DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The amendment filed 08/29/2025 has been entered. As directed, claims 1, 9 and 10 have been
amended, no claim is canceled or added. Thus claims 1 -10 remain pending in the application. The applicant’s amendments to the claims have overcome each and every objection previously set forth in the Non-Final Office Action mailed 05/29/2025.
Response to Arguments
With respect to the Applicant’s argued rejection under 35 U.S.C 101 in “Applicant Arguments/Remarks Made in an Amendment,”:
Applicant argues:
A. The claims do not recite a judicial exception under Prong One.
The Office alleges that independent claims 1, 9, and 10 "represent mathematical concepts" and "process[es] that, but for the recitation of generic computing components, ... can be reasonably performed in the human mind." Office Action at 13-16. Applicant respectfully disagrees.
Regarding the "mathematical process," M.P.E.P. § 2106.04(a)(2)(1) specifies that "[a] claim does not recite a mathematical concept (i.e., the claim limitations do not fall within the mathematical concept grouping), if it is only based on or involves a mathematical concept." (Emphases added).
Here, Applicant's independent claim 1 has been amended to recite:
1. An information processing device, comprising:
a memory configured to store therein time-series data including at least one of a dependent variable and an independent variable used for a thermal fluid analysis of an electronic apparatus;
one or more processors coupled to the memory and configured to:
generate a nonlinear function based on at least one of the dependent variable and the independent variable;
generate a linear regression equation of a thermal model of the electronic apparatus, by using the nonlinear function as a basis function;
estimate a coefficient of the linear regression equation;
calculate a product of the coefficient and a maximum value of the basis function corresponding to the coefficient, as a degree of influence;
correct the coefficient based on the degree of influence; and
output the linear regression equation expressed by the corrected coefficient.
Applicant respectfully submits that amended independent claim 1 does not merely recite "mathematical calculations" as asserted by the Office, because it is not directed to any mathematical relationships, formulas, or calculations in the abstract, but rather to a specific and practical implementation of an information processing device that estimates a coefficient of a linear regression equation of a thermal model of an electronic apparatus, and outputs the estimated linear regression equation. While mathematical operations are involved, they are not claimed in isolation. Instead, they are embedded within a broader technological framework that includes "generat[ing] a nonlinear function based on at least one of the dependent variable and the independent variable; generat[ing] a linear regression equation of a thermal model of the electronic apparatus, by using the nonlinear function as a basis function; estimate[ing] a coefficient of the linear regression equation; calculate[ing] a product of the coefficient and a maximum value of the basis function corresponding to the coefficient, as a degree of influence; correct[ing] the coefficient based on the degree of influence; and output[ting] the linear regression equation expressed by the corrected coefficient." These steps are not claimed as mathematical concepts per se, but as part of a concrete and applied technological solution to improve the accuracy of generating a thermal model of a thermal-fluid-analysis-target electronic apparatus. Thus, independent claim 1 does not actually recite a mathematical formula or calculation. Consequently, the claimed features of amended independent claim 1 cannot be considered to encompass "mathematical concept" as defined in M.P.E.P. § 2106.04(a)(2)(1).
Therefore, amended independent claim 1 is patent eligible at Step 2A - Prong 1. Although different in scope from independent claim 1, independent claims 9 and 10 have been amended to recite features similar to those discussed above with respect to independent claim 1 and are patent eligible at Step 2A - Prong 1 for at least similar reasons.
Dependent claims 2-8 are also patent eligible at Step 2A - Prong 1 at least due to their dependence from patent-eligible independent claim 1 and further in view of the additional features recited therein.
(see Response filed 8/29/2025 [pages 9-12]).
In response to applicant's argument, the examiner respectfully disagrees.
In MPEP 2106.04(II)(B): A claim may recite multiple judicial exceptions. For example, claim 4 at issue in Bilski v. Kappos, 561 U.S. 593, 95 USPQ2d 1001 (2010) recited two abstract ideas, and the claims at issue in Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 101 USPQ2d 1961 (2012) recited two laws of nature. However, these claims were analyzed by the Supreme Court in the same manner as claims reciting a single judicial exception, such as those in Alice Corp., 573 U.S. 208, 110 USPQ2d 1976.
a. The claims do recite a mental process
The limitation of “generate a nonlinear function based on at least one of the dependent variable and the independent variable; generate a linear regression equation of a thermal model of the electronic apparatus by using the nonlinear function as a basis function; estimate a coefficient of the linear regression equation; calculate a product of the coefficient and a maximum value of the basis function corresponding to the coefficient, as a degree of influence; correct the coefficient based on the degree of influence,” as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind. In MPEP 2016.04(a)(2)(III), “Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. As the Federal Circuit has explained, "[c]ourts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind." Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015). See also Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1318, 120 USPQ2d 1353, 1360 (Fed. Cir. 2016) (‘‘[W]ith the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper.’’); Mortgage Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314, 1324, 117 USPQ2d 1693, 1699 (Fed. Cir. 2016) (holding that computer-implemented method for "anonymous loan shopping" was an abstract idea because it could be "performed by humans without a computer").” Therefore, the limitation is a “mental process”, similar to the comparison steps in MPEP 2106.04(a)(2)(III).
b. The claims do recite a mathematical concept.
The limitations “generate a nonlinear function based on at least one of the dependent variable and the independent variable; generate a linear regression equation of a thermal model of the electronic apparatus by using the nonlinear function as a basis function; estimate a coefficient of the linear regression equation; calculate a product of the coefficient and a maximum value of the basis function corresponding to the coefficient, as a degree of influence; correct the coefficient based on the degree of influence," can be considered to represent mathematical concepts. In the instant specification, pages 9-12 discloses a mathematical concepts grouping is defined as mathematical relationships, mathematical formulas or equations, and mathematical calculations. For example, “The calculation module 15 calculates a degree of influence on the basis of the magnitude of the term (coefficient × basis function). The value of the basis function (for example, Ti−Tj) changes over time. Thus, the maximum value in the time-series data is assumed as a representative value of the basis function. More specifically, the degree of influence is expressed by magnitude of term=coefficient ξkj × representative value of basis function maxi|θik|. That is, the calculation module 15 calculates a product of the coefficient estimated by the estimation module 14 and the maximum value of the basis function corresponding to the coefficient, as a degree of influence.” In MPEP 2106.04(a)(2)(I), “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) (holding that claims to a ‘‘series of mathematical calculations based on selected information’’ are directed to abstract ideas); Digitech Image Techs., LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014) (holding that claims to a ‘‘process of organizing information through mathematical correlations’’ are directed to an abstract idea); and Bancorp Servs., LLC v. Sun Life Assurance Co. of Can. (U.S.), 687 F.3d 1266, 1280, 103 USPQ2d 1425, 1434 (Fed. Cir. 2012) (identifying the concept of ‘‘managing a stable value protected life insurance policy by performing calculations and manipulating the results’’ as an abstract idea). Further, in MPEP 2106.04(a)(2)(I)(C), “A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.” The claim does not need to recite equations explicitly, but reciting the determination/estimation of variables and generation/correction of mathematical model using mathematical methods can be considered as a mathematical concept. – See MPEP 2106.04(a)(2)(I). Therefore, the limitation is a “mathematical concepts”, similar to the comparison steps in MPEP 2106.04(a)(2)(I), and claims 1, 9 and 10 remain directed to a judicial exception under Step 2A, Prong 1.
With respect to the Applicant’s argued rejection under 35 U.S.C 101 in “Applicant Arguments/Remarks Made in an Amendment,”:
Applicant argues:
B. The claims integrate a practical application under Prong Two.
In the Step 2A - Prong Two analysis, the Office asserted that the additional elements recited in claim 1 are "insignificant extra solution data output." Office Action at 17-19.
According to M.P.E.P. § 2106.04(d)(1), a claim may integrate a judicial exception into a practical application when it, for example, improves the functioning of a computer or another technology or technical field, or applies the exception in a meaningful way beyond merely linking it to a technological environment.
Independent claim 1 satisfies this standard by providing a concrete and specific improvement to the technical field of modeling physical phenomena-particularly thermal fluid analysis.
As outlined in M.P.E.P. § 2106.04(d)(1), the Specification provides sufficient detail for a person of ordinary skill in the art to recognize the claimed invention as offering a technological improvement. Specifically, Applicant's Specification describes conventional techniques for obtaining a mathematical model describing a physical phenomenon from time-series data by applying symbolic regression, which is a type of machine learning. Applicant's published Application (US 2022/0366101 A1) at [0003]2. However, in the conventional techniques, it has been difficult to further improve the accuracy of generating a model of a physical phenomenon. Id. at [0004]. In order to improve the conventional technology, the claimed invention uses two distinct types of learning data -(1) time differential values (capturing short-term dynamics), and (2) differences from initial values (capturing long-term trends)-to estimate regression coefficients via machine learning.
This improvement is reflected in amended claim 1, which recites an information processing device which "generat[es] a nonlinear function based on at least one of the dependent variable and the independent variable; generate[s] a linear regression equation of a thermal model of the electronic apparatus, by using the nonlinear function as a basis function; estimate[s] a coefficient of the linear regression equation; calculate[s] a product of the coefficient and a maximum value of the basis function corresponding to the coefficient, as a degree of influence; correct[s] the coefficient based on the degree of influence; and output[s] the linear regression equation expressed by the corrected coefficient."
These steps are not generic or abstract, but rather specific, technical operations that improve the accuracy of generating a thermal model of the electronic apparatus. Applicant's published Application at [0035], [0068]-[0072].
Therefore, when considered as a whole, independent claim 1 recites a particular manner of generating a thermal model of the electronic apparatus, which can improve the accuracy of the generated model. Accordingly, independent claim 1 recites additional elements that reflect "an improvement to the other technology or technical filed," and/or "appl[y] or use[] the judicial exception in [a] meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception." M.P.E.P. § 2106.04(d)(1). Accordingly, independent claim 1 is also patent eligible at Step 2A - Prong 2.
Although different in scope, independent claims 9 and 10 have been amended recite features similar to those discussed above with respect to independent claim 1 and are also patent eligible at Step 2A - Prong 2. Dependent claims 2-8 are also patent eligible at Step 2A - Prong 2 at least due to their dependence from patent eligible independent claim 1 and further in view of the additional features recited therein.
(see Response filed 8/29/2025 [pages 12-14]).
With respect to applicant argument, “when considered as a whole, independent claim 1 recites a particular manner of generating a thermal model of the electronic apparatus, which can improve the accuracy of the generated model."
In response to applicant's argument, the examiner respectfully disagrees. The additional limitation of claims, "An information processing device, comprising: a memory configured to store therein time-series data including at least one of a dependent variable and an independent variable used for a thermal fluid analysis of an electronic apparatus; one or more processors coupled to the memory and configured to:” and “A computer program product comprising a non-transitory computer-readable medium including instructions stored thereon, the instructions causing a computer to execute:” and “An information processing method implemented by a computer …” which is mere instruction to implement an abstract idea on a computer, or merely uses a computer as tool to perform an abstract idea (see MPEP § 2106.05(f)) with the broad reasonable interpretation, which does not integrate a judicial exception into elements. Further, the following additional element – “store therein time-series data including at least one of a dependent variable and an independent variable used for a thermal fluid analysis of an electronic apparatus” and “output the linear regression equation expressed by the corrected coefficient.” which is merely a recitation of insignificant extra-solution data gathering (i.e., store time-series data) and data output (i.e., output linear regression equation) activity (see MPEP § 2106.05(g)) which does not integrate a judicial exception into practical application. The alleged “improvement” in model accuracy discussed by applicant does not come from any technical change to how a computer operates or how another technology works. Instead, it comes from refining the mathematical model itself. The claimed steps improve only the accuracy of the abstract mathematical model, not the functioning of a computer or any other technology (e.g., the physical thermal system). The improvement itself is part of the abstract idea and cannot be considered an additional element that integrates the judicial exception into a practical application. Therefore, the additional elements do not impose a meaningful limit on the abstract idea and do not integrate judicial exception into practical application under Step 2A - Prong 2.
With respect to the Applicant’s argued rejection under 35 U.S.C 101 in “Applicant Arguments/Remarks Made in an Amendment,”:
Applicant argues:
C. Under Step 2B, the claims recite "significantly more."
As explained above, Applicant submits that the claims are not "directed to" an abstract idea, which means that the claims qualify as patent eligible subject matter, and further analysis under Step 2B is not required. Nevertheless, solely to advance prosecution, Applicant presents the following analysis of the claims under Step 2B.
Here, amended independent claim 1 recites a specific and non-conventional configuration of an information processing device which "generat[es] a nonlinear function based on at least one of the dependent variable and the independent variable; generate[s] a linear regression equation of a thermal model of the electronic apparatus, by using the nonlinear function as a basis function; estimate[s] a coefficient of the linear regression equation; calculate[s] a product of the coefficient and a maximum value of the basis function corresponding to the coefficient, as a degree of influence; correct[s] the coefficient based on the degree of influence; and output[s] the linear regression equation expressed by the corrected coefficient."
This configuration reflects a purposeful and inventive approach to improving the accuracy of thermal modeling. By using two distinct types of learning data-(1) time differential values (capturing short-term dynamics) and (2) differences from initial values (capturing long-term trends)-the claimed invention enables more robust and reliable model generation. See Specification at [0035], [0068]-[0072].
Moreover, the claim recites specific technical steps that are implemented in a particular technological context. These steps go beyond generic data processing or abstract mathematical manipulation. Instead, they represent a non-routine and non- conventional combination of operations tailored to the domain of thermal fluid analysis. The use of machine learning in this context, with carefully selected learning inputs and a structured output, reflects an inventive concept that is not well-understood, routine, or conventional in the field.
Accordingly, the additional elements in the claims - when considered individually and as an ordered combination-amount to significantly more than any alleged abstract idea and transform the claim into a patent-eligible application under Step 2B. Therefore, the rejection of claims 1-10 under 35 U.S.C. § 101 should be withdrawn.
(see Response filed 8/29/2025 [pages 14-15]).
With respect to applicant argument, “the additional elements in the claims - when considered individually and as an ordered combination-amount to significantly more than any alleged abstract idea and transform the claim into a patent-eligible application under Step 2B.”
In response to applicant's argument, the examiner respectfully disagrees. The additional limitations “An information processing device, comprising: a memory configured to store therein time-series data including at least one of a dependent variable and an independent variable used for a thermal fluid analysis of an electronic apparatus; one or more processors coupled to the memory and configured to:” and “A computer program product comprising a non-transitory computer-readable medium including instructions stored thereon, the instructions causing a computer to execute:” and “An information processing method implemented by a computer …” and “store therein time-series data including at least one of a dependent variable and an independent variable used for a thermal fluid analysis of an electronic apparatus” and “output the linear regression equation expressed by the corrected coefficient” are generic computer components performing basic functions such as storing, processing, and outputting data. The functions of computer are well-understood, routine, and conventional in the field and do not add anything meaningful to the abstract idea. The alleged “improvement” such as improving the accuracy of generating a thermal model comes from changing the mathematical model itself, not from any change in how the computer or technology works. This type of improvement is part of the abstract idea and cannot be considered “significantly more.” Therefore, the additional elements, individually or in combination, amount to no more than applying conventional computer components to perform well-understood operations, which is insufficient to qualify as “significantly more” than the abstract idea under Step 2B. Therefore, the rejection of independent claims 1, 9 and 10 and the claims dependent thereon, under 35 U.S.C. § 101 is maintained.
Applicant's arguments filed under “Applicant Arguments/Remarks Made in an Amendment” on
08/29/2025, the applicant' s arguments with respect to rejection under 35 U.S.C. § 103 have been fully considered but they are not persuasive.
Applicant argues:
IV. Rejection under 35 U.S.C. § 103
Applicant respectfully traverses the rejection of claims 1-4, 6, 7, 9, and 10 under 35 U.S.C. § 103 as allegedly being unpatentable over Brunton in view of Kobayashi; the rejection of claim 5 under 35 U.S.C. 103 as allegedly being unpatentable over Brunton in view of Kobayashi and Slawski; and the rejection of claim 8 under 35 U.S.C. 103 as allegedly being unpatentable over Brunton in view of Kobayashi and Suzuki. Office Action at 23-35.
In rejecting independent claim 1 as previously presented, the Office Action admits that "Brunton fails to teach calculat[ing] a product of the coefficient and a maximum value of the basis function corresponding to the coefficient, as a degree of influence; correct the coefficient based on the degree of influence." Office Action at 25. Instead, the Office Action alleges that Kobayashi teaches this subject matter. Specifically, the Office Action alleges that, in Kobayashi:
the basis function evaluation unit 1052 sets an evaluation value v.sub.m of the unselected basis function .phi..sub.m(x) to 0, and sets an evaluation value v.sub.m of the selected basis function .phi..sub.m(x) to 1. For example, if w.sub.m'=0, the basis function evaluation unit 1052 sets an evaluation value v.sub.m' of a basis function .phi..sub.m'(x) corresponding to w.sub.m' to 0. If w.sub.m'.noteq.0, the basis function evaluation unit 1052 sets the evaluation value v.sub.m' of the basis function .phi..sub.m'(x) corresponding to w.sub.m' to 1. Examiner note: i.e., defines an evaluation value Vm for each basis function based on whether its coefficient is zero or non- zero); correct the coefficient based on the degree of influence ([0099] If the predetermined termination condition has been satisfied, the estimation function generation unit 105 generates the estimation function f(x) on the basis of a of a .tau.-th-generation basis function .phi..sub.m,.tau. (m=1 to M.sub..tau.) and a weight vector w having a largest AIC value. At this time, the basis function .phi..sub.m,.tau. corresponding to a zero element in the weight vector w is discarded. The estimation function f(x) generated by the estimation function generation unit 105 is input to the function output unit 106. If the estimation function f(x) is input, the function output unit 106 outputs the input estimation function f(x)).
Office Action at 25-26 (emphasis added).
However, even assuming the characterizations in the Office Action are correct (which Applicant does not concede), the above description concerning the evaluation value does not teach or even suggest "calculating a product of the coefficient and a maximum value of the basis function corresponding to the coefficient, as a degree of influence," as recited in independent claim 1. Thus, the "evaluation value" of Kobayashi is clearly different from the "degree of influence" recited in independent claim 1. Accordingly, amended independent claim 1 is allowable over Brunton and Kobayashi.
Moreover, Slawski and Suzuki fail to cure these deficiencies in Brunton and Kobayashi. Accordingly, amended independent claim 1 is allowable over the art of record. Claims 2-8 are allowable for at least the reason that they depend from an allowable claim. Although of different scope, independent claims 9 and 10 recite similar subject matter and are allowable for at least similar reasons.
Applicant therefore requests withdrawal of the rejection of claims 1-10 under 35 U.S.C. § 103.
(see Response filed 8/29/2025 [pages 15-17]).
In response to applicant's argument, the examiner respectfully disagrees. Under broadest reasonable interpretation (BRI), Kobayashi teaches “calculate a product of the coefficient and a maximum value of the basis function corresponding to the coefficient, as a degree of influence” ([0080], “If the vector w is obtained by the machine learning unit 1051, the estimation function generation unit 105 sets an evaluation value vm of the basis function φm(x) by the function of the basis function evaluation unit 1052. For example, the basis function evaluation unit 1052 sets an evaluation value vm of the unselected basis function φm(x) to 0, and sets an evaluation value vm of the selected basis function φm(x) to 1. For example, if wm′=0, the basis function evaluation unit 1052 sets an evaluation value vm′ of a basis function φm′(x) corresponding to wm′ to 0. If wm′≠0, the basis function evaluation unit 1052 sets the evaluation value vm′ of the basis function φm′(x) corresponding to wm′ to 1. Examiner note: the reference describes that the evaluation value Vm of each basis function φm(x) is determined based on the coefficient Wm output by the machine learning unit 1051 and the function of the basis function φm(x), as evaluated by the function of basis function evaluation unit 1052. The evaluation operation quantifies the contribution that each φm(x) makes to the overall estimation function f(x), depending on the magnitude of its corresponding coefficient. Under the broadest reasonable interpretation (BRI), the claimed “product of the coefficient and a maximum value of the basis function” includes any computational operation that determines the combined magnitude of a coefficient and its corresponding basis function when that basis function contributes its maximum effect to the estimation function . Specifically, the reference discloses that when Wm =0, the evaluation value Vm is set to 0 (indicating no contribution); and when Wm is not equal to 0, Vm is set to 1 (indicating the basis function contributes its full or maximum value). The evaluation value Vm represents the maximum contribution state of the basis function φm(x) relative to its coefficient Wm, and the determination of Vm based on Wm effectively expresses the interaction or product between the coefficient and the maximum value of the basis function. Therefore, the process described in paragraph [0080] reasonably reads on the limitation of “calculating a product of the coefficient and a maximum value of the basis function corresponding to the coefficient, as a degree of influence,” since the evaluation process quantifies each basis function’s influence on the estimation function by combining the coefficient magnitude and the basis function’s maximum (active) contribution state under BRI). Further, the newly cited reference Cox US 2013/0041513A1 teaches newly amended limitation “time-series data including at least one of a dependent variable and an independent variable used for a thermal fluid analysis of an electronic apparatus and generate linear regression equation of a thermal model of the electronic apparatus, by using the nonlinear function” ([0015], [0017], [0022], [0024] and [0031]). Therefore, Brunton in view of Kobayashi and Cox teach all limitations of claims 1, 9 and 10, the rejection under 35 U.S.C. 103 is maintained.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in
public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Instant Application 17/686,344
Co-pending Application 18/457,591
1. An information processing device, comprising: a memory configured to store therein time-series data including at least one of a dependent variable and an independent variable used for a thermal fluid analysis of an electronic apparatus; one or more processors coupled to the memory and configured to: generate a nonlinear function based on at least one of the dependent variable and the independent variable; generate a linear regression equation of a thermal model of the electronic apparatus, by using the nonlinear function is a basis function; estimate a coefficient of the linear regression equation; calculate a product of the coefficient and a maximum value of the basis function corresponding to the coefficient, as a degree of influence; correct the coefficient based on the degree of influence; and output the linear regression equation expressed by the corrected coefficient.
Examiner note: underlined text indicates additional limitations present in the instant application.
2. The device according to claim 1, wherein the one or more processors are configured to: update the linear regression equation by the corrected coefficient and then estimate the coefficient of the updated linear regression equation again; update the degree of influence by a product of the coefficient of the updated linear regression equation and a maximum value of the basis function corresponding to the coefficient of the updated linear regression equation; and again correct the coefficient of the updated linear regression equation based on the updated degree of influence, and the estimation of the coefficient, the calculation of the degree of influence, and the correction of the coefficient are repeated for a predetermined number of times.
4. The device according to claim 1, wherein the one or more processors are configured to correct a coefficient of the basis function, in which the degree of influence is equal to or less than a threshold, to zero.
5. The device according to claim 1, wherein the one or more processors are configured to estimate the coefficient by using a non-negative least square method.
8. The device according to claim 1, wherein a value of the dependent variable is expressed by a unit unified for each physical quantity represented by the dependent variable, and a value of the independent variable is expressed by a unit unified for each physical quantity represented by the independent variable.
9. An information processing method implemented by a computer, the method comprising: storing time-series data including at least one of a dependent variable and an independent variable used for a thermal fluid analysis of an electronic apparatus; generating a nonlinear function based on at least one of the dependent variable and the independent variable; generating a linear regression equation of a thermal model of the electronic apparatus, by using the nonlinear function is a basis function; estimating a coefficient of the linear regression equation; calculating a product of the coefficient and a maximum value of the basis function corresponding to the coefficient, as a degree of influence; correcting the coefficient based on the degree of influence; and outputting the linear regression equation expressed by the corrected coefficient.
Examiner note: underlined text indicates additional limitations present in the instant application. Further, the additional limitation of “implemented by a computer” merely specifies a conventional computer implementation of the method and would have been obvious to one of ordinary skill in the art to apply in the copending application.
10. A computer program product comprising a non-transitory computer-readable medium including instructions stored thereon, the instructions causing a computer to execute: storing time-series data including at least one of a dependent variable and an independent variable used for a thermal fluid analysis of an electronic apparatus; generating a nonlinear function based on at least one of the dependent variable and the independent variable; generating a linear regression equation of a thermal model of the electronic apparatus, by using the nonlinear function is a basis function; estimating a coefficient of the linear regression equation; calculating a product of the coefficient and a maximum value of the basis function corresponding to the coefficient, as a degree of influence; correcting the coefficient based on the degree of influence; and outputting the linear regression equation expressed by the corrected coefficient.
Examiner note: underlined text indicates additional limitations present in the instant application.
1. An information processing device comprising: a memory configured to store time-series data including at least one of a dependent variable and an independent variable; and one or more processors coupled to the memory and configured to: generate a plurality of nonlinear functions by a plurality of methods based on at least one of the dependent variable and the independent variable; mix the plurality of nonlinear functions to generate a linear regression equation used as a basis function; estimate a coefficient of the linear regression equation; calculate, for a nonlinear function generated by one of the plurality of methods among the plurality of nonlinear functions, a product of the coefficient and a maximum value of the basis function corresponding to the coefficient as a degree of influence; correct the coefficient based on the degree of influence; and output the linear regression equation represented by the corrected coefficient.
Examiner note: underlined text indicates additional limitations present in the copending application.
2. The device according to claim 1, wherein the one or more processors are configured to: update the linear regression equation with the corrected coefficient, and then estimate again a coefficient of the updated linear regression equation; update the degree of influence by a product of the coefficient of the updated linear regression equation and a maximum value of the basis function corresponding to the coefficient of the updated linear regression equation; and again correct the coefficient of the updated linear regression equation based on the updated degree of influence, and repeat the estimation of the coefficient, the calculation of the degree of influence, and the correction of the coefficient by predetermined times.
5. The device according to claim 1, wherein the one or more processors are configured to correct the coefficient of the basis function with the degree of influence equal to or less than a threshold to zero.
6. The device according to claim 1, wherein the one or more processors are configured to estimate the coefficient by a non-negative least squares method.
7. The device according to claim 1, wherein a value of the dependent variable is represented by a unit unified for each physical quantity indicated by the dependent variable, and a value of the independent variable is represented by a unit unified for each physical quantity indicated by the independent variable.
9. An information processing method comprising: storing time-series data including at least one of a dependent variable and an independent variable; generating a plurality of nonlinear functions by a plurality of methods based on at least one of the dependent variable and the independent variable; mixing the plurality of nonlinear functions to generate a linear regression equation used as a basis function; estimating a coefficient of the linear regression equation; calculating, for a nonlinear function generated by one of the plurality of methods among the plurality of nonlinear functions, a product of the coefficient and a maximum value of the basis function corresponding to the coefficient as a degree of influence; correcting the coefficient based on the degree of influence; and outputting the linear regression equation represented by the corrected coefficient.
Examiner note: underlined text indicates additional limitations present in the copending application.
10. A computer program product comprising a non-transitory computer-readable medium including programmed instructions, the instructions causing a computer to execute: storing time-series data including at least one of a dependent variable and an independent variable; generating a plurality of nonlinear functions by a plurality of methods based on at least one of the dependent variable and the independent variable; mixing the plurality of nonlinear functions to generate a linear regression equation used as a basis function; estimating a coefficient of the linear regression equation; calculating, for a nonlinear function generated by one of the plurality of methods among the plurality of nonlinear functions, a product of the coefficient and a maximum value of the basis function corresponding to the coefficient as a degree of influence; correcting the coefficient based on the degree of influence; and outputting the linear regression equation represented by the corrected coefficient.
Examiner note: underlined text indicates additional limitations present in the copending application.
Claims 1, 2, 4-5 and 8-10 are provisionally rejected on the ground of nonstatutory double patenting
as being unpatentable over claims 1-2, 5-7 and 9-10 of copending Application No. 18457591 in view of Cox US 20130041513A1. Suzuki ‘591 teaches the dependent variable and the independent variable and generate a linear regression equation; However, Suzuki ‘591 fails to teach variables used for a thermal fluid analysis of an electronic apparatus and generating linear regression equation of a thermal model of the electronic apparatus. Cox teaches variables used for a thermal fluid analysis of an electronic apparatus and generating linear regression equation of a thermal model of the electronic apparatus ([0015], “… the power consumption of a component (e.g., a microprocessor) can be sensed over a period of time. This type of power consumption and associated time interval data can then be correlated with direct temperature measurements of the component taken during testing.” [0017], “This is illustrated in the example, simulated temperature response graph of FIG. 1. This graph shows the simulated behavior of actual battery temperature (degrees) over time (minutes)…” Examiner note: time-series data (temperature over time) that includes an independent variable (power consumption) and a dependent variable (temperature) used for thermal analysis of an electronic apparatus); [0022], “A generic thermal management process or system that uses a thermal time constant to compute or estimate the real temperature behavior (over a given time interval) of a target location …” [0024], “…, more Sophisticated Statistical analysis techniques may be applied to predict the real thermal behavior of the target location—see FIG. 2C. As shown in that figure, the digital filter output of multiple sensors 201_1, 201_2. . . . (using multiple digital filters 203_1, 203_2. . . . ) are transformed through a mathematical “thermal model implemented by a temperature calculator 204, into a final prediction of the temperature at the target location. Techniques to build this thermal model include, but are not limited to, Principal Component Analysis and Multiple Linear Regression.” [0031], “In one case, a linear relationship between the two temperatures may be derived, in the form of Battery hot spot=K1+K2*processed RF temp sensor (equation 1) where K1 and K2 are constants that are selected to best fit a curve (here, a straight line) to the experimental data representing the actual battery hot spot temperature …” Examiner note: the reference teaches generating a thermal model ([0022] and [0024]) that estimates temperature behavior of a target location using nonlinear digital filtering (processed RF temperature), and deriving a linear regression equation ([0031]) between the estimated temperature (battery hot spot) and the processed RF temperature)). It would have been obvious to one of ordinary skill in the art, before the effective filing data, to have modified dependent variable and the independent variable and generate a linear regression equation recited in Suzuki ‘591 to have variables used for a thermal fluid analysis of an electronic apparatus and linear regression equation of a thermal model of the electronic apparatus, as taught by Cox, in order to improving regression stability and applicability of the regression system for modeling thermal characteristics of electronic apparatus.
Claim 3 is rejected on the ground of nonstatutory double patenting as being unpatentable
over claim 1 of U.S. Patent No. 18457591 in view of Cox US 20130041513A1 and “Discovering governing equations from data: Sparse identification of nonlinear dynamical systems” by Brunton, published on Sep. 2015. Suzuki ‘591 teaches the dependent variable and the independent variable; However, Suzuki ‘591 and Cox fail to teach the dependent variable and the independent variable have an unnormalized value. Brunton teaches the dependent variable and the independent variable have an unnormalized value (page.4, par. 1-3, “we are concerned with identifying the governing equations that underly a physical system based on data that may be realistically collected in simulations or experiments. Generically, …, For example, the Lorenz system in Eq. (22c) has very few terms in the space of polynomial functions … we collect a time-history of the state x(t) and its derivative ẋ(t) sampled at a number of instances in time t1, t2, … tm.” Examiner note: identifying the governing equations that underly a physical system based on data that may be realistically collected in simulations or experiments and Lorenz system is interpreted as dependent variable and independent variable are raw, unnormalized physical data are used). It would have been obvious to one of ordinary skill in the art, before the effective filing data, to have modified dependent variable and the independent variable recited in Suzuki ‘591 and Cox to have an unnormalized value, as taught by Brunton, in order to obtain a more accurate and physically regression model that reflects real-world data collected from simulations or experiment.
Claim 6 is rejected on the ground of nonstatutory double patenting as being unpatentable
over claim 1 of U.S. Patent No. 18457591 in view of Cox US 20130041513A1 and “Discovering governing equations from data: Sparse identification of nonlinear dynamical systems” by Brunton, published on Sep. 2015. Suzuki ‘591 teaches the memory is configured to store therein a plurality of types of the time-series data; However, Suzuki ‘591 and Cox fail to teach the types of time-series data are time-series data in which at least one of an initial condition and a boundary condition differs. Brunton teaches the types of time-series data are time-series data in which at least one of an initial condition and a boundary condition differs (page.12, par.1, “For this example, we use the standard parameters … For this example, we use the standard parameters … an initial condition ... Data is collected from t = 0 to t = 100 with a time-step of Δt = 0:001; see also figure. 5, trajectories of the Lorenz system. The exact system is shown in black … and the sparse identified system is shown in the dashed red arrow …; examiner note: different noise and initial condition settings). It would have been obvious to one of ordinary skill in the art, before the effective filing data, to have modified the types of time-series data recited in Suzuki ‘591 and Cox to have at least one of an initial condition and a boundary condition differs, as taught by Brunton, in order to capture a large range of dynamics and improve the model to reflect real-world data.
Claim 7 is rejected on the ground of nonstatutory double patenting as being unpatentable
over claim 1 of U.S. Patent No. 18457591 in view of Cox US 20130041513A1 and “Discovering governing equations from data: Sparse identification of nonlinear dynamical systems” by Brunton, published on Sep. 2015. Suzuki ‘591 teaches linear regression equation; However, Suzuki ‘591 and Cox fail to teach a left-hand side of the linear regression equation includes a time differential of the dependent variable. Brunton teaches a left-hand side of the linear regression equation includes a time differential of the dependent variable (Code 1: Sparse representation algorithm in Matlab. “Xi = Theta\dXdt;” examiner note: The Matlab code line shows the estimation of coefficient Ξ using a regression of dXdt (i.e., time derivative) against basis function Θ and dXdt is the derivative of the dependent variable with respect to time (see also equation (7) and derivatives ẋ is interpreted as left-hand side of the linear regression equation includes a time differential of the dependent variable ). It would have been obvious to one of ordinary skill in the art, before the effective filing data, to have modified linear regression equation recited in Suzuki ‘591 and Cox to have a time differential of the dependent variable on a left-hand side, as taught by Brunton, in order to model the evolution of system dynamics over time and facilitate sparse identification of differential equations that enables more accurate characterization of dynamic responses.
This is a provisional nonstatutory double patenting 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.
The claim(s) 1-10 are rejected under 35 USC § 101 because the claimed invention is
directed to judicial exception an abstract idea, it has not been integrated into practical application and the claims further do not recite significantly more than the judicial exception. Examiner has evaluated the claims under the framework provided in the 2019 Patent Eligibility Guidance published in the Federal Register 01/07/2019 and has provided such analysis below.
Step 1: Are the claims to a process, machine, manufacture or composition of matter?"
Yes, Claims 1-8 are directed to device and fall within the statutory category of machine;
Yes, Claims 9 is directed to method and fall within the statutory category of process;
Yes, Claims 10 is directed to a computer program product comprising non-transitory computer-readable storage medium and fall within the statutory category of article of manufacture.
In order to evaluate the Step 2A inquiry "Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?" we must determine, at Step 2A Prong 1, whether the claim recites a law of nature, a natural phenomenon or an abstract idea and further whether the claim recites additional elements that integrate the judicial exception into a practical application.
Step 2A Prong 1:
Claim 1: The limitations of “generate a nonlinear function based on at least one of the dependent variable and the independent variable;” as drafted, recites a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation (BRI), can be reasonably performed in the human mind. A person, for example, is capable of observing and evaluating relationship between dependent variable and independent variable mentally formulating a nonlinear function (The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011).).
Further, Claim 1: The limitations of “generate a linear regression equation of a thermal model of the electronic apparatus, by using the nonlinear function as a basis function;” as drafted, recites a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation (BRI), can be reasonably performed in the human mind. A person, for example, is capable of evaluating nonlinear terms (e.g., X2, sin(x)) and mentally formulating a regression equation representing a relationship between variables of a thermal model of an electronic apparatus, such as temperature versus power or current, using the terms as basis functions (The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011).).
Further, Claim 1: The limitations of “estimate a coefficient of the linear regression equation;” as drafted, recites a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation (BRI), can be reasonably performed in the human mind. A person, for example, is capable of analyzing the relationship between variables of the thermal model of the electronic apparatus (e.g., temperature, power, or current), evaluating all necessary variables and terms of the regression equation, mentally determining the coefficient values that best fit the regression relationship (The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011).).
Further, Claim 1: The limitations of “calculate a product of the coefficient and a maximum value of the basis function corresponding to the coefficient, as a degree of influence;” as drafted, recites a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation (BRI), can be reasonably performed in the human mind. A person, for example, is capable of identifying the coefficient value derived from the regression relationship, determining the maximum value of the basis function that corresponds to the coefficient (e.g., identifying sin(x) reaches 1 or X2 reaching its highest observed value within a given range), mentally performing a multiplication these two values to obtain a degree of influence that reflects the relative contribution of the corresponding term to the overall model (The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011).).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the
limitation in the mind but for the recitation of generic computer components, then it falls within the
“Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under
Prong I step 2A.
In MPEP 2106.04(II)(B): A claim may recite multiple judicial exceptions. For example, claim 4 at issue in Bilski v. Kappos, 561 U.S. 593, 95 USPQ2d 1001 (2010) recited two abstract ideas, and the claims at issue in Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 101 USPQ2d 1961 (2012) recited two laws of nature. However, these claims were analyzed by the Supreme Court in the same manner as claims reciting a single judicial exception, such as those in Alice Corp., 573 U.S. 208, 110 USPQ2d 1976.
Claim 1: The limitations of “generate a nonlinear function based on at least one of the dependent variable and the independent variable; generate a linear regression equation of a thermal model of the electronic apparatus, by using the nonlinear function as a basis function; estimate a coefficient of the linear regression equation; calculate a product of the coefficient and a maximum value of the basis function corresponding to the coefficient, as a degree of influence; correct the coefficient based on the degree of influence” as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation (BRI) in light of specification, can be reasonably considered to represent mathematical concept, specifically:
MPEP 2106.4(a)(2)(I): “The mathematical concepts grouping is defined as mathematical
relationships, mathematical formulas or equations, and mathematical calculations”.
MPEP 2106.04(a)(2)(I)(A), “A mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols.”
Further, MPEP recites: “For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.
The limitations of “generate a nonlinear function based on at least one of the dependent variable and the independent variable” can be considered to represent mathematical concepts, it recites the generation of a mathematical relationship between variables, expressed as a nonlinear function. In the specification, page. 9, “The nonlinear function generation module 12 generates a nonlinear function on the basis of at least one of the dependent variable and the independent variable. For example, the nonlinear function generation module 12 generates a nonlinear function on the basis of temperature Ti at a position i, and temperature T at a position j.” (See MPEP 2106.04(a)(2)(I)).
The limitations of “generate a linear regression equation of a thermal model of the electronic apparatus, by using the nonlinear function as a basis function;” can be considered to represent mathematical concepts, it recites the formulation of a mathematical relationship between a linear regression equation and a nonlinear function used as a basis function. In the specification, page. 9, “The regression equation generation module 13 generates a linear regression equation in which the nonlinear function generated by the nonlinear function generation module 12 is used as a basis function.” (See MPEP 2106.04(a)(2)(I)).
The limitations of “estimate a coefficient of the linear regression equation” can be considered to represent mathematical concepts, it recites the use of a mathematical formulas and calculations to determine regression coefficients. In the specification, page. 10, “the estimation module 14 estimates the coefficient of the linear regression equation generated by the regression equation generation module 13 using a non-negative least square method in the following equation (6) (step S2).” (See MPEP 2106.04(a)(2)).
The limitations of “calculate a product of the coefficient and a maximum value of the basis function corresponding to the coefficient, as a degree of influence” can be considered to represent mathematical concepts, it is using a mathematical formula and calculation defining how numerical values are derived. In the specification, page. 9-10, “The calculation module 15 calculates a degree of influence on the basis of the magnitude of the term (coefficient × basis function). The value of the basis function (for example, Ti−Tj) changes over time. Thus, the maximum value in the time-series data is assumed as a representative value of the basis function. More specifically, the degree of influence is expressed by magnitude of term=coefficient ξkj × representative value of basis function maxi|θik|. That is, the calculation module 15 calculates a product of the coefficient estimated by the estimation module 14 and the maximum value of the basis function corresponding to the coefficient, as a degree of influence.” (See MPEP 2106.04(a)(2)).
The limitations “correct the coefficient based on the degree of influence;” can be considered to represent mathematical concepts, it is using mathematical relationships and calculations that define how coefficients are numerically adjusted according to threshold conditions. In the specification, page. 11 - 12, “when the calculation module 15 calculates the degree of influence (magnitude of term) described above, and when the correction module 16 corrects the coefficient of the basis function in which the degree of influence is equal to or less than a threshold, to zero, the basis function in which the degree of influence is equal to or less than a threshold is deleted (step S3). For example, the threshold is represented by the right-hand side of the following equation (7). ξkj maxi∥θik ∥≤tol×Σ k(ξkj maxi∥θik∥) (7) … The threshold may also be the right-hand side of the following equation (8). That is, the correction module 16 may also correct the coefficient of the basis function θk corresponding to the term (ξkj maxi∥θik∥), which is equal to or less than tol times of the term that has affected the left-hand side most, to zero. ξkj maxi∥θik ∥≤tol×maxk(ξkj maxi∥θik∥) (8)”. (See MPEP 2106.04(a)(2)).
Claims 9 and 10 recite the similar elements as claim 1, and are rejected for the same reasons under 35 U.S.C. 101.
Therefore, claims 1, 9 and 10 recite judicial exceptions. The claims have been identified to recite judicial exceptions, Step 2A Prong 2 will evaluate whether the claims as a whole integrates the exception into a practical application of that exception.
Step 2A Prong 2: Claims 1, 9 and 10: The judicial exception is not integrated into a practical application.
In particular, the claims recite the following additional elements - "An information processing device, comprising: a memory configured to store therein time-series data including at least one of a dependent variable and an independent variable used for a thermal fluid analysis of an electronic apparatus; one or more processors coupled to the memory and configured to:” and “A computer program product comprising a non-transitory computer-readable medium including instructions stored thereon, the instructions causing a computer to execute:” and “An information processing method implemented by a computer …” which are mere instruction to implement an abstract idea on a computer, or merely uses a computer as tool to perform an abstract idea (see MPEP § 2106.05(f)) with the broad reasonable interpretation, which does not integrate a judicial exception into elements.
Further, the following additional element – “store therein time-series data including at least one of a dependent variable and an independent variable used for a thermal fluid analysis of an electronic apparatus” and “output the linear regression equation expressed by the corrected coefficient.” which is merely a recitation of insignificant extra-solution data gathering (i.e., store time-series data) and data output (i.e., output linear regression equation) activity (see MPEP § 2106.05(g)) which does not integrate a judicial exception into practical application. The insignificant extra-solution activities are further addressed below under step 2B as also being Well-Understood, Routine, and Conventional (WURC).
Therefore, "Do the claims recite additional elements that integrate the judicial exception into a practical application? No, these additional elements do not integrate the abstract idea into a practical application and they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
After having evaluated the inquires set forth in Steps 2A Prong 1 and 2, it has been concluded that claims 1 , 9 and 10 not only recite a judicial exception but that the claims are directed to the judicial exception as the judicial exception has not been integrated into practical application.
Step 2B: Claims 1, 9 and 10: The claims do not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than generic computing components which do not amount to significantly more than the abstract idea. Limitations that the courts have found not to be enough to qualify as "significantly more" when recited in a claim with a judicial exception include: i. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984 (see MPEP § 2106.05(f)); ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)); iii. Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g)) ; …
The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, …; iii. Electronic recordkeeping, … (updating an activity log).
Other examples where the courts have found the additional elements to be mere instructions to apply an exception, because they do no more than merely invoke computers or machinery as a tool to perform an existing process include: i. A commonplace business method or mathematical algorithm being applied on a general purpose computer, Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); ii. Generating a second menu from a first menu and sending the second menu to another location as performed by generic computer components, Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1243-44, 120 USPQ2d 1844, 1855-57 (Fed. Cir. 2016); iii. A process for monitoring audit log data that is executed on a general-purpose computer where the increased speed in the process comes solely from the capabilities of the general-purpose computer, FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016); iv. A method of using advertising as an exchange or currency being applied or implemented on the Internet, Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 715, 112 USPQ2d 1750, 1754 (Fed. Cir. 2014); v. Requiring the use of software to tailor information and provide it to the user on a generic computer, Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1370-71, 115 USPQ2d 1636, 1642 (Fed. Cir. 2015); and vi. A method of assigning hair designs to balance head shape with a final step of using a tool (scissors) to cut the hair, In re Brown, 645 Fed. App'x 1014, 1017 (Fed. Cir. 2016) (non-precedential).
The additional limitations “An information processing device, comprising: a memory configured to store therein time-series data including at least one of a dependent variable and an independent variable used for a thermal fluid analysis of an electronic apparatus; one or more processors coupled to the memory and configured to:” and “A computer program product comprising a non-transitory computer-readable medium including instructions stored thereon, the instructions causing a computer to execute:” and “An information processing method implemented by a computer …” and “store therein time-series data including at least one of a dependent variable and an independent variable used for a thermal fluid analysis of an electronic apparatus” and “output the linear regression equation expressed by the corrected coefficient” do not provide significantly more than the judicial exception. The recited steps merely describe well-understood, routine, and conventional computer functions, such as storing, processing, and outputting data, that are performed by a generic computing device. The claim does not recite any specific technical improvement to the functioning of the computer, or to any another technical field. Instead, the additional limitations simply apply the abstract operations (i.e., mathematical concepts and/or mental process) on a computer in a conventional and expected manner. Therefore, the additional elements, individually or in combination, amount to no more than applying conventional computer components to perform well-understood operations, which is insufficient to qualify as “significantly more” than the abstract idea under Step 2B.
Therefore, "Do the claims recite additional elements that amount to significantly more than the judicial exception? No, these additional elements, alone or in combination, do not amount to significantly more than the judicial exception. Having concluded analysis within the provided framework, claims 1, 9 and 10 do not recite patent eligible subject matter under 35 U.S.C. § 101.
Dependent claims 2-8 are also similar rejected under same rationale as cited above wherein these claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. These claims are merely further elaborate the mental process itself (and/or mathematical operations) or providing additional definition of process which does not impose any meaningful limits on practicing the abstract idea. Claims 2-8 are also rejected for incorporating the deficiency of their independent claim 1.
Claim 2: Recites, “The device according to claim 1, wherein the one or more processors are configured to: update the linear regression equation by the corrected coefficient and then estimate the coefficient of the updated linear regression equation again; update the degree of influence by a product of the coefficient of the updated linear regression equation and a maximum value of the basis function corresponding to the coefficient of the updated linear regression equation; and again correct the coefficient of the updated linear regression equation based on the updated degree of influence, and the estimation of the coefficient, the calculation of the degree of influence, and the correction of the coefficient are repeated for a predetermined number of times.”
The limitation merely specifies adjusting equation, estimating coefficient, then adjusting degree of influence as repeat correction until satisfies desired criteria; therefore, it merely extension of mental process and mathematical concepts similar to claim 1. Therefore, the office finds that the claim 2 is ineligible under 35 USC 101.
Claim 3: Recites, “The device according to claim 1, wherein the dependent variable and the independent variable have an unnormalized value.”
The limitation further defines dependent variable and the independent variable have an unnormalized value stored in the memory; therefore, it merely a recitation of insignificant extra-solution data gathering (i.e., store time-series data) activity (see MPEP § 2106.05(g)) which does not integrate a judicial exception into practical application. Therefore, the office finds that the claim 3 is ineligible under 35 USC 101.
Claim 4: Recites, “The device according to claim 1, wherein the one or more processors are configured to correct a coefficient of the basis function, in which the degree of influence is equal to or less than a threshold, to zero” as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation (BRI), covers performance of the limitation in the mind. A person, for example, is capable of evaluating a numerical value (i.e., coefficient), and make a judgement to adjust or update the value, such as reducing it, zeroing it, or otherwise modifying it (The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011).).
The limitation “correct a coefficient of the basis function” also can be considered to represent mathematical concepts. In the instant specification, page. 11 - 12, “when the calculation module 15 calculates the degree of influence (magnitude of term) described above, and when the correction module 16 corrects the coefficient of the basis function in which the degree of influence is equal to or less than a threshold, to zero, the basis function in which the degree of influence is equal to or less than a threshold is deleted (step S3). For example, the threshold is represented by the right-hand side of the following equation (7). ξkj maxi∥θik ∥≤tol×Σ k(ξkj maxi∥θik∥) (7) … The threshold may also be the right-hand side of the following equation (8). That is, the correction module 16 may also correct the coefficient of the basis function θk corresponding to the term (ξkj maxi∥θik∥), which is equal to or less than tol times of the term that has affected the left-hand side most, to zero. ξkj maxi∥θik ∥≤tol×maxk(ξkj maxi∥θik∥) (8)”. (See MPEP 2106.04(a)(2)). Therefore, the office finds that the claim 4 is ineligible under 35 USC 101.
Claim 5: Recites, “The device according to claim 1, wherein the one or more processors are configured to estimate the coefficient by using a non-negative least square method.” as drafted, recites a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation (BRI), can be reasonably performed in the human mind. A person, for example, is capable of evaluating all necessary variables and terms, analyzing the regression equation, mentally compute a coefficient by using a non-negative least square method (The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011).).
The limitation “estimate a coefficient” also can be considered to represent mathematical concepts. In the instant specification, page. 10, “he estimation module 14 estimates the coefficient of the linear regression equation generated by the regression equation generation module 13 using a non-negative least square method in the following equation (6) (step S2).” (See MPEP 2106.04(a)(2)). Therefore, the office finds that the claim 5 is ineligible under 35 USC 101.
Claim 6: Recites, “The device according to claim 1, wherein the memory is configured to store therein a plurality of types of the time-series data, and the types of time-series data are time-series data in which at least one of an initial condition and a boundary condition differs.”
The limitation further defines different types of time-series data including at least one of an initial condition and a boundary condition differs that stored in the memory; therefore, it merely a recitation of insignificant extra-solution data gathering (i.e., store time-series data) activity (see MPEP § 2106.05(g)) which does not integrate a judicial exception into practical application. Therefore, the office finds that the claim 6 is ineligible under 35 USC 101.
Claim 7: Recites, “The device according to claim 1, wherein a left-hand side of the linear regression equation includes a time differential of the dependent variable.”
The limitation further defines the generated linear regression equation includes a time differential of the dependent variable; therefore, it merely mathematical concepts (i.e., see instant specification, page.3, eq(3)). Therefore, the office finds that the claim 7 is ineligible under 35 USC 101.
Claim 8: Recites, “The device according to claim 1, wherein a value of the dependent variable is expressed by a unit unified for each physical quantity represented by the dependent variable, and a value of the independent variable is expressed by a unit unified for each physical quantity represented by the independent variable.”
The limitation further defines dependent variable and independent variable are expressed by a unit unified; therefore, it merely an extension of mental process and mathematical concepts. Therefore, the office finds that the claim 8 is ineligible under 35 USC 101.
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) 1-4, 6-7 and 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over
“Discovering governing equations from data: Sparse identification of nonlinear dynamical systems” by Brunton, published on Sep. 2015 in view of Kobayashi US 20120016822A1 and Cox US20130041513A1.
Claim 1, Brunton teaches An information processing device (page.6, Code 1: Sparse representation algorithm in Matlab; Note: a POSITA would understand that the Matlab is inherently executed on a computing system including memory and processor is interpreted as information processing device), comprising:
a memory configured to store therein time-series data including at least one of a dependent variable and an independent variable (page.4., Section 3 Sparse identification of nonlinear dynamics (SINDy), par.3, “To determine the form of the function f from data, we collect a time-history of the state x(t) and its derivative ẋ(t) sampled at a number of instances in time t1, t2, … tm.” In equation (3), x(t) is independent variable, ẋ(t) is dependent claim; page.3, par.1, “… we will generalize Eq. (3) to allow the dynamics f to vary in time,…”);
one or more processors coupled to the memory and configured to:
generate a nonlinear function based on at least one of the dependent variable and the independent variable (page.4, par.4, “Next, we construct an augmented library Θ(X) consisting of candidate nonlinear functions of the columns of X. For example, Θ(X) may consist of constant, polynomial and trigonometric terms: equation (5)”);
generate a linear regression equation of (page.5, “a library of nonlinear functions of the states, Θ(X), is constructed. This nonlinear feature library is used to find the fewest terms needed to satisfy X = Θ(X) Ξ . The few entries in the vectors of Ξ, solved for by sparse regression, …” Examiner note: The nonlinear function (e.g., 1, X, X2 …etc. (see eq (5)) form the basis function set Θ(X), the coefficients are determined by linear regression, since the equation is linear in the coefficients even though the basis functions are nonlinear);
estimate a coefficient of the linear regression equation (page.6, par.4, “An alternative is to implement the sequential threshold least-squares algorithm in Code (1).In this algorithm, we start with a least-squares solution for Ξ (note: i.e., coefficient matrix) and then threshold all coefficients that are smaller than some cutoff value λ. Once the indices of the remaining non-zero coefficients are identified, we obtain another least-squares solution for Ξ onto the remaining indices.” See also Code 1: Sparse representation algorithm in Matlab. “%% compute Sparse regression: sequential least squares Xi = Theta\dXdt; % initial guess: Least-squares” examiner note: i.e., a matrix solve to estimate the coefficients Ξ of the regression equation);
output the linear regression equation expressed by the corrected coefficient (page. 6, Code 1: Sparse representation algorithm in Matlab, “Xi(smallinds)=0; % and threshold”; examiner note: the code sets small coefficients in Ξ to zero that resulting sparse coefficient matrix with basis function in Θ(X) defines the corrected regression equation, a POSITA would understand the standard Matlab function includes a disp() or print() function for output the result.).
However, Brunton fails to teach calculate a product of the coefficient and a maximum value of the basis function corresponding to the coefficient, as a degree of influence; correct the coefficient based on the degree of influence.
Kobayashi teaches calculate a product of the coefficient and a maximum value of the basis function corresponding to the coefficient, as a degree of influence ([0080], “If the vector w is obtained by the machine learning unit 1051, the estimation function generation unit 105 sets an evaluation value vm of the basis function φm(x) by the function of the basis function evaluation unit 1052. For example, the basis function evaluation unit 1052 sets an evaluation value vm of the unselected basis function φm(x) to 0, and sets an evaluation value vm of the selected basis function φm(x) to 1. For example, if wm′=0, the basis function evaluation unit 1052 sets an evaluation value vm′ of a basis function φm′(x) corresponding to wm′ to 0. If wm′≠0, the basis function evaluation unit 1052 sets the evaluation value vm′ of the basis function φm′(x) corresponding to wm′ to 1.” - Examiner note: the reference describes that the evaluation value Vm of each basis function φm(x) is determined based on the coefficient Wm output by the machine learning unit 1051 and the function of the basis function φm(x), as evaluated by the function of basis function evaluation unit 1052. The evaluation operation quantifies the contribution that each φm(x) makes to the overall estimation function f(x), depending on the magnitude of its corresponding coefficient. Under the broadest reasonable interpretation (BRI), the claimed “product of the coefficient and a maximum value of the basis function” includes any computational operation that determines the combined magnitude of a coefficient and its corresponding basis function when that basis function contributes its maximum effect to the estimation function . Specifically, the reference discloses that when Wm =0, the evaluation value Vm is set to 0 (indicating no contribution); and when Wm is not equal to 0, Vm is set to 1 (indicating the basis function contributes its full or maximum value). The evaluation value Vm represents the maximum contribution state of the basis function φm(x) relative to its coefficient Wm, and the determination of Vm based on Wm effectively expresses the interaction or product between the coefficient and the maximum value of the basis function. Therefore, the process described in paragraph [0080] reasonably reads on the limitation of “calculating a product of the coefficient and a maximum value of the basis function corresponding to the coefficient, as a degree of influence,” since the evaluation process quantifies each basis function’s influence on the estimation function by combining the coefficient magnitude and the basis function’s maximum (active) contribution state under BRI); correct the coefficient based on the degree of influence ([0099] If the predetermined termination condition has been satisfied, the estimation function generation unit 105 generates the estimation function f(x) on the basis of a of a τ-th-generation basis function φm,τ (m=1 to Mτ) and a weight vector w having a largest AIC value. At this time, the basis function φm,τ corresponding to a zero element in the weight vector w is discarded. The estimation function f(x) generated by the estimation function generation unit 105 is input to the function output unit 106. If the estimation function f(x) is input, the function output unit 106 outputs the input estimation function f(x). Examiner note: the reference teaches when the estimation process terminates, the estimation function generation unit 105 generates the final estimation function f(x) using the set of basis functions φm,τ and corresponding weight vector w, while discarding basis functions that corresponding coefficient are zero. Under the broadest reasonable interpretation (BRI), the process reasonably reads on the limitation of “correct the coefficient based on the degree of influence,” since the system selectively eliminates or retains coefficients depending on the evaluated contribution (influence) to the estimation function, and discarding coefficients corresponding to non-influence basis functions as an adjustment or correction of the coefficient values based on the previously determined evaluation (degree of influence). See also [0157]-[0158] discloses the update and normalization steps directly depend on each coefficient’s contribution to the model output (error based influence measure)).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Brunton to incorporate the teachings of Kobayashi, and apply the coefficient-based evaluation of basis functions to determine and correct coefficient values based on the degree of influence in order to improve the regression accuracy and stability of the generated estimation function by eliminating or adjusting basis functions that contribute less influence to the overall model. In this case, Brunton teaches generating a nonlinear library of basis function and linear regression equation, estimating sparse coefficients and output a corrected sparse regression. Kobayashi teaches evaluating basis functions using a coefficient evaluation value and the basis functions corresponding to zero weight coefficients are eliminated as correction. The combination of teachings would provide benefit of refining coefficient values and reduces the effect of low impact basis functions, proving a more accurate and robust sparse regression model.
However, Brunton and Kobayashi fail to teach time-series data including at least one of a dependent variable and an independent variable used for a thermal fluid analysis of an electronic apparatus and generate linear regression equation of a thermal model of the electronic apparatus, by using the nonlinear function.
Cox teaches time-series data including at least one of a dependent variable and an independent variable used for a thermal fluid analysis of an electronic apparatus (intend use. [0015], “… the power consumption of a component (e.g., a microprocessor) can be sensed over a period of time. This type of power consumption and associated time interval data can then be correlated with direct temperature measurements of the component taken during testing.” [0017], “This is illustrated in the example, simulated temperature response graph of FIG. 1. This graph shows the simulated behavior of actual battery temperature (degrees) over time (minutes)…” Examiner note: time-series data (temperature over time) that includes an independent variable (power consumption) and a dependent variable (temperature) used for thermal analysis of an electronic apparatus); generate linear regression equation of a thermal model of the electronic apparatus (intend use), by using the nonlinear function ([0022], “A generic thermal management process or system that uses a thermal time constant to compute or estimate the real temperature behavior (over a given time interval) of a target location …” [0024], “…, more Sophisticated Statistical analysis techniques may be applied to predict the real thermal behavior of the target location—see FIG. 2C. As shown in that figure, the digital filter output of multiple sensors 201_1, 201_2. . . . (using multiple digital filters 203_1, 203_2. . . . ) are transformed through a mathematical “thermal model implemented by a temperature calculator 204, into a final prediction of the temperature at the target location. Techniques to build this thermal model include, but are not limited to, Principal Component Analysis and Multiple Linear Regression.” [0031], “In one case, a linear relationship between the two temperatures may be derived, in the form of Battery hot spot=K1+K2*processed RF temp sensor (equation 1) where K1 and K2 are constants that are selected to best fit a curve (here, a straight line) to the experimental data representing the actual battery hot spot temperature …” Examiner note: the reference teaches generating a thermal model ([0022] and [0024]) that estimates temperature behavior of a target location using nonlinear digital filtering (processed RF temperature), and deriving a linear regression equation ([0031]) between the estimated temperature (battery hot spot) and the processed RF temperature)).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Brunton and Kobayashi to incorporate the teachings of Cox, and apply time-series sensor data and thermal correlation modeling to enhance the regression-based thermal prediction process in order to improve modeling accuracy and reflect real-world thermal response characteristics of electronic apparatuses. In this case, Brunton teaches generating a nonlinear library of basis function and linear regression equation, estimating sparse coefficients and output a corrected sparse regression. Kobayashi teaches evaluating basis functions using a coefficient evaluation value and the basis functions corresponding to zero weight coefficients are eliminated as correction. Cox teaches correlating time-series power consumption and temperature data of an electronic device, processing the temperature signal via a nonlinear digital filter, and deriving a linear regression relationship to model the device’s thermal behavior. The combination of teachings provide benefit of improving regression stability and applicability of the regression system for modeling thermal characteristics of electronic apparatus.
Claim 2, Brunton teaches The device according to claim 1, wherein
the one or more processors are configured to:
update the linear regression equation by the corrected coefficient and then estimate the coefficient of the updated linear regression equation again;
update the degree of influence by a product of the coefficient of the updated linear regression equation and a maximum value of the basis function corresponding to the coefficient of the updated linear regression equation; and
again correct the coefficient of the updated linear regression equation based on the updated degree of influence, and
the estimation of the coefficient, the calculation of the degree of influence, and the correction of the coefficient are repeated for a predetermined number of times (page. 6, Code 1: Sparse representation algorithm in Matlab, “
%% compute Sparse regression: sequential least squares
Xi = Theta\dXdt; % initial guess: Least-squares
% lambda is our sparsification knob.
for k=1:10
smallinds = (abs(Xi)<lambda); % find small coefficients
Xi(smallinds)=0; % and threshold
for ind = 1:n % n is state dimension
biginds = ˜smallinds(:,ind);
% Regress dynamics onto remaining terms to find sparse Xi
Xi(biginds,ind) = Theta(:,biginds)\dXdt(:,ind);” Examiner note: the code indicates applies a threshold (correction), recalculates coefficients (estimate) and repeat the process 10 times (a predetermined number)).
However, Brunton fails to teach the degree of influence by a product of the coefficient and a maximum value of the basis function corresponding to the coefficient; correct the coefficient based on the degree of influence.
Kobayashi teaches the degree of influence by a product of the coefficient and a maximum value of the basis function corresponding to the coefficient; correct the coefficient based on the degree of influence ([0080], “If the vector w is obtained by the machine learning unit 1051, the estimation function generation unit 105 sets an evaluation value vm of the basis function φm(x) by the function of the basis function evaluation unit 1052. For example, the basis function evaluation unit 1052 sets an evaluation value vm of the unselected basis function φm(x) to 0, and sets an evaluation value vm of the selected basis function φm(x) to 1. For example, if wm′=0, the basis function evaluation unit 1052 sets an evaluation value vm′ of a basis function φm′(x) corresponding to wm′ to 0. If wm′≠0, the basis function evaluation unit 1052 sets the evaluation value vm′ of the basis function φm′(x) corresponding to wm′ to 1.” - Examiner note: the reference describes that the evaluation value Vm of each basis function φm(x) is determined based on the coefficient Wm output by the machine learning unit 1051 and the function of the basis function φm(x), as evaluated by the function of basis function evaluation unit 1052. The evaluation operation quantifies the contribution that each φm(x) makes to the overall estimation function f(x), depending on the magnitude of its corresponding coefficient. Under the broadest reasonable interpretation (BRI), the claimed “product of the coefficient and a maximum value of the basis function” includes any computational operation that determines the combined magnitude of a coefficient and its corresponding basis function when that basis function contributes its maximum effect to the estimation function . Specifically, the reference discloses that when Wm =0, the evaluation value Vm is set to 0 (indicating no contribution); and when Wm is not equal to 0, Vm is set to 1 (indicating the basis function contributes its full or maximum value). The evaluation value Vm represents the maximum contribution state of the basis function φm(x) relative to its coefficient Wm, and the determination of Vm based on Wm effectively expresses the interaction or product between the coefficient and the maximum value of the basis function. Therefore, the process described in paragraph [0080] reasonably reads on the limitation of “calculating a product of the coefficient and a maximum value of the basis function corresponding to the coefficient, as a degree of influence,” since the evaluation process quantifies each basis function’s influence on the estimation function by combining the coefficient magnitude and the basis function’s maximum (active) contribution state under BRI). [0099] If the predetermined termination condition has been satisfied, the estimation function generation unit 105 generates the estimation function f(x) on the basis of a of a τ-th-generation basis function φm,τ (m=1 to Mτ) and a weight vector w having a largest AIC value. At this time, the basis function φm,τ corresponding to a zero element in the weight vector w is discarded. The estimation function f(x) generated by the estimation function generation unit 105 is input to the function output unit 106. If the estimation function f(x) is input, the function output unit 106 outputs the input estimation function f(x). Examiner note: the reference teaches when the estimation process terminates, the estimation function generation unit 105 generates the final estimation function f(x) using the set of basis functions φm,τ and corresponding weight vector w, while discarding basis functions that corresponding coefficient are zero. Under the broadest reasonable interpretation (BRI), the process reasonably reads on the limitation of “correct the coefficient based on the degree of influence,” since the system selectively eliminates or retains coefficients depending on the evaluated contribution (influence) to the estimation function, and discarding coefficients corresponding to non-influence basis functions as an adjustment or correction of the coefficient values based on the previously determined evaluation (degree of influence). See also [0157]-[0158] discloses the update and normalization steps directly depend on each coefficient’s contribution to the model output (error based influence measure)).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Brunton to incorporate the teachings of Kobayashi, and apply the coefficient-based evaluation of basis functions to determine and correct coefficient values based on the degree of influence in order to improve the regression accuracy and stability of the generated estimation function by eliminating or adjusting basis functions that contribute less influence to the overall model. In this case, Brunton teaches generating a nonlinear library of basis function and linear regression equation, estimating sparse coefficients, output a corrected sparse regression and using Matlab to perform iteration for correction, estimation and set a predetermined repeat times. Kobayashi teaches evaluating basis functions using a coefficient evaluation value and the basis functions corresponding to zero weight coefficients are eliminated as correction. The combination of teachings would provide benefit of refining coefficient values and reduces the effect of low impact basis functions, proving a more accurate and robust sparse regression model.
Claim 3, Brunton further teaches The device according to claim 1, wherein the dependent variable and the independent variable have an unnormalized value (page.4, par. 1-3, “we are concerned with identifying the governing equations that underly a physical system based on data that may be realistically collected in simulations or experiments. Generically, …, For example, the Lorenz system in Eq. (22c) has very few terms in the space of polynomial functions … we collect a time-history of the state x(t) and its derivative ẋ(t) sampled at a number of instances in time t1, t2, … tm.” Examiner note: identifying the governing equations that underly a physical system based on data that may be realistically collected in simulations or experiments and Lorenz system is interpreted as dependent variable and independent variable are raw, unnormalized physical data are used).
Claim 4, However, Brunton fails to teach the one or more processors are configured to correct a coefficient of the basis function, in which the degree of influence is equal to or less than a threshold, to zero.
Kobayashi teaches correct a coefficient of the basis function, in which the degree of influence is equal to or less than a threshold, to zero ([0080], “If the vector w is obtained by the machine learning unit 1051, the estimation function generation unit 105 sets an evaluation value vm of the basis function φm(x) by the function of the basis function evaluation unit 1052. For example, the basis function evaluation unit 1052 sets an evaluation value vm of the unselected basis function φm(x) to 0, and sets an evaluation value vm of the selected basis function φm(x) to 1. For example, if wm′=0, the basis function evaluation unit 1052 sets an evaluation value vm′ of a basis function φm′(x) corresponding to wm′ to 0. If wm′≠0, the basis function evaluation unit 1052 sets the evaluation value vm′ of the basis function φm′(x) corresponding to wm′ to 1.” [0099] If the predetermined termination condition has been satisfied, the estimation function generation unit 105 generates the estimation function f(x) on the basis of a of a τ-th-generation basis function φm,τ (m=1 to Mτ) and a weight vector w having a largest AIC value. At this time, the basis function φm,τ corresponding to a zero element in the weight vector w is discarded. The estimation function f(x) generated by the estimation function generation unit 105 is input to the function output unit 106. If the estimation function f(x) is input, the function output unit 106 outputs the input estimation function f(x).” - Examiner note: the reference describes that the evaluation value Vm of each basis function φm(x) is determined based on the coefficient Wm output by the machine learning unit 1051 and the function of the basis function φm(x), as evaluated by the function of basis function evaluation unit 1052. The evaluation operation quantifies the contribution that each φm(x) makes to the overall estimation function f(x), depending on the magnitude of its corresponding coefficient. Under the broadest reasonable interpretation (BRI), the claimed “correct a coefficient of the basis function, in which the degree of influence is equal to or less than a threshold, to zero” includes any computational process that selectively eliminates or adjusts coefficient values based on the evaluated degree of influence. The reference explicitly discloses that when the estimation process terminates, basis functions correspond to coefficients having zero or low influence are discarded or corrected ([0099]), thereby efficiently setting the coefficients of insufficiently influential basis function to zero).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Brunton to incorporate the teachings of Kobayashi, and apply the coefficient-based evaluation of basis functions to determine and correct coefficient values based on the degree of influence in order to improve the regression accuracy and stability of the generated estimation function by selectively eliminating or adjusting basis functions that contribute less influence to the overall model. In this case, Brunton teaches generating a nonlinear library of basis function and linear regression equation, estimating sparse coefficients, output a corrected sparse regression. Kobayashi teaches evaluating basis functions using coefficient evaluation values and discarding basis functions corresponding to coefficients with zero or low influence as a corrective measure. The combination of teachings would provide benefit of refining coefficient values and reduces the effect of low impact basis functions to produce a more accurate and robust sparse regression model.
Claim 6, Brunton further teaches The device according to claim 1, wherein
the memory is configured to store therein a plurality of types of the time-series data, and
the types of time-series data are time-series data in which at least one of an initial condition and a boundary condition differs (page.12, par.1, “For this example, we use the standard parameters … For this example, we use the standard parameters … an initial condition ... Data is collected from t = 0 to t = 100 with a time-step of Δt = 0:001; see also figure. 5, trajectories of the Lorenz system. The exact system is shown in black … and the sparse identified system is shown in the dashed red arrow …; examiner note: different noise and initial condition settings).
Claim 7, Brunton further teaches The device according to claim 1, wherein a left-hand side of the linear regression equation includes a time differential of the dependent variable (Code 1: Sparse representation algorithm in Matlab. “Xi = Theta\dXdt;” examiner note: The Matlab code line shows the estimation of coefficient Ξ using a regression of dXdt (i.e., time derivative) against basis function Θ and dXdt is the derivative of the dependent variable with respect to time (see also equation (7) and derivatives ẋ is interpreted as left-hand side of the linear regression equation includes a time differential of the dependent variable ).
The elements of independent claims 9 and 10 are substantially the same as those of claims 1. Therefore, the elements of claims 9 and 10 are rejected due to the same reasons as outlined above for claims 1. Further, the additional elements of claim 10, “A computer program product comprising a non-transitory computer-readable medium including instructions stored thereon, the instructions causing a computer to execute:” (see Kobayashi, [0014]).
Claims 5 is rejected under 35 U.S.C. 103 as being unpatentable over Brunton and Kobayashi and
Cox as applied to claim 1 above, and further in view of “Non-negative least squares for high-dimensional linear models: consistency and sparse recovery without regularization” by Slawski, published on Feb. 2014.
Claim 5, However, Brunton and Kobayashi and Cox fail to teach the one or more processors are configured to estimate the coefficient by using a non-negative least square method.
Slawski teaches estimate the coefficient by using a non-negative least square method (abstract, “non-negativity constraints on the regression coefficients may be similarly effective as explicit regularization if the design matrix has additional properties, which are met in several applications of non-negative least squares (NNLS),” page.2, line 13-16“The NNLS problem is given by the quadratic program … which is a convex optimization problem that can be solved efficiently [27]. A minimizer β of (1.2) will be referred to as an NNLS estimator.”).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Brunton and Kobayashi and Cox to incorporate the teachings of Slawski, and apply Non-negative least squares for high-dimensional linear models because NNLS may have a better ℓ∞-rate in estimation and hence also advantages with respect to support recovery when combined with thresholding. From a practical point of view, NNLS does not depend on a regularization parameter and is hence easier to use. In this case, Brunton teaches generating a nonlinear library of basis function and linear regression equation, estimating sparse coefficients and output a corrected sparse regression. Kobayashi teaches evaluating basis functions using a coefficient evaluation value and the basis functions corresponding to zero weight coefficients are eliminated as correction. Cox teaches correlating time-series power consumption and temperature data of an electronic device, processing the temperature signal via a nonlinear digital filter, and deriving a linear regression relationship to model the device’s thermal behavior. Slawski teaches using NNLS for coefficient estimation. It would improve regression linear regression equation by eliminating low-impact basis functions; therefore, it provides benefit of robustness of the sparse model.
Claims 8 is rejected under 35 U.S.C. 103 as being unpatentable over Brunton and Kobayashi and
Cox as applied to claim 1 above, and further in view of Suzuki US 20170074813A1.
Claim 8, However, Brunton and Kobayashi and Cox fail to teach a value of the dependent variable is expressed by a unit unified for each physical quantity represented by the dependent variable, and a value of the independent variable is expressed by a unit unified for each physical quantity represented by the independent variable.
Suzuki teaches a value of the dependent variable is expressed by a unit unified for each physical quantity represented by the dependent variable ([0032], “…Examples of physical quantities indicated by indices (note: i.e., dependent variable) include, but should not be limited to, temperature, flow rate, electric power, electric energy, current value, and voltage…” examiner note: a POSITA would understand that physical quantities all have defined and consistent physical units, such as W, A, V to corresponding power, current and voltage), and a value of the independent variable is expressed by a unit unified for each physical quantity represented by the independent variable ([0033], “The index I is defined to be a dependent variable having each of the detected values x1, x2, . . . , and xn of the sensors 101 as an independent variable (note: i.e., independent variable). … The detected value, of the sensor 101a is an exemplary detected value corresponding to the environmental temperature. The detected value of the sensor 101b is an exemplary detected value corresponding to the flow rate of the airflow F (air volume of the fan 10i). The detected value of the sensor 101d is an exemplary detected value corresponding to the heat value of the heat generating element. [0054], “… the index may correspond to a different physical quantity including, for example, temperature, temperature of a heat generating element or environmental temperature…”; [0055], “… all of the sensors 101 involved in the calculation of the indices detect temperature.” Examiner note: a POSITA would understand that the multiple sensors measuring the same quantity with same unit, such as °C).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Brunton and Kobayashi and Cox to incorporate the teachings of Suzuki, and apply an electronic apparatus includes a housing, a heat generating element, a plurality of sensors, and an index calculator. The heat generating element is housed in the housing. The index calculator calculates an index corresponding to a physical quantity or an index indicating performance of the electronic apparatus, based on a correlation between detected values of the sensors and the index in order to allows users to conveniently identify an anomaly or an indication of anomaly in an electronic device or in ambient environment [0004]. In this case, Brunton teaches generating a nonlinear library of basis function and linear regression equation, estimating sparse coefficients and output a corrected sparse regression. Kobayashi teaches evaluating basis functions using a coefficient evaluation value and the basis functions corresponding to zero weight coefficients are eliminated as correction. Cox teaches correlating time-series power consumption and temperature data of an electronic device, processing the temperature signal via a nonlinear digital filter, and deriving a linear regression relationship to model the device’s thermal behavior. Suzuki teaches using physical sensor data (e.g., temperature or airflow) as independent variables and constructing indices (i.e., dependent variables) based on the independent variables, and detect same physical quantity to preserving unit consistence. It would improve regression linear regression equation by eliminating low-impact basis functions based on consistent physical unit input data; therefore, it provides benefit of robustness of the sparse model.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
“PySINDy: A Python package for the Sparse Identification of Nonlinear Dynamics from Data” by Silva, published on April. 2020, discloses PySINDy is a Python package for the discovery of governing dynamical systems models from data. In particular, PySINDy provides tools for applying the sparse identification of nonlinear dynamics (SINDy) approach to model discovery. (covers claims 1-3, 6-7).
“On the Convergence of the SINDy Algorithm” by Zhang, published on May. 2018, discloses time-series data is to identify the underlying dynamical system which generates it. This task can be done by selecting an appropriate model and a set of parameters which best fits the dynamics while providing the simplest representation (i.e. the smallest amount of terms). One such approach is the sparse identification of nonlinear dynamics framework [6] which uses a sparsity-promoting algorithm that iterates between a partial least-squares fit and a thresholding (sparsity-promoting) step. (covers claims (covers claims 1-3, 6-7).
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Any inquiry concerning this communication or earlier communications from the examiner should be directed to YI HAO whose telephone number is (571)270-1303. The examiner can normally be reached Monday - Friday.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Emerson Puente can be reached at (571)272-3652. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/YI . HAO/
Examiner, Art Unit 2187
/EMERSON C PUENTE/Supervisory Patent Examiner, Art Unit 2187
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