DETAILED ACTION
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on July 1st, 2025 has been entered.
This action is in response to the amendments filed on July 1st, 2025. A summary of this action:
Claims 1-7, 9, 11-20 have been presented for examination.
Claims 4-5, 7, 13, 15-16, 18-19 are objected to because of informalities
Claims 1-7, 9, 11-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite
Claims 1-7, 9, 11-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of a mathematical concept, certain methods of organizing human activity, and mental process without significantly more.
Claim(s) 1-3, 11-16 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Charlton, John, et al. "Measuring relative accuracy of malware detectors in the absence of ground truth." MILCOM 2018-2018 IEEE Military Communications Conference (MILCOM). IEEE, 2018 as taken in view of Zhu, Shuofei, et al. "Measuring and modeling the label dynamics of online {Anti-Malware} engines." 29th USENIX Security Symposium (USENIX Security 20). 2020.
Claim(s) 4-6, 17-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Charlton, John, et al. "Measuring relative accuracy of malware detectors in the absence of ground truth." MILCOM 2018-2018 IEEE Military Communications Conference (MILCOM). IEEE, 2018 as taken in view of Zhu, Shuofei, et al. "Measuring and modeling the label dynamics of online {Anti-Malware} engines." 29th USENIX Security Symposium (USENIX Security 20). 2020 in further view of Charlton, John, Pang Du, and Shouhuai Xu (hereinafter Du). "A new method for inferring ground-truth labels and malware detector effectiveness metrics." Science of Cyber Security: Third International Conference, SciSec 2021, Virtual Event, August 13–15, 2021, Revised Selected Papers 4. Springer International Publishing, 2021.
Claims 7 and 9 are not rejected under § 102/103. The closest art rejection is below, but it does not fairly teach what is recited in ordered combination in claims 7 and 9, nor does any other combination of prior art of record fairly teach the ordered combinations.
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 .
Response to Arguments/Amendments
Regarding the § 112(b) Rejection
Maintained, updated as necessitated by amendment.
With respect to the remarks, pages 8-9, there is no standard provided in the instant disclosure for an objective measure of what is considered as being “similar”, e.g. is a correlation value of 0.7 similar? Or 0.5? Or 0.2? Or 0.95? There is no threshold disclosed for an objective standard for what is similar and what is not. Also, ¶ 38: “It is understood that other
correlation scoring schemes can be used, e.g., 0 to 10, 1 to 100, etc.” – and the claim does not even recite that the correlation is to determine what is similar or what is not, but rather as a measure of how they are similar or the same voting outputs.
Considering these remarks, the Examiner notes that other terms may more readily represent what appears to be the intended claim scope, i.e. how correlated/related the voting output(s) are to each other, as these remarks appear to be intended to convey that “same or similar” is really just expressing it’s a measure of correlation between the two, but that is not what the claim expressly recites. ¶ 38: “The correlation value is a measure of correlation among the voting sources 108 under examination that is attributed to first-order interactions.”
Merriam Webster, Definition of “Correlation”, accessed electronically on Dec. 1st, 2025, URL merriam-webster(dot)com/dictionary/correlation, definition: “the state or relation of being correlated specifically: a relation existing between phenomena or things or between mathematical or statistical variables which tend to vary, be associated, or occur together in a way not expected on the basis of chance alone”.
To further clarify, the claim sets forth “similar” and “correlation” as distinct elements – these remarks imply that they are interchangeable, i.e. “similar” = “correlation”, whereas the issue for the rejection is, when reviewing these remarks, “similar” = “some unknown subjective level/range of correlation”.
Regarding the § 101 Rejection
Maintained, updated as necessitated by amendment.
With respect to the remarks, regarding the newly amended feature, the Examiner notes that weighted voting is still an abstract idea, and an abstract idea that has formed the basis of historical democracies, e.g. in the Roman Republic those with substantially more means were given a higher weight to their vote compare to those with little means, wherein the weights were assigned based on the degree of wealth of each person.
Regarding the § 102/103 Rejection
Rejection maintained, and updated as necessitated by amendment below.
With respect to the remarks, pages 10-11, # 1-2, the Examiner maintains the position previously stated in the prior final rejection.
As a point of clarity on # 2, the Examiner further notes precisely how the claim was mapped, i.e.: “…measuring consensus among the at least two voting sources using an agreement metric as a measure of how the at least two voting sources generate a same or similar voting output ...” [but, note it does not recite this is “over a period of time”], wherein Charlton, as cited, teaches: “Intuitively, the similarity between Di and Dk, Sik, is defined by the ratio of the number of decisions where Di and Dk agree with each other over the total number of files scanned by both detectors… and see in§ 111.B at# 1 the definitions of the "agreement matrix" and "count matrix"; and see§ Ill.A ,i,i 1-2… to clarify on the agreement metric - Charlton,§ II1.B, see the "Definition 2" which shows that each element at index ik in the similar matrix is the ik elements of "A" divided by "C" -A is the agreement matrix, i.e. "the number of files upon which detectors Di and Dk give the same labels [agrees]" and this is divided by "the number of files scanned by both detectors” – i.e. Charlton’s technique is using an agreement metric of whether or not the voting sources give the same output (note the “or” in the claim, as it only requires either “a same or similar voting output”, and Charlton reads on the “same”, as they “give the same labels [outputs agree as being the same]”.
With respect to the remarks, regarding Charlton, for the relative accuracy of malware detectors, and regarding the direction of future research in Charlton, the Examiner notes these appear to be referring to § II second to last paragraph of Charlton explicitly teaches: “In [5], the authors assumed the homogeneity of false positives in all detectors, an independent decision of each detector, and low false positives but high false negatives assumed for all detectors, and so forth. However, these assumptions should be removed in order to reflect real world applications.”
See Charlton, as was cited, including: “The extremely low relative accuracy of the last
four detectors can be attributed to the following: (i) these detectors match poorly with the
decisions of the other detectors;”, but then see Charlton § V ¶ 2 for the suggestion: “We plan to conduct the following future research: (1) develop a theoretical evaluation framework that can be used to judge under what environments the proposed algorithm works or does not work; (2) characterize the co-variance and correlation between the accuracy of detectors [example of a degree of correlation amount the detectors/voting sources]...”
Charlton does not anticipate this claim, nor was it relied upon as such, wherein the Examiner specifically pointed to (and stated) that the rejection was in view of a “suggestion” for further research by Charlton (but was not something done in Charlton on an anticipatory basis, rather this would have suggested to POSITA what to do next).
MPEP § 2142: “"To support the conclusion that the claimed invention is directed to obvious subject matter, either the references must expressly or impliedly suggest the claimed invention or the examiner must present a convincing line of reasoning as to why the artisan would have found the claimed invention to have been obvious in light of the teachings of the references." Ex parte Clapp, 227 USPQ 972, 973 (Bd. Pat. App. & Inter. 1985).”
MPEP § 2144(IV): “The reason or motivation to modify the reference may often suggest what the inventor has done, but for a different purpose or to solve a different problem. It is not necessary that the prior art suggest the combination to achieve the same advantage or result discovered by applicant. See, e.g., In re Kahn, 441 F.3d 977, 987, 78 USPQ2d 1329, 1336 (Fed. Cir. 2006) (motivation question arises in the context of the general problem confronting the inventor rather than the specific problem solved by the invention); Cross Med. Prods., Inc. v. Medtronic Sofamor Danek, Inc., 424 F.3d 1293, 1323, 76 USPQ2d 1662, 1685 (Fed. Cir. 2005) ("One of ordinary skill in the art need not see the identical problem addressed in a prior art reference to be motivated to apply its teachings."); In re Lintner, 458 F.2d 1013, 173 USPQ 560 (CCPA 1972) (discussed below); In re Dillon, 919 F.2d 688, 16 USPQ2d 1897 (Fed. Cir. 1990), cert. denied, 500 U.S. 904 (1991) (discussed below).”
MPEP § 2152.02(b): “…The two basic requirements that must be met by a prior art document in order to describe a claimed invention such that it is anticipated under AIA 35 U.S.C. 102 are the same as those under pre-AIA 35 U.S.C. 102. First, "each and every element of the claimed invention" must be disclosed either explicitly or inherently, and the elements must be "arranged or combined in the same way as in the claim." See In re Gleave, 560 F.3d 1331, 1334, 90 USPQ2d 1235, 1237-38 (Fed. Cir. 2009), citing Eli Lilly & Co. v. Zenith Goldline Pharms., Inc., 471 F.3d 1369, 1375, 81 USPQ2d 1324,1328 (Fed. Cir. 2006); Net MoneyIN, Inc. v. VeriSign, Inc., 545 F.3d 1359, 1370, 88 USPQ2d 1751, 1759 (Fed. Cir. 2008); In re Bond, 910 F.2d 831, 832-33, 15 USPQ2d 1566, 1567 (Fed. Cir. 1990). …An anticipatory description it is not required in order for a disclosure to qualify as prior art, unless the disclosure is being used as the basis for an anticipation rejection. In accordance with pre-AIA case law concerning obviousness, a disclosure may be cited for all that it would reasonably have made known to a person of ordinary skill in the art. Thus, the description requirement of AIA 35 U.S.C. 102(a)(1) and (a)(2) does not preclude an examiner from applying a disclosure in an obviousness rejection under AIA 35 U.S.C. 103 simply because the disclosure is not adequate to anticipate the claimed invention”
E.g. see Titanium Metals Corp. v. Banner, 778 F.2d 775, 227 USPQ 773 (Fed. Cir. 1985) in MPEP § 2131, including § III: “Claims to titanium (Ti) alloy with 0.8% nickel (Ni) and 0.3% molybdenum (Mo) were not anticipated by, although they were held obvious over, a graph in a Russian article on Ti-Mo-Ni alloys in which the graph contained an actual data point corresponding to a Ti alloy containing 0.25% Mo and 0.75% Ni.”
While Charlton, on a § 102 basis is directed to measuring the relative accuracy of the detectors, Charlton on a § 103 basis explicitly suggests the following features in § V: “We plan to conduct the following future research: (1) develop a theoretical evaluation framework that can be used to judge under what environments the proposed algorithm works or does not work; (2) characterize the co-variance and correlation between the accuracy of detectors; (3) develop an aggregation engine to incorporate detection labels of multiple malware detectors; and (4) identify the key characteristics of poor detectors in order to avoid them when aggregating the labels of multiple detector” – also, § 2 second to last paragraph further clarifies on this suggestion: “In [5], the authors assumed the homogeneity of false positives in all detectors, an independent decision of each detector, and low false positives but high false negatives assumed for all detectors, and so forth. However, these assumptions should be removed in order to reflect real world applications [i.e. it suggests for real-world application that independence assumption should be removed]” – thus, POSITA, when reading Charlton, would have found it obvious to continue work on the invention of Charlton, in the direction of future research suggested by Charlton, and to have modified Charlton’s teachings in a manner to “characterize the co-variance and correlation between the accuracy of detectors” and in doing so, when perusing the libraries and other such sources of material, would have found Zhu particular relevant to doing such a modification, as see Zhu abstract: “We show that hand-picked "trusted" engines do not always perform well, and certain groups of engines are strongly correlated and should not be treated independently.” And § 1 page 2362 ¶ 2: “Second, we model the relationships between different engines' labels, to examine the "independence" assumption made by existing works. By clustering engines based on their label sequences for the same files, we identify groups of engines with highly correlated or even identical labeling decisions. In addition, through a "causality" model, we identify engines whose labels are very likely to be influenced by other engines [example of a degree of first-order interaction]. Our results indicate that engines should not be weighted equally when aggregating their labels” – see the remaining citations of Zhu for further clarity, including the TSM.
In other words, while Charlton does not anticipate: “We plan to conduct the following future research: (1) develop a theoretical evaluation framework that can be used to judge under what environments the proposed algorithm works or does not work; (2) characterize the co-variance and correlation between the accuracy of detectors; (3) develop an aggregation engine to incorporate detection labels of multiple malware detectors; and (4) identify the key characteristics of poor detectors in order to avoid them when aggregating the labels of multiple detectors.” – it does suggest such a direction, and thus does contemplate/suggest it for purposes of obviousness under § 103, as MPEP § 2152.02(b): “…In accordance with pre-AIA case law concerning obviousness, a disclosure may be cited for all that it would reasonably have made known to a person of ordinary skill in the art. Thus, the description requirement of AIA 35 U.S.C. 102(a)(1) and (a)(2) does not preclude an examiner from applying a disclosure in an obviousness rejection under AIA 35 U.S.C. 103 simply because the disclosure is not adequate to anticipate the claimed invention”
As a final note, MPEP § 2141.03(I): “"A person of ordinary skill in the art is also a person of ordinary creativity, not an automaton." KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398, 421, 82 USPQ2d 1385, 1397 (2007). "[I]n many cases a person of ordinary skill will be able to fit the teachings of multiple patents together like pieces of a puzzle." Id. at 420, 82 USPQ2d 1397. Office personnel may also take into account "the inferences and creative steps that a person of ordinary skill in the art would employ." Id. at 418, 82 USPQ2d at 1396.”
With respect to the remarks regarding the combination of references, the Examiner respectfully disagrees for similar reasons as above, including noting that it is an “or” in the claim, i.e. “same or similar”, and Charlton teaches “same” for its agreement metric, as discussed in detail above. These remarks appear to read the claim as the claim requiring both “same and similar voting output”, but the claim does not expressly recite this. E.g. see MPEP § 2111 for In re Prater.
Furthermore, regarding the remarks such as “The PTO makes a parallel between
Applicant's agreement metric and Charlton's similarity matrix…. As noted above, Charlton's similarity matrix is not designed to incorporate a measure of how two voting sources generate a same or similar voting output over a period of time, and neither Charlton's "future work"…”- see Charlton, as was cited: “Intuitively, the similarity between Di and Dk, Sik, is defined by the ratio of the number of decisions where Di and Dk agree with each other over the total number of files scanned by both detectors” – and the other clarifying citations on page 451, col. 2, including: “To clearly define Sik in a modular fashion, two auxiliary matrices are defined: the agreement matrix, denoted by A…. Intuitively, Aik [the agreement matrix] is the number of files upon which detectors Di and Dk give the same labels [example of an agreement metric as it’s a measure of how the voting sources generate the same output, i.e. labels], namely”, also, see Charlton, page 452, ¶ 2: “The similarity matrix S resembles a well-known correlation
matrix consisting of correlation coefficients between a group of random variables. The major difference between these two kinds of matrices is that similarities are in the range of [0;1] while correlations are in the range of [1;1].”
Then, with respect to Zhu, see the cited sections such as § 5.1: “To measure the correlation between two engines, we examine how likely the two engines give the same labels to the same files around the same time. More specifically, given a pair of engines (A, B), we compare their label sequences on the same file to measure the similarity (or distance)…” and the other cited sections.
Claim Objections
Claims 4-5, 7, 13, 15-16, 18-19 are objected to because of the following informalities:
Claim 1 recites, in part: “a first-order interaction”, wherein claim 5 further recites:” “at least one first-order interaction” and claim 4 also recites: “a first-order interaction” – there is insufficient antecedent basis, as these do not clearly and explicitly refer back to the prior recitation, nor do they disambiguate with modifying phrases, e.g. first/second/third
The Examiner interprets these in view of the disclosure, ¶¶ 85-87 and elsewhere, noting the antecedent in the disclosure, and suggests amending to more clearly reflect what is disclosed
Claims 7, 18-19 are objected to under similar rationales
Claim 13 is objected to because it appears to further limit the assignment of the weight step, but does not do so explicitly (¶ 84)
Similar objection for claim 15 for “correlation” that is previously set forth in claim 14.
Also, claim 16 as well, as it appears to be intended to further limit the prior limitation, but does not do so explicitly - ¶ 86, ¶ 34
Appropriate correction is required.
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.
Claims 1-7, 9, 11-20 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.
The independent claims recite the phrase “similar voting output” – the term “similar” is a subjective term, and no standard is provided in the instant disclosure (¶¶ 28; 31) to ascertain what the meaning of this term is under the BRI without the exercise of the subjective judgement/opinion of POSITA, rendering the claim indefinite. See MPEP § 2173.05(b)(IV).
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 1-7, 9, 11-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of a mathematical concept, certain methods of organizing human activity, and mental process without significantly more.
Step 1
Claim 14 is directed towards the statutory category of a process.
Claims 1 and 12 are directed towards the statutory category of an apparatus.
Claims 12 and 14, 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 note, while the instant disclosure discusses in numerous paragraphs the application of the claimed and disclosed abstract idea to the field of virus screening (which is entirely unclaimed), this is not the focus of the alleged advance, as ¶ 39: “…but it is understood that the system 100 can be used to model correlation of any type of sourcing model 102…” makes it clear that the focus of the claimed advance lies not in computer technology, but rather in the realm of abstract ideas as discussed below in detail; also virus screening is an abstract idea.
MPEP § 2106.04(d): “In Finjan Inc. v. Blue Coat Systems, Inc., 879 F.3d 1299, 125 USPQ2d 1282 (Fed. Cir. 2018), the claimed invention was a method of virus scanning that scans an application program, generates a security profile identifying any potentially suspicious code in the program, and links the security profile to the application program. 879 F.3d at 1303-04, 125 USPQ2d at 1285-86. The Federal Circuit noted that the recited virus screening was an abstract idea, and that merely performing virus screening on a computer does not render the claim eligible. 879 F.3d at 1304, 125 USPQ2d at 1286. The court then continued with its analysis under part one of the Alice/Mayo test by reviewing the patent’s specification, which described the claimed security profile as identifying both hostile and potentially hostile operations. The court noted that the security profile thus enables the invention to protect the user against both previously unknown viruses and "obfuscated code," as compared to traditional virus scanning, which only recognized the presence of previously-identified viruses. The security profile also enables more flexible virus filtering and greater user customization. 879 F.3d at 1304, 125 USPQ2d at 1286.”
To clarify on the focus of the alleged advance, see also ¶ 67: “Now that the RlSM decomposition and RlSM- Tare introduced, they can be used to study first-order interactions among the antivirus engines in the VirusTotal-VT dataset” and ¶ 53. Then, see the instant inventors own publication on this disclosed invention for the inventors own words:
Joyce, Robert J., Edward Raff, and Charles Nicholas. "Rank-1 Similarity Matrix Decomposition For Modeling Changes in Antivirus Consensus Through Time." arXiv preprint arXiv:2201.00757 (2021).
Joyce, Abstract: “We introduce the Temporal Rank-1 Similarity Matrix decomposition (R1SM-T) in order to investigate the origins of these correlations and to model how consensus amongst antivirus engines changes over time” in § 3.1: “Hurier et al. [6] proposed a metric called overlap for computing pairwise detection consensus for a pair of antivirus engines. However, overlap does not consider that some antivirus engines may be missing from a scan report. Instead, we define a similar metric, which we call agreement, that corrects this issue.” – see ¶¶ 44-45 of the instant disclosure.
Joyce, § 4: “All current explanations for consensus between antivirus engines can be classified as first-order interactions, i.e. a single interaction between a pair of features. In order to test these widely held assumptions we introduce the Rank-1 Similarity Matrix (R1SM) decomposition. We later describe an extension to R1SM that reveals changes in first-order interactions within time-series data, which we call the Temporal Rank-1 Similarity Matrix Decomposition (R1SM-T). It was necessary to create this decomposition as, to our knowledge, no existing algorithm possesses this capability.” And see its subsections, including § 4.1. To clarify, see the instant disclosure, ¶¶ 53-67
In other words, the focus of the alleged advance lies not in technology, but rather in mathematical concepts used in data analysis, which has long been held to be an abstract idea (e.g. Parker v. Flook, Gottschalk v. Benson, SAP v. InvestPic, etc. as discussed in MPEP § 2106.04(a)(2)(I)), e.g. new mathematical concepts in the field of decomposition of tensors (Joyce, § 4 and its subsections; ¶¶ 53-67 of the instant disclosure), a new math equations to correct a math issue with the math equation from Hurier et al., (Joyce, § 3.1; instant disclosure ¶¶ 44-45), etc. And while the instant disclosure describes extensively its application to a dataset “VirusTotal” provided by “an online malware analysis service that scans files with a collection of antivirus engines” (¶¶ 5; 73; 76-78, etc. which all convey its WURC as well; see the step 2B analysis below for clarification) in view of ¶ 39 this is merely generally linking to a particular field of use by selecting what data is to be analyzed (e.g. Parker v. Flook; Electric Power Group; as discussed in MPEP § 2106.05(h); also see SAP v. InvestPic in MPEP § 2106.04(a)(2)(I)).
As a point of clarity on this, the Examiner notes that in the non-limiting exemplary embodiment in the disclosure, ¶ 56: “The RlSM decomposition is comparable to the existing CANDECOMP/PARAFAC (CP) decomposition, which also decomposes a tensor into a sum of rank-one outer products [10].” – this CP decomposition that it is “comparable to” was discovered before the invention of the computer, i.e. people have performed mental evaluations of such math before the invention of the computer (see example 45, discussion of the Arrhenius equation being performed mentally since the 1800’s). To clarify on this point:
See Hitchcock, Frank L. "The expression of a tensor or a polyadic as a sum of products." Journal of Mathematics and Physics 6.1-4 (1927): 164-189, which is the original publication on the CP decomposition, wherein § 6 of Hitchcock states: “The problem of decomposition of tensors into sums of products is thus (from a purely algebraic point of view) akin to the following problem: the determination of those properties of polyadics which are multiply invariant, and the discovery of the invariants, or other concomitants, on whose vanishing or non-vanishing the property depends”
To clarify, Kolda, Tamara G., and Brett W. Bader. "Tensor decompositions and applications." SIAM review 51.3 (2009): 455-500. § 3: “In 1927, Hitchcock [105, 106] proposed the idea of the polyadic form of a tensor, i.e., expressing a tensor as the sum of a finite number of rank-one tensors; and in 1944 Cattell [40, 41] proposed ideas for parallel proportional analysis and the idea of multiple axes for analysis (circumstances, objects, and features). The concept finally became popular after its third introduction [i.e. re-discovered three times], in 1970 to the psychometrics community, in the form of CANDECOMP (canonical decomposition) by Carroll and Chang [38] and PARAFAC (parallel factors) by Harshman [90]. We refer to the CANDECOMP/PARAFAC decomposition as CP, per Kiers [122]. M¨ocks [166] independently discovered CP in the context of brain imaging and called it the topographic components model” – and see § 3.1 ¶ 1: “The rank of a tensor X, denoted rank(X), is defined as the smallest number of rank-one tensors (see section 2.1) that generate X as their sum [105, 141]. In other words, this is the smallest number of components in an exact CP decomposition, where “exact” means that there is equality in (3.1). Hitchcock [105] first proposed this definition of rank in 1927, and Kruskal [141] did so independently 50 years later.” – i.e. decompositions “comparable” to what is disclosed were first discovered decades before the invention of the computer.
Also see Acar, Evrim, and Bülent Yener. "Unsupervised multiway data analysis: A literature survey." IEEE transactions on knowledge and data engineering 21.1 (2008): 6-20. § 1 ¶ 1: “MULTIWAY data analysis, dating back to 1920s to the studies of tensor decompositions by Hitchcock [1], [2], is the extension of two-way data analysis to higher-order data sets. Multiway analysis is often used for extracting hidden structures and capturing underlying correlations between variables in a multiway array. For example, multiway analysis of multichannel electroencephalogram (EEG) data enables us to capture the correlation between the channels by representing signals in both time and frequency domains. Multichannel EEG recordings are commonly represented as an I J matrix containing signals recorded for I time samples at J channels. In order to discover the brain dynamics, often frequency content of the signals, for instance signal power at K particular frequencies, also needs to be considered. In that case, EEG data can be arranged as an I J K three-way data set [3]… It has been shown in numerous research areas, including social networks [4], neuroscience [5], process analysis [6], and text-mining [7], that underlying information content of the data may not be captured accurately or identified uniquely by two-way data analysis… For example, in fluorescence spectroscopy, one of the most common multiway models, i.e., Parallel Factor Analysis (PARAFAC), can uniquely identify the pure spectra of chemicals from measurements of mixtures of chemicals..” – and see the remaining subsections of § 1 to further clarify, incl. fig. 4 as discussed in § 2 and its subsections.
In other words, the focus of the alleged advances (by both the instant disclosure and the inventors own words in Joyce) lies in the realm of mathematics, i.e. an allegedly new abstract idea without an integration of the abstract idea into a practical application (e.g. MPEP § 2106.04(d): “Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66, 80, 84, 101 USPQ2d 1961, 1968-69, 1970 (2012) (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,’’ but the Court in Gottschalk v. Benson ‘‘held that simply implementing a mathematical principle on a physical machine, namely a computer, was not a patentable application of that principle’’) and without additional elements that amount to significantly more (e.g. MPEP § 2106.05(I): “Mayo, 566 U.S. at 78, 101 USPQ2d at 1968 (after determining that a claim is directed to a judicial exception, "we then ask, ‘[w]hat else is there in the claims before us?") (emphasis added))…Instead, an "inventive concept" is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception, and is sufficient to ensure that the claim as a whole amounts to significantly more than the judicial exception itself. Alice Corp., 573 U.S. at 27-18, 110 USPQ2d at 1981 (citing Mayo, 566 U.S. at 72-73, 101 USPQ2d at 1966)…”, e.g. MPEP § 2106.05(d): “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))”)
MPEP § 2106.04(I): “The Supreme Court’s decisions make it clear that judicial exceptions need not be old or long-prevalent, and that even newly discovered or novel judicial exceptions are still exceptions. For example, the mathematical formula in Flook, the laws of nature in Mayo, and the isolated DNA in Myriad were all novel or newly discovered, but nonetheless were considered by the Supreme Court to be judicial exceptions because they were "‘basic tools of scientific and technological work’ that lie beyond the domain of patent protection." Myriad, 569 U.S. 576, 589, 106 USPQ2d at 1976, 1978 (noting that Myriad discovered the BRCA1 and BRCA1 genes and quoting Mayo, 566 U.S. 71, 101 USPQ2d at 1965); Flook, 437 U.S. at 591-92, 198 USPQ2d at 198 ("the novelty of the mathematical algorithm is not a determining factor at all"); Mayo, 566 U.S. 73-74, 78, 101 USPQ2d 1966, 1968 (noting that the claims embody the researcher's discoveries of laws of nature). The Supreme Court’s cited rationale for considering even "just discovered" judicial exceptions as exceptions stems from the concern that "without this exception, there would be considerable danger that the grant of patents would ‘tie up’ the use of such tools and thereby ‘inhibit future innovation premised upon them.’" Myriad, 569 U.S. at 589, 106 USPQ2d at 1978-79 (quoting Mayo, 566 U.S. at 86, 101 USPQ2d at 1971). See also Myriad, 569 U.S. at 591, 106 USPQ2d at 1979 ("Groundbreaking, innovative, or even brilliant discovery does not by itself satisfy the §101 inquiry."). The Federal Circuit has also applied this principle, for example, when holding a concept of using advertising as an exchange or currency to be an abstract idea, despite the patentee’s arguments that the concept was "new". Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 714-15, 112 USPQ2d 1750, 1753-54 (Fed. Cir. 2014). Cf. 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).”
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:
determine correlation among the at least two voting sources by measuring consensus among the at least two voting sources using an agreement metric as a measure of how the at least two voting sources generate a same or similar voting output over a period of time, the at least two voting sources including a first voting source and a second voting source; - math calculations in textual form, e.g. ¶ 33: “A similarity matrix of the voting output data organizes the voting output data so as to allow the correlation modeling module 110 to generate mathematical vectors that, when processed in accordance with the algorithms described herein, provide analytics as to the correlation among at least two voting sources 108.”, ¶ 34: “In an exemplary implementation, the correlation modeling module 110 can be configured to implement the agreement metric by dividing a number of occurrences in which a voting output from a first voting source 108 agrees with a voting output from a second voting source 108 by a number of occurrences in which the voting output from the first voting source 108 and the voting output from the second voting source 108 are present. As will be explained in detail later, the agreement metric provides a measure of overlap of pairwise detection consensus among two or more voting sources 108.”
Also, see ¶¶ 42-45, including the equation in ¶ 44, as discussed: “Instead, embodiments of the inventive method define a similar metric, referred to as "Agreement," that corrects this issue”
determine a degree of a first-order interaction among the at least two voting sources; determine a degree of the correlation among the at least two voting sources having they degree of first-order interaction; - math calculations in textual form, see the above citations to the disclosure, also see ¶ 35: “The correlation modeling module 110 can be configured to generate a matrix decomposition that identifies the first-order interaction among the at least two voting sources 108.”; ¶¶ 36-38. Also see ¶¶ 53-55; and ¶¶ 2-3 incl.: “Known explanations include different engines created by the same company, products "copying" the results of leading vendors, and vendors sublicensing their technology to others [12, 17]. All of the above explanations can be considered "first-order" interactions, since they create a direct link between the labeling decisions of two antivirus engines.”
To further clarify, this is akin to “v. organizing information and manipulating information through mathematical correlations, Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014). The patentee in Digitech claimed methods of generating first and second data by taking existing information, manipulating the data using mathematical functions, and organizing this information into a new form. The court explained that such claims were directed to an abstract idea because they described a process of organizing information through mathematical correlations, like Flook's method of calculating using a mathematical formula. 758 F.3d at 1350, 111 USPQ2d at 1721.” as discussed in MPEP §2106.04(a)(2)(I)(A)
and determine that the first voting source and the second voting source are generating voting outputs that are independent of each other or that are dependent on each other based on the degree of the correlation. – a continuation of the math calculations in textual form, when read in view of the above cited portions of the instant disclosure, as well as ¶¶ 27-31.
assign a weight factor to the first voting source and/or the second voting sources based on the degree of the correlation attributed to their respective first-order interactions. – math relationships in textual form, when read in view of ¶ 84 (using numerical weight values to mathematically relate the voting sources)
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:
determine correlation among the at least two voting sources by measuring consensus among the at least two voting sources using an agreement metric as a measure of how the at least two voting sources generate a same or similar voting output over a period of time, the at least two voting sources including a first voting source and a second voting source; determine a degree of a first-order interaction among the at least two voting sources; determine a degree of the correlation among the at least two voting sources having they degree of first-order interaction; and determine that the first voting source and the second voting source are generating voting outputs that are independent of each other or that are dependent on each other based on the degree of the correlation. – a mental process, but for the mere instructions to do it on a computer.
A person is readily able to mentally determine a correlation between voting sources in the manner claimed, e.g. mentally observing two sets of data (the voting sources), and mentally judging/evaluating how correlated are by evaluating/judging whether or not they agree with each other (consensus), wherein such correlation may readily be done mentally with an agreement metric (e.g. consensus is determined based on some measure/metric of how much the two sources they agree with each other; akin a teacher comparing two students tests (or papers), and trying to determine whether or not the students copied substantially from each other to ascertain whether they are cheating by determining how correlated the tests/papers are, following a school guideline that states there needs to be over a 70% match/consensus in the texts of the paragraphs to judge they cheated). See ¶ 31: “Consensus is a measure of how voting sources 108 generate the same or similar voting output over a period of time.” To do this over time is no less a mental process, e.g. the person observing tabulated results over time, e.g. a professor observing the records gathered over time of student test results, and mentally evaluating them at the end of the semester to determine if someone may have been cheating (¶ 29 for the cheating example).
Then a person would readily be able to determine a degree of first-order interaction between the sources. See ¶ 30: “A first-order interaction can be defined as an effect in which a pattern of values on one variable changes depending on the combination of values on two other variables.” – a person is readily able to mentally observe such an effect in a simple dataset (e.g. for each voting source, suppose there is a table comprising values of three variables; a person is readily able to observe patterns in such data, or evaluate such data with simple equations using physical aids such as pen, paper, and/or a calculator, to ascertain such effects), and then a person merely mentally judges/evaluates the first-order interactions by comparing them to determine a degree of the interaction.
Then a person would readily be able to mentally judge/evaluate a degree of correlation in a similar manner as discussed above (mental observations of data used to mentally evaluate/render a judgement about the data), and from their mentally render a judgement based on the results, e.g. determining, based on the degree of correlation, whether or not the students are cheating (¶ 29).
assign a weight factor to the first voting source and/or the second voting sources based on the degree of the correlation attributed to their respective first-order interactions. - a mental process of mental judgements/evaluations to do such a process given the generality recited, and certain methods of organizing human activity (such weighted voting is found in democratic processes that vote tabulation techniques such as ranked choice voting; and weighted representation is also found in the text of the United States Constitution – see the three-fifths compromise; also in the Roman Republic the Centuriate Assembly used weighted voting
Rauh, “DEVELOPMENT OF THE ROMAN CONSTITUTION”, Lecture Notes, Purdue University, URL: web(dot)ics(dot)purdue(dot)edu/~rauhn/roman-constitution(dot)htm, accessed via the WayBack Machine with an archive date of Apr. 10th, 2010 for: “The Military (Centuriate) Assembly was organized into some 193 centuries in all, 14 in the "equestrian" or Knights' class (i.e., those whose worth enabled them to own horses), 70 in the First Class. "Votes" would be cast according to centuries (the majority vote within a century carried that century as one vote). The top centuries voted first, and their votes counted most. As soon as the vote of a majority of centuries (97) was secured, the election was over. Since the wealthier citizens tended to see things in common, centuries of the Equites (the Knights) and the First Class typically voted alike. This gave a candidate or a legislative measure 84 centuries. Assuming the highest ranking centuries of the Second Class voted similarly, only 13 more centuries were necessary to secure a vote, at which point the voting would cease and the assembly disbanded. Typically, the lower classes would never be called on to vote. In short, this was a conservatively organized assembly in which the votes of the wealthiest citizens came first and counted most. Rarely were the centuries of the poorest citizens called to vote.”
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.”
The certain methods of organizing human activity recited in claim 1 is:
determine correlation among the at least two voting sources by measuring consensus among the at least two voting sources using an agreement metric as a measure of how the at least two voting sources generate a same or similar voting output over a period of time, the at least two voting sources including a first voting source and a second voting source; determine a degree of a first-order interaction among the at least two voting sources; determine a degree of the correlation among the at least two voting sources having they degree of first-order interaction; and determine that the first voting source and the second voting source are generating voting outputs that are independent of each other or that are dependent on each other based on the degree of the correlation. . – see MPEP § 2106.04(a)(2)(II)(C): “An example of a claim reciting social activities is Voter Verified, Inc. v. Election Systems & Software, LLC, 887 F.3d 1376, 126 USPQ2d 1498 (Fed. Cir. 2018). The social activity at issue in Voter Verified was voting. The patentee claimed "[a] method for voting providing for self-verification of a ballot comprising the steps of" presenting an election ballot for voting, accepting input of the votes, storing the votes, printing out the votes, comparing the printed votes to votes stored in the computer, and determining whether the printed ballot is acceptable. 887 F.3d at 1384-85, 126 USPQ2d at 1503-04. The Federal Circuit found that the claims were directed to the abstract idea of "voting, verifying the vote, and submitting the vote for tabulation", which is a "fundamental activity that forms the basis of our democracy" and has been performed by humans for hundreds of years. 887 F.3d at 1385-86, 126 USPQ2d at 1504-05.”
To clarify, the present claims are drawn to a very similar abstract idea, i.e. the comparing of votes from two different sources. While the present claims recite more particularity then a simple comparison, it is still reciting a similar abstract idea.
assign a weight factor to the first voting source and/or the second voting sources based on the degree of the correlation attributed to their respective first-order interactions. – certain methods of organizing human activity, for the reasons discussed above, i.e. weighted voting/representation/tabulation in democracy.
As such, the claims recite an abstract idea of a mathematical concept, certain methods of organizing human activity, and a mental process.
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 t