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 .
This action is responsive to the Amendment filed on 12/18/2025.
Claims 1, 3-6, 8-22 are pending in the case.
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/18/2025 has been entered.
Response to Arguments
Applicant’s remarks with regards to the interpretation of claim(s) 6 under 35 U.S.C. § 112(f) have been fully considered and are not persuasive. Therefore, the interpretation of claim(s) 6 under 35 U.S.C. § 112(f) is respectfully maintained. Regarding claim(s) 6,
Applicant argues “The term "processing unit" is a commonly used and well-understood term in the fields of computing, signal processing, and industrial inspection systems”.
Examiner respectfully disagrees. While processors, ASICs, FPGAs, DSPs, microcontrollers and integrated circuits are terms with definite structure, Examiner notes that a “processing unit” does not have a defined structure as understood by a person of ordinary skill in the art, but rather may be implemented in a variety of ways using software and/or hardware.
Applicant further argues that applicant’s disclosure connotes definite structure for "receiving unit" and "processing unit" and recites examples disclosed in the specification.
Examiner respectfully disagrees.
Applicant’s disclosure provides examples of “receiving unit”, system, “processing unit”, by using embodiments and disclosing what a structure for a component may be.
These do not constitute definite structure of components, but rather describe possible structure of components, and based on the Three-prong analysis of the terms,“receiving unit”, “processing unit” invoke interpretation under 35 U.S.C. § 112(f).
Therefore, Examiner notes that while a broadest reasonable interpretation of the structure of the terms "receiving unit" and "processing unit" is possible in light of applicant’s disclosure, applicant’s disclosure does not provide a definite structure for the terms. Therefore the terms "receiving unit" and "processing unit" invoke 35 U.S.C. § 112(f) and have been interpreted accordingly.
Applicant’s arguments and amendments with regards to the 35 U.S.C. § 102 and 103 rejection of claim(s) 1, 3-6, 8-22 for the limitations
“the one or more variables comprising at least one of instrument noise, transducer sensitivity variation, and expected defect morphology parameters” and
“wherein the priori data constrains the simulated data to physically realizable anomaly characteristics of the object”,
have been considered, but are moot because the new ground of rejection does not rely on any portions of reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant's arguments and amendments with regards to the 35 U.S.C. § 102 and 103 rejection of claim(s) 1, 3-6, 8-22, for the recitation of “the one or more variables statistically extrapolate or interpolate the experimental data such that variations and sources of variations in the experimental data is detected upon the application of the PDF“ in claims 1 and 6 have been considered, but are not persuasive.
Applicant argues that the amended claims are allowable due to the following:
“Srinivasamurthy is directed to creating well-balanced computer-based reasoning systems and using those to control systems. Paragraphs [20, 21, 34, 96, and 117] of Srinivasamurthy are directed to computer-based reasoning models that evaluate information gain, surprisal, and entropy-related metrics for assessing how informative newly received data is to an already- trained reasoning model. Nowhere does Srinivasamurthy contemplate that the one or more variables statistically extrapolate or interpolate the experimental data such that variations and sources of variations in the experimental data is detected upon the application of the PDF”
Examiner respectfully disagrees.
Srinivasamurthy [69-75, 80, 87, 94] teaches surprisal information is used to generate synthetic data using PDF estimates and historical data, synthetic data generates new data from the experimental data, new data may be generated using distribution and randomly sampling extrapolated or interpolated distribution (this could lead to previously absent (not present) data since the distribution has extrapolated or interpolated data), and
Srinivasamurthy [85] teaches synthetic data may have constraints for fitness,
and therefore sufficiently teaches
generating simulated data associated with the object based on at least one of the one or more PDF estimates and priori data associated with the testing of the object, wherein the priori data constrains the simulated data..., wherein the simulated data comprises one or more statistically synthesized anomalies not present in the experimental data along with the one or more anomalies of the experimental data.
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 following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
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.
Claim limitations "receiving unit configured to receive experimental data of the object” and
“processing unit coupled with the receiving unit, wherein the at least one processing unit is configured to:...apply a probability density function (PDF) upon one or more variables associated with the experimental data ..., generate simulated data associated with the object ..., and train a learning model based on the one or more new anomalies” in claim 6;
have been interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because they use a generic placeholder
"receiving unit" coupled with functional language "configured to receive experimental data of the object” and
“processing unit" coupled with functional language "coupled with the receiving unit, wherein the at least one processing unit is configured to:...apply a probability density function (PDF) upon one or more variables associated with the experimental data ..., generate simulated data associated with the object ..., and train a learning model based on the one or more new anomalies” in claim 6.
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.
A review of the specification shows that the following appears to be the corresponding structure described in the specification for the 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph limitation: according to [0024], "receiving unit", and "processing unit" are implemented as hardware/ processor(s) executing software modules to perform the instructions of the modules
If applicant wishes to provide further explanation or dispute the examiner’s interpretation of the corresponding structure, applicant must identify the corresponding structure with reference to the specification by page and line number, and to the drawing, if any, by reference characters in response to this Office action.
If applicant does not intend to have the claim limitation(s) treated under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112 , sixth paragraph, applicant may amend the claim(s) so that it/they will clearly not invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, or present a sufficient showing that the claim recites/recite sufficient structure, material, or acts for performing the claimed function to preclude application of 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 the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 3-6, 8-22 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1 and 6, each recite “measurement-domain variability”.
The original specification does not disclose domains or context, and therefore does not teach “measurement-domain variability”
Claims 1 and 6, each recite “wherein the priori data constrains the simulated data to physically realizable anomaly characteristics of the object”.
The original specification does not disclose constraining the simulated data to physically realizable anomaly characteristics of the object, and therefore does not teach “wherein the priori data constrains the simulated data to physically realizable anomaly characteristics of the object”.
The original specification in discloses
- generating simulated data using one of the one or more PDF estimates and priori data associated with the testing of the object,
- simulated data comprising one or more new anomalies which were unknown in the experimental data along with the one or more anomalies of the experimental data,
- priori data comprises one or more reference information which indicates the characteristics of the object, and
- the priori data may be a computer-aided design (CAD) model or any expert information which may indicate the characteristics of the object.
However the original specification is silent regarding
the priori data constrains the simulated data, and
constrains the simulated data to physically realizable anomaly characteristics of the object.
Therefore the above noted limitations of claims 1 and 6 do not have support in the original specification.
Claims 3-5, 8-22 merely recite additional functions performed by the inventions of claims 1 and 6.
Accordingly, claims 3-5, 8-22 are also rejected under 35 U.S.C. 112(a).
Applicant is requested to make appropriate amendments to the claims or clearly point of the specific portions of paragraphs in the specification that support the claim limitations.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 3-6, 8-13, 17-19, is/are rejected under 35 U.S.C. 103 as being unpatentable over Srinivasamurthy (US 20200371512 A1), in view of Gottschlich (US 20180330253 A1), De Beenhouwer (US 20210270755 A1) and Karlinsky (US 20170177997 A1).
Regarding claim 1, Srinivasamurthy teaches a method for detecting one or more anomalies in an object, the method comprising (Srinivasamurthy [23, 184, 221, 231, 278] software to detect anomalies in an object, anomalies may include defects that require repair in objects or timing or wear issues):
receiving experimental dataSrinivasamurthy [23, 184, 206, 221, 231, 241, 242] experiment data may be received, data may be related with anomalies in an object, anomalies may include defects that require repair in objects or timing or wear issues, data may have issue types for defects);
applying a probability density function (PDF) representative of measurement-domain variability upon one or more variablesSrinivasamurthy [20, 21, 34, 96, 117] probability density functions may be used to analyze variables in experimental data and determine surprisal values including future surprisal values, Srinivasamurthy [22, 34, 80, 85, 117, 126, 256] PDF may be context (domain) based and may be related to data, feature variability, distributions may be based on statistical extrapolation or interpolation) and ;
generating simulated data associated with the object based on at least one of the one or more PDF estimates and priori data associated with the testing of the object, wherein the priori data constrains the simulated data..., wherein the simulated data comprises one or more statistically synthesized anomalies not present in the experimental data along with the one or more anomalies of the experimental data (Srinivasamurthy [69-75, 80, 87, 94] surprisal information is used to generate synthetic data using PDF estimates and historical data, synthetic data generates new data from the experimental data, new data may be generated using distribution and randomly sampling extrapolated or interpolated distribution (this could lead to previously absent (not present) data since the distribution has extrapolated or interpolated data), Srinivasamurthy [85] synthetic data may have constraints for fitness); and
training a learning model based on the one or more new anomalies determined in the simulated data and the one or more anomalies of the experimental data, wherein the learning model is applied for detecting any anomaly in a new object related to the object for whom the learning model is trained (Srinivasamurthy [77, 115, 116, 180-183] model may be trained using synthetic data based on experimental data, trained model may be used to detect anomalies in real-world after training).
Srinivasamurthy does not specifically teach the one or more variables comprising at least one of instrument noise, transducer sensitivity variation, and expected defect morphology parameters, wherein the priori data constrains the simulated data to physically realizable anomaly characteristics of the object, validating the trained learning model using a portion of the experimental data of the object, wherein the object is one of a part, a product, a weld, a system, a instrument, and a component, and wherein the one or more anomalies is one of a size, shape, and orientation of the object.
However Gottschlich teaches validating the trained learning model using a portion of the experimental data of the object (Gottschlich [15, 18, 23] synthetic data may be used to train model used to detect anomalies, test inputs are used to validate trained model to determine model performance).
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention, to have incorporated the concept taught by Gottschlich of validating the trained learning model using a portion of the experimental data of the object, into the invention suggested by Srinivasamurthy; since both inventions are directed towards using synthetic data to train a model to detect anomalies, and incorporating the teaching of Gottschlich into the invention suggested by Srinivasamurthy would provide the added advantage of allowing the model to be validated using test inputs to determine model performance, and the combination would perform with a reasonable expectation of success (Gottschlich [15, 18, 23]).
Srinivasamurthy and Gottschlich does not specifically teach the one or more variables comprising at least one of instrument noise, transducer sensitivity variation, and expected defect morphology parameters, wherein the priori data constrains the simulated data to physically realizable anomaly characteristics of the object, wherein the object is one of a part, a product, a weld, a system, a instrument, and a component, and wherein the one or more anomalies is one of a size, shape, and orientation of the object.
However De Beenhouwer teaches wherein the priori data constrains the simulated data to physically realizable anomaly characteristics of the object (De Beenhouwer [78-81, 146] simulated data may be generated from distribution of received data, simulated data is based on physical characteristics of priori data using model, geometric (physical) and mechanical constraints may be considered).
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention, to have incorporated the concept taught by De Beenhouwer of wherein the priori data constrains the simulated data to physically realizable anomaly characteristics of the object, into the invention suggested by Srinivasamurthy and Gottschlich; since both inventions are directed towards generating simulated data using distribution of priori data, and incorporating the teaching of De Beenhouwer into the invention suggested by Srinivasamurthy and Gottschlich would provide the added advantage of using available data and model to generate simulated data that is in keeping with the received information, and the combination would perform with a reasonable expectation of success (De Beenhouwer [78-81, 146]).
Srinivasamurthy Gottschlich and De Beenhouwer does not specifically teach the one or more variables comprising at least one of instrument noise, transducer sensitivity variation, and expected defect morphology parameters, wherein the object is one of a part, a product, a weld, a system, a instrument, and a component, and wherein the one or more anomalies is one of a size, shape, and orientation of the object.
However Karlinsky teaches the one or more variables comprising ...expected defect morphology parameters (Karlinsky [16, 75, 76, 97, 117] input data may include ground truth information for defect information);
wherein the object is one of a part... and a component, and wherein the one or more anomalies is one of a size, ...and orientation of the object (Karlinsky [5, 103] size or orientation anomalies may be detected for a part of component, Karlinsky [8, 50, 52, 121-123, 127] method to detect object anomalies using experimental data using a trained model).
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention, to have incorporated the concept taught by Karlinsky of the one or more variables comprising ...expected defect morphology parameters, wherein the object is one of a part... and a component, and wherein the one or more anomalies is one of a size, ...and orientation of the object, into the invention suggested by Srinivasamurthy Gottschlich and De Beenhouwer; since both inventions are directed towards detecting object anomalies using experimental data using a trained model, and incorporating the teaching of Karlinsky into the invention suggested by Srinivasamurthy Gottschlich and De Beenhouwer would provide the added advantage of allowing the model to be used to detect size and orientation anomalies for parts and components based on reliable information (i.e. ground truth data), and the combination would perform with a reasonable expectation of success (Karlinsky [5, 103, 8, 50, 52, 121-123, 127).
Regarding claim 3, Srinivasamurthy, Gottschlich, De Beenhouwer and Karlinsky teach the invention as claimed in claim 1 above. Srinivasamurthy further teaches wherein the one or more variables comprises at least one of ... experimental variation... and defect probabilities (Srinivasamurthy [27, 96, 250] variables may be based on configurations of model parameters and data elements selected, and on defect probability).
Regarding claim 4, Srinivasamurthy, Gottschlich, De Beenhouwer and Karlinsky teach teaches the invention as claimed in claim 1 above. Srinivasamurthy further teaches wherein the experimental dataSrinivasamurthy [3, 33, 211, 214] experimental data contexts may be any combination of multi-dimensional data, data may include volume (fuel gauge) and time information).
Regarding claim 5, Srinivasamurthy, Gottschlich, De Beenhouwer and Karlinsky teach the invention as claimed in claim 1 above. Srinivasamurthy further teaches wherein the priori data comprises one or more characteristics of the object (Srinivasamurthy [214, 221, 230] object/ equipment sensor information may be used).
Regarding claim 11 Srinivasamurthy, Gottschlich, De Beenhouwer and Karlinsky teach the invention as claimed in claim 1 above. Srinivasamurthy does not specifically teach wherein the one or more variables comprises at least one of instrument noise, transducer sensitivity variations, expected defect morphologies, and precision and accuracy of the measurement.
However Karlinsky teaches wherein the one or more variables comprises ... accuracy... of the measurement (Karlinsky [5, 39] and Table 4, variables may include Segmentation Accuracy (measure of correct pixels vs wrong)).
Regarding claim 12, Srinivasamurthy, Gottschlich, De Beenhouwer and Karlinsky teach the invention as claimed in claim 1 above. Srinivasamurthy does not specifically teach integrating the learning model with a non-destructive evaluation/testing (NDE/NDT) hardware.
However Karlinsky teaches integrating the learning model with a non-destructive evaluation/testing (NDE/NDT) hardware (Karlinsky [5, 39, 40, 42], Fig. 1, NDE/NDT) hardware ( elements 101, 102) may be integrated with learning model).
Regarding claim 13, Srinivasamurthy, Gottschlich, De Beenhouwer and Karlinsky teach the invention as claimed in claim 12 above. Srinivasamurthy does not specifically teach wherein the learning model is applied with the NDE/NDT hardware for implementing an NDE/NDT process.
However Karlinsky teaches wherein the learning model is applied with the NDE/NDT hardware for implementing an NDE/NDT process (Karlinsky [5, 39, 40, 42, 52], Fig. 1, NDE/NDT) hardware ( elements 101, 102) may be integrated with learning model so model can be trained).
Regarding claim 14, Srinivasamurthy, Gottschlich, De Beenhouwer and Karlinsky teach the invention as claimed in claim 13 above. Srinivasamurthy does not specifically teach wherein the NDE/NDT process is one or more of a radiography testing, an ultrasonic phased array imaging, a liquid penetrant testing, a magnetic particle testing, and an active infrared imaging.
However De Beenhouwer teaches wherein the NDE/NDT process is one or more of a radiography testing (De Beenhouwer [9, 13, 22, 78, 81] using radiography projection testing for non-destructive testing and quality assurance of parts, process may advantageously provide good speed and accuracy with projection-based inspection).
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention, to have incorporated the concept taught by De Beenhouwer of wherein the NDE/NDT process is one or more of a radiography testing, into the invention suggested by Srinivasamurthy, Gottschlich, De Beenhouwer and Karlinsky; since both inventions are directed towards providing NDE/NDT using images, and incorporating the teaching of De Beenhouwer into the invention suggested by Srinivasamurthy, Gottschlich, De Beenhouwer and Karlinsky would provide the added advantage of good speed and accuracy with projection-based inspection, and the combination would perform with a reasonable expectation of success (De Beenhouwer [9, 13, 22, 78, 81]).
Claim 6 is directed towards a system performing instructions similar in scope to the instructions performed by the method of claim 1, and is rejected under the same rationale. Srinivasamurthy further teaches a system for detecting one or more anomalies in an object, the system comprising: a receiving unit configured to ...perform operations...; and at least a processing unit coupled with the receiving unit, wherein the at least one processing unit is configured to ...perform operations...(Srinivasamurthy [273-279]).
Claim(s) 8-10, 17-20 is/are dependent on claim 6 above, is/are directed towards a system performing instructions similar in scope to the instructions performed by the method of claim(s) 3-5, 11-14 respectively, and is/are rejected under the same rationale.
Claim(s) 15, 16, 21, 22, is/are rejected under 35 U.S.C. 103 as being unpatentable over Srinivasamurthy (US20200371512A1) in view of in view of Gottschlich (US 20180330253 A1), De Beenhouwer (US 20210270755 A1) and Karlinsky (US 20170177997 A1), and further in view of Holmes (US 20170030863 A1).
Regarding claim 15 Srinivasamurthy, Gottschlich, De Beenhouwer and Karlinsky teach the invention as claimed in claim 13 above. Srinivasamurthy does not specifically teach wherein the NDE/NDT process is implemented using an NDE/NDT transducer that is one or more of an ultrasonic transducer and a phased array probe.
However Holmes teaches wherein the NDE/NDT process is implemented using an NDE/NDT transducer that is one or more of an ultrasonic transducer (Holmes [3, 42, 60] ultrasonic transducer maybe used for NDE/NDT of parts, allows for detecting any number of flaws or defects within or on the surface of the structure).
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention, to have incorporated the concept taught by Holmes of wherein the NDE/NDT process is implemented using an NDE/NDT transducer that is one or more of an ultrasonic transducer, into the invention suggested by Srinivasamurthy, Gottschlich, De Beenhouwer and Karlinsky; since both inventions are directed towards NDE/NDT of parts, and incorporating the teaching of Holmes into the invention suggested by Srinivasamurthy, Gottschlich, De Beenhouwer and Karlinsky would provide the added advantage of detecting any number of flaws or defects within or on the surface of the structure, and the combination would perform with a reasonable expectation of success (Holmes [3, 42, 60]).
Regarding claim 16, Srinivasamurthy, Gottschlich, De Beenhouwer and Karlinsky teach the invention as claimed in claim 1 above. Claim 1 further teaches using priori data.
Srinivasamurthy does not specifically teach wherein the priori data is a computer-aided design (CAD) model.
However Holmes teaches wherein the ... data is a computer-aided design (CAD) model (Holmes [72] data may be from CAD model, [3, 42, 60] ultrasonic transducer maybe used for NDE/NDT of parts, allows for detecting any number of flaws or defects within or on the surface of the structure).
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention, to have incorporated the concept taught by Holmes of wherein the ... data is a computer-aided design (CAD) model, into the invention suggested by Srinivasamurthy, Gottschlich, De Beenhouwer and Karlinsky; since both inventions are directed towards detecting defects in parts, and incorporating the teaching of Holmes into the invention suggested by Srinivasamurthy, Gottschlich, De Beenhouwer and Karlinsky would provide the added advantage of detecting any number of flaws or defects within or on the surface of the structure and leveraging a CAD model, and the combination would perform with a reasonable expectation of success (Holmes [3, 42, 60, 72]).
Claim(s) 21, 22, is/are dependent on claim 6 above, is/are directed towards a system performing instructions similar in scope to the instructions performed by the method of claim(s) 15, 16, respectively, and is/are rejected under the same rationale.
Conclusion
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SANCHITA . ROY
Primary Examiner
Art Unit 2146
/SANCHITA ROY/Primary Examiner, Art Unit 2146