DETAILED ACTION
This action is in response to the claims filed on Mar. 1st, 2022. A summary of this action:
Claims 1-17 have been presented for examination.
Claim 1-17 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite
Claim(s) 1-6, 9, and 11-17 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wang, Randi, and Morad Behandish. "Surrogate modeling for physical systems with preserved properties and adjustable tradeoffs." arXiv preprint arXiv:2202.01139 (2022).
Claim(s) 10 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wang, Randi, and Morad Behandish. "Surrogate modeling for physical systems with preserved properties and adjustable tradeoffs." arXiv preprint arXiv:2202.01139 (2022) with the meaning of a term used in the primary reference explained/inherency demonstrated by Wang, Randi, and Vadim Shapiro (hereinafter Randi). "Topological semantics for lumped parameter systems modeling." Advanced Engineering Informatics 42 (2019): 100958.
Claim(s) 1-15 and 17 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Cao, Liwei, et al. "Identifying physico-chemical laws from the robotically collected data." (2019).
Multiple § 102 rejections are made in view of MPEP § 2120(I): “Prior art rejections should ordinarily be confined strictly to the best available art. Exceptions may properly be made, for example, where:…(C) for cases examined under the first inventor to file provisions of the AIA , the most pertinent disclosure could be shown not to be prior art by invoking an exception in a 37 CFR 1.130 affidavit or declaration of attribution or prior public disclosure”
Claim(s) 7-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang, Randi, and Morad Behandish. "Surrogate modeling for physical systems with preserved properties and adjustable tradeoffs." arXiv preprint arXiv:2202.01139 (2022) with the meaning of a term used in the primary reference explained/inherency demonstrated by Michopoulos, John G., et al. "Metacomputing for directly computable multiphysics models." Journal of Computing and Information Science in Engineering 23.6 (2023): 060820 in view of Kalyuzhnaya, Anna V., et al. "Towards generative design of computationally efficient mathematical models with evolutionary learning." Entropy 23.1 (2020): 28.
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as obvious over Cao, Liwei, et al. "Identifying physico-chemical laws from the robotically collected data." (2019) in view of Schmidt, Michael, and Hod Lipson. "Distilling free-form natural laws from experimental data." science 324.5923 (2009): 81-85.
This action is non-final
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 .
Notice on the specification
¶ 55: “"Computer-readable medium" or "non-transitory, computer-readable medium," as used herein, refers to any non-transitory storage and/or transmission medium that participates in providing instructions to a processor for execution.”
The claims do not presently include a CRM claim, however if one were recited, in view of ¶ 55, even with the modifier “non-transitory”, it would be rejected under § 101 as signals per se (i.e. transmission mediums), for this has been re-defined in the specification to include signals per se.
Should it later be sought a claim for a CRM, the Examiner strongly suggests amending the specification to remove this re-definition. ¶ 55 gives a list of examples, but then: “Such a medium may include, but is not limited to, non-volatile media and volatile media.”, i.e. it does not exclude signals per se.
Claim Rejections - 35 USC § 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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 1-17 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. The dependent claims inherit the deficiencies of the claims they depend upon.
MPEP § 2173.05(b)(IV): “A claim term that requires the exercise of subjective judgment without restriction may render the claim indefinite. In re Musgrave, 431 F.2d 882, 893, 167 USPQ 280, 289 (CCPA 1970). Claim scope cannot depend solely on the unrestrained, subjective opinion of a particular individual purported to be practicing the invention. Datamize LLC v. Plumtree Software, Inc., 417 F.3d 1342, 1350, 75 USPQ2d 1801, 1807 (Fed. Cir. 2005));”
Independent claims recite the phrase “representing a plurality of testable hypotheses each as a network or graph-like structure comprising physical relationships among the physical variables”, wherein the term “graph-like” is a subjective term that renders the claim indefinite because there is no standard provided in the instant disclosure (¶ 30) for POSITA to ascertain the scope of the present claims without relying on their own unrestrained, subjective opinion when practicing the invention.
To clarify, at issue is that it is indefinite as to the extent of the boundaries of this claim, for the claim does not simply recite “graph”, but also includes anything that is “graph-like”, which is purely subjective as to what is like a graph and what is not, with no objective standard in the disclosure.
Claim 8 recites more subjective terminology, see “simpler”, “complexity”, and “adequately” with no standard in the disclosure (¶¶ 28 and 33).
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 1-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of both a mathematical concept and mental process without significantly more.
Step 1
Claim 1 is directed towards the statutory category of a process.
Claim 17 is directed towards the statutory category of an apparatus.
Claims 17, and the dependents thereof, are rejected under a similar rationale as representative claim 1, and the dependents thereof.
Step 2A – Prong 1
The claims recite an abstract idea of both a mental process and mathematical concept.
As an initial matter, this is entirely focused on an abstract idea. There is no improvement to technology, for this is an application which seeks to automate the scientific method of scientists, e.g. Einstein, as its core focus. ¶ 14: “The present disclosure relates methods and systems for artificial intelligence (AI)assisted generation of viable hypotheses. More specifically, the present disclosure describes a 'cyber-physicist' (CyPhy), an AI research associate for early-stage scientific process of hypothesis generation and initial validation or invalidation, grounded in the most invariable mathematical foundations of classical and relativistic physics.”
The thrust of the alleged advance in ¶ 14: “The framework distinguishes itself from existing rule-based reasoning, statistical learning, and hybrid AI methods by: (1) an ability to rapidly enumerate and test a diverse set of mathematically sound and parsimonious physical hypotheses, starting from a few basic assumptions on the embedding spacetime topology; (2) a distinction between non-negotiable mathematical truism (e.g., conservation laws or symmetries), that are directly implied by properties of spacetime, and phenomenological relations (e.g., constitutive laws), whose characterization relies indisputably on empirical observation, justifying targeted use of data-driven methods (e.g., machine learning (ML) or polynomial regression); and (3) a "simple-first" strategy (following Occam's razor) to search for new hypotheses by incrementally introducing latent variables that are expected to 20 exist based on topological foundations of physics” is merely the use of generic machine learning, or other commonplace algorithms (MPEP § 2106.05(f)) to seek to claim the process of discovery of math equations itself, but with a computer.
To further clarify, ¶ 15: “Further, the AI research associate may bridge multiple levels of abstraction, using a domain-agnostic representation scheme (referred to herein as an interaction network or I-net) to express a wide range of mathematically viable physical hypotheses (e.g., candidates for theories/laws) from Kepler's and Newton's laws to elastodynamics in composite materials, by exploiting common structural invariants across physics. Said approach entails: (a) defining a relatively unbiased ontology that is rooted in fundamental abstractions (also referred to as conservation laws) that are common to all known theories of classical and relativistic physics; (b) constructing a constrained search space to enumerate viable hypotheses with postulated invariants (e.g., built-in conservation laws that are consistent with the presupposed spacetime 30 topology); and (c) automatically assembling interpretable ML architectures for each hypothesis, to estimate parameters for phenomenological relations (also referred to herein as constitutive laws) from empirical data.”
This is not a data structure, but rather a mathematical representation/abstraction. ¶ 16: “At the core of (a) is a powerful mathematical abstraction of physical governing equations rooted in algebraic topology and differential geometry, leading to an ontological commitment to the relationship between physical measurement and basic properties of the embedding spacetime - but nothing more, to leave room for innovation and surprise”.
To clarify, ¶ 18: “The interaction networks or I-nets described herein may be based on a
generalization of Tonti diagrams that is expressive and versatile enough to accommodate novel scientific hypotheses, while retaining a basic commitment to philosophical principles such as parsimony (Occam's razor), measurement-driven classification of variables, and separation of non-negotiable mathematical properties of spacetime (homology) from domain-specific empirical knowledge (phenomenology).”
To further clarify, ¶ 22: “An abstract (symbolic) I-net may be defined on a single 'D-space as a finite collection of primary and/or secondary co-chain complexes that are inter-connected by phenomenological links (also referred to herein as constitutive laws), as illustrated in FIG. IA” – note, in § 101, constitutive laws, e.g. Hook’s laws, are generally referred to as Laws of Nature, “e.g., candidates for theories/laws) from Kepler's and Newton's laws to elastodynamics in composite materials,” in ¶ 15.
And ¶ 23: “The cross-sequence links can thus represent both single-physics constitutive relations and multi-physics coupling interactions. Conservation laws, on the other hand, are represented by a balance between the output of a topological operator and an external source/sink, the latter being represented by a loop.”
See ¶ 28 as well, which alleges no new computer data structure, but rather the use of a generic one: “The search space may be defined by a directed acyclic graph (DAG) whose nodes (i.e., states) represent symbolic I-net instances” in its ordinary capacity.
MPEP § 2106.05(a): “Examples that the courts have indicated may not be sufficient to show an improvement in computer-functionality… vii. Providing historical usage information to users while they are inputting data, in order to improve the quality and organization of information added to a database, because "an improvement to the information stored by a database is not equivalent to an improvement in the database’s functionality," BSG Tech LLC v. Buyseasons, Inc., 899 F.3d 1281, 1287-88, 127 USPQ2d 1688, 1693-94 (Fed. Cir. 2018); and” and MPEP § 2106.04(II)(A)(2): “See, e.g., RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1327, 122 USPQ2d 1377 (Fed. Cir. 2017) ("Adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract"); Genetic Techs. Ltd. v. Merial LLC, 818 F.3d 1369, 1376, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016) (eligibility "cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself.").”
See ¶ 41 to further clarify in its discussion of fig. 2, i.e. its directed at the data stored in a DAG, not some new inventive data structure. And the data is of the utmost abstract nature, with links between the data being Laws of Nature itself, and the data itself being purely mathematical in nature, e.g. variables, math operators, etc., all of which form a “search space” for discovering new equations. See ¶ 42 as well, further clarifying on this point, concluding at ¶ 43: “In this example, the best tis achieved with ff1(0) = c1sin0 and ff2(w) = c2w where c2/c1 = -g Ir- The latent variables L(f0) and T(f1) tum out to be the familiar notions of angular momentum and torque, respectively, although the software need not know anything about angular momentum and torque to generate and test what-if scenarios about the existence of angular momentum and torque and the correlations of angular momentum and torque with angular position and velocity. Hence, the interpretability of the discovered relationships by a human scientist does not require predisposing the AI associate to such interpretations.”
Furthermore, human scientists are known to have very complex minds, e.g. physicists such as Einstein, Stephen Hawking, etc., and have long known and worked in multiple different fields of physics, e.g. Marie Curie holds a Nobel Prize in both Chemistry and Physics, and her family has received numerous Nobel Prizes as well.
Furthermore, see ¶ 44: “When adding new dangling branches to the I-net structure, the search algorithm may prioritize actions that produce I-net structures similar to existing Tonti diagrams by assigning a penalty factor to every violation of the common structure (e.g., diagonal phenomenological links connecting non-dual cells).”.
The end resulting data as an equation – see ¶ 45.
With respect to the generic nature of the machine learning, ¶ 46: “As a result, the generated hypotheses may be evaluated using any number of existing machine learning or symbolic regression frameworks that standardize on ordinary differential equations and/or partial differential equations (ODE/PDE) inputs” – this is not rooted in computer technology, but rather directed towards purely the abstract, and uses a computer as a tool to perform the abstract idea of discovery itself.
¶ 47: “Next, an algebraic simplification may be performed (e.g., using the software) to identify equivalence classes of hypotheses that, despite coming from different I-net structures, lead to the same ODE upon differential interpretation of the I-nets” – entirely abstract, but do it on a computer. E.g. see the math equations for the small-signal model of a transistor. These are derived from the base ODEs itself, with numerous assumptions so to simplify it to a math representation readily usable by people, e.g. such as was used to create the first computer.
In the inventors’ own words, see Behandish, Morad, John Maxwell III, and Johan de Kleer. "AI research associate for early-stage scientific discovery." arXiv preprint arXiv:2202.03199 (2022), page 2, col.2, ¶ 2 “The key enabler of our AI framework is a simple type system for (a) physical variables, based on how they are measured in spacetime; and (b) physical relations, based on their (topological vs. metric) nature, and the variables they connect. Following the ground-breaking discoveries by a number of mathematicians, physicists, and electrical engineers (Kron 1963; Roth 1955; Branin 1966) towards a general network theory, Tonti explained the fascinating analogies across classical and relativistic physics in his pioneering life-long work (Tonti 2013) by reframing them in the language of cellular homology, leading to informal classification diagrams. Tonti diagrams can be formalized as directed graphs with strongly typed nodes for variables and edges for relations.” And see fig. 1: “A topology-aware representation for physics (Tonti 2013):”, i.e. the work of another.
Also, on page 2, paragraph split between the columns: “…(b) constructing a constrained search space to enumerate viable hypotheses with postulated invariants, e.g., built-in conservation laws that are consistent with the presupposed spacetime topology; and…At the core of (a) is a powerful mathematical abstraction of physical governing equations rooted in algebraic topology and differential geometry (Frankel 2011). This abstraction leads to an ontological commitment to the relationship between physical measurement and basic properties of the embedding spacetime but nothing more, to leave room for innovation and surprise. This relationship has been shown to be responsible for the analogies and common structure across physics (Tonti 2013), exploited in (b), along with search heuristics based on analogical reasoning.”
See figure 2 to further clarify: “Tonti diagrams are recipes to generate governing equations in different contexts, defined by a continuum, discrete, or semi-discrete setting and a topological embedding of the variables based on how they are measured. The conservation laws in terms of co-boundary operators result directly from assumed properties of space (or spacetime), while constitutive relations must be learned from data (e.g., via regression/ML). [e.g., by a computer and commonplace software; see MPEP § 2106.05(f))]”
To further clarify, as to what the focus of the claimed advance actually is: “We present a novel representation, called `interaction networks' (I-nets), based on a generalization of Tonti diagrams that is expressive and versatile enough to accommodate novel scientific hypotheses, while retaining a basic commitment to philosophical principles such as parsimony (Occam's razor), measurement-driven classification of variables, and separation of non-negotiable mathematical properties of spacetime (homology) from domain-specific empirical knowledge (phenomenology).” - simply continuing the work of Tonti, who continued the work of “a number of mathematicians, physicists, and electrical engineers” per page 2, col. 2, ¶ 2.
In other words, the focus of the claimed advance is simply a new mathematical representation, readily drawn on paper – see figures 3-4 of Behandish which shows this, i.e. an allegedly new abstract idea. MPEP § 2106.04(I): “Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151, 120 USPQ2d 1473, 1483 (Fed. Cir. 2016) ("a new abstract idea is still an abstract idea") (emphasis in original).”
And for what its intended uses include, see the words of the inventors, “The good news is that we can directly interpret the same I-net instance in integral form to generate equations over larger regions in space and/or time, to make the computations more resilient to noise.” In Behandish 2022, page 8, col. 1, ¶ 2, i.e. to generate new math equations, and to clarify the conclusion section: “Data-driven regression is targeted at the latter to enable distilling governing equations from sparse and noisy data, while providing deep insights into the mathematical foundations.”
As a further point of clarity, given that the work of Tonti is “exploited” (Behandish, page 2, col. 2, ¶ 1), see Tonti, Enzo. The mathematical structure of classical and relativistic physics. Vol. 10. Basel, Switzerland:: Birkhäuser, 2013.
Chapter 8, § 8.1, including fig. 8.3:
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See the remaining parts of this chapter for numerous visual representations as aids to understand what Tonti diagrams are, e.g. fig. 8.6, and § 8.4 discusses “How to Combine Space and Time” – e.g. the figure on page 228.
§ 8.5 further clarifies on the nature of Tonti diagrams. Note § 8.6.8: “One of the beautiful features of a classification diagram is that it respects the tensorial nature of the variables inside every box.” – note, this is the mathematical field of tensor calculus that Tonti is discussing. Appendix B clarifies on this, including B.1: “Due to its geometrical nature, vector calculus does not require the use of coordinate systems to express physical laws, i.e. it gives rise to an intrinsic formulation. This is the case of the fundamental equation of motion for a particle, i.e. ma = F. Nevertheless, performing calculations necessitates working with components instead of with vectors themselves. This prompts us to inquire as to how components are transformed in passing from one basis to another. The transformation formulae are the subject of tensor analysis. The aim of tensor analysis is to introduce notations and rules to write variables and equations in a way which is independent of the coordinate system chosen and of the basis chosen. In dealing with tensors, some authors start with tensor calculus, others with tensor algebra. Authors of the first group start with coordinate systems, while those of the second group start with the basis in a vector space.”.
Then see § 8.6.9 which discusses “composing the equations” from the diagram.
Many more diagrams are available in chapter 9, pages 267-271, also in chapter 10, pages 310-323, and in several of the later chapters as well.
Appendix D provides a detailed discussion of the history of the diagrams.
See MPEP § 2106.04: “...In other claims, multiple abstract ideas, which may fall in the same or different groupings, or multiple laws of nature may be recited. In these cases, examiners should not parse the claim. For example, in a claim that includes a series of steps that recite mental steps as well as a mathematical calculation, an examiner should identify the claim as reciting both a mental process and a mathematical concept for Step 2A Prong One to make the analysis clear on the record.”
To clarify, see the USPTO 101 training examples, available at https://www.uspto.gov/patents/laws/examination-policy/subject-matter-eligibility.
The mathematical concept recited in claim 1 is:
representing a plurality of testable hypotheses each as a network or graph-like structure comprising physical relationships among the physical variables, wherein the physical relationships are selected from the relationship types, and wherein, within the network or graph-like structure, the physical variables are nodes and the physical relationships are edges;
interpreting at least one of the testable hypotheses into analytical and/or computational forms with a combination of known and unknown variables;
and validating or invalidating the at least one of the testable hypotheses by (a) fitting the unknown parameters to data relating to the physical system and (b) evaluating a goodness of fit for the fitting.
This is simply in textual form claiming a mathematical graph/network (math relationships/equations in textual form, i.e. the physical relationships are the mathematical relationships between math variables, e.g. laws of nature as discussed above), and then (note ¶ 32: “The analytical and/or computational forms may be one or more of: a differential equation, an integral equation, an integro-differential equation, a discrete-algebraic equation, and a system model.”; also note ¶ 42, and ¶ 45, etc., the term “hypotheses” is merely a broad term conveying, under the BRI consistent with the disclosure, series of math equations/relationships, i.e. the interpreting limitation is merely combining variables and equations together to discover new math equations, and the validating/invalidating is merely the math concept of discovering new equations/relationships, followed by mathematically testing them by math calculations to ensure they “fit”, e.g. by using another math equation (¶ 42: “A loss function can, for example, be defined as a mean-squared-error (MSE) to penalize violations uniformly over the time series period”.
Under the broadest reasonable interpretation, the claim recites a mathematical concept – the above limitations are steps in a mathematical concept such as mathematical relationships, mathematical formulas or equations, and mathematical calculations. If a claim, under its broadest reasonable interpretation, is directed towards a mathematical concept, then it falls within the Mathematical Concepts grouping of abstract ideas. In addition, as per MPEP § 2106.04(a)(2): “It is important to note that a mathematical concept need not be expressed in mathematical symbols, because "[w]ords 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). See, e.g., SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163, 127 USPQ2d 1597, 1599 (Fed. Cir. 2018)”
See MPEP § 2106.04(a)(2).
To clarify, see the USPTO 101 training examples, available at https://www.uspto.gov/patents/laws/examination-policy/subject-matter-eligibility.
The mental process recited in claim 1 is:
A method for identifying, generating, and/or evaluating scientific hypotheses, the method comprising:
describing a context for a physical system in terms of an underlying topology and a domain of interest;
defining a plurality of physical variables and relation types based on the underlying topology and the domain of interest;
representing a plurality of testable hypotheses each as a network or graph-like structure comprising physical relationships among the physical variables, wherein the physical relationships are selected from the relationship types, and wherein, within the network or graph-like structure, the physical variables are nodes and the physical relationships are edges;
interpreting at least one of the testable hypotheses into analytical and/or computational forms with a combination of known and unknown variables;
and validating or invalidating the at least one of the testable hypotheses by (a) fitting the unknown parameters to data relating to the physical system and (b) evaluating a goodness of fit for the fitting.
Purely a mental process (note the lack of even a computer, see In re Prater in MPEP § 2111) without even a requirement to use a computer.
A person, e.g. a scientists/physicist/engineer, is readily able, and has long been able to (e.g. Tonti, Enzo. The mathematical structure of classical and relativistic physics. Vol. 10. Basel, Switzerland:: Birkhäuser, 2013, appendix D; also see Behandish, Morad, John Maxwell III, and Johan de Kleer. "AI research associate for early-stage scientific discovery." arXiv preprint arXiv:2202.03199 (2022), page 2, col. 2, ¶ 2), creating Tonti and Tonti-like diagrams to represent equations and variables, readily done with pen and paper, such as the ones depicted in both of these references (see Tonti as cited more specifically above for numerous examples; compare and contrast figures 3-4 of Behandish to further clarify), just as a scientist or engineer would readily be able to mentally evaluate a scientific problem, e.g. finding an equation to fit measured data, and then go through the archive of their own mind (and/or other sources such as textbooks) to recall laws of nature and other such mathematical representations of physics, then in their evaluation of the problem ascertain which ones are the right ones that could be used, and then perform a mental trial-and-error process to derive an equation to model the data. E.g. "Likewise, Einstein could not patent his celebrated law that E=mc2; nor could Newton have patented the law of gravity." Id. Nor can one patent "a novel and useful mathematical formula," Parker v. Flook, 437 U.S. 584, 585, 198 USPQ 193, 195 (1978) in MPEP § 2106.04(b)(I), wherein Einstein did not simply wake up one day and write that equation on the blackboard, but rather spent quite some time to derive that equation from the various governing equations, likely involving the use of numerous blackboards and reams of paper.
And the scientific method readily calls for testing hypotheses, this is a core concept of the scientific method, part of the definition of the word itself. Merriam Webster Dictionary, Definition of “scientific method”, accessed on Jan. 5th, 2026, URL: www(dot)merriam-webster(dot)com/dictionary/scientific%20method: “principles and procedures for the systematic pursuit of knowledge involving the recognition and formulation of a problem, the collection of data through observation and experiment, and the formulation and testing of hypotheses”
In summary, the focus of this claimed advance is simply to use graphs (e.g. Tonti diagrams, or any other such similar diagrams, long in use by the minds of engineers and physicist in varying forms) to perform the scientific method to discover a new equation.
MPEP § 2106.04(I): “The Supreme Court has explained that the judicial exceptions reflect the Court’s view that abstract ideas, laws of nature, and natural phenomena are "the basic tools of scientific and technological work", and are thus excluded from patentability because "monopolization of those tools through the grant of a patent might tend to impede innovation more than it would tend to promote it." Alice Corp., 573 U.S. at 216, 110 USPQ2d at 1980 (quoting Myriad, 569 U.S. at 589, 106 USPQ2d at 1978 and Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 (2012)). The Supreme Court’s concern that drives this "exclusionary principle" is pre-emption. Alice Corp., 573 U.S. at 216, 110 USPQ2d at 1980. The Court has held that a claim may not preempt abstract ideas, laws of nature, or natural phenomena, even if the judicial exception is narrow (e.g., a particular mathematical formula such as the Arrhenius equation). See, e.g., Mayo, 566 U.S. at 79-80, 86-87, 101 USPQ2d at 1968-69, 1971 (claims directed to "narrow laws that may have limited applications" held ineligible); Flook, 437 U.S. at 589-90, 198 USPQ at 197 (claims that did not "wholly preempt the mathematical formula" held ineligible). This is because such a patent would "in practical effect [] be a patent on the [abstract idea, law of nature or natural phenomenon] itself." Benson, 409 U.S. at 71- 72, 175 USPQ at 676. The concern over preemption was expressed as early as 1852. See Le Roy v. Tatham, 55 U.S. (14 How.) 156, 175 (1852) ("A principle, in the abstract, is a fundamental truth; an original cause; a motive; these cannot be patented, as no one can claim in either of them an exclusive right.").” – and this is a claim seeking to patent the scientific method itself, but do it with simple graphs, so as to discover new equations. Such is not eligible subject matter.
To be clear on the breadth and focus of this present claim, see:
E.g. Alotto, Piergiorgio, Fabio Freschi, and Maurizio Repetto. "Multiphysics problems via the cell method: The role of Tonti diagrams." IEEE Transactions on Magnetics 46.8 (2010): 2959-2962. See figure 1, and § IV.A: “The global variables used in stationary current problems are the electric scalar potential , the voltage and the electric current . Their placement in a Tonti diagram is reported in Fig. 1. Ohm’s discrete constitutive matrix for tetrahedra can be built by making use of Whitney edge-elements [6]. Following the primal cycle, i.e. starting from the electric scalar potential on the primal complex in order to obtain given sources on the dual complex, it is easy to [mentally] obtain the equation:” – and see §§ IV(B and C) for additional examples, i.e. these figures teach the “describing…defining…representing…” limitations, with a with the “interpreting…” taught by the “easy” task of obtaining the equations corresponding to the Tonti diagrams.
E.g. Specogna, Ruben, and Francesco Trevisan. "Eddy-Currents Computation With T-$\Omega $ Discrete Geometric Formulation for an NDE Problem." IEEE transactions on magnetics 44.6 (2008): 698-701. See § I and § II, including subsections, noting in particular fig. 2, then see the derivation in § III as “Tonti’s diagram is a useful tool to deduce the final system of algebraic equations”
E.g., Tratkanov, Dimitri. Geometric and energy-based methods for modeling and simulation of multi-physics systems in electrical engineering. Diss. Université de Technologie de Compiègne, 2020. § 2.2: “Our work was greatly influenced by their work and vision. E. Tonti and its "cell-method" also had a major influence on our work, which is why this method will be briefly presented in the next subsection, including its main characteristics and features… The first two equations are topological equations and must be preserved structurally to ensure the consistency of conservation laws. The third equation is the metrics of the problem and may be approximated (or relaxed), because phenomenological equations are just approximation of reality. Although this distinction between these three kinds of laws is not a recent discovery (they are used for a long time in the field of engineering, see [Cannon, 2012] for example), the fact that it can be presented so clearly is, from our point of view, what corresponds to the essential contribution of Tonti's diagrams.”, e.g. § 2.2.3 and fig. 2.16
In summary, what is claimed is the fundamental principle of the scientific method, as taken in combination with a mental tool of simple 2D drawings on paper, or purely mentally, long-used and commonly used by engineers and scientists in its ordinary capacity (the Tonti Diagram, or variants thereof), which is not eligible subject matter.
Under the broadest reasonable interpretation, these limitations are process steps that cover mental processes including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of physical aids but for the recitation of a generic computer component. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the "Mental Process" grouping of abstract ideas. A person would readily be able to perform this process either mentally or with the assistance of physical aids. See MPEP § 2106.04(a)(2).
To clarify, see the USPTO 101 training examples, available at https://www.uspto.gov/patents/laws/examination-policy/subject-matter-eligibility. In particular, with respect to the physical aids, see example # 45, analysis of claim 1 under step 2A prong 1, including: “Note that even if most humans would use a physical aid (e.g., pen and paper, a slide rule, or a calculator) to help them complete the recited calculation, the use of such physical aid does not negate the mental nature of this limitation.”; also see example # 49, analysis of claim 1, under step 2A prong 1: “Moreover, the recited mathematical calculation is simple enough that it can be practically performed in the human mind. Even if most humans would use a physical aid, like a pen and paper or a calculator, to make such calculations, the use of a physical aid would not negate the mental nature of this limitation.”
As such, the claims recite an abstract idea of both a mental process and mathematical concept.
Step 2A, prong 2
The claimed invention does not recite any additional elements that integrate the judicial exception into a practical application. Refer to MPEP §2106.04(d).
The following limitations are merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f), including the “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more”:
Claim 17 adds a generic computer and generic computer components to be used as a tool to automate the abstract idea. ¶¶ 55-58 clarify on the generic nature of these components. Claim 1 has no express recitation of a computer (In re Prater; as discussed in MPEP § 2111), and thus does not require it. Even if claim 1 was to recite one, it would amount to no more then the mere instructions to use a computer and generic computer components as a tool to perform the abstract idea.
A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. See MPEP § 2106.04(d).
MPEP 2106.04(II)(A)(2) “…Instead, under Prong Two, a claim that recites a judicial exception is not directed to that judicial exception, if the claim as a whole integrates the recited judicial exception into a practical application of that exception. Prong Two thus distinguishes claims that are "directed to" the recited judicial exception from claims that are not "directed to" the recited judicial exception…Because a judicial exception is not eligible subject matter, Bilski, 561 U.S. at 601, 95 USPQ2d at 1005-06 (quoting Chakrabarty, 447 U.S. at 309, 206 USPQ at 197 (1980)), if there are no additional claim elements besides the judicial exception, or if the additional claim elements merely recite another judicial exception, that is insufficient to integrate the judicial exception into a practical application. See, e.g., RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1327, 122 USPQ2d 1377 (Fed. Cir. 2017) ("Adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract"); Genetic Techs. Ltd. v. Merial LLC, 818 F.3d 1369, 1376, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016) (eligibility "cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself."). For a claim reciting a judicial exception to be eligible, the additional elements (if any) in the claim must "transform the nature of the claim" into a patent-eligible application of the judicial exception, Alice Corp., 573 U.S. at 217, 110 USPQ2d at 1981, either at Prong Two or in Step 2B” and MPEP § 2106(I): “Mayo, 566 U.S. at 80, 84, 101 USPQ2dat 1969, 1971 (noting that the Court in Diamond v. Diehr found “the overall process patent eligible because of the way the additional steps of the process integrated the equation into the process as a whole,”” – and see MPEP § 2106.05(e).
To further clarify, MPEP § 2106.04(II)(A)(1): “Alice Corp., 573 U.S. at 216, 110 USPQ2d at 1980 (citing Mayo, 566 US at 71, 101 USPQ2d at 1965). Yet, the Court has explained that ‘‘[a]t some level, all inventions embody, use, reflect, rest upon, or apply laws of nature, natural phenomena, or abstract ideas,’’ and has cautioned ‘‘to tread carefully in construing this exclusionary principle lest it swallow all of patent law” See also Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335, 118 USPQ2d 1684, 1688 (Fed. Cir. 2016) ("The ‘directed to’ inquiry, therefore, cannot simply ask whether the claims involve a patent-ineligible concept, because essentially every routinely patent-eligible claim involving physical products and actions involves a law of nature and/or natural phenomenon").”
As a point of clarity, RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1327, 122 USPQ2d 1377 (Fed. Cir. 2017) ("Adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract"); Genetic Techs. Ltd. v. Merial LLC, 818 F.3d 1369, 1376, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016) (eligibility "cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself." discussed in MPEP § 2106.04(II)(A)(2) as well as MPEP § 2106.04(I): “Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151, 120 USPQ2d 1473, 1483 (Fed. Cir. 2016) ("a new abstract idea is still an abstract idea") (emphasis in original).
The claimed invention does not recite any additional elements that integrate the judicial exception into a practical application. Refer to MPEP §2106.04(d).
Step 2B
The claimed invention does not recite any additional elements/limitations that amount to significantly more.
The following limitations are merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f), including the “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more”:
Claim 17 adds a generic computer and generic computer components to be used as a tool to automate the abstract idea. ¶¶ 55-58 clarify on the generic nature of these components. Claim 1 has no express recitation of a computer (In re Prater; as discussed in MPEP § 2111), and thus does not require it. Even if claim 1 was to recite one, it would amount to no more than the mere instructions to use a computer and generic computer components as a tool to perform the abstract idea.
The claimed invention is directed towards an abstract idea of both a mathematical concept and a mental process without significantly more.
Regarding the dependent claims
Claim 2 is further limiting the abstract idea itself
Claim 3 – further limiting the abstract idea itself, also should it be found its not its merely generally linking to a variety of fields of use
Claim 4 - further limiting the abstract idea itself, e.g. mentally evaluating data from a physical system, or just mental observations, e.g. observing the system is an electrical circuit, so therefore variables such as voltage and current are judged to be in the system
Claim 5 – further limiting the abstract idea itself, including explicit recitations of math operators for use in math equations/relationships/calculations
Claim 6 – further limiting the abstract idea itself to using mathematical expressions of laws of nature, readily mentally judged
Claim 7 – Tonti diagrams, as discussed above, and readily the result of the mental processes as discussed above, wherein Tonti diagrams are directed cyclic graphs. See Michopoulos, John G., et al. "Metacomputing for directly computable multiphysics models." Journal of Computing and Information Science in Engineering 23.6 (2023): 060820, Michopoulos, page 4, col. 2, ¶ 2: “To the authors’ knowledge, the first attempt to utilize graphs (weighted and undirected) for describing circuit networks was given by Kron [59]. Subsequently, equational representations of dynamical systems in the form of Bond graphs were introduced by Paynter [60]. Then, DAGs were introduced for the first time as ASGs by Mast [61–63] for representing and solving elasticity problems expressed over tensor quantities defined in the algebra of complex numbers and in Ref. [9], it was proposed to extend them for multiphysics of continua. Independently and unaware of the ASG efforts, Tonti introduced DAGs [the Tonti diagrams] for equational theories representation [64–66] with all quantities labeling DAG nodes and edges of the graphs defined over the field of reals to denote equational theories. In the 1980s, Deschamps utilized DAGs with nodes representing scalar and vector quantities to represent Maxwellian electromagnetics. Finally, “Formal” graphs were introduced by Deschamps [67] for electromagnetics, where the nodes and edges were labeled by scalar and vector-based equational components” – wherein reference # 64 is “Tonti, E., 1972, “On the Mathematical Structure of a Large Class of Physical Theories,” Atti Accad. Naz. Lincei Rend. Cl. Sci. Fis. Mat. Nat., 52(8), pp. 48–56.” – all this claim 7 requires is the mental process of a person searching through a plurality of Tonti diagrams, or a variant thereof, wherein the person mentally adds to the math concept inn the Tonti diagram, e.g. add a new law of nature/constitutive law, etc. Thus, it is both a math concept and a mental process.
Also, directed acyclic graphs long predate computers. Boriel, “Understanding, Directed Acyclic Graph(DAG) - History and Use Cases”, Oct. 16th 2018, Linkedin article, www(dot)linkedin(dot)com/pulse/understanding-directed-acyclic-graph-dag-history-use-bariel-g – “An ancient use case of DAGs is creating a family tree… ''Depictions of family trees as DAGs have been recorded in Ancient Rome by Pliny the Elder who described the graphs decorating the walls of Roman patrician houses. Prior to this, DAGs may not have been recorded, but often described when explaining family histories''”
Should the use of the “directed cyclic graph” be found not to be part of the abstract idea, then it would be considered as part of the mere instructions to do it on a computer, as well as generally linking to a particular technological environment (akin to the “in the context of XML” in MPEP § 2106.05(f) and similarly see the discussion of XML in MPEP § 2106.05(h))
Claim 8 – merely repeating the abstract idea for multiple hypothesis, i.e. repeating the scientific method using graphs/diagram as a visual aid such as on paper, until one finds a correct hypothesis (the Examiner further notes that such trial-and-error recitations are a core part of the scientific method as well, i.e. perform trials, evaluate the results for errors, and try again with changes, and so own, until one arrives at a tested hypothesis that works). This is simply routine experimentation with application of the scientific method, a fundamental basic tool of all scientific and technological work. To clarify, all claim 8 conveys is simply that a person should perform the trial-and-error mental process of the application of the scientific method, using Tonti diagrams (or variants thereof) until one finds the simplest equation that explains the data, e.g. Newton’s law of gravity being discovered was likely using this process after Sir Issac Newton observed an apple falling from a tree and sought to find a math equation/relationship that would express the scientific truth of gravity.
Claim 9 is further limiting the abstract idea by adding equations expressly to the graph (both mental and math), and people are readily able to fit math equations to data, e.g. linear regressions to fit measured data have long been used mentally/manually with physical aids since before the invention of the computer (should evidence be needed of this historical fact, OFFFICAL NOTICE is taken of it). For an example of a person who fit equations to data, see the discussion of the formula derived by Carter mentally, and/or with physical aids, in Mackay Radio & Tel. Co. v. Radio Corp. of Am., 306 U.S. 86, 59 S. Ct. 427, 83 L. Ed. 506, 40 U.S.P.Q. 199 (1939) (cited to MPEP § 2106.04(I)) based on available data in a graph, wherein this was before the inventor of the computer.
Claim 10 – further limiting the abstract idea
Claim 11 – adding in a litany of math equations/relationships to the abstract idea, recited with such generality that people may readily mentally evaluate them such as with physical aids
Claim 12 – mere data gathering, which is WURC in view of MPEP § 2106.05(d)(II): “iii. Electronic recordkeeping, Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 225, 110 USPQ2d 1984 (2014) (creating and maintaining "shadow accounts"); Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log); iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93”, e.g. this is so broad it literally encompasses: “Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66, 67, 101 USPQ2d 1961, 1964 (2010) provides an example of additional elements that were not an inventive concept because they were merely well-understood, routine, conventional activity previously known to the industry, which were not by themselves sufficient to transform a judicial exception into a patent eligible invention. Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66, 79-80, 101 USPQ2d 1969 (2012) (citing Parker v. Flook, 437 U.S. 584, 590, 198 USPQ 193, 199 (1978) (the additional elements were "well known" and, thus, did not amount to a patentable application of the mathematical formula)). In Mayo, the claims at issue recited naturally occurring correlations (the relationships between the concentration in the blood of certain thiopurine metabolites and the likelihood that a drug dosage will be ineffective or induce harmful side effects) along with additional elements including telling a doctor to measure thiopurine metabolite levels in the blood using any known process. 566 U.S. at 77-79, 101 USPQ2d at 1967-68. The Court found this additional step of measuring metabolite levels to be well-understood, routine, conventional activity already engaged in by the scientific community because scientists "routinely measured metabolites as part of their investigations into the relationships between metabolite levels and efficacy and toxicity of thiopurine compounds." 566 U.S. at 79, 101 USPQ2d at 1968. Even when considered in combination with the other additional elements, the step of measuring metabolite levels did not amount to an inventive concept, and thus the claims in Mayo were not eligible. 566 U.S. at 79-80, 101 USPQ2d at 1968-69” as discussed in MPEP § 2106.05(d), - for additional WURC evidence see ¶¶ 32 and 35, for the specification goes into no details but as is preferable omits what is WURC, as per MPEP § 2106.07(a)(III): “(A) A citation to an express statement in the specification or to a statement made by an applicant during prosecution that demonstrates the well-understood, routine, conventional nature of the additional element(s). A specification demonstrates the well-understood, routine, conventional nature of additional elements when it describes the additional elements as well-understood or routine or conventional (or an equivalent term), as a commercially available product, or in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. 112(a).” and To clarify, see MPEP § 2164.01: “A patent need not teach, and preferably omits, what is well known in the art. In re Buchner, 929 F.2d 660, 661, 18 USPQ2d 1331, 1332 (Fed. Cir. 1991); Hybritech, Inc. v. Monoclonal Antibodies, Inc., 802 F.2d 1367, 1384, 231 USPQ 81, 94 (Fed. Cir. 1986), cert. denied, 480 U.S. 947 (1987); and Lindemann Maschinenfabrik GMBH v. American Hoist & Derrick Co., 730 F.2d 1452, 1463, 221 USPQ 481, 489 (Fed. Cir. 1984).” Also see MPEP § 2163(II)(A)(3)(a): “What is conventional or well known to one of ordinary skill in the art need not be disclosed in detail. See Hybritech Inc. v. Monoclonal Antibodies, Inc., 802 F.2d at 1384, 231 USPQ at 94. See also Capon v. Eshhar, 418 F.3d 1349, 1357, 76 USPQ2d 1078, 1085 (Fed. Cir. 2005) ("The ‘written description’ requirement must be applied in the context of the particular invention and the state of the knowledge…. As each field evolves, the balance also evolves between what is known and what is added by each inventive contribution."). If a skilled artisan would have understood the inventor to be in possession of the claimed invention at the time of filing, even if every nuance of the claims is not explicitly described in the specification, then the adequate description requirement is met.”
Claim 13 – further limiting the abstract idea
Claim 14 – mere data outputting and displaying, WURC in view of MPEP § 2106.05(d)(II), also see example 46, claim 1, for its 2B consideration for the displaying step
Claim 15 – mere data gathering, followed by repeating the abstract idea step of the validating/invalidating with new data. WURC evidence: see MPEP § 2106.05(d)(II): “iii. Electronic recordkeeping, Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 225, 110 USPQ2d 1984 (2014) (creating and maintaining "shadow accounts"); Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log); iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93
Claim 16 – further limiting the math concept. ¶ 50: “I-net subtype in which the variables are tensors of numerical values associated to various cells in the cell complex, ordered arbitrarily, and co-boundary operators are defined concretely by sparse tensor multiplications with incidence tensors, which are populated by O or 1 values for bookkeeping incidence relations within the cell complex”, i.e. tensor as used herein is referring the mathematical term “tensor”, such as commonly used in the mathematical field of tensor calculus, and the term “operator” is referring the operators in equations indicating math operations - ¶ 21: “, a differential operator, an integral operator, and an interpolative operator.”, e.g. ¶ 42 for the “* operator”, wherein doing this “in a computational framework” is merely in the mathematical computational framework, e.g. ¶ 32: “computational forms may be one or more of: a differential equation, an integral equation, an integro-differential equation, a discrete-algebraic equation, and a system model.”, wherein ¶ 35 discusses a litany of different generic algorithms may be used to accomplish this mapping
The claimed invention is directed towards an abstract idea of both a mathematical concept and a mental process without significantly more.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-6, 9, and 11-17 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wang, Randi, and Morad Behandish. "Surrogate modeling for physical systems with preserved properties and adjustable tradeoffs." arXiv preprint arXiv:2202.01139 (2022).
As a point of clarity on multiple prior art rejections, see MPEP § 2120(I): “Prior art rejections should ordinarily be confined strictly to the best available art. Exceptions may properly be made, for example, where: …(C) for cases examined under the first inventor to file provisions of the AIA , the most pertinent disclosure could be shown not to be prior art by invoking an exception in a 37 CFR 1.130 affidavit or declaration of attribution or prior public disclosure”
Wang is not a named inventor of the instant application, and therefore qualifies as prior art under § 102(a)(1).
Regarding Claim 1
Wang teaches:
A method for identifying, generating, and/or evaluating scientific hypotheses, the method comprising:
describing a context for a physical system in terms of an underlying topology and a domain of interest; defining a plurality of physical variables and relation types based on the underlying topology and the domain of interest; representing a plurality of testable hypotheses each as a network or graph-like structure comprising physical relationships among the physical variables, wherein the physical relationships are selected from the relationship types, and wherein, within the network or graph-like structure, the physical variables are nodes and the physical relationships are edges; interpreting at least one of the testable hypotheses into analytical and/or computational forms with a combination of known and unknown variables; and validating or invalidating the at least one of the testable hypotheses by (a) fitting the unknown parameters to data relating to the physical system and (b) evaluating a goodness of fit for the fitting. (Wang, abstract, then see section “A Data-Driven Approach”: “In a recent article [34], common reference semantics for lumped parameter system modeling were presented, based on algebraic topological foundations of network theory [27, 7], which can serve as a unifying abstraction of system modeling languages such as Modelica [11], Simulink [10], linear graphs [28], and bond graphs [23], etc. A key advantage of using this abstraction is the ability to automatically map a given topological structure for the LPM (e.g., a circuit graph or mass/spring/damper network) to a set of governing ODEs. These ODEs have built-in conservation laws in the LPM context such as Kirchhoff's current and voltage laws for electrical and thermal circuits, superposition of forces and Newton's laws of motion in multibody dynamics, and so on. They also include constitutive laws associated with lumped components such as springs and dampers in mechanical systems, resistors and capacitors in analog circuits, conductors in heat transfer, and so on. The recipe for generating the ODEs from system topology in [34] is given by Tonti diagrams [33] of network theory (Fig. 4 (c)). The Tonti diagram is a composition of topological and algebraic operators that map data associated with different cells in an oriented cell complex representation of the LPM network; for instance, in an electrical circuit, the superposition of incoming/outgoing currents on incident wires (i.e., 1cells) to a junction (i.e., 0cells) is captured by a boundary operator (from 1cells to 0cells), whereas resistance, capacitance, inductance, and other constitutive relations are in-place algebraic relations that keep data on 1cells. These operators are represented by different types of arrows on the Tonti diagram. The key advantage of using this approach to equation generation is its generalizability to various domains of physics and possible multi-physics, as the underlying topological and algebraic operations are common to mechanical, electrical, thermal, and other systems”
Then see ¶ 2: “Except in cases where an LPM is directly generated from a system model with modular components (e.g., actual springs/dampers in an automobile suspension assembly), the parameters for the artificial LPM components are not easily obtained from geometric and material properties. These parameters must be estimated from data by solving an optimization problem (e.g., least squares regression).”
To clarify, ¶ 3: “Figure 4 illustrates the work ow for the data-driven LPM construction. Given an experimental or simulation data set and an LPM topology in Fig. 4 (a), we first convert the LPM from the domain-specific format (e.g., Modelica) to the domain-agnostic canonical form (i.e., oriented cell complex) as shown in Fig. 4 (b), using the semantics provided in [34]. Each 1cell is associated with a symbolic constitutive relation and given an initial value to the constitutive parameter. After selecting a state variable of interest, the state equations (i.e., system of second-order ODEs/DAEs) are generated by tracing groups of paths along the Tonti diagram of network theory with the appropriate physical types [34]. There are a total of 8 different options for state variables, each of which corresponds to a different groups of paths [34]. Once the system of ODEs/DAEs are assembled, we use ordinary least squares regression although other objective functions and optimization techniques are certainly applicable|to iteratively update the constitutive parameters until the solution of the state equation fits the given data…. In a later section, we will apply this method to obtain an LPM for a mechanical problem (single physics) and a thermo-mechanical problem (coupled Multiphysics)”
To clarify, also see the section “Preliminary Results”, ¶ 2, incl.: “We apply an ordinary least squares regression [21] to find the values of the lumped masses, stiffness coefficients, and damping coefficients and compare the simulation result for the optimal LPM against the ROM and the HFM (Fig. 5 (e)). It can be observed that the simulation results between the ROM and the HRM are close and the simulation results between the optimal LPM and the ROM match well, with a normalized root-mean-square error (NRMSE)4 of 4.52%. The relative H2error bound computed from (1) against HFM is 0.184.” and ¶ 4 incl.: “To retrieve interpretability, we initialize a pair of connected oriented cell complexes representing a mechanical LPM (mass-spring-damper network) connected to a thermal LPM (resistance-conductance network) by a transformer (TF) (Fig. 11), where we label the 1cells with L3, L6 for masses, L2, L5 for springs, L1, L4 for dampers, L7, L10, L11 for thermal resistors, and L8, L9 for thermal conductors. The Tonti diagram of generalized network systems [34] is shown in Fig. 9 (a) and the paths traced to generate ODEs of the multidomain lumped parameter systems are shown in Fig. 9 (b). We apply an ordinary least squares regression to obtain the optimal values of the lumped mass, stiffness coefficients, damping coefficients, thermal conductance, and thermal resistance. We compare the solutions of the HFM, the ROM and the optimal LPM in Fig. 12, where the NRMSE of the parameter estimation is 3.11% and the relative H2error bound computed from (1) against the HFM is 0.0124. Note that the mechanical vibration time scale in this case is much faster than the heat diffusion, so the displacement reaches steady-state value of 1:393 104 meters early on. The displacement visible in the figure is caused by thermal expansion.”
For relevance, also see the paragraph split between the columns on page 3, and caption of figure 4: “The user provides (a) a topology for the LPM (i.e., symbolic network of inter-connected components), which is then converted to (b) the common language of abstract oriented cell complexes [34]. (c) The Tonti diagram [33] converts the cell complex representation to (d) a system of symbolic ODEs with unknown constitutive parameters. The parameters are learned from data using standard system identification techniques.” – see the steps labeled 1-6 in fig. 4 to further clarify
Regarding Claim 2
Wang teaches:
The method of claim 1, wherein the underlying topology pertains to a physical space of the physical system, a time of the physical system, a spacetime of the physical system, an abstract system network of the physical system, or any combination thereof. (Wang, as cited above, see the figures of the various Tonti diagrams, e.g. fig. 4)
Regarding Claim 3
Wang teaches:
The method of claim 1, wherein the domain of interest comprises a mechanical domain, an electrical domain, a thermal domain, or any combination thereof. (Wang, fig. 4(c) for mechanical; and on pg. 6: “Next, we apply the approach to a slightly more complex problem with weak thermo-elastic coupling, over a piston geometry shown in Fig. 8 (a),” and in section “A Data-Driven Approach” see “The key advantage of using this approach to equation generation is its generalizability to various domains of physics and possible multi-physics, as the underlying topological and algebraic operations are common to mechanical, electrical, thermal, and other systems”
Regarding Claim 4
Wang teaches:
The method of claim 1, wherein the types of physical variables are parameters within and/or derived from the data relating to the physical system. (Wang, as cited above, for the fitting of the unknown parameters with regressions, e.g. OLS regression (page 6, col. 2 and elsewhere as cited above)
Regarding Claim 5
Wang teaches:
The method of claim 1, wherein the relationship types comprise one or more selected from the group consisting of: a topological relation, a metric relation, an algebraic relation, a differential operator, an integral operator, and an interpolative operator. (Wang, page 4 col. 1 ¶ 2: “The Tonti diagram is a composition of topological and algebraic operators that map data associated with different cells in an oriented cell complex representation of the LPM network; for instance, in an electrical circuit, the superposition of incoming/outgoing currents on incident wires (i.e., 1cells) to a junction (i.e., 0cells) is captured by a boundary operator (from 1cells to 0cells), whereas resistance, capacitance, inductance, and other constitutive relations are in-place algebraic relations that keep data on 1cells.”
Regarding Claim 6
Wang teaches:
The method of claim 1, wherein the relationship types are derived by prescribing, defining, and/or constraining a conservation law and/or a constitutive law. (Wang, page 4 col. 1 ¶ 2: “These ODEs have built-in conservation laws in the LPM context such as Kirchhoff's current and voltage laws for electrical and thermal circuits, superposition of forces and Newton's laws of motion in multibody dynamics, and so on…The Tonti diagram is a composition of topological and algebraic operators that map data associated with different cells in an oriented cell complex representation of the LPM network; for instance, in an electrical circuit, the superposition of incoming/outgoing currents on incident wires (i.e., 1cells) to a junction (i.e., 0cells) is captured by a boundary operator (from 1cells to 0cells), whereas resistance, capacitance, inductance, and other constitutive relations are in-place algebraic relations that keep data on 1cells.”
Regarding Claim 9
Wang teaches:
The method of claim 1, wherein the network or graph-like structure comprises one or more equations in terms of the physical variables and the known and unknown parameters, and wherein the validating or invalidating comprises fitting the one or more equations to available data. (Wang, as was cited above for the Tonti diagrams on pages 4-5 and fig. 4, see the other clarification citations as well, wherein ordinary least squares regression was used to fit the values of the parameters in the equations to the data (see section Preliminary results)
Regarding Claim 11.
Wang teaches:
The method of claim 1, wherein the fitting is guided by a loss function, an error function, a cost function, an objective function, a utility function, or penalty function that quantifies how well a testable hypothesis explains the data. (Wang, as cited above, is using “ordinary least squares regression” for fitting the data), e.g. page 4 last paragraph
Regarding Claim 12.
Wang teaches:
The method of claim 1, wherein the data is provided by simulation, experiment, or a combination of both. (Wang, abstract: “We present both a model-based and a data-driven strategy to generate surrogate models. The former starts from a high- fidelity model generated from first principles and applies a bottom-up model order reduction (MOR) that preserves stability and convergence while providing a priori error bounds, although the resulting reduced- order model may lose its interpretability. The latter generates interpretable surrogate models by fitting artificial constitutive relations to a presupposed topological structure using experimental or simulation data.”
Regarding Claim 13.
Wang teaches:
The method of claim 1, wherein the analytical and/or computational forms comprises one or more of:
a differential equation, an integral equation, an integro-differential equation, a discrete-algebraic equation, and a system model. (Wang, see figure 4(d) # 3 and see its accompanying description)
Regarding Claim 14.
Wang teaches:
The method of claim 1 further comprising:
outputting and/or displaying at least one of: (a) the underlying topology and the domain of interest, (b) the network or graph-like structure for the at least one of the testable hypotheses, (c) the analytical and/or computational forms for the at least one of the testable hypotheses, (d) the search space, (e) the validation or invalidation for the at least one of the testable hypotheses, and (f) the goodness of fit for the at least one of the testable hypotheses. (Wang, as cited above, include seeing fig. 4(d) # 6, also see fig. 12)
Regarding Claim 15.
Wang teaches:
The method of claim 1 further comprising: collecting additional data; and validating or invalidating at least some of the plurality of testable hypotheses with the additional data. (Wang, ¶ 1 on page 5: “to iteratively update the constitutive parameters until the solution of the state equation fits the given data.” And page 4, col. 2, last paragraph: “Each 1cell is associated with a symbolic constitutive relation and given an initial value to the constitutive parameter.” – i.e. in the first iteration, it receives a first data and validates/invalidates it, and when it invalidates it, it then receives/collects by the “update” the parameter and validates/invalidates whether or not it will work, and so on “until the solution of the state equation fits the given data”
Regarding Claim 16.
Wang teaches:
The method of claim 1, wherein the interpreting of the at least one of the testable hypotheses comprises mapping the physical variables to tensor data and physical relationships to computational operators in a computational framework. (Wang, section “Data-Drive Approach” : “A key advantage of using this abstraction is the ability to automatically map a given topological structure for the LPM (e.g., a circuit graph or mass/spring/damper network) to a set of governing ODEs…. The Tonti diagram is a composition of topological and algebraic operators that map data associated with different cells in an oriented cell complex representation of the LPM network; for instance, in an electrical circuit, the superposition of incoming/outgoing currents on incident wires (i.e., 1cells) to a junction (i.e., 0cells) is captured by a boundary operator (from 1cells to 0cells), whereas resistance, capacitance, inductance, and other constitutive relations are in-place algebraic relations that keep data on 1cells. These operators are represented by different types of arrows on the Tonti diagram.” And see fig. 4 caption: “The user provides (a) a topology for the LPM (i.e., symbolic network of inter-connected components), which is then converted to (b) the common language of abstract oriented cell complexes [34]. (c) The Tonti diagram [33] converts the cell complex representation to (d) a system of symbolic ODEs with unknown constitutive parameters. The parameters are learned from data using standard system identification techniques.” And the figure itself
Regarding Claim 17.
Rejected under a similar rationale as above, wherein Wang teaches:
A computing system comprising: a processor; a memory coupled to the processor; and instructions provided to the memory, wherein the instructions are executable by the processor to cause the system to perform a method comprising: (Wang, as cited above, e.g. fig. 4, teaches it was software executed, also see page 7 ¶ 1
Claim(s) 10 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wang, Randi, and Morad Behandish. "Surrogate modeling for physical systems with preserved properties and adjustable tradeoffs." arXiv preprint arXiv:2202.01139 (2022) with the meaning of a term used in the primary reference explained/inherency demonstrated by Wang, Randi, and Vadim Shapiro (hereinafter Randi). "Topological semantics for lumped parameter systems modeling." Advanced Engineering Informatics 42 (2019): 100958.
See MPEP § 2131: “Normally, only one reference should be used in making a rejection under 35 U.S.C. 102. However, a 35 U.S.C. 102 rejection over multiple references has been held to be proper when the extra references are cited to: (A) Prove the primary reference contains an "enabled disclosure;" (B) Explain the meaning of a term used in the primary reference; or
(C) Show that a characteristic not disclosed in the reference is inherent.”
Regarding Claim 10.
Wang teaches:
The method of claim 1, wherein the at least one of the testable hypotheses comprises at least one of conservation laws derived from first principles applied to (a) the underlying topology, (b) phenomenological, empirical, constitutive, material, or multi-physics interaction laws expressed in algebraic terms with the unknown parameters, and (c) initial or boundary conditions. (Wang, as was cited above for its use of the Tonti diagrams with the constitutive laws was applied, in particular note the various citations to reference # 34 which is Randi on page 4, e.g., in the last paragraph, and note figure 4
As taken in view of Randi, § 3.2, ¶ 2: “In what follows we will adopt Tonti's convention and distinguish between configuration type variables, that are modeled as cochains on primary cell complex decomposition of space, and source variables that are modeled as cochains on the dual cell complex decomposition of the same space…This conceptualization of physical quantities in terms cochains on dual cell complexes is not arbitrary: it arises from first principles based on how the postulated quantities are measured. In each case, the measurement process implies the intrinsic dimension of the associated quantity (e.g., displacements are measured at a point, currents are measured across the surface, voltage drop is measured along a path, and so on). The decision whether a particular quantity belongs to the primal or dual complex is determined by the oddness principle that requires change of sign under change of orientation of the relevant cell.”
Claim(s) 1-15 and 17 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Cao, Liwei, et al. "Identifying physico-chemical laws from the robotically collected data." (2019).
Regarding Claim 1
Cao teaches:
A method for identifying, generating, and/or evaluating scientific hypotheses, the method comprising: (Cao, abstract)
describing a context for a physical system in terms of an underlying topology and a domain of interest; defining a plurality of physical variables and relation types based on the underlying topology and the domain of interest; representing a plurality of testable hypotheses each as a network or graph-like structure comprising physical relationships among the physical variables, wherein the physical relationships are selected from the relationship types, and wherein, within the network or graph-like structure, the physical variables are nodes and the physical relationships are edges; interpreting at least one of the testable hypotheses into analytical and/or computational forms with a combination of known and unknown variables; and validating or invalidating the at least one of the testable hypotheses by (a) fitting the unknown parameters to data relating to the physical system and (b) evaluating a goodness of fit for the fitting. (Cao, abstract: “A mixed-integer nonlinear programming (MINLP) formulation for symbolic regression was proposed to identify physical models from noisy experimental data. The formulation was tested using numerical models and was found to be more efficient than the previous literature example with respect to the number of predictor variables and training data points. The globally optimal search was extended to identify physical models and to cope with noise in the experimental data predictor variable. The methodology was coupled with the collection of experimental data in an automated fashion, and was proven to be successful in identifying the correct physical models describing the relationship between the shear stress and shear rate for both Newtonian and non-Newtonian fluids, and simple kinetic laws of reactions”
then see § 1, last paragraph, and figure 1, then § 2.1 incl.: “Use will be made of the Directed Acyclic Graph (DAG) description of algebraic functions throughout this work [27]. The MINLP formulation is based on a balanced binary tree superstructure for the representation of the equations describing a physical model. The overall goal is to enable the assembly of free-form algebraic functions by connecting predictor variables and operators, such that the resulting function predicts the dependent variable values accurately.” And furthermore see eq. 1 and its accompanying description: “Consequently, by using the tree structure and the index assignment given (Tables 1-3), the optimization problem was formulated with the objective to minimize the sum of squared errors (SSE) between the values computed by the model and the experimental data, Eq. 1, according to Ref.” and see fig. 3 in § 2.4 and its accompanying description which shows the “Framework for the Automated Identification and Selection of Physical Models via Symbolic Regression”
e.g. see § 3.1 for the “Proof of concept” incl. fig. 5 for “Binary expression tree for Arrhenius equation.” And eq. 28 wherein the system rediscovered it (compare with eq. 27)
then see § 3.2, wherein “The data collected from a commercially available emulsion sample by means of the automated capillary viscometer were used to identify the simple linear relationship between shear stress (𝜏) and shear rate (𝛾̇) at the wall of the tubing (Eq. 30).” Wherein it was to identify “Newton’s Law of Viscosity [physical relationship among the physical variables]” then: “For the parameter identification an expression tree with three layers, including the shear rate
(𝛾̇ ) as the only predictor variable, and ten experimental data points were used. Two data points were not included in the training set and used for the calculation of the extrapolation error. The set of operators included the basic operators and a power law ℱ = {𝑖𝑑,+, −, ∙, /, ^}.” – i.e. it defined the tree [example of a “Directed Acyclic Graph” per § 2.1 ¶ 1 as cited above] of the variables and the topology of the tree for the domain of interest, and sought the math operations for the physical relationships in the tree between the variables (e.g. fig. 5), wherein table 4 provides the results, and the lowest complexity model [each model being testable hypotheses that were tested/validated in the form of equations, validated for the fitting per eq. 1] is in the form of Newton’s law and was validated, i.e. “However, the comparison of the extrapolation errors shows the superiority of Newton’s law model (𝐶 = 3), whereas the other identified models suffer from overfitting. Overall, the Newton’s law model can be selected as the sparsest model with the highest generalisation capability, and can be easily interpreted to generate knowledge about the physics of the system under investigation.” – to clarify, this selects the least complex acceptable equation (see pages 10-11 for description of eq. 25)
See §§ 3.3-3.5 for additional examples
Regarding Claim 2
Cao teaches:
The method of claim 1, wherein the underlying topology pertains to a physical space of the physical system, a time of the physical system, a spacetime of the physical system, an abstract system network of the physical system, or any combination thereof. (Cao, as cited above, teaches the topology pertains to the physical system and its governing laws, e.g. §§ 3.1-3.5)
Regarding Claim 3
Cao teaches:
The method of claim 1, wherein the domain of interest comprises a mechanical domain, an electrical domain, a thermal domain, or any combination thereof. (Cao, as cited above, teaches the topology pertains to the physical system and its governing laws, e.g. §§ 3.1-3.5, wherein § 3.1 shows a thermal domain
Regarding Claim 4
Cao teaches:
The method of claim 1, wherein the types of physical variables are parameters within and/or derived from the data relating to the physical system. (Cao, as cited above, the variables were selected/derived based on the law)
Regarding Claim 5
Cao teaches:
The method of claim 1, wherein the relationship types comprise one or more selected from the group consisting of: a topological relation, a metric relation, an algebraic relation, a differential operator, an integral operator, and an interpolative operator. (Cao, as cited above, teaches at least algebraic relations, e.g. § 3.2)
Regarding Claim 6
Cao teaches:
The method of claim 1, wherein the relationship types are derived by prescribing, defining, and/or constraining a conservation law and/or a constitutive law. (Cao, § 3.2 shows application to Newton’s Law of Viscosity, i.e. the relationship type was prescribed/defined by this law)
Regarding Claim 7
Cao teaches:
The method of claim 1, wherein the plurality of testable hypotheses are arranged in a search space that is represented by a directed acyclic graph whose nodes are the testable hypotheses and edges are the actions in the search space representing one or more of: (a) adding one or more new relations among existing physical variables; or (b) defining one or more new physical variables linked to one or more existing variables with one or more new physical relations. (Cao, as cited above, § 2.1 ¶ 1: “Use will be made of the Directed Acyclic Graph (DAG) description of algebraic functions throughout this work [27]. The MINLP formulation is based on a balanced binary tree superstructure for the representation of the equations describing a physical model. The overall goal is to enable the assembly of free-form algebraic functions by connecting predictor variables and operators, such that the resulting function predicts the dependent variable values” and page 5 last pargraph: “With regard to the branch-nodes, there are binary variables 𝛿4,> assigned for operator selection, where an operator is active at node n if 𝛿4,> = 1 and inactive if 𝛿4,> = 0. If active, each binary operator is applied using both children nodes while a unary operator uses only the value of the left node 𝑉@4,2 . Five binary operators (addition, subtraction, multiplication, division and power law) and three unary operators (identity, exponential and square root) were implemented. It should be noted that the list of” also § 2.4 ¶ 3: “Furthermore, this constraint filters mathematical invariants including more terms from the search space.”
And see §§ 3.1-3.5 as discussed above, including the tables with the resulting equations, which shows that it added relations between existing variables, e.g. table 4 shows the different relations added to the variables, and see table 7 which shows new variables were added to the selected existing PNPA variable along with relations between the variables (e.g. last row, compared to first)
Regarding Claim 8
Cao teaches:
The method of claim 7 further comprising:
performing the interpreting and the validating or invalidating for multiple of the plurality of testable hypotheses, wherein the interpreting and the validating or invalidating for the multiple of the plurality of testable hypotheses is performed for simpler testable hypotheses and proceeds to other testable hypotheses that adds complexity incrementally if the simpler hypotheses do not explain the data adequately. (Cao, as cited and discussed above for the complexity penalty, in § 2.4 eq. 25)
Regarding Claim 9
Cao teaches:
The method of claim 1, wherein the network or graph-like structure comprises one or more equations in terms of the physical variables and the known and unknown parameters, and wherein the validating or invalidating comprises fitting the one or more equations to available data. (Cao, as cite above, teaches that the graph-like structure represents one or more equations, along with known/unknown parameters, and fits it to experimental data (or simulation data in the case of § 3.1, as the datapoints were calculated for this section)
Regarding Claim 10.
Cao teaches:
The method of claim 1, wherein the at least one of the testable hypotheses comprises at least one of conservation laws derived from first principles applied to (a) the underlying topology, (b) phenomenological, empirical, constitutive, material, or multi-physics interaction laws expressed in algebraic terms with the unknown parameters, and (c) initial or boundary conditions. (Cao, page 2, last paragraph: “The possibility of building data-driven interpretable and generalizable models for complex and not well understood physical systems is important as these models share the similar structure to those based on first principles and can be transferred to analogous systems, whereas surrogate models cannot be easily generalized [17]”, and see § 3.2 as an example where it is to test Newton’s Law of Viscosity
Regarding Claim 11.
Cao teaches:
The method of claim 1, wherein the fitting is guided by a loss function, an error function, a cost function, an objective function, a utility function, or penalty function that quantifies how well a testable hypothesis explains the data. (Cao, eq. 1 as discussed above)
Regarding Claim 12.
Cao teaches:
The method of claim 1, wherein the data is provided by simulation, experiment, or a combination of both. (Cao, §§ 3.1-3.5 as discussed above, 3.1 is an example of simulation, and 3.2 is an example of experiment)
Regarding Claim 13.
Cao teaches:
The method of claim 1, wherein the analytical and/or computational forms comprises one or more of:
a differential equation, an integral equation, an integro-differential equation, a discrete-algebraic equation, and a system model. (Cao, as cited above, is finding discrete algebraic equations)
Regarding Claim 14.
Cao teaches:
The method of claim 1 further comprising:
outputting and/or displaying at least one of: (a) the underlying topology and the domain of interest, (b) the network or graph-like structure for the at least one of the testable hypotheses, (c) the analytical and/or computational forms for the at least one of the testable hypotheses, (d) the search space, (e) the validation or invalidation for the at least one of the testable hypotheses, and (f) the goodness of fit for the at least one of the testable hypotheses. (Cao, as cited above, teaches that the equations were output, including both the selected equation and the other equations in the search space, and the goodness of fit for the validation/invalidate, e.g. table 5 and the other tables in §§ 3.1-3.5, also see the plots, e.g. fig. 8(b) which were displayed
Regarding Claim 15.
Cao teaches:
The method of claim 1 further comprising: collecting additional data; and validating or invalidating at least some of the plurality of testable hypotheses with the additional data. (Cao, §§ 3.4-3.5, wherein § 3.5 ¶ 1: “In the second attempt experimental data collected at different pH were included in the training algorithm to identify the dependence of the kinetic constant on the [𝑂𝐻D] concentration.”, i.e. more data was collected and the hypotheses were tested again)
Regarding Claim 17.
Rejected under a similar rationale as claim 1 above, wherein Cao was software executed by a computer per the citations above.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 7-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang, Randi, and Morad Behandish. "Surrogate modeling for physical systems with preserved properties and adjustable tradeoffs." arXiv preprint arXiv:2202.01139 (2022) with the meaning of a term used in the primary reference explained/inherency demonstrated by Michopoulos, John G., et al. "Metacomputing for directly computable multiphysics models." Journal of Computing and Information Science in Engineering 23.6 (2023): 060820 in view of Kalyuzhnaya, Anna V., et al. "Towards generative design of computationally efficient mathematical models with evolutionary learning." Entropy 23.1 (2020): 28.
See MPEP § 2131: “Normally, only one reference should be used in making a rejection under 35 U.S.C. 102. However, a 35 U.S.C. 102 rejection over multiple references has been held to be proper when the extra references are cited to: (A) Prove the primary reference contains an "enabled disclosure;" (B) Explain the meaning of a term used in the primary reference; or
(C) Show that a characteristic not disclosed in the reference is inherent.”
As a point of clarity on the date, MPEP § 2112(II): “There is no requirement that a person of ordinary skill in the art would have recognized the inherent disclosure at the relevant time, but only that the subject matter is in fact inherent in the prior art reference. Schering Corp. v. Geneva Pharm. Inc., 339 F.3d 1373, 1377, 67 USPQ2d 1664, 1668 (Fed. Cir. 2003) (rejecting the contention that inherent anticipation requires recognition by a person of ordinary skill in the art before the critical date and allowing expert testimony with respect to post-critical date clinical trials to show inherency); see also Toro Co. v. Deere & Co., 355 F.3d 1313, 1320, 69 USPQ2d 1584, 1590 (Fed. Cir. 2004) ("[T]he fact that a characteristic is a necessary feature or result of a prior-art embodiment (that is itself sufficiently described and enabled) is enough for inherent anticipation, even if that fact was unknown at the time of the prior invention."); Abbott Labs v. Geneva Pharms., Inc., 182 F.3d 1315, 1319, 51 USPQ2d 1307, 1310 (Fed. Cir. 1999) ("If a product that is offered for sale inherently possesses each of the limitations of the claims, then the invention is on sale, whether or not the parties to the transaction recognize that the product possesses the claimed characteristics."); Atlas Powder Co. v. IRECO, Inc., 190 F.3d 1342, 1348-49, 51 USPQ2d 1943, 1947 (Fed. Cir. 1999) ("Because ‘sufficient aeration’ was inherent in the prior art, it is irrelevant that the prior art did not recognize the key aspect of [the] invention.... An inherent structure, composition, or function is not necessarily known."); SmithKline Beecham Corp. v. Apotex Corp., 403 F.3d 1331, 1343-44, 74 USPQ2d 1398, 1406-07 (Fed. Cir. 2005) (holding that a prior art patent to an anhydrous form of a compound "inherently" anticipated the claimed hemihydrate form of the compound because practicing the process in the prior art to manufacture the anhydrous compound "inherently results in at least trace amounts of" the claimed hemihydrate even if the prior art did not discuss or recognize the hemihydrate); In re Omeprazole Patent Litigation, 483 F.3d 1364, 1373, 82 USPQ2d 1643, 1650 (Fed. Cir. 2007) (The court noted that although the inventors may not have recognized that a characteristic of the ingredients in the prior art method resulted in an in situ formation of a separating layer, the in situ formation was nevertheless inherent. "The record shows formation of the in situ separating layer in the prior art even though that process was not recognized at the time. The new realization alone does not render that necessary [sic] prior art patentable.").”
Regarding Claim 7
While Wang does not explicitly teach the following in full, Wang in view of Kalyuzhnaya does teach it:
The method of claim 1, wherein the plurality of testable hypotheses are arranged in a search space that is represented by a directed acyclic graph whose nodes are the testable hypotheses and edges are the actions in the search space representing one or more of: (a) adding one or more new relations among existing physical variables; or (b) defining one or more new physical variables linked to one or more existing variables with one or more new physical relations. (Wang, section “A Data-Driven Approach” on page 4 as cited above including: “In a recent article [34], common reference semantics for lumped parameter system modeling were presented…The recipe for generating the ODEs from system topology in [34] is given by Tonti diagrams [33] of network theory (Fig. 4 (c))” … and on the next column: “we first convert the LPM from the domain-specific format (e.g., Modelica) to the domain-agnostic canonical form (i.e., oriented cell complex) as shown in Fig. 4 (b), using the semantics provided in [34]…. There are a total of 8 different options for state variables, each of which corresponds to a different groups of paths [34]…”
In particular, note the citation is to “Tonti, E. 2013. The mathematical structure of classical and relativistic physics. Springer”
Then see Michopoulos, page 4, col. 2, ¶ 2: “To the authors’ knowledge, the first attempt to utilize graphs (weighted and undirected) for describing circuit networks was given by Kron [59]. Subsequently, equational representations of dynamical systems in the form of Bond graphs were introduced by Paynter [60]. Then, DAGs were introduced for the first time as ASGs by Mast [61–63] for representing and solving elasticity problems expressed over tensor quantities defined in the algebra of complex numbers and in Ref. [9], it was proposed to extend them for multiphysics of continua. Independently and unaware of the ASG efforts, Tonti introduced DAGs for equational theories representation [64–66] with all quantities labeling DAG nodes and edges of the graphs defined over the field of reals to denote equational theories. In the 1980s, Deschamps utilized DAGs with nodes representing scalar and vector quantities to represent Maxwellian electromagnetics. Finally, “Formal” graphs were introduced by Deschamps [67] for electromagnetics, where the nodes and edges were labeled by scalar and vector-based equational components” – wherein reference # 64 is “Tonti, E., 1972, “On the Mathematical Structure of a Large Class of Physical Theories,” Atti Accad. Naz. Lincei Rend. Cl. Sci. Fis. Mat. Nat., 52(8), pp. 48–56.”
Thus, Michopoulos demonstrates that the Tonti diagrams of Wang are in fact a form/example of directed acyclic graph[s], by both inherency and by explanation of the meaning of Tonti diagrams
As to the adding of equations/relationships, see fig. 4, also fig. 7 and 9, i.e. as the Tonti graph was constructed relationships and variables were added - to clarify, fig. 4 caption: “The work ow for the data-driven approach to LPM construction. The user provides (a) a topology for the LPM (i.e., symbolic network of inter-connected components), which is then converted to (b) the common language of abstract oriented cell complexes [34]. (c) The Tonti diagram [33] converts the cell complex representation to (d) a system of symbolic ODEs with unknown constitutive parameters. The parameters are learned from data using standard system identification techniques.” – to clarify, section “A Data-Driven Approach”: “A key advantage of using this abstraction is the ability to automatically map a given topological structure for the LPM (e.g., a circuit graph or mass/spring/damper network) to a set of governing ODEs.”
But Wang does not explicitly teach that it is used as a search space, i.e. in Wang, it creates the DAG of the Tonti diagram from the topology directly, but does not search DAGs
However, this would have been obvious when Wang, incl. the paragraph split between pages 4-5 discussing the use of OLS “to iteratively update the constitutive parameters until the solution of the state equation fits the given data.” And its note that “although other objective functions and optimization techniques are certainly applicable”
taken in view of Kalyuzhnaya, abstract: “In this paper, we describe the concept of generative design approach applied to the automated evolutionary learning of mathematical models in a computationally efficient way. To formalize the problems of models’ design and co-design, the generalized formulation of the modeling workflow is proposed. A parallelized evolutionary learning approach for the identification of model structure is described for the equation-based model and composite machine learning models.” Then see § 2 ¶ 1: “An extensive literature review shows many attempts for mathematical models design in the different fields [9,10]. In particular, the methods of the automated model design is highly valuable part of the various researches [11]. As an example, the equation-free methods allow building the models that represent the multi-scale processes [12]. Another example is building of the physical laws from data in form of function [13], ordinary differential equations system [14], partial differential equations (PDE) [15]. The application of the automated design of ML models or pipelines (which are algorithmicaly close notions) are commonly named AutoML [8] although most of them work with models of fixed structure, some give opportunity to automatically construct relatively simple the ML structures. Convenient notation for such purpose is representation of a model as a directed acyclic graph (DAG) [16]. Another example of popular AutoML tool for pipelines structure optimization is TPOT [17]”
Then see § 3 ¶ 1: “A problem of the generative design of mathematical models requires a model representation as a flexible structure and appropriate optimization methods for maximizing a measure of the quality of the designed model. To solve this optimization problem, different approaches can be applied. The widely used approach is based on evolutionary algorithms (e.g., genetic optimization implemented in TPOT [35] and DarwinML [16] frameworks) because it allows solving both exploration and exploitation tasks in a space of model structure variants. The other optimization approaches like the random search of Bayesian optimization also can be used, but the populational character of evolutionary methods makes it possible to solve the generative problems in a multiobjective way and produce several candidates model” and in ¶ 3 of § 3: “In the context of problem of computer model generative design, the model M should have flexible structure that can evolve by changing (or adding/eliminating) the properties of a set of atomic parts (“building blocks”). For such task, the model M can be described as a graph (or more precisely as a DAG):” – and see fig. 2 – then see page 5 ¶ 1 last 4 lines, followed by: “So, the task of mathematical model generative design can be formulated as optimization task:… model M is a space of possible model structures…
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings from Wang on a system which generated equations based on Tonti diagrams which are an example of directed acyclic graphs with the teachings from Kalyuzhnaya on automated generative design of math models by using evolutionary algorithms The motivation to combine would have been that it’s “because it allows solving both exploration and exploitation tasks in a space of model structure variants…The other optimization approaches like the random search of Bayesian optimization also can be used, but the populational character of evolutionary methods makes it possible to solve the generative problems in a multiobjective way and produce several candidates model”.
Also, the KSR rationale of “(A) Combining prior art elements according to known methods to yield predictable results;” and “(B) Simple substitution of one known element for another to obtain predictable results;” both apply, i.e. Tonti diagrams were known in the art, and were directed acyclic graphs representing mathematical relationships and their variables for generating equations (see Wang); and it was well-known to use evolutionary algorithms for generative design of math models (“widely used approach is based on evolutionary algorithms”; Kalyuzhnaya, § 3 ¶ 1) and “Convenient notation for such purpose is representation of a model as a directed acyclic graph (DAG) [16].” (§ 2, ¶ 1); also note in § 1 ¶ 1: “In this section, we describe the problem of generative co-design of mathematical models and computational resources in terms of the genetic programming approach.”
It other words, in evolutionary algorithms to generate math models (equation/sets of equations), it was already well-known to use DAGs in combination with evolutionary algorithms to search through a plurality of DAGs to find an optimal math model, wherein the DAG represents the equation set. It was also well-known to represent math relationships such as for generating equations in Tonti diagrams. Tonti diagrams are a form of DAGs (which POSITA would have readily recognized).
The only distinction would be a simple substitution, e.g. initiate the evolutionary algorithm starting with the DAG of Wang (Wang fig. 4, (b and c)) so as to use a different optimization technique already contemplated as obvious variant by Wang (Wang, page 5 ¶ 1: “although other objective functions and optimization techniques are certainly applicable”) to find a better math model/series of math models, and in doing so achieve the benefit that “the populational character of evolutionary methods makes it possible to solve the generative problems in a multiobjective way and produce several candidates model.” (§ 3 ¶ 1 of Kalyuzhny).
Regarding Claim 8
Wang in view of Kalyuzhnaya teaches:
The method of claim 7 further comprising: performing the interpreting and the validating or invalidating for multiple of the plurality of testable hypotheses, wherein the interpreting and the validating or invalidating for the multiple of the plurality of testable hypotheses is performed for simpler testable hypotheses and proceeds to other testable hypotheses that adds complexity incrementally if the simpler hypotheses do not explain the data adequately. (Wang as was taken in view of Kalyuzhnaya as cited above teaches this, i.e. start with the simple equation set resultant from the DAG of Wang, and if that does not fit well, then optimize it with the evolutionary algorithms of Kalyuzhnaya to add in more complex math models)
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as obvious over Cao, Liwei, et al. "Identifying physico-chemical laws from the robotically collected data." (2019) in view of Schmidt, Michael, and Hod Lipson. "Distilling free-form natural laws from experimental data." science 324.5923 (2009): 81-85.
Regarding Claim 16.
While Cao does not explicitly teach the following, Cao in view of Schmidt teaches:
The method of claim 1, wherein the interpreting of the at least one of the testable hypotheses comprises mapping the physical variables to tensor data and physical relationships to computational operators in a computational framework. (Cao, as cited above for claim 1 teaches that the physical relationships are mapped to math computational operations between the variables, wherein Cao maps the variables to data in the DAG of binary trees (e.g. fig. 5)
But Cao does not teach mapping to tensor data – however this would have been obvious in view of Schmidt:, see page 81, col. 3, ¶¶ 1-3: “Initial expressions are formed by randomly combining mathematical building blocks such as algebraic operators {+, –, ÷, ×}, analytical functions (for example, sine and cosine), constants, and state variables… Although symbolic regression is typically used to find explicit (12–14) and differential equations (15), this method cannot readily find conservation laws or invariant equations…” and page 82 ¶ 1: “The key insight into identifying nontrivial conservation laws computationally is that the candidate equations should predict connections between dynamics of subcomponents of the system. More precisely, the conservation equation should be able to predict connections among derivatives of groups of variables over time, relations that we can also readily calculate from new experimental data.” – then, see fig. 2, wherein its mapping to tensor data – to clarify, page 82, ¶ 3 in col. 1: “An important consequence of the partial derivative– pair measure is that it can also identify relations that represent other nontrivial identities of the system beyond invariants and conservation laws. For example, if the system is confined to a manifold, the manifold equation can also derive accurate partial-derivative pairs. Similarly, the partial-derivative pair can identify equations such as Lagrangian equations, the energy equivalent to the equation of motion in classical mechanics, which summarize the systems dynamics but are not invariant.”, e.g. “Given position and velocity data over time, the algorithm converged on the energy laws of each system (Hamiltonian and Lagrangian equations).” On page 83 col. 1, ¶ 2
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings from Cao on a system which was to identify physical models from experimental data, including laws of nature, with the teachings from Schmidt on “Distilling Free-Form Natural Laws from Experimental Data” (title of Schmidt). The motivation to combine would have been that “We have demonstrated the discovery of physical laws, from scratch, directly from experimentally captured data with the use of a computational search. We used the presented approach to detect nonlinear energy conservation laws, Newtonian force laws, geometric invariants, and system manifolds in various synthetic and physically implemented systems without prior knowledge about physics, kinematics, or geometry. The concise analytical expressions that we found are amenable to human interpretation and help to reveal the physics underlying the observed phenomenon. Many applications exist for this approach, in fields ranging from systems biology to cosmology, where theoretical gaps exist despite abundance in data. Might this process diminish the role of future scientists? Quite the contrary: Scientists may use processes such as this to help focus on interesting phenomena more rapidly and to interpret their meaning.” (page 85, last two paragraphs of Schmidt)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Affenzeller, Michael, et al. "Gaining deeper insights in symbolic regression." Genetic programming theory and practice xi. New York, NY: Springer New York, 2014. 175-190. Page 182
Cornforth, Theodore, and Hod Lipson. "Symbolic regression of multiple-time-scale dynamical systems." Proceedings of the 14th annual conference on Genetic and evolutionary computation. 2012. Abstract and § 2.4
Cozad, Alison, and Nikolaos V. Sahinidis. "A global MINLP approach to symbolic regression." Mathematical Programming 170.1 (2018): 97-119. Abstract and §§ 2-3 including subsections
DiCarlo, Antonio, et al. "Solid and physical modeling with chain complexes." Proceedings of the 2007 ACM symposium on Solid and physical modeling. 2007. Abstract and §§ 1.2 and 2.3
Eldred, Christopher. Structure-preserving numerical discretizations for domains with boundaries. No. SAND2021-11517. Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2021. §§ 2.2-2.2.1
de França, Fabrício Olivetti. "A greedy search tree heuristic for symbolic regression." Information Sciences 442 (2018): 18-32. Abstract and §§ 2-4
de Franca, Fabricio Olivetti, and Maira Zabuscha de Lima. "Interaction-transformation symbolic regression with extreme learning machine." Neurocomputing 423 (2021): 609-619. Abstract and §§ 1-3
Kettunen, Lauri, and Tuomo Rossi. "Systematic derivation of partial differential equations for second order boundary value problems." International Journal of Numerical Modelling: Electronic Networks, Devices and Fields 36.3 (2023): e3078. Abstract, §§ 1-6
Neumann, Pascal, et al. "A new formulation for symbolic regression to identify physico-chemical laws from experimental data." Chemical Engineering Journal 387 (2020): 123412. Final publication of Cao et al.
Paoluzzi, Alberto, et al. "Topological computing of arrangements with (co) chains." ACM Transactions on Spatial Algorithms and Systems (TSAS) 7.1 (2020): 1-29. Abstract.
Schmidt, Michael, and Hod Lipson. "Comparison of tree and graph encodings as function of problem complexity." Proceedings of the 9th annual conference on Genetic and evolutionary computation. 2007. Abstract and § 1
Françoso Dal Piccol Sotto, Léo, et al. "Graph representations in genetic programming." Genetic Programming and Evolvable Machines 22.4 (2021): 607-636. §§ 2.3-2.4
Tonti, Enzo. "Why starting from differential equations for computational physics?." Journal of Computational Physics 257 (2014): 1260-1290. Abstract
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