DETAILED ACTION
This action is in response to the amendments filed on Dec. 17th, 2025 A summary of this action:
Claims 1, 3-20, 22 have been presented for examination.
Claims 1, 3-20, 22 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement
Claims 1, 3-20, 22 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.
Claim(s) 1, 3-5, 8-13, 15-18, 20, 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang, Xiaofeng, Zhenjie Zhu, and Guoliang Lu. "Multiple regression analysis for change detection in multi-sensory monitoring data with application to induction motor speed condition monitoring." Measurement Science and Technology 31.9 (2020): 095103 In view of Sridhar, Prasanna, Asad M. Madni, and Mo Jamshidi. "Hierarchical data aggregation in spatially correlated distributed sensor networks." 2006 World Automation Congress. IEEE, 2006.
Claim(s) 6-7, 14, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang, Xiaofeng, Zhenjie Zhu, and Guoliang Lu. "Multiple regression analysis for change detection in multi-sensory monitoring data with application to induction motor speed condition monitoring." Measurement Science and Technology 31.9 (2020): 095103 In view of Sridhar, Prasanna, Asad M. Madni, and Mo Jamshidi. "Hierarchical data aggregation in spatially correlated distributed sensor networks." 2006 World Automation Congress. IEEE, 2006 and in further view of Cormode, Graham, Srikanta Tirthapura, and Bojian Xu. "Time-decaying sketches for robust aggregation of sensor data." SIAM Journal on Computing 39.4 (2010): 1309-1339.
This action is 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(a) Rejection
Withdrawn in view of amendment, new grounds as necessitated by amendment.
Regarding the § 101 Rejection
Maintained, updated as necessitated by amendment.
With respect to the remarks, at prong 1, the Examiner respectfully disagrees. See the below rejection for how the presently amended claim is rejected under § 101. The Examiner also notes, for compact prosecution, Appeal 2025-002572, for this was also time-series analysis with an abstract idea of both a mental process and a math concept. Note its discussion of Ex parte Desjardins as well. Also, note its discussion of the limitation “detecting, by the best respective trained machine learning model, an anomaly in a time-series” – and example 47, limitation (d) of claim 2, for its discussion of a similar limitation.
With respect to the August 2025 memorandum, see footnote 17: “Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025) (steps incidental to automating an abstract idea were not sufficient to confer eligibility)” – see MPEP §2106.05(g): “The term "extra-solution activity" can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim” – and also, see example 47, for “using the trained ANN”. Note, in Recentive it was the “training” act that was “incident” as discussed in the opinion: “The requirements that the machine learning model be “iteratively trained” or dynamically adjusted in the Machine Learning Training patents do not represent a technological improvement. Recentive’s own representations about the nature of machine learning vitiate this argument: Iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning.”
So, see the rejection below for further clarity.
With respect to the remarks for prong 2, regarding the mere data gathering, see example 45, claim 3, prong 2: “Moreover, as discussed above for claim 1, the evaluation of whether this limitation is insignificant extra-solution activity in Step 2A Prong One does not take into account whether or not the limitation is well-known. Limitation (a) in the claim is thus insignificant extra-solution activity. Further, this determination precludes the ARCXY thermocouple from being considered to be a “particular machine,” because its involvement in the claim is only as insignificant extra-solution activity. See MPEP 2106.05(b), particularly the third factor.” So see MPEP § 2106.05(b)(III): “Whether its involvement is extra-solution activity or a field-of-use, i.e., the extent to which (or how) the machine or apparatus imposes meaningful limits on the claim. Use of a machine that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not integrate a judicial exception or provide significantly more. See Bilski, 561 U.S. at 610, 95 USPQ2d at 1009 (citing Parker v. Flook, 437 U.S. 584, 590, 198 USPQ 193, 197 (1978)), and CyberSource v. Retail Decisions, 654 F.3d 1366, 1370, 99 USPQ2d 1690 (Fed. Cir. 2011) (citations omitted) ("[N]othing in claim 3 requires an infringer to use the Internet to obtain that data. The Internet is merely described as the source of the data. We have held that mere ‘[data-gathering] step[s] cannot make an otherwise nonstatutory claim statutory.’" 654 F.3d at 1375, 99 USPQ2d at 1694 (citation omitted))”
To clarify on this, see MPEP § 2106.05(d): “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.”
For, “Alice Corp., 573 U.S. at 21-18, 110 USPQ2d at 1981 (citing 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)); 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"). 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).” MPEP § 2106.05(I).
In summary, an act of mere data of necessary data for use in the abstract idea is not a practical application that merely integrates the abstract idea. See MPEP § 2106.05(g) for In re Grams for more clarity in the opinion of In re Grams; also see Electric Power Group as well.
To clarify, prong 2 is the equivalent of the “directed to” inquiry in the courts (MPEP § 2106.04(d)(1)), and at issue is that a mere data gathering step of necessary data for the abstract idea does not change the nature of what the abstract idea itself is, but only what source of data it uses.
Hence, example 45, claim 3, does a 2B WURC consideration instead, and any allegations that something is not “well-known” (as alleged in these remarks) must be made at 2B at the WURC (MEPP § 2106.04(d)(1): “Specifically, the "improvements" analysis in Step 2A determines whether the claim pertains to an improvement to the functioning of a computer or to another technology without reference to what is well-understood, routine, conventional activity.”
With respect to the remarks for prong 2, regarding the “using a …computer based numerical modeling method”, the Examiner stated in the rejection its merely generally linking to the technological environment of computers, e.g. “iv. Specifying that the abstract idea of monitoring audit log data relates to transactions or activities that are executed in a computer environment, because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer, FairWarning v. Iatric Sys., 839 F.3d 1089, 1094-95, 120 USPQ2d 1293, 1295 (Fed. Cir. 2016);”, e.g. “x. Requiring that the abstract idea of creating a contractual relationship that guarantees performance of a transaction (a) be performed using a computer that receives and sends information over a network, or (b) be limited to guaranteeing online transactions, because these limitations simply attempted to limit the use of the abstract idea to computer environments, buySAFE Inc. v. Google, Inc., 765 F.3d 1350, 1354, 112 USPQ2d 1093, 1095-96 (Fed. Cir. 2014).”, e.g. “For example, an examiner could explain that employing generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment, does not add significantly more, similar to how limiting the abstract idea in Flook to petrochemical and oil-refining industries was insufficient” in MPEP § 2106.05(h), e.g. “Also, the claim invokes a generic DNN merely as a tool for making the recited mathematical calculation rather than purporting to improve the technology or a computer. See MPEP 2106.05(f). Therefore, the limitation represents no more than mere instructions to apply the judicial exception on a computer. It can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of computers.” In example 48.
With respect to the remarks for prong 2, regarding practical application, first see the discussion above, incl. of the August 2025 memorandum discussing Recentive Analytics.
As to example 47 claim 3, these remarks do not even point out the additional elements for the practical application (in particular, the additional elements which integrate the abstract idea itself in to a practical application). “e.g., 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,” MPEP § 2106.04(d) and MPEP § 2106.05€ to further clarify
Furthermore, example 47 points specifically to (d-f) as integrating a practical application, in view of SRI Int’l, Inc. v. Cisco Systems, Inc., 930 F.3d 1295, 1303 (Fed. Cir. 2019), as discussed in the example and clarified on in MPEP § 2106.04(a)(2)(III)(A) as cited to: “See SRI Int’l, Inc. v. Cisco Systems, Inc., 930 F.3d 1295, 1304 (Fed. Cir. 2019) (declining to identify the claimed collection and analysis of network data as abstract because "the human mind is not equipped to detect suspicious activity by using network monitors and analyzing network packets as recited by the claims");”
The present claims are not reasonably analogous (e.g. they lack limitations such as (d-f) in example 47), for they do not recite any additional elements that integrate the abstract idea itself into the practical application. The abstract idea itself cannot be the practical application, for, per MPEP § 2106.04(II)(A)(2): “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.").”
To clarify, see MPEP § 2106.04(I): “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.").
The alleged improvement is merely in post-measurement analysis of measured data (emphasis that it is post measurement in view of Thales), so also see “i. Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989);” in MPEP § 2106.05(g), wherein in the opinion: “Thus, step [a] requires the performance of [**1826] clinical laboratory tests on an individual to obtain data for the parameters (e.g., sodium content). The remaining steps, [b]-[e], analyze that data to ascertain the existence and identity of an abnormality, and possible causes thereof. In that regard, steps [b]-[e] are in essence a mathematical algorithm, in that they represent "[a] procedure for solving a given type of mathematical problem." Gottschalk v. Benson, 409 U.S. 63, 65, 93 S.Ct. 253, 254, 34 L.Ed.2d 273 (1972)…The sole physical process step in Grams' claim 1 is step [a], i.e., performing clinical tests on individuals to obtain data. The specification does not bulge with disclosure on those tests. To the contrary, it focuses on the algorithm itself, although it briefly refers to, without describing, the clinical tests that provide data. Thus, it states: "The [computer] program was written to analyze the results of up to eighteen clinical laboratory tests produced by a standard chemical analyzer that measures the levels of the chemical biological components listed...."”
Again, a 2B WURC consideration akin to example 45, claim 3 for the data gathering (see above citations for more clarification), and Recentive Analytics for the newly amended subject matter per footnote 17 of the Aug. memorandum, as well as the “using the trained ANN” discussion in example 47.
No remarks submitted for 2B.
Regarding the § 102/103 Rejection
Maintained, updated as necessitated by amendment.
With respect to the remarks, these are addressing the newly amended features and as such, see the rejection below for how these were taught by Wang.
With respect to the remarks regarding Sridhar, these are general conclusory remarks that do not address the teachings of Sridhar that were relied upon.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 3-20, 22 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. The dependent claims inherit the deficiencies of the claims they depend upon.
See MPEP 2163(II)(A): "For example, in Hyatt v. Dudas, 492 F.3d 1365, 1371, 83 USPQ2d 1373, 1376-1377 (Fed. Cir. 2007), the examiner made a prima facie case by clearly and specifically explaining why applicant’s specification did not support the particular claimed combination of elements, even though applicant’s specification listed each and every element in the claimed combination. The court found the "examiner was explicit that while each element may be individually described in the specification, the deficiency was lack of adequate description of their combination" and, thus, "[t]he burden was then properly shifted to [inventor] to cite to the examiner where adequate written description could be found or to make an amendment to address the deficiency.""
Also, see MPEP 2163(I) for Lockwood v. Amer. Airlines, Inc., 107 F.3d 1565, 1572, 41 USPQ2d 1961, 1966 (Fed. Cir. 1997).
Independent claims recite (claim 1 as representative): training a machine learning model using a second computer-based numerical modeling method, the machine learning model being a single time series model based on the patterns; predicting a future condition of the system using the machine learning model with current data of the system, the future condition of the system represented by a quantified state of the system;
This is not sufficiently described.
¶ 22: “Aspects of this embodiment include: forming the MxN matrix to reduce high-level dimension period data for each user defined cycle, based on weights applied to the data; defining one method based on the pattern to predict the trend; and using a machine learning model and a data transformation method based on the pattern to predict the trend.”
¶ 68: “…Equation 2 using a first computer-based numerical modeling method such as a least square method.”
¶ 61: “The modeling module 220 may be programmed to use a least square method to solve for the Beta vector, although embodiments are not limited to a least square method.”
There is insufficient support for the particular combination of features now recited in the claims. Nothing in the specification discloses an act of “training”, and the only mention of the time-series model is in ¶ 22 of a single embodiment “this embodiment”, not using it for a single time series model (¶ 22 states “trend” but this term is not expressed elsewhere in the disclosure).
To further clarify, ¶ 62: “a second computer-based numerical modeling method to form a linear regression for the patterns and sensor data.”
A linear regression is not a machine learning model, unless one wishes to define the term machine learning model to include a math concept discovered in the 1800s long before computers and routinely used by engineers and scientists since then. Even the term “regression” was coined long before the computer, to name this math concept that was routinely performed mentally as evidenced below in the § 101 rejection.
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, 3-21 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 11 is directed towards the statutory category of an article of manufacture. Claim 16 is directed towards the statutory category of an apparatus.
Claims 11 and 16, 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.
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:
creating matrices based on the multi-dimensional time series data; - mathematical calculations/relationships in textual form. See ¶¶ 46—55 including: “…In embodiments, the modeling module 220 calculates a respective MxN matrix 405 for each of plural different cycles of the time series data collected by the sensors, where each individual cycle corresponds to a respective record…”
determining, using a first computer-based numerical modeling method, patterns based on the matrices; - math calculations/relationships in textual form. See ¶¶ 56-63: “In embodiments, the pattern determined by the modeling module 220 for a particular MxN matrix is a vector of coefficients B that satisfy Equation 2 for values included in the particular MxN matrix and for a target value y…. In this example, the modeling module 220 uses a first computer-based numerical modeling method to solve for a vector Beta[Bl,B2,B3,B4] (referred to herein as a Beta vector) that satisfies Equation 5 using the target value y and the values Xmn of the MxN matrix for this particular record… According to aspects of the invention, after determining the plural patterns (e.g., the Beta vectors)” – also see ¶ 68
predicting a future condition of the system using the machine learning model with current data of the system, the future condition of the system represented by a quantified state of the system - math calculations/relationships in textual form, but use a computer as a tool to do the calculations. See ¶ 63 :”… In embodiments, the modeling module 220 is programmed to use a second computer-based numerical modeling method to form a linear regression for the patterns and sensor data…ARMA… For example, after determining plural Beta vectors Bl, B2, B3, .. , BN in the manner described herein, the modeling module 220 then uses those plural Beta vectors with an ARMA model to create a time series model that predicts a future Beta vector B(N+ 1)…” – also see ¶ 69, then see ¶ 64: “In embodiments, the modeling module 220 uses the time series model to predict a Beta vector for a future cycle that has not yet happened, e.g., at time t(N+ 1). The modeling module 220 then uses the data contained in the MxN matrix for the current time period, e.g., at time t(N), with the predicted Beta vector to determine the target value y at time t(N+ 1). For example, the modeling module 220 may solve Equation 2 for y using the MxN matrix for time t(N) and the precited Beta vector for time t(N+ 1).” – also see ¶ 70
To clarify on what an “ARMA” model is, in view of MPEP § 2111.01(I and III) and MPEP § 2111.01(III), and in view of ¶¶ 63 and 69 of the instant disclosure which do not define this term, see Zhang, “Time Series: Autoregressive models AR, MA, ARMA, ARIMA”, Oct. 23rd, 2018, Lecture Notes for CS 3750 Advanced Topics in Machine Learning (ISSP 3535) at University of Pittsburgh, accessed via URL: people(dot)cs(dot)pitt(dot)edu/~milos/courses/cs3750/ - see slide 25 including: “Note that AR and MA are two widely used linear models that work on stationary time series, and I is a preprocessing procedure to “stationarize" time series if needed.” – the see slide 53 which provides the “Definition”, as reproduced below:
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wherein each matrix of the matrices is an MxN matrix where M is a number of the groups of the sensors and N is a number of dimensions of the multi-dimensional time series data, the number of dimensions being a number of different types of data collected by each one of the groups of the sensors. – merely further limiting the matrix dimensions for use in the math concept
As a point of clarity, to distinguish on a critical point from Thales Visionix, Inc. v. United States, 850 F.3d 1343, 1348-49, 121 USPQ2d 1898, 1902-03 (Fed. Cir. 2017) in MPEP § 2106.04(a)(2)(I), the Examiner notes that math herein is not tied to a particular configuration of sensors, but rather the math itself is what defines the arrangement, i.e. rather than claiming a particular arrangement of sensors wherein processing of the raw data from the sensors to provide the measurement result (Thales) just to complete the invention so as to provide the measurement result (“Thales Visionix, Inc. v. United States, 850 F.3d 1343, 121 USPQ2d 1898, 1902 (Fed. Cir. 2017) ("That a mathematical equation is required to complete the claimed method and system does not doom the claims to abstraction.")”; MPEP § 2106.04(a)), the present claims are applicable to any sensor configuration that meets the broad requirements of the claims (i.e. there is a plurality of sensors, grouped based on distance to each other).
In other words, to complete the math itself, it merely requires a broad range of sensor arrangements, as discussed in further detail below, for the mere data gathering necessary to perform the abstract idea (example 45, claim 3, prong 2, citing to MPEP § 2106.05(b)(III)), for post-measurement analysis of previously measured “data” as claimed. MPEP § 2106.05(g): “i. Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989);… vi. Determining the level of a biomarker in blood, Mayo, 566 U.S. at 79, 101 USPQ2d at 1968. See also PerkinElmer, Inc. v. Intema Ltd., 496 Fed. App'x 65, 73, 105 USPQ2d 1960, 1966 (Fed. Cir. 2012) (assessing or measuring data derived from an ultrasound scan, to be used in a diagnosis)”…
Or as summarized in MPEP § 2106.05(g): “Whether the limitation amounts to necessary data gathering and outputting, (i.e., all uses of the recited judicial exception require such data gathering or data output)”
See the prong 2 and 2B analysis below for more clarification on this point.
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:
creating matrices based on the multi-dimensional time series data; - a mental process, but for the mere instructions to do it on a computer. For example, a person may readily observe collected data, and then create matrices based on the observed data as a mental evaluation/judgement, e.g. by writing out the matrices using pen and paper in a tabular format, e.g. for a 2x2 matrix, creating a 2x2 (2 rows, 2 columns) table
determining, using a first computer-based numerical modeling method, patterns based on the matrices; - a mental process, but for the mere instructions to do it on a computer. For example, the person would readily be able to observe the matrices on paper, such as ones represented by tables on paper, and identifying by a mental observation/evaluation/judgments patterns based on the observed matrices
For example, the person, having mentally determined the pattern as discussed above, would have been able to mentally judge what the pattern is and mentally evaluate the pattern to create a time series model of the data, e.g. observing that it is a linear data pattern, and thus mentally judging to using a linear model for the data, e.g. y=mx+b (or, for a time series, x=mt+b, or other similar simple equations), and evaluate the observed data, such as with pen, paper, and/or a calculator, to fit the equation as a model to the data, thus creating the model.
predicting a future condition of the system using the machine learning model with current data of the system, the future condition of the system represented by a quantified state of the system. - a mental process, but for the mere instructions to do it on a computer, e.g. For example, the person, having mentally determined the pattern as discussed above, would have been able to mentally judge what the pattern is and mentally evaluate the pattern to create a time series model of the data, e.g. observing that it is a linear data pattern, and thus mentally judging to using a linear model for the data, e.g. y=mx+b (or, for a time series, x=mt+b, or other similar simple equations), and evaluate the observed data, such as with pen, paper, and/or a calculator, to fit the equation as a model to the data, thus creating the model, then performs simple calculations as mental evaluations, e.g. suppose the equation is x=mt+b, and m and b are known (e.g., 1 and 1), and the values of x and t are known for t = 0 to 1 second, then to predict x at t = 1.5 second, one would simply evaluate this equation, such as with a calculation, for t= 1.5, i.e. x = 1*1.5+1 = 2.5.
Furthermore, in view of the time series model as disclosed including, as per ¶ 63: “linear regression”, the Examiner notes that such models predate the invention of a computer. To further clarify on this point (MPEP § 2111.01(I and III)), first see example 45 of the Oct. 2019 PEG Appendix 1, page 21, ¶ 4: “This limitation also falls into the “mental process” group of abstract ideas, because the recited mathematical calculation is simple enough that it can be practically performed in the human mind, e.g., scientists and engineers have been solving the Arrhenius equation in their minds since it was first proposed in 1889.” – then see:
Howarth, Richard J. "A history of regression and related model-fitting in the earth sciences (1636?-2000)." Natural Resources Research 10 (2001): 241-286. Abstract and introduction which describe this is a work reviewing the history of regression fitting, then see page 245, col. 2, ¶¶ 2-3 which describe examples in the 1700’s of “fitting a linear equation…”, then see the section “The Coming of ‘Least Squares’” starting on page 246, which describe how Gauss in the early 1800’s developed a regressing fitting technique “using the principle of minimization of the sum of squared residuals…”, along with a contemporaneous discovery by Legendre in 1805, and a second contemporaneous discovery in 1808 by Robert Adrian, wherein fig. 2 provides a visual comparison of these different fitting methods for a linear fitting to data, and see table 2b which provides examples of the “Parameters for Linear Models…Fitted to data of Table 2A by Laplace (1799) and by Least Squares” including the use of “OLS” [ordinary least squares] by “Adrian, 1818”. In addition, see the section “Least-Squares Gains Ground” on page 254, including ¶ 2: “The principle of least squares is probably most familiar to modern earth scientists in the guise of fitting a ‘regression’ equation modeling the behavior of a dependent variate (y) as a function of a set of one or more predictors (X). The term regression was coined originally by the innovative English gentleman scientist and an ‘ingenious amateur’ statistician (Hald, 1998, p. 599; Forrest, 1974) Sir Francis Galton (1822–1911; Kt. 1909) in 1877.” and including col. 2 on page 254, ¶¶ 1-3 including: “…Yule (1897) then demonstrated that, even if the error distribution for y is nonnormal, if the goal is to predict the average value of the dependent variate as a linear function of X, this is achieved by minimization of the overall sum of squared residuals between the observed values of y and those predicted by the regression equation…[see equation] In other words, the familiar least-squares criterion. Furthermore, the estimated values of the unknown regression coefficients ¯0 and ¯1 are given by solving the system of ‘normal equations’ (Legendre, 1805; Gauss, 1809). Yule’s results freed regression analysis from its association with Galton’s work on inherited characteristics, and provided a powerful new tool for both statistical modeling and prediction which was taken up by scientists in all fields.”, see page 255, col. 2, ¶ 2 and the section “The Computer Revolution” which describes the history of computers being used as a tool to implement regression fitting
Jeffrey M. Stanton (2001) Galton, Pearson, and the Peas: A Brief History of Linear Regression for Statistics Instructors, Journal of Statistics Education, 9:3. § 2: “Besides his role as a colleague of Galton's and a researcher in Galton's laboratory, Karl Pearson also became Galton's biographer after the latter's death in 1911 (Pearson 1922). In his four-volume biography of Galton, Pearson described the genesis of the discovery of the regression slope (Pearson 1930). In 1875, Galton had distributed packets of sweet pea seeds to seven friends; each friend received seeds of uniform weight (also see Galton 1894), but there was substantial variation across different packets. Galton's friends harvested seeds from the new generations of plants and returned them to him (see Appendix A). Galton plotted the weights of the daughter seeds against the weights of the mother seeds. Galton realized that the median weights of daughter seeds from a particular size of mother seed approximately described a straight line with positive slope less than 1.0: "Thus he naturally reached a straight regression line, and the constant variability for all arrays of one character for a given character of a second. It was, perhaps, best for the progress of the correlational calculus that this simple special case should be promulgated first; it is so easily grasped by the beginner." (Pearson 1930, p. 5)…” – see the remaining parts of this section, including the figures, for additional clarification, also see § 3, then § 4 including: “In 1896, Pearson published his first rigorous treatment of correlation and regression in the Philosophical Transactions of the Royal Society of London . In this paper, Pearson credited Bravais (1846) with ascertaining the initial mathematical formulae for correlation” and § 6: “The simulated sweet pea data from Figure 1 illustrate the basic concepts of plotting data in columns, regression to the mean, and hand fitting a line to the data using the means of the columns.”
and determining whether the quantified state of the system satisfies a threshold value, - a mental process, e.g. a person comparing mentally two numbers, e.g. is 5 greater than 4?, and providing a result of such a simple mental judgement.
wherein each matrix of the matrices is an MxN matrix where M is a number of the groups of the sensors and N is a number of dimensions of the multi-dimensional time series data, the number of dimensions being a number of different types of data collected by each one of the groups of the sensors. – merely further limiting the mental process. To clarify, the claim places no restriction on what the values of M and N are, e.g. let M=4, and N=2, and a person would readily be able to mentally perform the abstract idea as discussed above. See fig. 4 and ¶ 47 to clarify on this, and see the discussion Gottschalk v. Benson, 409 U.S. 63, 65, 175 USPQ2d 673, 674 (1972) and Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1139, 120 USPQ2d 1473, 1474 (Fed. Cir. 2016) in MPEP § 2106.04(a)(2)(III) to further clarify
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 1 – the preamble; claim 11 - A computer program product comprising one or more computer readable storage media having program instructions collectively stored on the one or more computer readable storage media, the program instructions executable to; claim 16 - A system comprising: a processor, a computer readable memory, one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable to:
… using a first computer-based numerical modeling method… using a second computer-based numerical modeling method… - part of the mere instructions to use a computer, and generic computer components, as a tool to implement the abstract idea, as well as mere instructions to apply the abstract idea on a computer. To clarify on the BRI, ¶¶ 68-69: “by solving Equation 2 using a first computer-based numerical modeling method such as a least square method…using a second computer-based numerical modeling method such as an ARMA model, for example.”
The following limitations are generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h):
… using a first computer-based numerical modeling method… using a second computer-based numerical modeling method… - generally linking to the technological environment of computers
The following limitations are adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g):
obtaining multi-dimensional time series data from sensors that collect the multi- dimensional time series data in a system during a time, wherein the sensors are arranged in groups of sensors defined according to their relative distances to respective ones of center points in the groups of sensors, and an individual sensor in an individual group of sensors is physically closer to a respective center point of the respective individual group of sensors where the individual sensor belongs than the respective center point of any other of the groups of sensors;
The “training…” step, and the “using the machine learning model” limitation, are considered under a similar rationale as example 47, claim 2, for its recitation of “using the trained ANN” in (d-e) limitations, i.e. mere instructions to do it on a computer, mere instructions to generally “apply it” without placing any restrictions on how it operates, and generally linking to the field of use of machine learning.
In addition, in view of footnote 17 of the August 2025 memorandum, “Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025) (steps incidental to automating an abstract idea were not sufficient to confer eligibility”; and Ex parte Desjardins, the Examiner notes that training is incidental to the act of using a trained model, and MPEP § 2106.05(g) defines extra-solution activity to be incident. Therefore, it’s an insignificant extra-solution activity as part of the steps incidental to automating the abstract idea itself.
As a point of clarity, the specification describes no improvement to the technology of machine learning itself. ¶ 22: “using a machine learning model and a data transformation method based on the pattern to predict the trend” is the only mention of it. Thus, it is not an improvement to machine learning technology, e.g. neural networks and similar such software algorithms (Ex parte Desjardins), but rather this claim, and disclosure, merely invoke it as part of using a computer as a tool to perform the abstract idea (Recentive Analytics).
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).
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 1 – the preamble; claim 11 - A computer program product comprising one or more computer readable storage media having program instructions collectively stored on the one or more computer readable storage media, the program instructions executable to; claim 16 - A system comprising: a processor, a computer readable memory, one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable to:
… using a first computer-based numerical modeling method… using a second computer-based numerical modeling method… - part of the mere instructions to use a computer, and generic computer components, as a tool to implement the abstract idea, as well as mere instructions to apply the abstract idea on a computer. To clarify on the BRI, ¶¶ 68-69: “by solving Equation 2 using a first computer-based numerical modeling method such as a least square method…using a second computer-based numerical modeling method such as an ARMA model, for example.”
The following limitations are generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h):
… using a first computer-based numerical modeling method… using a second computer-based numerical modeling method… - generally linking to the technological environment of computers
The following limitations are adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g):
obtaining multi-dimensional time series data from sensors that collect the multi- dimensional time series data in a system during a time, wherein the sensors are arranged in groups of sensors defined according to their relative distances to respective ones of center points in the groups of sensors, and an individual sensor in an individual group of sensors is physically closer to a respective center point of the respective individual group of sensors where the individual sensor belongs than the respective center point of any other of the groups of sensors;
The “training…” step, and the “using the machine learning model” limitation, are considered under a similar rationale as example 47, claim 2, for its recitation of “using the trained ANN” in (d-e) limitations, i.e. mere instructions to do it on a computer, mere instructions to generally “apply it” without placing any restrictions on how it operates, and generally linking to the field of use of machine learning.
In addition, in view of footnote 17 of the August 2025 memorandum, “Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025) (steps incidental to automating an abstract idea were not sufficient to confer eligibility”; and Ex parte Desjardins, the Examiner notes that training is incidental to the act of using a trained model, and MPEP § 2106.05(g) defines extra-solution activity to be incident. Therefore, it’s an insignificant extra-solution activity as part of the steps incidental to automating the abstract idea itself.
As a point of clarity, the specification describes no improvement to the technology of machine learning itself. ¶ 22: “using a machine learning model and a data transformation method based on the pattern to predict the trend” is the only mention of it. Thus, it is not an improvement to machine learning technology, e.g. neural networks and similar such software algorithms (Ex parte Desjardins), but rather this claim, and disclosure, merely invoke it as part of using a computer as a tool to perform the abstract idea (Recentive Analytics).
Further, such a generic recitation of machine learning in the specification ¶ 22 is evidence that it is WURC, as the specification preferably omitted any details well-known to POSITA.
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).”
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.”
For additional evidence see the previously cited evidence of record, e.g.:
THIYAGARAJAN, “Sensor Failure Detection and Faulty Data Accommodation Approach for Instrumented Wastewater Infrastructures”, 2018, see § II ¶¶ 1-2, incl.: “Computational modeling using artificial neural networks is widely used for detecting sensor failures mainly because of its adaptability to dynamic environments [19], [20]. This method works by training the neurons and developing a structure based on the training data for comparing with the sensor measurements to detect the sensor failure [21].”
Gajowniczek et al., “Whole Time Series Data Streams Clustering: Dynamic Profiling of the Electricity Consumption”, 2020, page 2, ¶ 2.
Yun et al., “Deployment Support for Sensor Networks in Indoor Climate Monitoring”, 2013, § 2 ¶ 1
Yang et al., “Temporal Data Clustering via Weighted Clustering Ensemble with Different Representations”, 2011, § 1 ¶ 2.
Zhang et al., “Multi-Dimensional Joint Prediction Model forIoT Sensor Data Search”, 2019, § 1 ¶ 2: “Prediction models can be used to sketch the future readings of the sensors, and analyze data streams to infer patterns and correlations. However, the traditional sensor-data prediction models often use the auto-regressive moving average model (ARMA) and the auto-regressive integrated moving average (ARIMA) model [7] only based on the historical data
time series of the target sensor to predict the time series of the sensor data. As machine learning methods grow popular gradually, more researchers focused on the studies to establish nonlinear prediction model based on a large scale of historical data. Typical models such as the support vector regression”
In addition, the following are also considered as well-understood, routine, and conventional activities, as discussed in MPEP § 2106.05(d):
obtaining multi-dimensional time series data from sensors that collect the multi- dimensional time series data in a system during a time, wherein the sensors are arranged in groups of sensors defined according to their relative distances to respective ones of center points in the groups of sensors, and an individual sensor in an individual group of sensors is physically closer to a respective center point of the respective individual group of sensors where the individual sensor belongs than the respective center point of any other of the groups of sensors;
¶¶ 1-3 of the instant disclosure
Heng, Li, et al. "Multi-Steps Weighted ARMA Identification Algorithm for the Multi-sensors System with Unknown Parameters." Proceedings of the 2nd International Conference on Algorithms, Computing and Systems. 2018. Abstract including: “…our algorithm which differs from the conventional 2-steps Algorithm…” then see § 1: “Multi-sensors information fusion refers to the synthesis of data and information from multiple sensors to obtain more detailed and accurate reasoning than a single sensor. AR model is an important model in the field of state estimation. State equation can be transformed into ARMA model by mathematical method, and ARMA could be approximated by mathematical method, that is the reason AR model is used to describe the stationary time series in engineering… here we call it “2-steps identification method”: the first step is to get the local estimates of the unknown parameters by using the least squares method, to get the fusion estimates by using the arithmetic mean of the local estimates; the second step is to get the linear functions by correlation function…”
Sharma, A. B., Golubchik, L., and Govindan, R. 2010. Sensor faults: Detection methods and prevalence in real-world datasets. ACM Trans. Sensor Netw. 6, 3, Article 23 (June 2010), 39 pages. § 1: “With the maturation of sensor network software, we are increasingly seeing longer-term deployments of wireless sensor networks in real mode settings. As a result, research attention is now turning towards drawing meaningful scientific inferences from the collected data [Tolle et al. 2005].
Sridhar, Prasanna, Asad M. Madni, and Mo Jamshidi. "Hierarchical data aggregation in spatially correlated distributed sensor networks." 2006 World Automation Congress. IEEE, 2006. § 1.1 ¶¶ 1-2
Cormode, Graham, Srikanta Tirthapura, and Bojian Xu. "Time-decaying sketches for robust aggregation of sensor data." SIAM Journal on Computing 39.4 (2010): 1309-1339. § 1 ¶¶ 1-2
Alberg, Dima, and Mark Last. "Short-term load forecasting in smart meters with sliding window-based ARIMA algorithms." Vietnam Journal of Computer Science 5 (2018): 241-249. Abstract, then see § 1 ¶¶ 1-2, then see § 2 ¶¶ 2-3, and then see §3.2 ¶¶ 1-2
To clarify on the sensors, in view of Thales (MPEP § 2106.04(a)(2)(I) and MPEP § 2106.05(a)) as well as Diamond v. Diehr (see example 45, claim 3; also MPEP § 2106.05(a)(II)), the Examiner notes that the claim does not recite any particular unconventional arrangement of sensors, but rather conveys well-known arrangements of sensors.
To clarify, it merely requires sensors to surround a “user-defined” center point (¶ 48, incl.: “The center points CP1, CP2, CP3, CP4 may be defined as corresponding to certain devices in the environment, such as air conditioners, for example.”
In the field of use (¶¶ 47-48) for an HVAC unit with temperature and humidity sensors, such arrangements are WURC:
Yoganathan, Duwaraka, et al. "Optimal sensor placement strategy for office buildings using clustering algorithms." Energy and buildings 158 (2018): 1206-1225. § 1 ¶¶ 1-2, then see § 2.1¶ 1, then see § 2.2 ¶¶ 1-2 and fig. 1, then see § 3: “Typically, a large number of sensors are installed in buildings today. This facilitates obtaining a complete picture of the indoor conditions. The floorplan of the case study office area is presented in Fig. 4. Various sensors such as TelosB4to measure indoor parameters luminance, relative humidity and temperature, Dlink SmartPlugs5to measure plug load energy consumption, and PPM IAQmonitor6to measure indoor air quality parameters such as CO2, formaldehyde and total volatile organic compounds are instrumented in the target office area” – also, see fig. 17-18 and its accompanying description, as well as fig. 23-24.
Yang, Chen, et al. "Sensor placement algorithm for structural health monitoring with redundancy elimination model based on sub-clustering strategy." Mechanical Systems and Signal Processing 124 (2019): 369-387. Abstract, § 1 ¶¶ 1-3, then see § 3.3 including fig. 1
Hong, Dezhi, et al. "Towards automatic spatial verification of sensor placement in buildings." Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings. 2013. Abstract, then see § 1 ¶¶ 1-2 including: “Typically, placement information is embedded in the name or associated metadata for each sensor in the building. These are used to group sensors by location. For example, in our building data, all sensors that contain the string `410' in their name are in room 410. Processes typically group streams in this fashion: using regular-expression matching or field-matching queries on the characters in the sensor name or metadata. If these are not updated to reflect changes then such group-by query results will not accurately represent true spatial relationships.”- then see ¶ 3 of § 1. Also, see § 4.1 ¶ 1 including fig. 3, and see § 4.3 ¶ 2, then see § 4.5 ¶¶ 1 -2: “To compare with our threshold-based method, we also cluster using a baseline approach. The pairwise corrcoeff for sensors in different rooms can be interpreted as a distance" between them. A larger coefficient indicates a closer \distance", and vice versa….”
Lee, Sang-yeon, et al. "Optimal sensor placement for monitoring and controlling greenhouse internal environments." Biosystems Engineering 188 (2019): 190-206. Abstract, then see § 1 ¶ 2: “Proper environmental control of the internal condition including the air temperature, humidity, and CO2 concentration in a greenhouse are necessary to affect the growth, productivity, and quality of crops. In a large greenhouse such as an information and communication technology (ICT)-applied greenhouse or smart farm, the internal conditions are monitored using sensors to control the greenhouse environment via greenhouse actuators, such as air conditioners, circulation fans, and heat pumps. For measuring the environment inside a greenhouse, different sensors are installed and used by growers and researchers. However, growers generally install a limited number of sensors, owing to economic limitations and management challenges. Typically, the sensor locations are decided according to the experience of growers and greenhouse designers. The centre location of facilities has been generally considered as the representative location for monitoring environmental factors such as the air temperature and humidity (Feng, Li, & Zhi, 2013). However, there is uncertainty regarding whether the environment measured at the centre of facilities properly represents the entire environment inside greenhouses. Therefore, it is necessary to select optimal installation locations for the limited number of sensors to accurately monitor the internal environments of large greenhouses.”
Northeast Document Conservation Center, “Monitoring Temperature and Relative Humidity”, Preservation Leaflet, URL: www(dot)nedcc(dot)org/free-resources/preservation-leaflets/2(dot)-the-environment/2(dot)2-monitoring-temperature-and-relative-humidity, accessed via WayBack Machine with archive date of Sept. 2020, see the section “monitoring the Environment”: “Computerized building management systems (BMS) are often used by facilities staff to monitor climate conditions and control HVAC equipment in large buildings or groups of buildings. While these systems can be used to provide temperature and relative humidity data for analysis, there are a few important considerations: The system’s sensors must be recalibrated periodically to ensure accuracy. Sensors must be located properly to ensure that they reflect the climate conditions the collection is experiencing. Some sensors should be located in return air ducts to measure air from the controlled space. The computerized management system must contain correct locations for the sensors.” – then, see the “Map showing placement options for monitors”, and the section “Placement of Monitoring Equipment” for its accompanying description.
Rogers, “Where should I Mount a Wall, Duct or Outside Air Relative Humidity Sensor?”, Sept. 1st, 2016, Setra blog posting, URL: www(dot)setra(dot)com/blog/mounting-installation-guidelines-hr-transmitters/2013/01/16 which discusses placement location of humidity sensors for the different types of humidity sensors commercially available in 2016.
World Health Organiziation, “Temperature and humidity monitoring systems for fixed storage areas”, “Technical supplement to WHO Technical Report Series, No. 961, 2011 Annex 9: Model guidance for the storage and transport of time- and temperature-sensitive pharmaceutical products”, May 2015, URL: cdn(dot)who(dot)int/media/docs/default-source/medicines/norms-and-standards/guidelines/distribution/trs961-annex9-supp6(dot)pdf?sfvrsn=f7bf9011_2 – see §§ 1.1.1-1.1.2, then § 2.3 , then § 2.3.2, then § 2.3.3, also see § 2.3.4, then see § 2.3.5 including: “A typical monitoring system consists of a network of sensors which are linked together to form an integrated electronic temperature and event logger system.” – e.g. see the three types on pages 13-14, note there are multiple sensors in the left-hand rooms wherein there are multiple different types of sensors (page 15, first bullet point to clarify on this). See § 2.5.1 which clarifies on the “Number of monitoring points”, as § 2.5.2 for the location of these points.
Li, Bing, et al. "Optimal sensor placement using data-driven sparse learning method with application to pattern classification of hypersonic inlet." Mechanical Systems and Signal Processing 147 (2021): 107110. § 1 ¶¶ 1-3
Arnesano, Marco, Gian Marco Revel, and Federico Seri. "A tool for the optimal sensor placement to optimize temperature monitoring in large sports spaces." Automation in Construction 68 (2016): 223-234. Abstract and § 1 ¶ 1
Fu, Yong, et al. "Thermal modeling for a HVAC controlled real-life auditorium." 2014 IEEE 34th International Conference on Distributed Computing Systems. IEEE, 2014. Abstract, then see § 1 ¶ 2, then § III.B
Hayat, Hasan, et al. "The state-of-the-art of sensors and environmental monitoring technologies in buildings." Sensors 19.17 (2019): 3648. Fig 1(a) and page 3, ¶ 1, then see § 2 and table 1, see §§ 2.1 and 2.3 to further clarify.
Yun, Jaeseok, and Jaeho Kim. "Deployment support for sensor networks in indoor climate monitoring." International Journal of Distributed Sensor Networks 9.9 (2013): 875802. Abstract, thewn see page 2, col. 1, ¶¶ 1 -2
… using a first computer-based numerical modeling method… using a second computer-based numerical modeling method… - see:
Guclu, Adem, et al. "Prognostics with Autoregressive Moving Average for Railway Turnouts." Annual Conference of the PHM Society. Vol. 2. No. 1. 2010. § 3: “Autoregressive moving average (ARMA) models are used in time series analysis to describe time series data… Least squares regression is used in training to find the best values of the parameters that minimize the error term…”
Heng, Li, et al. "Multi-Steps Weighted ARMA Identification Algorithm for the Multi-sensors System with Unknown Parameters." Proceedings of the 2nd International Conference on Algorithms, Computing and Systems. 2018. Abstract including: “…our algorithm which differs from the conventional 2-steps Algorithm…” then see § 1: “Multi-sensors information fusion refers to the synthesis of data and information from multiple sensors to obtain more detailed and accurate reasoning than a single sensor. AR model is an important model in the field of state estimation. State equation can be transformed into ARMA model by mathematical method, and ARMA could be approximated by mathematical method, that is the reason AR model is used to describe the stationary time series in engineering… here we call it “2-steps identification method”: the first step is to get the local estimates of the unknown parameters by using the least squares method, to get the fusion estimates by using the arithmetic mean of the local estimates; the second step is to get the linear functions by correlation function…”
Wang, Kedong, Shaofeng Xiong, and Yong Li. "Modeling with noises for inertial sensors." Proceedings of the 2012 IEEE/ION Position, Location and Navigation Symposium. IEEE, 2012. § 1: “…ARMA (auto-regressive moving average) is one of the popular methods to model sensor's colored noise for stationary time sequence [1-4]….
Betros, Robert S. "ARMA models for real-time system identification of smart structures." First European Conference on Smart Structures and Materials. Vol. 1777. SPIE, 1992. Abstract, then see § 2: “…More simple algorithms like least squares ARMA and Kalman filters appear to have been overlooked in recent work for their simplicity…. The least squares ARMA algorithm has shown quick convergence, stability, and accuracy in identifying slowly time varying structural transfer functions. The simplicity of the algorithm makes real -time implementation possible on existing DSP chips…” – and see § 3
Howarth, Richard J. "A history of regression and related model-fitting in the earth sciences (1636?-2000)." Natural Resources Research 10 (2001): 241-286 as was discussed above
Sharma, A. B., Golubchik, L., and Govindan, R. 2010. Sensor faults: Detection methods and prevalence in real-world datasets. ACM Trans. Sensor Netw. 6, 3, Article 23 (June 2010), 39 pages. § 1, then see § 3.3 ¶¶ 1-2
Kapetanios, George. "A note on an iterative least-squares estimation method for ARMA and VARMA models." Economics Letters 79.3 (2003): 305-312. § 1: “The class of univariate and multivariate ARMA models is a flexible and powerful modelling tool applicable in a variety of situations that has appealing theoretical properties”
Alberg, Dima, and Mark Last. "Short-term load forecasting in smart meters with sliding window-based ARIMA algorithms." Vietnam Journal of Computer Science 5 (2018): 241-249. Abstract, then see § 1 ¶¶ 1-2, then see § 2 ¶¶ 2-3, and then see §3.2 ¶¶ 1-2
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 3 is a math calculation in textual form using a math operation in textual form of a “weighted average”, wherein this is recited with enough generality that a person would readily be able to do the calculation as a mental evaluation, e.g. a weighted average of 3 data points from 3 sensors (¶ 50), wherein physical aids such as a calculator may be used to aid the mental evaluation, e.g. the person observes the data from multiple sensors such as on a print-out in a table, manually plots it on graph paper in a 2D plot as a cluster of data points with pen and paper, and uses a ruler and/or the graph paper to observe/judge the distance to a center point of the cluster of data points (e.g. 3 data points, e.g. the center of a triangle formed by these 3 data points).
Claim 4 is considered as math calculations/equations/relationships in textual form, when read in view of ¶ 52, wherein claim 4 is recited with enough generality that a person would readily be able to perform this as a mental evaluation with physical aids. Also, see ¶ 48: “user-defined center points”, and to clarify on clustering being a mental step, when recited at this level of generality, see page 7 of the July 2024 Subject Matter Eligibility Examples: “Specifically, step (b) recites discretizing continuous training data to generate input data by processes including rounding, binning, or clustering continuous data, which may be practically performed in the human mind using observation, evaluation, judgment, and opinion”
Claim 5 recites a mental step of “defining a number of windows…” – e.g. a person mentally observing that the collected data represented 2 minutes of time readings, and thus mentally judges to define 4 windows of time, each representing 30 seconds of the 2 minutes. The claim then recites a math calculation/relationship in textual form of: “determining a respective vector of coefficients for each one of the windows…” – see ¶¶ 56-57 to clarify on the BRI, wherein this is recited with enough generality that a person may readily perform a mental evaluation of the math to perform this step such as with the use of physical aids, e.g. a calculator, and/or pen and paper, but for the mere instructions to do it on a computer and/or apply the abstract idea on a computer. Should it be found that the “defining a number of windows…” is not part of the mental process, then the Examiner submits that this would be an insignificant extra-solution activity of mere data gathering for the later calculation, wherein this is the WURC use of windows in time series analysis as evidenced by:
Gajowniczek, Krzysztof, Marcin Bator, and Tomasz Ząbkowski. "Whole time series data streams clustering: dynamic profiling of the electricity consumption." Entropy 22.12 (2020): 1414. Abstract: “Time series data streams have their own specifics in terms of the data processing and data analysis because, usually, it is not possible to process the whole data in memory as the large data volumes are generated fast so the processing and the analysis should be done incrementally using sliding windows.” – see § 1 ¶¶ 2-3
Li, Bo. Multi-dimensional data stream compression for embedded systems. Diss. Concordia University, 2019. Abstract, § 1.1, then see § 2.1: “Moreover, many summarization methods apply Sliding window [13] technique which maintains a window that moves with new data coming. It ensures that the methods always use fresh data for analysis and statistics by keeping the most recent items of the stream or all items within a specific time period in given bound memory [26].”
Zhang, “Time Series: Autoregressive models AR, MA, ARMA, ARIMA”, Oct. 23rd, 2018, Lecture Notes for CS 3750 Advanced Topics in Machine Learning (ISSP 3535) at University of Pittsburgh, accessed via URL: people(dot)cs(dot)pitt(dot)edu/~milos/courses/cs3750/ - slide 63: “Rolling statistics with sliding window of 12 months”, and slide 26: “The size of the moving average window”
Alberg, Dima, and Mark Last. "Short-term load forecasting in smart meters with sliding window-based ARIMA algorithms." Vietnam Journal of Computer Science 5 (2018): 241-249, page 1613, col. 1, ¶ 2
Cormode, Graham, Srikanta Tirthapura, and Bojian Xu. "Time-decaying sketches for robust aggregation of sensor data." SIAM Journal on Computing 39.4 (2010): 1309-1339. Page 1311, ¶ 4, also page 1314 ¶¶ 2-3 and page 1315 ¶¶ 1-3
Wang, Xiaofeng, Zhenjie Zhu, and Guoliang Lu. "Multiple regression analysis for change detection in multi-sensory monitoring data with application to induction motor speed condition monitoring." Measurement Science and Technology 31.9 (2020): 095103 – see eq. 6 and its description
Claim 6 is considered as math calculations in textual form (see ¶¶ 58-59) recited with such generality that a person would readily be able to mentally evaluate the math
Claim 7 is rejected under a similar rationale
Claim 8 is rejected under a similar rationale as the features of “computer-based numerical modeling” as discussed above for claim 1, i.e. mere instructions to use a computer as a tool to implement the abstract idea, mere instructions to apply the abstract idea on a computer, and generally linking to the technological environment of computers, wherein this is also considered WURC in view of the evidence discussed above for claim 1.
Claim 9 is considered as a mental step of a mental judgement in view of ¶ 20: “operator of the environment may adjust one or more system controls” and ¶ 71, but for the mere instructions to use a computer as a tool to implement this step, and should it be found that this is not a mental step then claim 9 is considered as mere instructions to “apply it” as this is a results-oriented limitation with no restriction on how the adjusting is performed and no restriction on how the predicted future condition is used to perform the adjusting, wherein there is also no particularity in what the system is; wherein this would also be considered as an insignificant post-solution activity of an insignificant application that is WURC in view of:
Brisette et al., US 2018/0180314, ¶¶ 2-7
Nabi et al. US 2022/0316736 ¶¶ 2-7
Danielson US 2019/0032940 ¶¶ 2-5
Claim 10 is adding additional steps to both the math concept and the mental process, akin to the predicting step in claim 1 and rejected under a similar rationale as discussed above
Claim 12 is rejected under a similar rationale as claims 3-4 as discussed above
Claim 13 is rejected under a similar rationale as claim 5 above
Claim 14 is rejected under a similar rationale as claim 6-7 above
Claim 15 is rejected under a similar rationale as claim 9 above
Claim 17 is rejected under a similar rationale as claims 3-4 as discussed above
Claim 18 is rejected under a similar rationale as claim 5 above
Claim 19 is rejected under a similar rationale as claim 6-7 above
Claim 20 is rejected under a similar rationale as claim 9 above
Claim 22 is rejected as akin to the act of cutting hair with scissors in In re Brown as cited in MPEP § 2106.05(f and g) as both mere instructions to “apply it” as well as an insignificant application of the abstract idea itself. To further clarify, ¶ 20: “the operator of the environment may adjust one or more system controls (e.g., adjust a temperature of the system) cooling system to lower a to avoid the predicted undesirable future condition.” And ¶ 45: “The system controls 230 are controls that affect the operation of the system in which the sensors 205a-n are arranged. In the example of the manufacturing environment where the sensors 205a-n collect temperature and humidity data, the system controls 230 may be used to control a heating, ventilation, and air conditioning (HV AC) system that controls the temperature and humidity in the manufacturing environment. In embodiments, the system controls 230 are controlled by computer such as the computing device 210 and/or another computer in the system.” – thus, it is also considered as mere instructions to automate a mental judgement, in particular note the “operator” in ¶ 20, i.e. a person, as compared to the optional computer in ¶ 45 (note it says on “In embodiments”, i.e. in some embodiments
Also, this is WURC, given (1) the generic nature of the descprition of the HVAC system and how this control is suppose to be indicates it is well-known to POSITA by preferable omission of well-known details for how to do this; and (2) see:
Arnesano et al., “A tool for the optimal sensor placement to optimize temperature monitoring in large sports spaces” 2016 § 1 ¶ 1, then see ¶ 2: “The data acquired from the field are used by control and optimization modules to increase the facility efficiency in terms of thermal comfort and energy consumption” , and the next paragraph: “Considering that [8] pointed out that the air temperature is one of the most influential variable on the comfort evaluation inside sports environments and considering the literature review reported in the previous paragraphs, the level of accuracy of the air temperature measure is a crucial task to address a fine climate control operated by HVAC systems, leading to objectives such as energy efficiency and maximization of the comfort level, as done by the BMS developed in SportE2”
Yun et al., “Deployment Support for Sensor Networks in Indoor Climate Monitoring”, § 1 ¶ 2: “Since smart energy systems have become a prime target for energy savings and occupant comfort, indoor climate monitoring based on wireless sensor networks (WSNs) have been widely employed in attempts to collect various parameters from buildings, including temperature, humidity, CO2, light, and occupancy. These signals could be used to analyze the building environment condition and infer the occupant’s comfort level and finally control electric outlets, HVAC system, and lighting in order to improve building energy efficiency while preserving the occupant’s comfort level”, § 2, incl: “Sensor and actuator technologies based on ubiquitous computing and WSNs have been employed in attempts to implement responsive environments. The office at Xerox PARC is one of the examples of such responsive environments, where electric outlets, HVAC systems, and lightings were automatically controlled in response to the occupants’ preferences [4].”
Northeast Document Conservation Center, “Monitoring Temperature and Relative Humidity”, Preservation Leaflet, URL: www(dot)nedcc(dot)org/free-resources/preservation-leaflets/2(dot)-the-environment/2(dot)2-monitoring-temperature-and-relative-humidity, accessed via WayBack Machine with archive date of Sept. 2020, section “EVALUATING CLIMATE AND COLLECTION NEEDS”
Brisette et al., US 2018/0180314, ¶¶ 2-7
Danielson, US 2019/0032940, ¶¶ 2-5, in particular ¶ 4
Nabi, US 2022/0316736, ¶¶ 2-3
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 § 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) 1, 3-5, 8-13, 15-18, 20, 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang, Xiaofeng, Zhenjie Zhu, and Guoliang Lu. "Multiple regression analysis for change detection in multi-sensory monitoring data with application to induction motor speed condition monitoring." Measurement Science and Technology 31.9 (2020): 095103 In view of Sridhar, Prasanna, Asad M. Madni, and Mo Jamshidi. "Hierarchical data aggregation in spatially correlated distributed sensor networks." 2006 World Automation Congress. IEEE, 2006.
Regarding Claim 1
Wang teaches:
A computer-implemented method, comprising: (Wang, abstract and § 6)
obtaining multi-dimensional time series data from sensors that collect the multi-dimensional time series data in a system during a time (Wang, abstract, including: “…This paper presents a novel framework, based on multiple regression analysis (MRA), for the on-line monitoring of induction motors in order to detect changes in motor rotational speed during the course of successive operations. To utilize the synergistic information of multiple sensors, a prediction model is established based on MRA…” – to clarify, see fig. 1, as discussed in part in § 2.1.1: “Let us assume that X1:j is the collected multidimensional condition data up to inspection time j, i.e. X1:j = [x11 :j; x21 :j; · · · , xi 1:j] ′ , where i is the number of sensors; [·] ′ stands for the transposition operation. Each dimension, e.g. xi 1:j = [xi 1, xi 2, xi 3, · · · , xij ] where xij is the observation value of ith sensor at time j, may be expresed as a periodic form, considering the symmetry of physical structure of motor machinery [37]….” – and see equation 1 and its accompanying description including: “where xn,v contains observations of vth phase in n+1th cycle sensed by a measurement system consisting of the number of i sensors,”
creating matrices based on the multi-dimensional time series data; (Wang, abstract and § 2.1.1 as discussed above, and see the last paragraph in § 2.1.1: “Moreover, in order to fully utilize the advantage of LASSO, a sliding-window strategy was adopted for model training [42], thus…” – and see the equation, i.e. the “sliding window” creates matrices based on the data
to be specific, see the equation 6, then see equations 1-3, then see § 2.1.2 ¶ 1, then see fig. 2 and its accompanying description, i.e. in equation 1, there is a matrix representing the data from each of “i sensors” from time 0 to time “nT+v”, wherein the “sliding-window strategy” in § 2.1.1 creates matrices from this matrix of smaller windows of time, as visible depicted in fig. 2 and its accompanying description (include seeing table 1, e.g. “The window including first 5 cycles are adopted for model training;”
in addition, in § 2.1.2, eq. 7, there would have been matrices for each sensors data for the time periods, e.g. matrices created for “0” to “nT+v” to “c”, and additional matrices for “c” to “nT+v”
determining, using a first computer-based numerical modeling method, patterns based on the matrices; (Wang, as discussed above including the abstract and § 2.1.1-2.1.2, include seeing eq. 3 -4: “Meanwhile, in order to utilize the synergistic information existing in multi-sensory data, a novel prediction strategy is presented, as shown in figure 1, and the prediction model is established by the extension of the multivariate linear model, thus where the element number of each coefficient vector b is equal to the number of sensors, i.e. b = [b1, b2, · · · , bi]′ , but where their specific values are unequal for different dimensions… On the basis of several completed cycles, the coefficients of the different rows may be estimated by minimizing a costing function in the model training process, by means of the well - known least square regression (LRS) method:…”
to clarify, then see § 2.1.2 eq. 7: “By virtue of the established MRA model, an appropriate statistical metric should be adopted to quantify temporal anomalies for decision making [21]. When the motor condition [each motor condition being an example of a pattern] changes at time c, the generation mechanism of the data changes accordingly, which means the coefficients of trained models before and after c are distinct [the patterns were determined for the coefficient determination] ; i.e. {b10 , b20 , · · · , bi 0 } changes to {b11 , b21 , · · · , bi 1 }. Such a distribution change of data may be depicted through a piecewise regression model, given as“
In addition, for a second example of this feature, also see § 2.1.1 last paragraph: “Moreover, in order to fully utilize the advantage of LASSO, a sliding-window strategy was adopted for model training [42], thus …where w is the size of the window” – as taken in view of equations 1-3, i.e. the “coefficient vector” was determined for the “sliding window” during the training, see fig. 2 to clarify – specifically the “Slid window” to the “Model establishment” [incl. determination of the “coefficient vector”] as detailed in table 1: “Step 1. The window including first 5 cycles are adopted for model training…Step 3. The window including training cycles moves forward by one cycle and the model is updated accordingly…Step 1. The window moves forward by one cycle, and the model is updated using equations (3), (5), and (6), accordingly….”, i.e. in each window there is a “coefficient vector” which represents a determined pattern
training a machine learning model using a second computer-based numerical modeling method, the machine learning model being a single time series model based on the patterns; predicting a future condition of the system using the machine learning model with current data of the system, the future condition of the system represented by a quantified state of the system; and determining whether the quantified state of the system satisfies a threshold value, (Wang, abstract: “This paper presents a novel framework, based on multiple regression analysis (MRA), for the on-line monitoring of induction motors in order to detect changes in motor rotational speed during the course of successive operations. To utilize the synergistic information of multiple sensors, a prediction model is established based on MRA. By virtue of this model, the residual between model output and sensor observation is defined as a dynamic stability indicator, for the purpose of characterizing the running status of a motor.” – then see § 2.1.1-2.1.2 as discussed above, including seeing fig. 1 for the “Model establishment” step wherein this provides “prediction[s]” for time series as visibly depicted, wherein this is based on the patterns as discussed in § 2.1.2 (the “piecewise regression model…which allows for a trend-type [pattern] change… Assuming that {b10 , b20 , · · · , bi 0 } has already been estimated by means of model training, the predictions of xn,v at the inspected n+1th cycle can be calculated and denoted byˆxn,v.”)
to clarify on the training, page 3, col. 2, last paragraph: “On the basis of several completed cycles, the coefficients of the different rows may be estimated by minimizing a costing function in the model training process,…” to page 3: “Moreover, in order to fully utilize the advantage of LASSO, a sliding-window strategy was adopted for model training” and § 2.1.2: “When the motor condition changes at time c, the generation mechanism of the data changes accordingly, which means the coefficients of trained models before and after c are distinct ;”
as to the quantifying, Wang § 2.1.2: “Next, an anomaly score Qn+1 is defined, which quantifies the extent of motor deviation from normal, considering the offset of residuals and the effect of cycle length, thus”, and § 2.2: “Let us assume that the motor’s condition was inspected as normal prior to the n+1th cycle. The anomaly scores of historical cycles {Q1, Q2, · · · , Qn} will follow independent and identical distribution, and will vary within an interval ranging from zero to pre-defined limit… Once a change occurs at n+1th cycle, i.e. nT +v > c, the anomaly score Qn+1 will increase and exceed the limit, which can be detected via hypothesis testing as… If H0 is satisfied, then change decision making will take place; otherwise, no change occurs.” – see fig. 2 to further clarify, for the “On-line monitoring” followed by the “Decision making” portions
While Wang does not explicitly teach the following feature, Wang in view of Sridhar teaches: wherein the sensors are arranged in groups of sensors defined according to their relative distances to respective ones of center points in the groups of sensors, and an individual sensor in an individual group of sensors is physically closer to a respective center point of the respective individual group of sensors where the individual sensor belongs than the respective center point of any other of the groups of sensors;… wherein each matrix of the matrices is an MxN matrix where M is a number of the groups of the sensors and N is a number of dimensions of the multi-dimensional time series data, the number of dimensions being a number of different types of data collected by each one of the groups of the sensors. .(Wang, as discussed above including §§ 2.1.2-2.1.2 for the matrices, wherein each matrix is for a single sensor, with a set of “observations” for each sensor, and how this is used in a “piecewise regression model” in § 2.1.2 as was discussed above, also Wang, abstract: “Multi-sensory configuration enables the collection of comprehensive information relating to the operating condition of machinery in use” and § 1; see Wang table 5 and fig. 5-6 which also clarify that the number of sensors was 4, and each sensor collected a different type of information, i.e. a “Sound” sensor; a “Current Sensor”, and note in fig. 5(a) that the vibration sensors collected two different types of vibration data (as clarified in fig. 5(b), note the right-most red lines as these two indicate schematically that the first vibration sensor was collecting “Stepper motor” vibration; and the second vibration was collecting “Gearbox” vibration, i.e. N = 4 = 4 types of information
taken in further view of Sridhar, abstract: “The central idea of using sensor networks for monitoring events and conditions is to exploit the distributed nature provided by tiny and low powered devices. Multiple sensors can be used collaboratively to monitor events or space more effectively than a single sensor... These sensors in general are prone to failure due to their inherent characteristics. In this paper, we propose a robust fault tolerant data aggregation scheme in sensor networks.” – then see § 1.1 including: “Parallel fused data from multiple sensors can represent decision milestones which will incur less communication cost than serially processing raw data acquired by individual sensors. It is an intractable problem to actually detect if a sensor is faulty by looking at the raw data acquired from the sensors. However, because of faulty sensors, the fused data will deviate from the actual physical value being sensed. In order to reduce the impact of faulty information prior to fusing, we propose a novel approach of weighted average aggregation of data from these sensors.” – see §2.2 to further clarify: “Clustering of randomly deployed sensor nodes based on some metric (say, distance) has the advantage of dividing the problem space into several sub-problems and solving each sub-problem for estimation; a divide-and-conquer approach…. Consider three overlapping sensing regions. The region of interest is the aggregated data obtained around the region of the intersection of these sensing regions. For a large deployment scenario, these sensing regions can be extended to cluster regions… In hierarchical structure, sensor information is fused in each cluster to produce a local estimate which is then fused to obtain a global estimate of the sensed information. Several fusion steps are needed in each cluster, however, each of these local estimates can be done in parallel. Weighted adaptation can be easily managed resulting in more reliable information from each sensor/cluster heads…. Each sensor node has a weighting factor at any instance of time t, given by wi(t). In the event of sensor failure, the proposed… In order to estimate Δwi(t), we use the concept of spatial correlation…. The sensors deployed in large numbers are thus correlated spatially within the region of events, that is, the sensor I reads the same event value (with minimal variation) as the neighboring k sensors which are closely deployed…” and see equations 2-3 - then see “Theorem 1” and “Corollary 1” in § 2.2.1
with respect to the sensors all being closest in a group, see Sridhar, fig. 1 which shows such a configuration
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 “Multi-sensory configuration enables the collection of comprehensive information relating to the operating condition of machinery in use. However, the complex properties of multi-sensory monitoring data create serious challenges for data modelling and analysis. This paper presents a novel framework, based on multiple regression analysis (MRA), for the on-line monitoring of induction motors in order to detect changes in motor rotational speed during the course of successive operations…” (Wang, abstract) with the teachings from Sridhar on “Multiple sensors can be used collaboratively to monitor events or space more effectively than a single sensor” and “In this paper, we propose a robust fault tolerant data aggregation scheme in sensor networks” (Sridhar, abstract). The motivation to combine would have been that “Multiple sensors can be used collaboratively to monitor events or space more effectively than a single sensor. Several applications can be envisioned with sensor networks ranging from military and commercial applications to environment and earth sciences. Typical examples include: traffic monitoring of vehicles, military reconnaissance and surveillance, target tracking, cross-border infiltration, habitat monitoring and structural monitoring, to name a few. These sensors in general are prone to failure due to their inherent characteristics. In this paper, we propose a robust fault tolerant data aggregation scheme in sensor networks” (Sridhar, abstract) Also, see § 1.1, ¶ 2 for an additional motivation to combine, and § 3: “Data aggregation in sensor networks in general helps to reduce communication cost. However, a faulty reading by a sensor can represent a false estimate for the aggregated data. In this paper, we propose a robust mechanism to aggregate data from different sensors with some tolerance to faults”
Regarding Claim 3
Wang, in view of Sridhar, as discussed above, teaches: The method of claim 1, wherein each value in the MxN matrix is a weighted average of values of plural respective ones of the sensors in the respective individual group of sensors. (See Wang as was taken in view of Sridhar as discussed above for claim 1 incl. in Sridhar the abstract, and §§ 1.1, 2.2, and 2.2.1 as were cited above)
Regarding Claim 4
Wang, in view of Sridhar, as discussed above, teaches:
The method of claim 3, wherein respective weights of the plural respective ones of the sensors in the respective individual group of sensors are based on a closest distance to the respective center point of the respective individual group of sensors. (Wang, as discussed above including §§ 2.1.2-2.1.2 for the matrices, wherein each matrix is for a single sensor, with a set of “observations” for each sensor, and how this is used in a “piecewise regression model” in § 2.1.2 as was discussed above, also Wang, abstract: “Multi-sensory configuration enables the collection of comprehensive information relating to the operating condition of machinery in use” and § 1
as taken in view of Sridhar, abstract, § 1.1, and § 2.2 as discussed above, in particular see § 2.2: “Clustering of randomly deployed sensor nodes based on some metric (say, distance) has the advantage of dividing the problem space into several sub-problems and solving each sub-problem for estimation; a divide-and-conquer approach…. Consider three overlapping sensing regions. The region of interest is the aggregated data obtained around the region of the intersection of these sensing regions. For a large deployment scenario, these sensing regions can be extended to cluster regions… In hierarchical structure, sensor information is fused in each cluster to produce a local estimate which is then fused to obtain a global estimate of the sensed information. Several fusion steps are needed in each cluster, however, each of these local estimates can be done in parallel. Weighted adaptation can be easily managed resulting in more reliable information from each sensor/cluster heads…. Each sensor node has a weighting factor at any instance of time t, given by wi(t). In the event of sensor failure, the proposed… In order to estimate Δwi(t), we use the concept of spatial correlation…. The sensors deployed in large numbers are thus correlated spatially within the region of events, that is, the sensor I reads the same event value (with minimal variation) as the neighboring k sensors which are closely deployed…” and see equations 2-3 - then see “Theorem 1” and “Corollary 1” in § 2.2.1 – in other words, the weights are determined based on the sensors in the group being “correlated spatially” in the cluster (i.e. that the neighboring sensors are “closely deployed”, see fig. 1 to further clarify which visually depicts a center point of the cluster of the sensors)
The rationale to combine is the same as discussed above for claim 1
Regarding Claim 5
Wang teaches:
The method of claim 1, wherein the determining the patterns comprises: defining a number of windows each representing a respective period of the time; and determining a respective vector of coefficients for each one of the windows, wherein the vector of coefficients for a particular one of the windows represents a pattern between a condition of the system measured during the respective period of the time and the multidimensional-time series data collected during the respective period of the time. (Wang, abstract and §§ 2.1.1-2.1.2 as discussed above, including the “piecewise regression model” in § 2.1.2 – in particular note the determined vector of coefficients: “When the motor condition changes at time c, the generation mechanism of the data changes accordingly, which means the coefficients of trained models before and after c are distinct ; i.e. {b10 , b20 , · · · , bi 0 } changes to {b11 , b21 , · · · , bi 1 }.” (see § 2.1.1: “each coefficient vector b” in the description of eq. 3) – wherein eq. 7 provides two windows of time, see the “0 ≤ nT+v < c,” and “c ≤ nT+v”, i.e. this is a “piecewise regression model”
In addition, also see § 2.1.1 last paragraph: “Moreover, in order to fully utilize the advantage of LASSO, a sliding-window strategy was adopted for model training [42], thus …where w is the size of the window” – as taken in view of equations 1-3, i.e. the “coefficient vector” was determined for the “sliding window” during the training, see fig. 2 to clarify – specifically the “Slid window” to the “Model establishment” [incl. determination of the “coefficient vector”] as detailed in table 1: “Step 1. The window including first 5 cycles are adopted for model training…Step 3. The window including training cycles moves forward by one cycle and the model is updated accordingly…Step 1. The window moves forward by one cycle, and the model is updated using equations (3), (5), and (6), accordingly….”, i.e. in each window there is a “coefficient vector” for use in establishing the model
Regarding Claim 8
Wang teaches: The method of claim 1, wherein the first computer-based numerical modeling method is different than the second computer-based numerical modeling method. (Wang, § 2.1.1: “On the basis of several completed cycles, the coefficients of the different rows may be estimated by minimizing a costing function in the model training process, by means of the well -
known least square regression (LRS) method:” – which is an example of a first numerical modeling method used, then see § 2.1.2 which shows the created model is a “piecewise regression model” (example of a second modeling method)
to clarify on the BRI, ¶ 61: “The modeling module 220 may be programmed to use a least square method to solve for the Beta vector, although embodiments are not limited to a least square method.” And ¶ 63: “In embodiments, the modeling module 220 is programmed to use a second computer-based numerical modeling method to form a linear regression for the patterns and sensor data… For example, after determining plural Beta vectors Bl, B2, B3, .. , BN in the manner described herein, the modeling module 220 then uses those plural Beta vectors with an ARMA model to create a time series model that predicts a future Beta vector B(N+ 1).”
Regarding Claim 9
While Wang does not anticipate the following feature, it would have been obvious in view of Wang: The method of claim 1, further comprising adjusting a control of the system based on the predicted future condition. (Wang, abstract, incl. “…By virtue of this model, the residual between model output and sensor observation is defined as a dynamic stability indicator, for the purpose of characterizing the running status of a motor…” then see § 1 ¶ 1: “With these applications, one of the major goals is to detect changes (e.g. faults, anomalies, and switching/transit points) in the running status of dynamic motors at an early stage, based on sensor signals [4, 5]. Such changes relate to a fluctuation in signal statistics, which is difficult to detect and analyze compared with an abrupt change [6]. The capacity to detect such changes enables abnormal running behavior to be observed and highlighted so as to help users to formulate corrective schedules and/or carry out predictive maintenance [7]. Change detection is also desirable in advanced applications where appropriate actions or adaptive adjustments need to be carried out as soon as possible once a change alarm is received”
Then see page 7, last paragraph: “…Moreover, our proposed approach achieved the best performance, which indicates that the proposed method is superior to state-of-the-art competitors, and is applicable to real-life engineering applications.” And see § 6
As such, a skilled person would have been suggested, or at least found it obvious, to apply Wang’s technique to “real-life engineering applications”, such as ones where Wang’s method of “Change detection” is used to provide “appropriate actions or adaptive adjustments [which] need to be carried out as soon as possible once a change alarm is received”, wherein POSITA would have been motivated to make this combination because Wang’s “proposed approach achieved the best performance, which indicates that the proposed method is superior to state-of-the-art competitors, and is applicable to real-life engineering applications” and would have been motivated to do so because “Change detection is also desirable in advanced applications where appropriate actions or adaptive adjustments need to be carried out as soon as possible once a change alarm is received” – additional rationales to combine are discussed below for claim 22, and incorporated herein by reference for conciseness
Regarding Claim 10.
Wang teaches: The method of claim 1, wherein the predicting comprises: predicting a future pattern using the machine learning model; and predicting a future target value of the system using the future pattern. (Wang, see § 2.1.1 last paragraph: “Moreover, in order to fully utilize the advantage of LASSO, a sliding-window strategy was adopted for model training [42], thus …where w is the size of the window” – as taken in view of equations 1-3, i.e. the “coefficient vector” was determined for the “sliding window” during the training, see fig. 2 to clarify – specifically the “Slid window” to the “Model establishment” [incl. determination of the “coefficient vector”] as detailed in table 1: “Step 1. The window including first 5 cycles are adopted for model training…Step 3. The window including training cycles moves forward by one cycle and the model is updated accordingly…Step 1. The window moves forward by one cycle, and the model is updated using equations (3), (5), and (6), accordingly….”, i.e. the prediction is predicting the future pattern (the future window) and predicting a target value in the future pattern)
Regarding Claim 11.
This is rejected under a similar rationale as claim 1 above, wherein Wang teaches: A computer program product comprising one or more computer readable storage media having program instructions collectively stored on the one or more computer readable storage media, the program instructions executable to: (Wang, abstract and § 6)
obtain multi-dimensional time series data from sensors that collect the multi-dimensional time series data in a system during a time corresponding to a user-defined cycle…(Wang, abstract, including: “…This paper presents a novel framework, based on multiple regression analysis (MRA), for the on-line monitoring of induction motors in order to detect changes in motor rotational speed during the course of successive operations. To utilize the synergistic information of multiple sensors, a prediction model is established based on MRA…” – to clarify, see fig. 1, as discussed in part in § 2.1.1: “Let us assume that X1:j is the collected multidimensional condition data up to inspection time j, i.e. X1:j = [x11 :j; x21 :j; · · · , xi 1:j] ′ , where i is the number of sensors; [·] ′ stands for the transposition operation. Each dimension, e.g. xi 1:j = [xi 1, xi 2, xi 3, · · · , xij ] where xij is the observation value of ith sensor at time j, may be expresed as a periodic form, considering the symmetry of physical structure of motor machinery [37]….” – and see equation 1 and its accompanying description including: “where xn,v contains observations of vth phase in n+1th cycle sensed by a measurement system consisting of the number of i sensors,” – wherein POSITA would have inferred that the cycle of Wang was used defined by Wang as input parameter, or at least would have found it obvious to have the cycle be user defined because this would have been “making adjustable” the cycle of Wang (MPEP § 2144.04(V)(D)) – to clarify, the claim recites no particular method of how a user is to define what the cycle is, but only that it is user defined
Regarding Claim 12.
This is rejected under a similar rationale as claims 1 and 3-4 as discussed above. With respect to the recitation of “user-defined”, this is rejected under a similar rationale as the similar recitation in claim 11, i.e. it would have been inferred that the “fusion node” of Sridhar (e.g. fig. 1) was user defined; or at least it would have obvious to have made this user-defined as this would be “making adjustable” the center point (MPEP § 2144.04(V)(D)).
Regarding Claim 13.
This is rejected under a similar rationale as claim 5 above.
Regarding Claim 15.
This is rejected under a similar rationale as claim 9 above.
Regarding Claim 16.
This is rejected under a similar rationale as claim 1 above, wherein Wang teaches: A system comprising: a processor, a computer readable memory, one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable to: (Wang, abstract and § 6)
Regarding Claim 17.
This is rejected under a similar rationale as claims 1 and 3-4 as discussed above. With respect to the center point corresponding to a device in an environment, this would have been taught by Wang, in view of Sridhar - see Wang, fig. 5-6 and table 5 show where the sensors are and what they are, then see Sridhar, abstract: “Multiple sensors can be used collaboratively to monitor events or space more effectively than a single sensor.” And Sridhar fig. 1 – i.e. in combination, the multiple sensors would have been positioned to monitor the same space as the single sensor, and that space would correspond to the device to be monitored in Wang by the single sensor (e.g. Wang, fig. 5). The rationale to combine is the same as discussed above
Regarding Claim 18.
This is rejected under a similar rationale as claim 5 above.
Regarding Claim 20.
This is rejected under a similar rationale as claim 9 above.
Regarding Claim 22.
Wang teaches:
The method of claim 1, wherein the threshold value represents a value below which a control of the system is adjusted to raise the quantified state of the system above the threshold value. (Wang § 2.1.2: “Next, an anomaly score Qn+1 is defined, which quantifies the extent of motor deviation from normal, considering the offset of residuals and the effect of cycle length, thus”, and § 2.2: “Let us assume that the motor’s condition was inspected as normal prior to the n+1th cycle. The anomaly scores of historical cycles {Q1, Q2, · · · , Qn} will follow independent and identical distribution, and will vary within an interval ranging from zero to pre-defined limit… Once a change occurs at n+1th cycle, i.e. nT +v > c, the anomaly score Qn+1 will increase and exceed the limit, which can be detected via hypothesis testing as… If H0 is satisfied, then change decision making will take place; otherwise, no change occurs.” – see fig. 2 to further clarify, for the “On-line monitoring” followed by the “Decision making” portions
Then see § 1 ¶ 1: “…The capacity to detect such changes enables abnormal running behavior to be observed and highlighted so as to help users to formulate corrective schedules and/or carry out predictive maintenance [7]. Change detection is also desirable in advanced applications where appropriate actions or adaptive adjustments need to be carried out as soon as possible once a change alarm is received” – thus Wang renders this obvious, because § 1 ¶ 1 clarifies once the alarm is receiving “appropriate actions or adaptive adjustments need to be carried out”, wherein POSITA would have been motivated to do so because “Change detection is also desirable in advanced applications” – also, see the KSR rationale of “(A) Combining prior art elements according to known methods to yield predictable results;” which is also relied upon for readily apparent reasons (note it’s the introduction section); and furthermore should it be found that Wang is merely suggesting manual adjustments, then it would still have been obvious because per MPEP § 2144(III): “In re Venner, 262 F.2d 91, 95, 120 USPQ 193, 194 (CCPA 1958) (Appellant argued that claims to a permanent mold casting apparatus for molding trunk pistons were allowable over the prior art because the claimed invention combined "old permanent-mold structures together with a timer and solenoid which automatically actuates the known pressure valve system to release the inner core after a predetermined time has elapsed." The court held that broadly providing an automatic or mechanical means to replace a manual activity which accomplished the same result is not sufficient to distinguish over the prior art.)” – and POSITA would have been motivated to do so because computers are faster than people, i.e. it would have caused a faster “adaptive adjustment” when a computer controlled the system in response to the alarm
Claim(s) 6-7, 14, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang, Xiaofeng, Zhenjie Zhu, and Guoliang Lu. "Multiple regression analysis for change detection in multi-sensory monitoring data with application to induction motor speed condition monitoring." Measurement Science and Technology 31.9 (2020): 095103 In view of Sridhar, Prasanna, Asad M. Madni, and Mo Jamshidi. "Hierarchical data aggregation in spatially correlated distributed sensor networks." 2006 World Automation Congress. IEEE, 2006 and in further view of Cormode, Graham, Srikanta Tirthapura, and Bojian Xu. "Time-decaying sketches for robust aggregation of sensor data." SIAM Journal on Computing 39.4 (2010): 1309-1339.
Regarding Claim 6
While Wang, in view of Sridhar, does not explicitly teach the following feature, Wang in view of Sridhar and Cormode teaches: The method of claim 1, wherein the first computer-based numerical modeling method utilizes an algorithm that includes a first factor based on attenuation of the data over the time. (Wang, as was cited above for §§ 2.1.1-2.1.2 including the use of a “piecewise regression model”, and “a sliding-window strategy”
As taken in further view of Cormode, abstract: “The sketch has the following properties which make it useful in communication-efficient aggregation in distributed streaming scenarios, such as sensor networks:… is also time decaying, so that the weight of a data item in the sketch can decrease with time according to a user-specified decay function.”
To clarify, page 1310, ¶ 2: “Lastly, we observe that in any evolving setting, recent data are more reliable than older data. We should therefore weight newer observations more heavily than older ones. This can be formalized in a variety of ways: we may only consider observations that fall within a sliding window of recent time (say, the last hour) and ignore (assign zero weight to) any that are older, or, more generally, use an arbitrary function that assigns a weight to each observation as a function of its initial weight and its age [18, 14]. A data summary should allow such decay functions to be applied and give us guarantees relative to the exact answer”
Then see page 1311, definition 1.1, in particular: “The decayed weight of an element (v,w, t, id) at time c ≥ t is f(w, c − t). An example decay function is the sliding window model [18, 22, 34], where f(w, x) is defined as follows. For some window size W, if x ≤ W, then f(w, x) = w; otherwise, f(w, x) = 0. Other popular decay functions include exponential decay f(w, x) =
w · exp(−ax) and polynomial decay f(w, x) = w · (x + 1)−a, where a is a constant.” – i.e. the “w” is an example of an attenuation factor, and the “a” defines the speed of the attenuation (note its location in the exponential decay in particular))
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 “Multi-sensory configuration enables the collection of comprehensive information relating to the operating condition of machinery in use. However, the complex properties of multi-sensory monitoring data create serious challenges for data modelling and analysis. This paper presents a novel framework, based on multiple regression analysis (MRA), for the on-line monitoring of induction motors in order to detect changes in motor rotational speed during the course of successive operations…” (Wang, abstract) with the teachings from Cormode, abstract: “The sketch has the following properties which make it useful in communication-efficient aggregation in distributed streaming scenarios, such as sensor networks:… is also time decaying, so that the weight of a data item in the sketch can decrease with time according to a user-specified decay function.”
The motivation to combine would have been that as per page 1310, ¶ 2: “Lastly, we observe that in any evolving setting, recent data are more reliable than older data. We should therefore weight newer observations more heavily than older ones. This can be formalized in a variety of ways: we may only consider observations that fall within a sliding window of recent time (say, the last hour) and ignore (assign zero weight to) any that are older, or, more generally, use an arbitrary function that assigns a weight to each observation as a function of its initial weight and its age [18, 14]. A data summary should allow such decay functions to be applied and give us guarantees relative to the exact answer”
Regarding Claim 7
Wang in view of Sridhar and Cormode teaches: The method of claim 6, wherein the algorithm includes a second factor that defines a speed of the attenuation. (Wang, as was cited above for §§ 2.1.1-2.1.2 including the use of a “piecewise regression model”, and “a sliding-window strategy”
As taken in further view of Cormode, abstract: “The sketch has the following properties which make it useful in communication-efficient aggregation in distributed streaming scenarios, such as sensor networks:… is also time decaying, so that the weight of a data item in the sketch can decrease with time according to a user-specified decay function.”
To clarify, page 1310, ¶ 2: “Lastly, we observe that in any evolving setting, recent data are more reliable than older data. We should therefore weight newer observations more heavily than older ones. This can be formalized in a variety of ways: we may only consider observations that fall within a sliding window of recent time (say, the last hour) and ignore (assign zero weight to) any that are older, or, more generally, use an arbitrary function that assigns a weight to each observation as a function of its initial weight and its age [18, 14]. A data summary should allow such decay functions to be applied and give us guarantees relative to the exact answer”
Then see page 1311, definition 1.1, in particular: “The decayed weight of an element (v,w, t, id) at time c ≥ t is f(w, c − t). An example decay function is the sliding window model [18, 22, 34], where f(w, x) is defined as follows. For some window size W, if x ≤ W, then f(w, x) = w; otherwise, f(w, x) = 0. Other popular decay functions include exponential decay f(w, x) =
w · exp(−ax) and polynomial decay f(w, x) = w · (x + 1)−a, where a is a constant.” – i.e. the “w” is an example of an attenuation factor, and the “a” defines the speed of the attenuation (note its location in the exponential decay in particular))
The rationale is the same as discussed above for claim 6
Regarding Claim 14.
This claim is rejected under a similar rationale as claims 6-7 as discussed above.
Regarding Claim 19.
This claim is rejected under a similar rationale as claims 6-7 as discussed above.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/David A Hopkins/Primary Examiner, Art Unit 2188
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