corresponding to the molecules, (ii) edges corresponding to the molecular interactions, and (iii) compartment membership data grouping the nodes into data compartments based on identical data characteristics" (independent claims 1 and 13);
• " computational"; (independent claims 1, 13 and 35);
• "(II) a visualization unit comprising: (a )an electronic display device; and (b) a data processor configured to: (i) receive the interaction network data structure and at least a portion of the simulation results, (ii) generate a graphical visualization reflecting the compartment membership of the nodes and the temporal evolution of the state values, and (iii) render the graphical visualization on the electronic display device" (independent claim 1);
• "one or more processors of a computing system, the one or more processors coupled to anon-transitory computer-readable storage medium comprising instructions executable by the one or more processors" (independent claim 13);
• "(f) generating, by the one or more processors, based at least in part on the interaction network data structure and at least a portion of the simulation results, a graphical visualization reflecting the compartment membership of the nodes and the temporal evolution of the state values" (independent claim 13);
• "(g) rendering the graphical visualization on the electronic display device" (independent claim 13);
• "(b) generate, from the structured molecular interaction data, an interaction network data structure comprising nodes representing the molecules and edges representing the interactions, wherein generating the interaction network data structure comprises grouping the nodes into functional modules based at least in part on the data characteristic indicative of the biological function" (independent claim 35);
• "(c) generate an executable dynamic model derived from the interaction network data structure, the executable dynamic model defining state information associated with at least a subset of the nodes and rules for updating the state information based on regulatory relationships encoded by the edges" (independent claim 35);
• "(e) generate and render, on an electronic display device, an interactive graphical visualization based on the interaction network data structure and the simulation results, wherein the interactive graphical visualization is updated to reflect changes in the state information across the plurality of computational iterations represented by the simulation results" (independent claim 35); and
• "(i) selectively presenting graphical elements representing the functional modules or the nodes within the functional modules based on a zoom level of the interactive graphical visualization" (independent claim 35).
Dependent claims 12 and 27-28 recite further details about the visualization unit; dependent claim 18 recites further details about "generating the interaction network data" and dependent claim 24 recites further details about "generating graphical visualizations."
Considerations under Step 2A, Prong Two
The recited limitations in claims 1, 13 and 35 are interpreted as requiring the use of a computer. Hence, the claims explicitly recite steps executed by computers and therefore can be described as computer functions or instructions to implement on a generic computer. Further steps directed to additional non-abstract elements of a computing device/computer do not describe any specific computational steps by which the "computer parts" perform or carry out the judicial exceptions, nor do they provide any details of how specific structures of the computer are used to implement these functions. The claims state nothing more than a generic computer which performs the functions that constitute the judicial exceptions. The instant claims state nothing more than that a generic computer performs the functions that constitute the abstract idea (MPEP 2106.05(f)).
Limitations in claims 1, 13 and 35 are considered to perform the claimed abstract idea with a computer, which is not sufficient to integrate an abstract idea into a practical application (see MPEP 2106.05(f)); since steps that can be performed mentally and merely performing the mental process in a computer environment do not negate the fact that something that can be carried out in the human mind. See MPEP 2106.04(a)(2).III.C.
Claims reciting "accessing" data read on data gathering activities; not amounting into a practical application.
Claims reciting "display device" (claims 1, 13 and 35); graphical display" (claims 24 and 27); "visualization unit" (claims 1, 12, 27-28); "graphical visualization" (claims 13, 24 and 35); "generating an interaction network map" (claims 1, 13 and 35); "generating/generate a dynamic model" (claims 1, 13 and 35); "rendering/render a graphical visualization" (claims 1, 13 and 35) are interpreted as data output and as such insignificant extra-solution activity.
Hence, these are mere instructions to apply the abstract idea using a computer and insignificant extra-solution activity and therefore the claims do not integrate that abstract idea into a practical application (see MPEP 2106.04(d) § I; 2106.05(f); and 2106.05(g)).
In Step 2A, Prong One above, claim steps and/or elements were identified as part of one or more judicial exceptions (JEs).
In this Step 2A, Prong Two immediately above claim steps and/or elements were identified as part of one or more additional elements. Additional elements are further discussed in Step 2B below.
Here in Step 2A, Prong Two, no additional step or element clearly demonstrates integration of the JE(s) into a practical application.
[Step 2A Prong Two: claims 1-2, 4-6, 8-9, 11-13, 16-18, 20-22, 24 and 27-35: No]
Step 2B: Do the claims recite a non-conventional arrangement of elements in addition to any identified judicial exception(s) (MPEP 2106.05)?
According to analysis so far, the additional elements described above do not provide significantly more than the judicial exception. A determination of whether additional elements provide significantly more also rests on whether the additional elements or a combination of elements represents other than what is well-understood, routine, and conventional. Conventionality is a question of fact and may be evidenced as: a citation to an express statement in the specification or to a statement made by an applicant during examination that demonstrates a well-understood, routine or conventional nature of the additional element(s); a citation to one or more of the court decisions as discussed in MPEP 2106(d)(II) as noting the well-understood, routine, conventional nature of the additional element(s); a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s); and/or a statement that the examiner is taking official notice with respect to the well-understood, routine, conventional nature of the additional element(s).
Claims 1, 13 and 35 recite a computer or computer functions, interpreted as instructions to apply the abstract idea using a computer, where the computer does not impose meaningful limitations on the judicial exceptions; which can be performed without the use of a computer (MPEP 2106.04(d) § I; and MPEP 2106.05(f)).
Further, the courts have found that outputting data are well-understood, routine, and conventional functions of a computer when claimed in a generic manner or as insignificant extra-solution activity (see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network), Versa ta Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015), and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93, as discussed in MPEP 2106.05(d)(Il)(i)).
When the claims are considered as a whole, they do not integrate the abstract idea into a practical application; they do not confine the use of the abstract idea to a particular technology; they do not solve a problem rooted in or arising from the use of a particular technology; they do not improve a technology by allowing the technology to perform a function that it previously was not capable of performing; and they do not provide any limitations beyond generally linking the use of the abstract idea to a broad technological environment. See MPEP 2106.05(a) and 2106.05(h).
The instant claims constitute insignificant extra solution activity, and when considered individually, are insufficient to constitute inventive concepts that would render the claims significantly more than an abstract idea (see MPEP 2106.05(g)). Hence, these elements, when considered individually, are insufficient to constitute inventive concepts that would render the claims significantly more than an abstract idea (see MPEP 2106.05(d)).
[Step 2B: claims 1-2, 4-6, 8-9, 11-13, 16-18, 20-22, 24 and 27-35: No]
Conclusion: Instant claims are directed to non-statutory subject matter
For the reasons above, the claims in this instant application, when the limitations are considered individually and as a whole, are directed to an abstract idea and lack an inventive concept not clearly anything significantly more.
Response to applicant's remarks in regard to Claim Rejection 35 U.S.C. ~ 101
The Remarks of 03/04/2026 have been fully considered but are not persuasive for the reasons below:
Applicant asserts in pg. 12 para. 2-3
Claim 1, for example, involves a computing system that includes… The claimed system cannot practically be performed in the human mind. … This structural tethering of discrete computational logic to empirical, in vivo drug-response data provides a concrete, practical application. Claim 1 is thus not directed to an abstract idea, and withdrawal of the§ 101 rejection is respectfully requested
It is respectfully submitted that this is not persuasive because despite the fact that steps related to the visualization of the claimed data structured indeed relies on a computing system (i.e. which have been identified at step 2A prong 2), the claimed steps related to the execution of a dynamic model that simulates interactions is indeed based on algorithms that employ parameters (i.e. math) and logic operations which can be performed in the human mind (i.e. as described in Step 2A prong 1). See instant specification at pg. 1 para. 1, which discloses an algorithm implemented in the processing unit which generates a network map based on the plurality of datasets in the database and which allows for identifying nodes within network map based on predefined parameters; and at pg. 9 para. 3 which discloses the execution of logical operations.
Furthermore, regarding the argued "in vivo drug-response data", there is no evidence in the claims that any of the identified additional elements are integrating the judicial elements into a practical application. Claims 1, 13 and 33-34 are directed to “administration of a drug”; merely limiting the data being integrated into the network map, therefore limiting the type of data in the step that was identified as abstract. There is no limitation that actually requires that a drug be administered in the metes and bounds of the claim, since administering a drug is not an active step; and rather, just limits what data is included in the database.
Claim Rejections - 35 USC § 103
The following is a quotation of pre-AIA 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, 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 negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under pre-AIA 35 U.S.C. 103(a) 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.
A. Claims 1, 4-6, 8-9, 12-13, 16-18, 20-21, 24, 27 and 29-34 are rejected under 35 U.S.C. 103 as being unpatentable over Cannataro (“Protein-to-Protein Interactions: Technologies, Databases, and Algorithms” ACM Computing Surveys 43(1):1-36 (2010)) as evidenced by Lagoa (“The role of initial trauma in the host’s response to injury and hemorrhage: insights from a correlation of mathematical simulation and hepatic transcriptomic analysis” Shock 26(6):592-600 (2006)) in view of Vodovotz (“Mechanistic simulations of inflammation: Current state and future prospects” Mathematical Biosciences 217:1–10 (2009) as evidenced by An (“Translational Systems Biology: Introduction of an Engineering Approach to the Pathophysiology of the Burn Patient” J Burn Care Res. 29(2): 277–285 (2008) and An09 (“Detailed qualitative dynamic knowledge representation using a BioNetGen model of TLR-4 signaling and preconditioning” Mathematical Biosciences 217 53–63 (2009)), as cited on the attached Form PTO-892. Any newly recited portions are necessitated by claim amendment.
Claims 1 and 13 recite:
wherein said inflammation comprises inflammation resolution, the system comprising: (I) a processing unit coupled to a non-transitory computer-readable storage medium comprising instructions executable by the processing unit to:
(a) access a database comprising a plurality of structured datasets each comprising:
(i) at least an identifier for a molecule identified from a damage associated molecular pattern (DAMP) or from a pathogen associated molecular pattern (PAMP),
(ii) data on molecular interactions of the molecule with one or more other molecules, and
(iii) at least one data characteristic indicative of a biological function of the molecule in inflammation
• Cannataro teaches a system and method for predicting the interactions between two or more proteins can be obtained by mining interaction networks stored in databases (i.e. step (I)(a)) (pg. 1 para. 1) via computational analysis (i.e. system) (pg. 4 para. 1); wherein studying and storing protein-protein interactions requires computer-based tools (i.e. non-transitory computer-readable storage medium comprising instructions executable by the processing unit) (pg. 2 para. 1); wherein one database can integrate various types of datasets by applying a probabilistic model and then predicts protein-protein interaction networks in humans (pg. 17 para. 2); wherein users can input a set of protein identifiers and search for them in databases (i.e. step (I)(a)(i)-(ii)) (pg. 28 para. 6); wherein said protein-protein interactions can demonstrate an interrelation between their modifications and disease (pg. 5 para. 2); wherein discovered PPI interactions is stored together with core interaction data (pg. 10 para. 2).
• Cannataro does not teach "inflammation resolution" and "a molecule identified from a damage associated molecular pattern (DAMP) or from a pathogen associated molecular pattern (PAMP)." Vodovotz teaches various modeling approaches for the effective characterization of a complex system such as inflammation (pg. 2 col. 1 para. 2); including pathobiology of inflammation (i.e. molecules identified from a pathogen associated molecular pattern) and the intertwined damage/healing response (i.e. inflammation resolution), with a mechanistic, rational basis to the design and implementation of therapies via the extraction of relevant data from the scientific literature (pg. 8 col. 1 para. 1).
• Cannataro does not teach step (I)(a)(iii). However, Vodovotz teaches various modeling approaches of mechanistic simulations of inflammation in the larger context of health and disease, from intracellular signaling to whole-animal physiology (i.e. step (I)(a)(iii)) (pg. 1 para. 1); wherein mathematical and computational methods (i.e. system/method) for the effective characterization of a complex system such as inflammation (i.e. simulating molecular interactions involved in inflammation in a subject) (pg. 2 col. 1 para. 2).
(b) generate from the structured datasets, an interaction network data structure comprising:
(i) nodes corresponding to the molecules, (ii) edges corresponding to the molecular interactions, and (iii) compartment membership data grouping the nodes into data compartments based on identical data characteristics
(c) generate, from the interaction network data structure, an executable dynamic model comprising:
(i) a state value, selected from a finite set of state values, assigned to each of at least a subset of the nodes, and (ii) state transition functions specifying how a future state value of a node is determined from current state values of one or more regulator nodes according to regulatory relationships encoded by one or more edges of the interaction network data structure…
(e) execute the executable dynamic model over successive computational iterations to generate simulation results representing a temporal evolution of the state values for at least the subset of the nodes
• Cannataro teaches a system and method for predicting the interactions between two or more proteins can be obtained by mining interaction networks stored in databases (pg. 1 para. 1); wherein the information representable as a network is mostly modeled using graph theory via a structure composed of a set of nodes and the edges linking them (i.e. step (b)(i)-(ii)) (pg. 2 para. 2); wherein the software presents as output a network of interacting proteins and the size of the result depends on the chosen depth parameter (pg. 28 para. 6); including (i) the modeling of the PPI network in a suitable data structure; (ii) the existence of appropriate algorithms for mapping, that is, identification of the correspondence of nodes in a subnetwork (i.e. compartments) and those stored in the database (pg. 12 para. 2); and application of graph-theoretic methods to explain different topological motifs in the resulting network (pg. 23 para. 5); wherein the interaction databases (i.e. the datasets that compose each database) can be categorized on the basis of (i) the kind of the stored interactions (i.e. step (b)(iii)) and (ii) the kind of data submission and extraction (pg. 18 para. 2); wherein dynamic graphs (i.e. an executable dynamic model) are not fixed in time and can evolve through local changes with the use of appropriate meta-information associated with both nodes and edges (pg. 31 para. 4); wherein the algorithmic computation causes a particular state in which each node is equally attracted from the others with nodes within dense regions are more attracted than the ones that are in the same region (pg. 21 para. 5); wherein the operation that describes the state of each other being attracted to others is based on an inflation parameter greater than 1 (i.e. step (c)(i)) in which the greater the inflation parameter, the greater the number of clusters (pg. 21 para. 6); wherein each value of a matrix represents the tendency of a node to be attracted by the other ones in which the evolution of the system, that is, of the flow, is computed by calculating (i.e. state functions) the next power of this matrix (i.e. step (c)(ii) and step (e)) (pg. 21 para. 5).
(d) integrate molecular expression data to calibrate at least a portion of the state transition functions, wherein the molecular expression data comprise transcriptomics data obtained from the subject following administration of a drug to the subject; and
• Cannataro teaches that one database can integrate various types of datasets (pg. 17 para. 2); including the integration of PPI information with biochemical pathways, gene (regulatory) pathways, and transcriptomics (pg. 32 para. 4).
• Cannataro does not teach the "following administration of a drug." However, Vodovotz teaches various modeling approaches of mechanistic simulations of inflammation in the context of health and disease (pg. 1 para. 1); wherein a graphically based modeling toolkit, BioNetGen is used to produce a model of TLR4 which generates a network of 76 possible molecular species and 202 reactions (pg. 4 col. 2 para. 2); wherein identified parameters may be utilized for the design of anti-inflammatory therapeutic strategies (pg. 5 col. 1 para. 1); wherein sepsis (i.e. inflammation process) modeling studies were performed on data matched to in-vivo models of endotoxin administration in mice (i.e. inflammation modeling studies based on data obtained from a subject that has been administered a drug) as evidenced by An (pg. 4 para. 3 An).
• It is interpreted that "to calibrate at least a portion of the state transition functions" reads on intended use and therefore it is not a requirement within the scope of the claim.
(II) a visualization unit comprising:
(a )an electronic display device; and
(b) a data processor configured to:
(i) receive the interaction network data structure and at least a portion of the simulation results,
(ii) generate a graphical visualization reflecting the compartment membership of the nodes and the temporal evolution of the state values, and
(iii) render the graphical visualization on the electronic display device
• Cannataro teaches a system and method for predicting the interactions between two or more proteins can be obtained by mining interaction networks stored in databases (pg. 1 para. 1); wherein the software presents as output a network of interacting proteins and the size of the result depends on the chosen depth parameter (pg. 28 para. 6); including (i) the modeling of the PPI network in a suitable data structure; (ii) the existence of appropriate algorithms for mapping, that is, identification of the correspondence of nodes in a subnetwork (i.e. compartments) and those stored in the database (pg. 12 para. 2).
• Cannataro does not teach the "graphical visualization on the electronic display device" neither the rendering step. However, Vodovotz teaches various modeling approaches of mechanistic simulations of inflammation in the context of health and disease (pg. 1 para. 1); wherein a graphically based modeling toolkit, BioNetGen is used to produce a model of TLR4 which generates a network of 76 possible molecular species and 202 reactions (pg. 4 col. 2 para. 2); wherein identified parameters may be utilized for the design of anti-inflammatory therapeutic strategies (i.e. step (II)(b)(i)) (pg. 5 col. 1 para. 1); wherein BioNetGen tool taught by Vodovotz is a software with a visual interface/display configured to receive the network map from the processing unit and navigation functionality and rendering graphical image (i.e. step (II)(b)(ii)-(iii)) as evidenced by An09 that teaches BioNetGen as a software platform for modeling intracellular signaling pathways (pg. 53 para. 1) displaying both a text-based interface, and a visual graphics tool (pg. 54 col. 2 para. 4).
Claims 4 and 16 recite:
wherein said DAMP and/or said PAMP are derived from evaluation of publications in public scientific databases using an automated evaluation algorithm which identifies molecules suspected to be involved in inflammation
Claims 5 and 17 recite:
wherein said biological function of a molecule in inflammation is selected from the group consisting of: Mast Cell degranulation, Macrophage Differentiation, Myeloid Cell Differentiation, Lymphocyte Differentiation, Immune Cell Differentiation, Regulation of Hemopoiesis, Initiation of Innate Immune Response, Pattern recognition Receptor Signaling Pathways, Cytokine Production, Regulation of Adaptive Immune Responses, T-cell Mediated Immune Response, T cell Selection, Regulation of B cell Proliferation, Regulation of Immunoglobulin Secretion, Regulation of T cell Proliferation, Regulation of Lymphocyte Proliferation, Inflammation Resolution, Gene Expression Regulation, Protein Modification, Blood Vessel Development, Immune Response, Hemopoiesis, Neuronal Development, and Protein Transport
Claim 31 recites:
wherein PAMP comprises at least one selected from: lipopolysaccharide (LPS), lipoteichoic acid, flagellin, peptidoglycan, unmethylated CpG DNA, and double-stranded RNA
Claim 32 recites:
wherein the DAMP comprises at least one selected from: HMGB 1, S100A8 and/or S100A9, heat-shock protein 70, extracellular ATP, uric-acid crystals, and mitochondrial DNA
• Cannataro does not teach the recitations above. However, Vodovotz various modeling approaches for the effective characterization of a complex system such as inflammation (pg. 2 col. 1 para. 2); including pathobiology of inflammation (i.e., reading on molecules identified from a pathogen associated molecular pattern) and the intertwined damage/healing response (i.e., inflammation resolution), with a mechanistic, rational basis to the design and implementation of therapies via the extraction of relevant data from the scientific literature (pg. 8 col. 1 para. 1); wherein lipopolysaccharide (LPS) is considered a central canonical acute inflammatory stimulus in sepsis and related diseases (pg. 3 col. 2 para. 2); wherein HMGB1 is considered a pro-inflammatory mediator of sepsis related to cellular stress and/or death in models (pg. 5 col. 1 para. 3).
Claims 6 and 18 recite:
wherein the processing unit applies algorithmic rules for gene prioritization, determining node degree, determining betweenness centrality, motif identification, and/or determining association with inflammation
• Cannataro teaches a system and method for predicting the interactions between two or more proteins can be obtained by mining interaction networks stored in databases (pg. 1 para. 1) via computational analysis (pg. 4 para. 1); wherein one database can integrate various types of datasets (pg. 17 para. 2); including (i) the modeling of the PPI network in a suitable data structure; (ii) the existence of appropriate algorithms for mapping, that is, identification of the correspondence of nodes in a subnetwork and those stored in the database (pg. 12 para. 2); and application of graph-theoretic methods to explain different topological motifs in the resulting network (pg. 23 para. 5).
Claims 8 and 20 recite:
wherein the transcriptomics data are used to model the datasets in the database
• Cannataro teaches predicting the interactions between two or more proteins can be obtained by mining interaction networks stored in databases (pg. 1 para. 1); including the integration of PPI information with biochemical pathways, gene (regulatory) pathways, and transcriptomics (pg. 32 para. 4).
Claims 9 and 21 recite:
eliminate datasets from the database if no match is found between the identifier for a molecule in the dataset and the molecule identifier in the transcriptomics data
• Cannataro teaches the integration of PPI information with transcriptomics (pg. 32 para. 5); wherein microarray datasets are loaded into the analysis software, with a comprehensive, computable, biological knowledge base that dynamically computes a set of relevant genes and presents this information in the form of activated networks in terms of significance (i.e. eliminated datasets when identified not relevant) as evidenced by Lagoa (pg. 597 col. 2 para. 4 Lagoa).
Claims 29 and 30 recite:
wherein motif identification comprises identification of feedback or feedforward loops
• Cannataro teaches that the work considers the data of Saccharomices Cervisiae and applies graph-theoretic methods to explain different topological motifs in the resulting network (i.e. motif identification).
• Cannataro does not teach "feedback or feedforward loops." However, Vodovotz teaches that the mechanistic detail a dynamic model allows finer grained examination of the mechanisms of signal attenuation and tolerance behavior, and suggests the necessity for multiple nested feedback loops both in terms of explaining the overall dynamics (pg. 4 col. 2 para. 2).
Claim 12 recites:
wherein the visualization unit is configured to graphically arrange highly connected nodes close to each other
Claim 24 recites:
wherein generating the one or more graphical visualizations comprises graphically arranging highly connected nodes close to each other and/or identifying only nodes for graphical display that are intramodularly connected
Claim 27 recites:
wherein the visualization unit is configured to include only nodes for graphical display that are intramodularly connected
• Cannataro teaches predicting the interactions between two or more proteins can be obtained by mining interaction networks stored in databases (pg. 1 para. 1) via computational analysis (i.e., system) (pg. 4 para. 1); with the existence of appropriate algorithms for mapping, that is, identification of the correspondence of nodes in a subnetwork and those stored in the database (pg. 12 para. 2).
• Cannataro does not teach " graphical visualizations with intramodularly/ highly connected nodes." However, Vodovotz teaches various modeling approaches of mechanistic simulations of inflammation in the context of health and disease (pg. 1 para. 1); wherein a graphically based modeling toolkit, BioNetGen (i.e. generating the one or more graphical visualization as in claim 24) is used to produce a model of TLR4 which generates a network of 76 possible molecular species and 202 reactions (i.e. visualization of simulated interactions) (pg. 4 col. 2 para. 2); wherein BioNetGen is a software platform for modeling intracellular signaling pathways creating a modular component of an overall model as evidenced by An09 (pg. 53 Abstract An09) with circular nodes denoting complexes composed of attached molecules (pg. 56 Fig. 1 An09) (i.e. the modular type of modelling in BioNetGen comprises the connected nodes depicted in Fig. 1 - hence nodes for graphical display that are intramodularly and highly connected as in claims 12, 24 and 27); wherein identified parameters may be utilized for the design of anti-inflammatory therapeutic strategies (pg. 5 col. 1 para. 1).
Claim 33 recites:
administering the drug to the subject before obtaining the transcriptomics data from the subject.
Claim 34 recites:
wherein the transcriptomics data comprises measurements obtained at two or more time points following administration of the drug to the subject, and integrating the transcriptomics data onto the interaction network data structure comprises integrating time-series data to simulate a temporal evolution of the molecular interactions involved in inflammation in the subject
• Cannataro teaches predicting the interactions between two or more proteins can be obtained by mining interaction networks stored in databases (pg. 1 para. 1); including the integration of PPI information with biochemical pathways, gene (regulatory) pathways, and transcriptomics (pg. 32 para. 4).
• Cannataro does not teach obtaining "data following administration of the drug to the subject, and integrating the transcriptomics data onto the interaction network data structure comprises integrating time-series data to simulate a temporal evolution of the molecular interactions involved in inflammation in the subject." However, Vodovotz teaches various modeling approaches of mechanistic simulations of inflammation in the context of health and disease (pg. 1 para. 1); wherein simulation algorithms included interactions among evolving quantities such as endotoxin in the system (i.e. two or more time points following administration of the drug) (pg. 2 col. 2 para. 4); wherein sepsis (i.e. inflammation process) modeling studies were performed on data matched to in-vivo models of endotoxin administration in mice (i.e. inflammation modeling studies based on data obtained from a subject that has been administered a drug) as evidenced by An (pg. 4 para. 3 An).
Rationale for combining (MPEP §2142-2143)
Regarding claims 1, 4-6, 8-9, 12-13, 16-18, 20-21, 24, 27 and 29-34, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Cannataro in view of Vodovotz because all references disclose methods for the investigation of molecular interactions involved in in signaling pathways. The motivation would have been to unify mechanisms described in the scientific literature using methods and tools developed to uncover novel insights into the pathobiology of inflammation and the intertwined damage/healing response (pg. 8 col. 1 para. 1 Vodovotz).
Therefore it would have been obvious to one of ordinary skill in the art to substitute analysis of molecular interactions in signaling pathways of Cannataro to the methods by Vodovotz because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for investigating molecular interactions involved in in signaling pathways.
B. Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Cannataro and Vodovotz as applied to claim 1 above further in view of Nathan (“Points of control in inflammation” Nature 420.6917: 846-852 (2002)), as cited on the attached Form PTO-892. Any newly recited portions are necessitated by claim amendment.
Claim 2 recites:
wherein the inflammation resolution is characterized by biosynthesis of specialized pro-resolving mediators (SPMs)
• Cannataro nor Vodovotz explicitly teach the recited limitation above. However, Nathan teaches points of control in inflammation (pg. 846 Title); wherein anti-inflammatory lipoxins are synthesized as stopping signals during inflammation (i.e. reading on specialized pro-resolving mediators) (pg. 849 col. 2 para. 2).
Rationale for combining (MPEP §2142-2143)
Regarding claim 2, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Cannataro and Vodovotz in view of Nathan because all references disclose methods for the investigation of molecular interactions involved in in signaling pathways. The motivation would have been to investigate molecules responsible for inflammation response and suppression (pg. 846 para. 1 Nathan).
Therefore it would have been obvious to one of ordinary skill in the art to substitute analysis of molecular interactions in signaling pathways of Cannataro and Vodovotz to the methods by Nathan because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for investigating molecular interactions involved in in signaling pathways.
C. Claims 11 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Cannataro and Vodovotz as applied to claims 1 and 13 further in view of Schneider (“Traumeel – an emerging option to nonsteroidal anti-inflammatory drugs in the management of acute musculoskeletal injuries” Int J Gen Med. 4:225–234 (2011)), as cited on the attached Form PTO-892. Any newly recited portions are necessitated by claim amendment.
Claim 11 recites:
wherein said multicomponent drug is comprises Achillea millefolium, Aconitum napellus, Arnica montana, Atropa bella-donna, Bellis perennis, Calendula officinalis, Echinacea purpurea, Echinacea, Hamamelis virginiana, Hepar sulfuris, Hypericum perforatum, Matricaria recutita, Mercurius solubilis Hahnemanni, and Symphytum officinale
Claim 22 recites:
wherein the drug is a multicomponent drug
• Cannataro nor Vodovotz explicitly teach the recited limitation above. However, Schneider teaches anti-inflammatory drug made by a fixed combination of biological and mineral extracts – Traumeel – (i.e., reading on multicomponent drug Traumeel) that acts in the management of inflammation which includes family of transcription factors in the expression of genes that control the inflammatory response (pg. 225 para. 1 and pg. 229 col. 2 para. 2); also teaching the analysis of drug actions related to treating inflammation (pg. 227 Table 1); wherein the drug components comprise wherein said multicomponent drug is comprises Achillea millefolium, Aconitum napellus, Arnica montana, Atropa bella-donna, Bellis perennis, Calendula officinalis, Echinacea purpurea, Echinacea, Hamamelis virginiana, Hepar sulfuris, Hypericum perforatum, Matricaria recutita, Mercurius solubilis Hahnemanni, and Symphytum officinale (pg. 228 Table 2).
Rationale for combining (MPEP §2142-2143)
Regarding claims 11 and 22, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Cannataro and Vodovotz in view of Schneider because all references disclose methods for the investigation of interaction among molecules. The motivation would have been to investigate molecules responsible for inflammation response and suppression (pg. 846 para. 1 Schneider).
Therefore it would have been obvious to one of ordinary skill in the art to substitute analysis of interaction among molecules of Cannataro and Vodovotz to the methods by Schneider because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for the investigation of interaction among molecules.
D. Claims 28 and 35 are rejected under 35 U.S.C. 103 as being unpatentable over Cannataro and Vodovotz as applied to claim 1 further in view of Ponto (“Giga-stack: A method for visualizing giga-pixel layered imagery on massively tiled displays” Future Generation Computer Systems 26:693-700 (2010)), as cited on the attached Form PTO-892. Any newly recited portions are necessitated by claim amendment.
Claim 28 recites:
wherein the visualization unit is configured to apply tiling techniques to divide images into a matrix of smaller images to manage computational resources in generating a multi-layered visualization
• Cannataro nor Vodovotz explicitly teach the recited limitation above. However, Ponto teaches a technique for the interactive visualization and interrogation of multi-dimensional giga-pixel imagery (i.e. pg. 693 para. 1) (i.e. multi-layered visualization); allowing users to view single, high-resolution image planes on tiled displays (pg. 693 col. 2 para. 1); wherein a resource management system is used to control loading, display, and replacement of image data (pg. 694 col. 2 para. 2).
Claim 35 recites:
a processing unit coupled to a non-transitory computer-readable storage medium storing instructions executable by the processing unit to:
(a) access structured molecular interaction data describing molecules involved in inflammation and interactions among the molecules, wherein the structured molecular interaction data comprises at least one data characteristic indicative of a biological function of the molecules in inflammation
• Cannataro teaches a system and method for predicting the interactions between two or more proteins can be obtained by mining interaction networks stored in databases (i.e. step (a)) (pg. 1 para. 1) via computational analysis (i.e. system) (pg. 4 para. 1); wherein studying and storing protein-protein interactions requires computer-based tools (i.e. non-transitory computer-readable storage medium comprising instructions executable by the processing unit) (pg. 2 para. 1); wherein one database can integrate various types of datasets by applying a probabilistic model and then predicts protein-protein interaction networks in humans (pg. 17 para. 2); wherein users can input a set of protein identifiers and search for them in databases (pg. 28 para. 6); wherein said protein-protein interactions can demonstrate an interrelation between their modifications and disease (pg. 5 para. 2); wherein discovered PPI interactions is stored together with core interaction data (pg. 10 para. 2).
• Cannataro does not teach "inflammation resolution" and "wherein the structured molecular interaction data comprises at least one data characteristic indicative of a biological function of the molecules in inflammation." However, Vodovotz teaches various modeling approaches for the effective characterization of a complex system such as inflammation (pg. 2 col. 1 para. 2); including pathobiology of inflammation and the intertwined damage/healing response (i.e. inflammation resolution), with a mechanistic, rational basis to the design and implementation of therapies via the extraction of relevant data from the scientific literature (pg. 8 col. 1 para. 1) applied via mechanistic simulations of inflammation in the larger context of health and disease, from intracellular signaling to whole-animal physiology (pg. 1 para. 1); wherein mathematical and computational methods (i.e. system/method) for the effective characterization of a complex system such as inflammation (i.e. data characteristic indicative of a biological function of the molecules in inflammation) (pg. 2 col. 1 para. 2).
(b) generate, from the structured molecular interaction data, an interaction network data structure comprising nodes representing the molecules and edges representing the interactions, wherein generating the interaction network data structure comprises grouping the nodes into functional modules based at least in part on the data characteristic indicative of the biological function;
(c) generate an executable dynamic model derived from the interaction network data structure, the executable dynamic model defining state information associated with at least a subset of the nodes and rules for updating the state information based on regulatory relationships encoded by the edges;
(d) execute the executable dynamic model over a plurality of computational iterations to generate simulation results representing a temporal evolution of the state information; and
• Cannataro teaches a system and method for predicting the interactions between two or more proteins can be obtained by mining interaction networks stored in databases (pg. 1 para. 1); wherein the information representable as a network is mostly modeled using graph theory via a structure composed of a set of nodes and the edges linking them (i.e. step (b) an interaction network data structure comprising nodes representing the molecules and edges representing the interactions) (pg. 2 para. 2); wherein the interaction databases (i.e. the datasets that compose each database) can be categorized on the basis of (i) the kind of the stored interactions (i.e. step (b) grouping the nodes into functional modules based at least in part on the data characteristic indicative of the biological function) and (ii) the kind of data submission and extraction (pg. 18 para. 2); wherein the software presents as output a network of interacting proteins and the size of the result depends on the chosen depth parameter (pg. 28 para. 6); including (i) the modeling of the PPI network in a suitable data structure; (ii) the existence of appropriate algorithms for mapping, that is, identification of the correspondence of nodes in a subnetwork (i.e. compartments) and those stored in the database (pg. 12 para. 2); and application of graph-theoretic methods to explain different topological motifs in the resulting network (pg. 23 para. 5); wherein dynamic graphs (i.e. an executable dynamic model) are not fixed in time and can evolve through local changes with the use of appropriate meta-information associated with both nodes and edges (pg. 31 para. 4); wherein the algorithmic computation causes a particular state in which each node is equally attracted from the others with nodes within dense regions are more attracted than the ones that are in the same region (pg. 21 para. 5); wherein the operation that describes the state of each other being attracted to others is based on an inflation parameter greater than 1 (i.e. step (c) executable dynamic model defining state information associated with at least a subset of the nodes and rules) in which the greater the inflation parameter, the greater the number of clusters (pg. 21 para. 6); wherein each value of a matrix represents the tendency of a node to be attracted by the other ones in which the evolution of the system, that is, of the flow, is computed by calculating (i.e. state functions) the next power of this matrix (i.e. step (c) updating the state information based on regulatory relationships encoded by the edges and step (d)) (pg. 21 para. 5).
(e) generate and render, on an electronic display device, an interactive graphical visualization based on the interaction network data structure and the simulation results, wherein the interactive graphical visualization is updated to reflect changes in the state information across the plurality of computational iterations represented by the simulation results …
(ii) dynamically arranging the nodes presented within a respective functional module based on inter-modular connections between the respective functional module and other functional modules, so as to reduce visual overlap or crossing of edges representing the inter-modular connections
• Cannataro teaches a system and method for predicting the interactions between two or more proteins can be obtained by mining interaction networks stored in databases (pg. 1 para. 1); wherein the information representable as a network is mostly modeled using graph theory via a structure composed of a set of nodes and the edges linking them (i.e. step (b) an interaction network data structure comprising nodes representing the molecules and edges representing the interactions) (pg. 2 para. 2); wherein each value of a matrix represents the tendency of a node to be attracted by the other ones in which the evolution of the system, that is, of the flow, is computed by calculating (i.e. state functions) the next power of this matrix (i.e. dynamically arranging the nodes and updated to reflect changes in the state information across the plurality of computational iterations represented by the simulation results) (pg. 21 para. 5).
• Cannataro does not teach "(e) …generate and render, on an electronic display device, an interactive graphical visualization based on the interaction network data" and "(ii) …respective functional module based on inter-modular connections between the respective functional module and other functional modules However, Vodovotz teaches various modeling approaches of mechanistic simulations of inflammation in the context of health and disease (pg. 1 para. 1); wherein a graphically based modeling toolkit, BioNetGen is used to produce a model of TLR4 which generates a network of 76 possible molecular species and 202 reactions (pg. 4 col. 2 para. 2); wherein identified parameters may be utilized for the design of anti-inflammatory therapeutic strategies (pg. 5 col. 1 para. 1); wherein BioNetGen tool taught by Vodovotz is a software with a visual interface/display configured to receive the network map from the processing unit and navigation functionality and rendering graphical image (i.e. generate and render, on an electronic display device, an interactive graphical visualization based on the interaction network data) as evidenced by An09 that teaches BioNetGen as a software platform for modeling intracellular signaling pathways (pg. 53 para. 1) displaying both a text-based interface, and a visual graphics tool (pg. 54 col. 2 para. 4); wherein BioNetGen is a software platform for modeling intracellular signaling pathways creating a modular component of an overall model as evidenced by An09 (pg. 53 Abstract An09) with circular nodes denoting complexes composed of attached molecules (pg. 56 Fig. 1 An09) (i.e. the modular type of modelling in BioNetGen comprises the connected nodes depicted in Fig. 1 - hence nodes for graphical display that are intramodularly and highly connected as in claims 12, 24 and 27); wherein identified parameters may be utilized for the design of anti-inflammatory therapeutic strategies (pg. 5 col. 1 para. 1).
(i) selectively presenting graphical elements representing the functional modules or the nodes within the functional modules based on a zoom level of the interactive graphical visualization
• Cannataro nor Vodovotz teach the "selectively presenting graphical elements" and the "zoom level of the interactive graphical visualization." However, Ponto teaches a technique for the interactive visualization and interrogation of multi-dimensional giga-pixel imagery where users can freely pan and zoom, while swiftly transitioning through data layers, enabling intuitive analysis of massive multi-spectral or time-varying records (pg. 693 Abstract).
Rationale for combining (MPEP §2142-2143)
Regarding claims 28 and 35, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Cannataro and Vodovotz in view of Ponto because all references disclose methods for the investigation of image analysis. The motivation would have been to analyze large data sets simultaneously (pg. 700 col. 1 para. 2 Ponto).
Therefore it would have been obvious to one of ordinary skill in the art to substitute analysis of images displayed of Cannataro and Vodovotz to the methods by Ponto because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for investigation of image analysis.
Response to applicant's remarks in regard to Claim Rejection 35 U.S.C. ~ 103
The Remarks of 03/04/2026 have been fully considered but are not persuasive for the reasons below:
Applicant asserts in pg.
The cited art fails to teach or suggest the discrete modeling architecture required by Claim 1. As the Examiner correctly identified in the Office Action, Vodovotz utilizes "ordinary differential equations (ODEs) or agent-based models (ABM)." An ODE is a continuous mathematical model, not a discrete model utilizing a finite set of state values. Neither Vodovotz nor Cannataro teaches or suggests using empirical post-drug transcriptomic data to calibrate discrete state transition functions mapped to a DAMP/PAMP compartmentalized network. The prior art's use of continuous OD Es or general predictive networking does not teach or suggest the specific Boolean or discrete state calibration recited in Claim 1. Furthermore, regarding the visualization unit, the rejection relied on An09; however, the Office Action acknowledges that An09 teaches modeling pathways displayed in static diagrams. A static diagram inherently cannot reflect the "temporal evolution of the state values" across successive computational iterations. Furthermore, Ponto merely teaches the spatial tiling of high resolution images to manage memory, which provides no teaching or suggestion of visualizing the temporal evolution of discrete biological state values across dynamic compartments. Because the cited references fail to teach or suggest the claimed combination of features of the pending claims, the claims are not obvious in view of the references, alone or in combination. Accordingly, withdrawal of the rejection under 35 U.S.C. §103 and allowance of the claims is respectfully requested. Similarly, independent Claim 35 is patentable because it recites a specific technological improvement by employing semantic zooming and topology-aware node arrangement based on biological inter-modular connectivity. While the cited art may disclose static pathway diagrams (An09) or generic spatial image tiling (Ponto), it does not teach or suggest a rendering architecture that organizes and presents dynamic inflammation-simulation results using biologically meaningful modules and inter-modular relationships
It is respectfully submitted that this is not persuasive because the claims recite an "executable dynamic model comprising: (i) a state value, selected from a finite set of state values, assigned to each of at least a subset of the nodes, and (ii) state transition functions specifying how a future state value of a node is determined from current state values of one or more regulator nodes according to regulatory relationships encoded by one or more edges of the interaction network data structure" and the presented art read on the recited model as described in the rejection above. There is no recitation in the instant claims of the argued "specific Boolean or discrete state calibration recited in Claim 1." The art to Cannataro (pg. 21 para. 5-6) describes the discrete state and stat functions related to the executable dynamic model as described in detail in the Claim Rejections above.
Regarding the argued "static diagram inherently cannot reflect the "temporal evolution of the state values" across successive computational iterations", "One cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references" (MPEP 2145 § IV). This argument is unpersuasive, because it analyzes the teachings of the references separately and independently, whereas the rejection is based on the combined teachings of the references. The argued "static diagram" related to the images being visualized in the BioNetGen tool taught by Vodovotz which is not the art that is being used to teach the actual "temporal evolution of the state values." The argued "temporal evolution of the state values" is being taught by Cannataro that discloses dynamic graphs not fixed in time and can evolve through local changes with the use of appropriate meta-information associated with both nodes and edges (pg. 31 para. 4).
The same explanation above applies to the argued "Ponto merely teaches the spatial tiling of high resolution images to manage memory, which provides no teaching or suggestion of visualizing the temporal evolution of discrete biological state values across dynamic compartments" and "(Ponto), it does not teach or suggest a rendering architecture that organizes and presents dynamic inflammation-simulation results." Ponto is not the art used to teach the argued elements. Cannataro indeed teaches a software that presents as output a network of interacting proteins and the size of the result depends on the chosen depth parameter (pg. 28 para. 6); including (i) the modeling of the PPI network in a suitable data structure; (ii) the existence of appropriate algorithms for mapping, that is, identification of the correspondence of nodes in a subnetwork (i.e. compartments) and those stored in the database (pg. 12 para. 2). Furthermore Vodovotz teaches the map from the processing unit and navigation functionality and rendering graphical image as described in the Claim Rejections above. While none of the references teach all claim limitations, and the examiner does not dispute Appellant's identification of material missing from each one, all the claim limitations are taught by the combination of references, as explained previously.
Conclusion
No claims are allowed.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FRANCINI A FONSECA LOPEZ whose telephone number is (571)270-0899. The examiner can normally be reached Monday - Friday 8AM - 5PM ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Olivia Wise can be reached at (571) 272-2249. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/F.F.L./Examiner, Art Unit 1685
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685
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