DETAILED ACTION
This communication is in response to Application No. 18/002,862 filed on December 22, 2022 in which claims 1-8 are presented for examination.
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 .
Priority
Acknowledgment is made of applicant's claim for foreign priority based on an application filed in Japan on 07/15/2020 and an application filed under the Patent Cooperation Treaty (PCT) on 07/15/2020. Acknowledgment is also made of receipt of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file.
Information Disclosure Statement
The information disclosure statement submitted on 12/22/2022 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement was considered by the examiner.
Specification
The contents of the disclosure are sufficient for examination purposes.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are:
“model creation unit” in Claim 6.
“feature amount extraction unit” in Claim 6.
“prediction analysis unit” in Claim 6.
“data extraction unit” in Claim 6.
“curve creation unit” in Claims 6 and 7.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1-8 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Regarding Claim 1, the claims recite the limitations “step of creating . . .”, “step of extracting . . .”, “step of analyzing . . .”, “step of specifying . . .”, and “step of creating . . .” that invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. As a result, the scope of the claimed elements is not clear. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claims so that the claim limitations will no longer be interpreted as limitations under
35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure,
material, or acts perform the entire claimed function, without introducing any new matter (35
U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure,
material, or acts disclosed therein to the function recited in the claims, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the
corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or
inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Additionally, Claim 1 recites the limitation “a first damage rate of a pipeline for each pipeline characteristic” (Ln. 3). It is not clear whether the intended meaning of this limitation is a single “first damage rate . . . for each pipeline characteristic” or multiple “first damage rate[s]”, one “for each pipeline characteristic.” As a result, the scope of the claim is indefinite therefore it is rejected. Applicant should amend the claim to clarify the intended meaning of the claim limitation.
Regarding Claim 2, the claim recites at least one of the limitations “step of creating . . .”, “step of extracting . . .”, “step of analyzing . . .”, “step of specifying . . .”, and “step of creating . . .”, which as discussed above, invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. Therefore, as elaborated above, the claim is indefinite and is rejected under 35 U.S.C. 112(b). Applicant may amend or respond in the manner outlined in the rejection of Claim 1 above. Additionally, the claim is rejected based on its dependence on a rejected claim.
Regarding Claims 3 and 4, the claims are rejected based on their dependence on a rejected claim.
Regarding Claim 5, the claim recites at least one of the limitations “step of creating . . .”, “step of extracting . . .”, “step of analyzing . . .”, “step of specifying . . .”, and “step of creating . . .”, which as discussed above, invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. Therefore, as elaborated above, the claim is indefinite and is rejected under 35 U.S.C. 112(b). Applicant may amend or respond in the manner outlined in the rejection of Claim 1 above. Additionally, the claim is rejected based on its dependence on a rejected claim.
Regarding Claim 6, the claim recites the limitations “a model creation unit”, “a feature amount extraction unit”, “a prediction analysis unit”, “a data extraction unit”, and “a curve creation unit” that invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. As a result, the scope of the claimed elements is not clear. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claims so that the claim limitations will no longer be interpreted as limitations under
35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure,
material, or acts perform the entire claimed function, without introducing any new matter (35
U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure,
material, or acts disclosed therein to the function recited in the claims, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the
corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or
inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Additionally, Claim 6 recites the limitation “a first damage rate of a pipeline for each pipeline characteristic” (Ln. 3-4). It is not clear whether the intended meaning of this limitation is a single “first damage rate . . . for each pipeline characteristic” or multiple “first damage rate[s]”, one “for each pipeline characteristic.” As a result, the scope of the claim is indefinite therefore it is rejected. Applicant should amend the claim to clarify the intended meaning of the claim limitation.
Regarding Claim 7, the claim recites at least one of the limitations “a model creation unit”, “a feature amount extraction unit”, “a prediction analysis unit”, “a data extraction unit”, and “a curve creation unit”, which as discussed above, invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. Therefore, as elaborated above, the claim is indefinite and is rejected under 35 U.S.C. 112(b). Applicant may amend or respond in the manner outlined in the rejection of Claim 6 above. Additionally, the claim is rejected based on its dependence on a rejected claim.
Regarding Claim 8, the claim is rejected based on their dependence on a rejected claim.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract ideas without significantly more.
Regarding Claim 1:
Step 1: Claim 1 is a method claim. Therefore, Claims 1-5 are directed to a statutory category of eligible subject matter.
Step 2A Prong 1: 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. Here, steps of the claimed method are mental processes. Specifically, the claim recites
“A damage rate curve creation method comprising: a step of . . . predict and output a first damage rate of a pipeline for each pipeline characteristic by use of first pipeline damage data” (mental process – amounts to exercising judgement to evaluate observed data to form a set of characteristic-specific opinions on damage rates, which may be aided by pen and paper);
“a step of extracting a feature amount having high contribution to the prediction” (mental process – amounts to exercising judgement and evaluation to form an opinion on which subset of observed data, which may be aided by pen and paper);
“a step of analyzing a change in the first damage rate related to a change in the feature amount” (mental process – amounts to exercising judgement and evaluation to compared observed data with a previously determined damage rate, which may be aided by pen and paper);
“a step of specifying a value of the feature amount at an inflection point of the change in the first damage rate as an extreme value, and extracting, from the first pipeline damage data, data having a value of the feature amount at which a difference from the extreme value is equal to or less than a threshold value as second pipeline damage data” (mental process – amounts to exercising judgement and evaluation to form an opinion, based on a known threshold value, on a subset of observed data, which may be aided by pen and paper); and
“a step of creating a damage rate curve indicating a second damage rate based on the second pipeline damage data” (mental process – amounts to exercising judgement to evaluate a subset of observed data to form an opinion on damage rates, which may be aided by pen and paper).
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
The claim recites the additional elements:
“creating a plurality of machine learning models that . . . for each of the machine learning models” (amounts to mere instructions to apply the judicial exception on generic and unspecialized computer components, which do not impose any meaningful limits on practicing the abstract idea) and
“including information on presence or absence of earthquake damage and pipeline characteristics” (amounts to generally linking the use of the judicial exception to a particular technological environment or field of use).
Step 2B: The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional elements:
“creating a plurality of machine learning models that . . . for each of the machine learning models” (mere instructions to apply the exception using generic computer components does not provide an inventive concept) and
“including information on presence or absence of earthquake damage and pipeline characteristics” (merely generally linking the use of the judicial exception to a particular technological environment or field of use does not provide an inventive concept).
For the reasons above, Claim 1 is rejected as being directed to an abstract idea without significantly more. This rejection applies equally to dependent claims 2-5. The additional limitations of the dependent claims are addressed below.
Regarding Claim 2:
Step 2A Prong 1: See the rejection of Claim 1 above, which Claim 2 depends on. Here, the claim recites additional elements, which fall within the “Mathematical Concepts” grouping of abstract ideas because they are mathematical relationships or formulas. Alternatively, these additional elements are also mental processes. Specifically, the claim recites
“wherein the step of creating the damage rate curve includes a step of creating the damage rate curve by use of a fitting function” (mathematical concept - amounts to inputting data into a fitting function, which is a mathematical formula, in order to generate a curve as output; mental process – alternatively, amounts to exercising judgment and evaluation to modify a set of observed data, based on a known set of steps, which may be aided by pen and paper).
Step 2A Prong 2 & Step 2B: There are no elements left for consideration of implementation within a practical application or for consideration of significantly more.
Accordingly, Claim 2 is rejected as being directed to an abstract idea without significantly more.
Regarding Claim 3:
Step 2A Prong 1: See the rejection of Claim 2 above, which Claim 3 depends on. Here, the claim recites additional elements that are mathematical concepts or, alternatively, mental processes. Specifically, the claim recites
“wherein the fitting function is a sigmoid function” (mathematical concept - amounts to inputting data into a sigmoid fitting function, which is a mathematical formula, in order to generate a curve as output; mental process – alternatively, amounts to exercising judgment and evaluation to modify a set of observed data, based on a known set of specific steps, which may be aided by pen and paper).
Step 2A Prong 2 & Step 2B: There are no elements left for consideration of implementation within a practical application or for consideration of significantly more.
Accordingly, Claim 3 is rejected as being directed to an abstract idea without significantly more.
Regarding Claim 4:
Step 2A Prong 1: See the rejection of Claim 1 above, which Claim 4 depends on.
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
The claim recites the additional element:
“wherein the feature amount having high contribution is an earthquake motion index” (amounts to generally linking the use of the judicial exception to a particular technological environment or field of use).
Step 2B: The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional element:
“wherein the feature amount having high contribution is an earthquake motion index” (merely generally linking the use of the judicial exception to a particular technological environment or field of use does not provide an inventive concept).
Accordingly, Claim 4 is rejected as being directed to an abstract idea without significantly more
Regarding Claim 5:
Step 2A Prong 1: See the rejection of Claim 1 above, which Claim 5 depends on.
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
The claim recites the additional element:
“wherein in the step of creating the damage rate curve, a rate of the number of damaged pipelines in the second pipeline damage data in the total number of pipelines in the second pipeline damage data or the number of damaged places per unit length of the pipelines in the second pipeline damage data is calculated as the second damage rate” (amounts to generally linking the use of the judicial exception to a particular technological environment or field of use).
Step 2B: The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional element:
“wherein in the step of creating the damage rate curve, a rate of the number of damaged pipelines in the second pipeline damage data in the total number of pipelines in the second pipeline damage data or the number of damaged places per unit length of the pipelines in the second pipeline damage data is calculated as the second damage rate” (merely generally linking the use of the judicial exception to a particular technological environment or field of use does not provide an inventive concept).
Accordingly, Claim 5 is rejected as being directed to an abstract idea without significantly more
Regarding Claim 6:
Step 1: Claim 6 is a machine claim. Therefore, Claims 6-8 are directed to a statutory category of eligible subject matter.
Step 2A Prong 1: 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. Here, the claim recites limitations that are substantially the same as the limitations of Claim 1. As a result, and as elaborated above, these limitations are abstract ideas because they are mental processes.
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
The claim recites the additional elements:
“A damage rate curve creation device comprising: a model creation unit configured to create a plurality of machine learning models that . . . a feature amount extraction unit configured to . . . a prediction analysis unit configured to . . . a data extraction unit configured to . . . and a curve creation unit configured to” (amounts to mere instructions to apply the judicial exception on generic and unspecialized computer components, which do not impose any meaningful limits on practicing the abstract idea) and
“including information on presence or absence of earthquake damage and pipeline characteristics” (merely generally linking the use of the judicial exception to a particular technological environment or field of use does not provide an inventive concept).
Step 2B: The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional elements:
“A damage rate curve creation device comprising: a model creation unit configured to create a plurality of machine learning models that . . . a feature amount extraction unit configured to . . . a prediction analysis unit configured to . . . a data extraction unit configured to . . . and a curve creation unit configured to” (mere instructions to apply the exception using generic computer components does not provide an inventive concept) and
“including information on presence or absence of earthquake damage and pipeline characteristics” (merely generally linking the use of the judicial exception to a particular technological environment or field of use does not provide an inventive concept).
For the reasons above, Claim 6 is rejected as being directed to an abstract idea without significantly more. This rejection applies equally to dependent claims 7-8. The additional limitations of the dependent claims are addressed below.
Regarding Claim 7, the claim recites additional limitations that are substantially the same as the additional limitations of Claim 2, in the form of a machine. The claim is also directed to performing mathematical concepts and/or mental processes without integration into a practical component or significantly more.
Accordingly, Claim 7 is rejected under the same rationale.
Regarding Claim 8:
Step 2A Prong 1: See the rejection of Claim 6 above, which Claim 8 depends on.
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
The claim recites the additional element:
“A program for causing a computer to function as the damage rate curve creation device according to claim 6” (amounts to mere instructions to apply the judicial exception on generic and unspecialized computer components, which do not impose any meaningful limits on practicing the abstract idea).
Step 2B: The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional element:
“A program for causing a computer to function as the damage rate curve creation device according to claim 6” (mere instructions to apply the exception using generic computer components does not provide an inventive concept).
Accordingly, Claim 8 is rejected as being directed to an abstract idea without significantly more
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2 and 4-8 are rejected under 35 U.S.C. 103 as being unpatentable over Bagriacik et al. (hereinafter Bagriacik) (“Comparison of statistical and machine learning approaches to modeling
earthquake damage to water pipelines”) in view of Folkman (“Water Main Break Rates In the USA and Canada: A Comprehensive Study”) and Card et al. (hereinafter Card) (“Regression Kink Design: theory and Practice”).
Regarding Claim 1, Bagriacik teaches a damage rate curve creation method (Pg. 79, Col. 2, Para. 2, “The purpose of this analysis is to develop models to predict if an individual pipe will be damaged in a specified earthquake as a function of characteristics . . . In addition to the conventional RR method from the literature, three other model types were considered”, where “from the literature” demonstrates the “RR method” is the method discussed in regard to prior research, which “develop[s]” a “repair rate” “curve” that is within the broadest reasonable interpretation of a damage rate curve, see Pg. 77, Col. 1, Para. 3, “we found, empirical modeling efforts have used the same general approach, which for convenience we call the repair rate method (RR). They have aimed to develop a curve that relates repair rate (number of repairs, i.e., damage locations, per km. of pipe) to a measure of ground motion, ground deformation, or strain”)
. . .
a step of creating a plurality of machine learning models that predict and output a first damage . . . [probability] of a pipeline for each pipeline characteristic (Pg. 79, Col. 2, Para. 2, “The purpose of this analysis is to develop models to predict if an individual pipe will be damaged in a specified earthquake as a function of characteristics . . . In addition to the conventional RR method from the literature . . . other model types were considered . . . [including] boosted regression trees (BRT) . . . [a] nonparametric machine learning technique”; where the “BRT” is a plurality of machine learning models, see Pg. 80, Col. 1, Para. 4, “BRT creates many simple models and linearly combines them”; Pg. 81, Table 4, “pˆi= estimated probability of damage for pipe i”; Pg. 76, Col. 2, Para. 2, “These model types allow investigation of multiple explanatory variables and interactions among them. They can use each length of pipe as a unit of analysis rather than repair rate for a region, ensuring that the variables refer more directly to a specified pipe”, where the “probability of damage” is for each pipeline characteristic because it “refer[s]” to each “explanatory variable” feature amount that is a characteristic of “a specified pipe”, for more information see Pgs. 77-78, Sect. “3. Explanatory variables”)
by use of first pipeline damage data including information on presence or absence of earthquake damage and pipeline characteristics (Pg. 76, Abstract, “A large dataset of water pipeline damage from the February and June 2011 earthquakes in Christchurch, New Zealand is used”, where the “dataset” includes information on the presence or absence of earthquake damage, see Pg. 79, Col. 1, Para. 4, “The original dataset included 117,335 pipes . . . only 0.3% of the observations without LRI data were damaged versus 2.8% of those with the LRI data”, and pipeline characteristics, see Pg. 76, Table 3, where the “descriptive statistics” for the “datasets” includes feature amounts of “Pipe material” and “Pipe type”, which are characteristics of pipelines in the dataset; for more information see Pgs. 77-78, Sect. “3. Explanatory variables”);
a step of extracting a feature amount having high contribution to the prediction for each of the machine learning models (Pg. 86, Col. 1-2, Para. 4-1, “To investigate this tradeoff between improved prediction and increased complexity and data needs, we examined the predictive power of the four reduced models to the full models for both the logit and BRT models (Table 9). These results suggest first, that in terms of error in the total count, the PGV and pipe length alone provide the same level of predictive ability that the full set of variables does (R1 vs. Full)”, where the feature amounts “PGV” and “pipe length”, which contribute a significant portion of the “predictive ability” of “the full set of variables”, must be extract to be used in a reduced “R1” model, and where, as discussed above, the “BRT” is a plurality of ML models; see also Pg. 84, Col. 1-2, Para. 1-1, “in examining the importance of the explanatory variables, we seek to . . . clarify lingering ambiguity from past research about which pipe attributes are really influential . . . While the RR method cannot provide insight on the relative importance of different variables, each of the other methods can”; Pg. 80, Col. 2, Para. 3, “BRT has the advantages that . . . the relative importance of variables are based on reduction in out-of-sample error and can be compared across numerical and categorical variables; Pg. 76, Abstract, “the relative importance of pipe and earthquake attributes in predicting damage”);
a step of analyzing a change in the first damage . . . [probability] related to a change in the feature amount (Pg. 86, Table 9, where feature amounts, such as “PGV”, “pipe length”, and “pipe material”, are changed by including or excluding them in the “BRT” model and the probability of damage is analyzed to determine a change in its “predictive performance”; see also Pg. 86, Col. 1, Para. 1, “The rightmost column of Table 8 presents the marginal effects for the statistically significant variables in the full logit model. It suggests, for example, that changing a pipe from galvanized iron, GI, the reference pipe material, to MPVC reduces the probability of damage by 0.1, all else being equal. Changing the trench type from pre-1984 local, the reference level, to post-2005, AP20 does the same. Fig. 5 offers another way to view the variable effects. Holding all other variables at their mean or reference level, it shows, for the full logit model, how the probability of a pipe being damaged, pi, varies with PGV and pipe material (Fig. 5a) or PGV and LRI (Fig. 5b)”; where, while discussion is “focus[ed] on the logoit model” in order to “allow examination of the influence of different specific pipe materials”, the change based analysis is applicable to the “BRT” at the category level, such as “material as a whole”, see Pg. 84-85, Col. 2-1, Para. , “We first focus on the logit model results in particular because unlike BRT . . . they allow examination of the influence of different specific pipe materials, pipe types, and trench types (as opposed to pipe material as a whole)”);
a step of specifying a value of the feature amount . . . in the first damage . . . [probability], and extracting, from the first pipeline damage data, data having a value of the feature amount . . . [equal to or greater than] . . a threshold value as second pipeline damage data (Pg. 79, Col. 1, Para. 4, “The original dataset included 117,335 pipes . . . For all variables except LRI, a small percentage of observations had randomly distributed missing values . . . We considered three methods of handling the missing data . . . Finally, we restricted the analysis to the region that experienced PGV ≥45 cm/s, for which all observations were complete”, where “PGV” is a feature amount with a value “≥45 cm/s”, which is in the first damage probability because, as discussed above, it is an “explanatory variable” used to create a first damage probability for “a specific pipe”, see Pg. 76, Col. 2, Para. 2, “These model types allow investigation of multiple explanatory variables and interactions among them. They can use each length of pipe as a unit of analysis rather than repair rate for a region, ensuring that the variables refer more directly to a specified pipe”); and
a step of creating a damage rate curve indicating a . . . damage rate based on the . . . pipeline damage data (Pg. 79, Col. 2, Para. 2, “The purpose of this analysis is to develop models to predict if an individual pipe will be damaged in a specified earthquake as a function of characteristics . . . In addition to the conventional RR method from the literature, three other model types were considered”, where, as discussed above, the “RR method” “develop[s]” a “repair rate” “curve” that is within the broadest reasonable interpretation of a damage rate curve, and uses the pipeline damage data, see Pg. 76, Abstract, “A large dataset of water pipeline damage from the February and June 2011 earthquakes in Christchurch, New Zealand is used”).
Bagriacik does not teach . . . comprising . . . rate . . . rate . . . at an inflection point of the change . . . rate . . . as an extreme value . . . at which a difference from the extreme value is equal to or less than . . . second . . . second . . . .
However, Folkman teaches . . . [a first damage] rate [of a pipeline for each pipeline material characteristic] (Pg. 25, Fig. 20, “BREAK RATES OF EACH PIPE MATERIAL FROM THE BASIC SURVEY”; Pg. 29, Fig. 26, “PERCENT OF FAILURES PER DECADE OF INSTALLED PIPE MATERIAL”)
. . . [analyzing the first damage] rate (Pg. 3, Col. 2, Para. 1, “Water main break rates are calculated for all pipe materials used in the transport of water to create a measurement to judge pipe performance and durability . . . in aggregate, break rates produce a compelling story which can aid in asset management decision making as it relates to defining pipe criticality and costs of repairing and replacing our underground water pipes”, where “judg[ing]” and “defining” are analyzing)
. . . [specifying of a value in the first damage] rate . . . (Pg. 24, Col. 2, Para. 3, “Figure 20 illustrates the failure rates as a function of material type. In both the 2012 and 2018 surveys, PVC was the pipe material with the lowest break rate”).
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the creation and output of a first damage probability for each pipeline characteristic, analyzing change in the first damage probability, and specifying a value in the first damage probability of Bagriacik with the creation and output of a first damage rate for each pipeline characteristic, analyzing change in the first damage rate, and specifying a value in the first damage rate of Folkman in order to evaluate pipelines damage rates instead of damage probability, where damage rates are the most important and critical factor (Folkman, Pg. 3, Col. 1-2, Para. 3-1, “The most important and critical factor used to quantify the condition and occurrences of failing underground pipe networks is water main break rates”).
Additionally, Card teaches . . . [a rate plot of curvature creation method] comprising . . . [specifying a feature amount] at an inflection point of the change . . . as an extreme value [, based on knowledge of its importance and impact the slope of a first rate plot of curvature] (Pg. 1, Para. 2, “an RKD arises when a policy variable of interest is determined . . . The idea of the regression kink design is to examine the slope of the relationship between the outcome of interest and the assignment variable at the exact location of the kink”, where the “assignment variable [feature] at the exact location of the kink” is specified as an extreme value, which is assumed to be importance based on a “policy rule” and then “show[n]” to be a point of “pronounced slope change”, which is within the broadest reasonable interpretation of an inflection point, see Pg. 19, Para. 3, “Both plots show pronounced slope changes at the threshold where the benefit cap starts to bind, and the kink is especially salient in the 2008-2013 sample”; Pg. 1, Para. 2, “in a regression kink design the policy rule is assumed to have a kink in the relationship between the policy variable and the underlying assignmFent variable”; Pg. 26, Fig. 3a-3b, where rates are plotted; see also Pg. 9, Para. 1, “Figures 3a and 3b present analogous plots for our main outcome variable, the logarithm of time to the next job. These figures also show discernible kinks”)
. . . at which a difference from the extreme value is equal to or less than [a threshold value] . . . (Pg. 4, Para. 4, “the ease of implementation of a uniform kernel – which converts the RKD problem to a simple OLS/2SLS problem using data within a window of width h from the threshold – makes it an attractive choice, and we use it in our applications below”, where “a window of width h from the threshold”, demonstrates the “data” is at a difference equal to or less than “h” of the extreme value at the “threshold”, see generally Pg. 1, Para. 2, “Provided that individuals on either side of the kink threshold are “similar”, any kink in the outcome can be attributed to the treatment effect of the policy variable. As in an RDD, implementing an RKD involves the estimation of quantities “close” to a threshold using (local) polynomial regressions”)
. . . [as second data] (Pg. 1-2, Para. 3-1, “The kinks . . . allow us to estimate the elasticity . . . for a subpopulation . . . near the kink point”, where the “subpopulation” is the second data)
. . . [creating a rate plot of curvature indicating a] second [rate] . . . [based on the] second [data] (Pg. 17, Para. 1, “if we plot the relationship between baseline earnings and the log time to next job not just locally around the kink points as in Figures 3a and 3b, but globally for the whole range of baseline earnings available in the data, the relationship is highly nonlinear”, where the “global” plot is the first rate, see Pg. 20, Fig. 6, and the “local” plot are the second rate, see Pg. 26, Fig. 3a-3b, which are based on the second data “Bottom Kink Sample” and “Top Kink Sample”).
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the separate steps of creating a plurality of machine learning models to determine a damage rate for each pipeline characteristic, extracting high contribution feature amounts, analyzing changes in the damage rate related to changes in a feature amount, selecting second data based on a specified value of a feature amount, and creating a damage rate curve indicating a damage rate based on pipeline damage data of Bagriacik in view of Folkman with the method for creating a rate plot of curvature indicating a second rate comprising: determining data variable importance in a first rate plot of curvature of first data; selecting second data from the first dataset, where the second data is data within a threshold window of an inflection point for an extreme variable; and using the subset of data to create the second rate plot of curvature of Card in order to develop a pipeline damage rate curve that locally visualizes a rate relationship to clearly display aspects associated with important data values (Card, Pg. 17, Para. 1, “if we plot the relationship between baseline earnings and the log time to next job not just locally around the kink points as in Figures 3a and 3b, but globally for the whole range of baseline earnings available in the data, the relationship is highly nonlinear . . . this general pattern renders the kinks at the policy thresholds less salient”, where the aspects associated with data deemed important, “kinks”, would otherwise be displayed in a “less salient” way; see also Card, Pg. 9, Para. 1, “These figures average away some of the noise seen in the raw scatter plots”), which, in the context of Bagriacik, would require the steps of “creating”, “extracting”, and “specifying” to comprise the method of “damage curve creation” in order for the important data to be determined, extracted, and specified for use as second data for the damage curve.
Notably, Bagriacik further motivates this combination to allow for instances where users seek to utilize the benefits of the RR method (Bagriacik, Pg. 86, Col. 2, Para. 2, “The repair rate (RR) method used almost exclusively in previous studies performs very well in terms of predicting the total number of damaged pipes in the city and is far simpler to apply”), but needs to reduce data demands by retaining only the most predictive data values (Bagriacik, Pg. 87, Col. 1, Para. 1, “Finally, while all of the pipe and ground motion/deformation attributes examined based on the conceptual framework were statistically significant, a comparison of reduced models suggests that a majority of the predictive power can be obtained by including only the pipe length, PGV, LRI, and pipe material, so if it is necessary to limit the data demands of applying a model for prediction, one might focus on those”; Bagriacik, Pg. 86, Col. 1-2, Para. 4-1, “To investigate this tradeoff between improved prediction and increased complexity and data needs, we examined the predictive power of the four reduced models to the full models for both the logit and BRT models (Table 9). These results suggest first, that in terms of error in the total count, the PGV and pipe length alone provide the same level of predictive ability that the full set of variables does (R1 vs. Full)”, where subsets of the most predictive values are also show to be effective and thus it would be obvious to try a subset of only “PGV”).
Regarding Claim 2, Bagriacik in view of Folkman and Card teach the damage rate curve creation method according to claim 1, wherein the step of creating the damage rate curve includes a step of creating the damage rate curve (Bagriacik, Pg. 79, Col. 2, Para. 2, “The purpose of this analysis is to develop models to predict if an individual pipe will be damaged in a specified earthquake as a function of characteristics . . . In addition to the conventional RR method from the literature, three other model types were considered”, where, as discussed above, the RR method is used to develop a “repair rate” “curve” that is within the broadest reasonable interpretation of a damage rate curve, see Bagriacik, Pg. 77, Col. 1, Para. 3, “we found, empirical modeling efforts have used the same general approach, which for convenience we call the repair rate method (RR). They have aimed to develop a curve that relates repair rate (number of repairs, i.e., damage locations, per km. of pipe) to a measure of ground motion, ground deformation, or strain”)
by use of a fitting function (Bagriacik, Pg. 79-80, Col. 2-1, Sec. “5.1. Repair rate method”, “a single value of repair rate, RR, was computed (total number of repairs/length of pipe), producing a data pair of RR and ground motion level . . . Applying ln() transforms of the resulting 12 observations of paired data, linear regression was used to fit the model: ln(RR) = 2.188 + 15.627ln(PGV)”, where “linear regression was used to fit the model” is within the broadest reasonable interpretation of by use of a fitting function).
Regarding Claim 4, Bagriacik in view of Folkman and Card teach the damage rate curve creation method according to claim 1, wherein the feature amount having high contribution is an earthquake motion index (Bagriacik, Pg. 87, col. 1, Para. 1, “a comparison of reduced models suggests that a majority of the predictive power can be obtained by including . . . PGV . . . so if it is necessary to limit the data demands of applying a model for prediction, one might focus on those”, where, among other variables, “PGV” is determined to have high contribution, which is within the broadest reasonable interpretation of an earthquake motion index because it indexes earthquake motion, see generally Bagriacik, Pg. 77, Col. 2, Para. 4, “All previous empirical models have used some measure of earthquake-induced ground motion/deformation . . . PGV has become the most common measure”).
Regarding Claim 5, Bagriacik in view of Folkman and Card teach the damage rate curve creation method according to claim 1, wherein in the step of creating the damage rate curve (Bagriacik, Pg. 79, Col. 2, Para. 2, “The purpose of this analysis is to develop models to predict if an individual pipe will be damaged in a specified earthquake as a function of characteristics of the pipe . . . In addition to the conventional RR method from the literature”, where the “RR method from the literature”, discussed in detail below, is used in the method),
a rate of the number of damaged pipelines in the second pipeline damage data in the total number of pipelines in the second pipeline damage data or the number of damaged places per unit length of the pipelines in the second pipeline damage data is calculated as the second damage rate (Bagriacik, Pg. 77, Col. 1, Para. 3, “the repair rate method (RR). They have aimed to develop a curve that relates repair rate (number of repairs, i.e., damage locations, per km. of pipe)”, where rate is number of damage places, “damage locations”, per unit length, “per km. of pipe”; and where the “repair rate”, which as discussed in regard to Claim 1 is a damage rate, and in view of Card, uses second pipeline data).
The reasons for obviousness are discussed above in regard to the rejection of Claim 1 and remain applicable here.
Regarding Claim 6, Bagriacik in view of Folkman and Card teach a damage rate curve creation device comprising (Bagriacik, Pg. 79, Col. 2, Para. 2, “The models were fitted using R software v3.3.1 with default settings”, where “R software” requires hardware to run, which is within the broadest reasonable interpretation of a damage rate curve creation when it is performing the method of Bagriacik in view of Folkman and Card):
a model creation unit configured to . . . (Bagriacik, Pg. 79, Col. 2, Para. 2, “The purpose of this analysis is to