DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
2. 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.
Response to Arguments
3. Applicant's arguments received 10/10/2025 respect to the rejection under 35 USC 101 have been considered but they are not persuasive.
Applicant argues that (REMARKS, p.1):
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Examiner respectfully disagrees. Applicant is advised that, according to MPEP 2106 and the 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG), the USPTO determines claim eligibility under 35 U.S.C. § 101 using the Alice/Mayo framework. The analysis under Step 2A - Prong 1 evaluates whether the claim recites a judicial exception. Step 2A - Prong 2 asks does the claim recite additional elements that integrate the judicial exception into a practical application, and, if necessary, Step 2B further analyzes whether or not the claim provides an Inventive Concept. That is, the claim needs to be analyzed limitation by limitation, and/or element by element, following the MPEP/2019 PEG guidelines.
In the instant case, focusing on what the inventors have invented exactly and giving the broadest reasonable interpretation (BRI) to the claims, Examiner asserts that the pending claims 1 and 3-5 are directed to an abstract idea of feature-level ultrasonic data fusion as input for defect classification/recognition but without reciting any additional elements that amount to “significantly more” than the judicial exception. Specifically, as set forth in section 5 below in this office action, Examiner identifies that the combination of the step 2 – step 6 and the wherein clause about noise reduction recited in the representative claim 1 constitutes a judicial exception that falls within the combination of the “Mathematical Concepts” and “Mental Process” Groupings of abstract Ideas defined by the 2019 PEG. As such, Applicant’s argument in this regard is unpersuasive.
Applicant further argues that (REMARKS, p.1-2):
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Examiner respectfully disagrees. Examiner reminds to the Applicant that during patent examination, the pending claims must be given the broadest reasonable interpretation consistent with the specification. Under a broadest reasonable interpretation (BRI), words of the claim must be given their plain meaning, unless such meaning is inconsistent with the specification. The plain meaning of a term means the ordinary and customary meaning given to the term by those of ordinary skill in the art at the relevant time. See MPEP 2111.01. Moreover, although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
In the instant case, under the BRI to the claim, Examiner maintains the position that although the claims recite that data is “obtained” by “a machine scanning a steel object”, the limitation of “obtaining steel defect data by machine scanning a steel object” is still recited at a high level of generality, which requires only a generic scanning machine for scanning any steel object. The “obtaining” does not appear to be done in any particular manner and the limitation merely describes the type of data obtained, not how it is obtained in any meaningful way. Thus, the limitation of “obtaining steel defect data by machine scanning a steel object” represents merely a pre-solution activity of data gathering to the identified judicial exception. As to the limitations of the steel defect data including ultrasonic A scan data and ultrasonic S scan data wherein the ultrasonic A scan data is ultrasound ultrasonic sequence data, and the ultrasonic S scan data is sector image data, they are merely data characterization and/or descriptive of the information being observed/obtained. None of these elements is considered to be qualified for a significant or meaningful limitation because, at most, they only generally link the judicial exception to a particular technological environment or field of use. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Applicant further argues that (REMARKS, p.2):
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Examiner respectfully disagrees. Under its BRI, the limitation of step 5 including “determining a type of defect of the steel object among one or more of crack, pore, slag inclusion, incomplete penetration, or non-fusion by performing a classification task based on a result of the feature evaluation and screening” is considered an extension of the abstract idea. Performing a “determination” based on information that has been obtained and calculated is a mental process, e.g. data evaluation or judgment, and/or mathematical concept as it specifically determines the defect according to the various mathematical calculations/algorithm (e.g. see claim 4 describing “the step 4 comprises :…”). As such, the recited “determination” is not an additional element that could be considered a meaningful limit on the abstract idea to integrate into a practical application. Moreover, the types of defects of the steel object being “crack, pore, slag inclusion, incomplete penetration, or non-fusion” is mere data characterization. For example, a human being is capable of determining the specific type of defect in a steel object through observation and data evaluation. At most, the listed defects of the steel object describe the type of data being analyzed which could be considered a field of use limitation.
Applicant further argues that (REMARKS, p.2):
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Examiner respectfully disagrees. Applicant is further advised that, under the 2019 PEG, claim subject matter needs to be analyzed limitation by limitation, and/or element by element, to determine the eligibility. Simply distinguishing over the prior art does not guarantee that a claim is eligible under 35 U.S.C. 101; the claimed subject matter must also fall within the statutory categories of patentable subject matter (processes, machines, manufactures, or compositions of matter) and not be directed to a judicial exception like laws of nature, abstract ideas, or natural phenomena, even if it is novel and non-obvious compared to the prior art. Applicant’s arguments in this regard are therefore unpersuasive.
The rest of the Applicant’s arguments are reliant upon the issues discussed above or have been fully addressed by the analysis under the 2019 PEG as set forth below in the previous office action, thus are deemed unpersuasive as well. The rejection is maintained.
Claim Rejections - 35 USC § 101
4. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 101 that form the basis for the rejections under this section made in this Office action:
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.
5. Claims 1 and 3-5 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Under the 2019 PEG (now been incorporated into MPEP 2106), the revised procedure for determining whether a claim is "directed to" a judicial exception requires a two-prong inquiry into whether the claim recites: (1) any judicial exceptions, including certain groupings of abstract ideas (i.e., mathematical concepts, certain methods of organizing human interactions such as a fundamental economic practice, or mental processes); and (2) additional elements that integrate the judicial exception into a practical application (see MPEP § 2106.05(a)-(c), (e)-(h)).
Only if a claim (1) recites a judicial exception and (2) does not integrate that exception into a practical application, do we then look to whether the claim: (3) adds a specific limitation beyond the judicial exception that is not "well-understood, routine, conventional" in the field (see MPEP § 2106.0S(d)); or (4) simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception.
Claims 1 and 3-5 are directed to an abstract idea of feature-level ultrasonic data fusion as input for defect classification/recognition.
Specifically, representative claim 1 recites:
An intelligent recognition method based on the fusion of a defect pulse signal and an image, characterized by comprising the following steps:
step 1, obtaining steel defect data by machine scanning a steel object, the steel defect data including ultrasonic A scan data and ultrasonic S scan data capable of characterizing defect information on the basis of data obtained by machine scanning a defect, wherein the ultrasonic A scan data is ultrasound ultrasonic sequence data, and the ultrasonic S scan data is sector image data;
step 2, pre-processing the ultrasonic A scan data and the ultrasonic S scan data;
step 3, extracting feature information from the pre-processed ultrasonic A scan data and ultrasonic S scan data, and taking the feature information as an index for distinguishing the defects, and using the feature information respectively extracted from the ultrasonic A scan data and ultrasonic S scan data as an input variable for classification, comprising:
step 3.1, extracting ultrasonic A scan data features, comprising extracting time-domain features and geometric features of a feature waveform; when extracting the time-domain features, firstly decomposing the A scan pulse into a fourth layer by wavelet packet decomposition, and taking a ratio of the energy of each node of first three nodes of 16 nodes in the fourth layer to total energy of the A scan pulse as one time-domain feature; wherein the calculation formula is Efi=E / Ei，i=1,2,3; Efi represents the time-domain feature value calculated by an ith node of the fourth layer after decomposition, E represents the total energy of the A scan pulse, and Ei represents the energy value on the ith node of the fourth layer; if first three nodes are selected in total, three time-domain features are obtained; wherein in the step 3.1, the geometric features of the feature waveform comprise an envelope length, an area enclosed by the envelope and a horizontal axis, envelope gradient features, two groups of 1-D LBP feature values, a wave root width and a kurtosis;
wherein the extracting the feature information includes:
(1) calculating the envelope length and the area enclosed by the envelope and the horizontal axis by calculating the length of the envelope and the area enclosed by the envelope and the horizontal axis by means of differentiation of discrete data, wherein the length of the envelope is calculated by means of differentiation and integration; according to a sampling step length h = 1 of the pulse signal and a pulse amplitude corresponding to each sampling point, the calculation formula of the longitudinal axis distance between adjacent sampling points is ∆y=f(x+h)-f(x), where x represents a value of the horizontal axis, ∆y is a distance of the longitudinal axis between two adjacent sampling points, and f () represents an amplitude value of the pulse at a certain point, i.e., a value of the longitudinal axis; then, the calculation formula of envelope length is
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where b represents a sampling length of the feature interval, L represents the length feature, and ∑0b() is a summation formula; the method for calculating the area enclosed by the envelope and the horizontal axis includes, firstly, calculating ∆y=f(x+h)-f(x); on this basis, the area enclosed by the envelope and the horizontal axis is calculated by
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where S represents an area feature value;
(2) calculating of the envelope gradient features, comprising: if an amplitude value of an initial sampling point in the feature interval is set to y0 and an amplitude value of a next adjacent sampling point is set to y1, setting the difference value between the two to Δy= y1- y0; if Δy is less than 0, recording Δy; if Δy is greater than 0, not recording; then moving backward to the next sampling point, calculating Δy between the second and third sampling points, successively calculating sampling points of the feature interval, and summing all the recorded values to obtain an envelope gradient feature value;
(3) conducting an envelope 1-D LBP feature extraction method, comprising: taking any point in the feature interval as an intermediate point, and respectively selecting three points before and after the intermediate point or four points before and after the intermediate point; when the three points before and after the intermediate point are selected, calculating a difference value between each point of the selected three points before and after the intermediate point and the intermediate point respectively; if the difference value is greater than or equal to 0, assigning the sampling point as 1; if less than 0, assigning the sampling point as 0; then combining six sampling points except the intermediate point into a group of binary numbers and converting the binary numbers into decimal numbers; circularly taking each sampling point as the intermediate point and sequentially calculating the corresponding decimal value of each sampling point, wherein since three points are taken before and after the intermediate point, the decimal points after the conversion are all within (0, 63), this interval is divided into 7 parts equally, and the size of each interval is 9; calculating the number of occurrence times of these decimal points in (0, 7) and (54, 63) as the 1-D LBP feature value; when the four points are taken before and after the intermediate point, dividing (0, 255) into 10 parts equally; calculating the number of occurrence times in the (0, 25.5) and (229.5, 255) as the feature value;
(4) taking the wave root width as a width between the peak starting position and a decline end position of the feature wave, wherein the wave root width is measured by the number of sampling points in the interval;
(5) calculating kurtosis using calculation formula including:
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where A(k) represents amplitude value information of each sampling point; Aa represents a mean value, Kurt represents a kurtosis, and n represents the number of sampling points; M4, m2 are transition parameters;
step 4, performing feature evaluation and screening; and
step 5, determining a type of defect of the steel object among one or more of crack, pore, slag inclusion, incomplete penetration, or non-fusion by performing a classification task based on a result of the feature evaluation and screening,
wherein the pre-processing in step 2 comprises noise reduction of the S scan data and noise reduction of the A scan data, the noise reduction of the S scan data including: median filtering the detect image to obtain a filtered image, graying the filtered image, and extracting the image features using the feature extraction method after the graying the image: and the noise reduction of ultrasonic A scan data including: performing the noise reduction for the ultrasonic A scan data, based on a sym7 wavelet basis function, to obtain noise reduction data, and normalizing the noise reduction data to be (0, 1).
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements”.
The highlighted portion of the claim constitutes an abstract idea under the 2019 Revised Patent Subject Matter Eligibility Guidance and the additional elements are NOT sufficient to amount to significantly more than the judicial exceptions, as analyzed below:
Step
Analysis
1. Statutory Category ?
Yes.
Method
2A - Prong 1: Judicial Exception Recited?
Yes.
Under its broadest reasonable interpretation (BRI), the limitation of “step 2, pre-processing …” reads on a data analysis and/or manipulation process which can be performed by employing mathematical relationships (such as formatting data values, labelling raw data based on math relationship for feature fusion, etc.).
Under its BRI, each or the combination of the limitations of “step 3, extracting feature information ….”, “step 4, performing feature evaluation and screening” and “step 5, determining a type of defect …” encompasses mathematical concepts and wavelet packet decomposition technique to extract feature information from the collected ultrasonic scan data as an index for distinguishing defects, and using the feature information as an input variable for classification (see Spec. paragraphs 55-80). The generation of mathematical terms/quantities further encompasses mental processes that can be performed mentally with aid of pen and paper because other than reciting “ultrasonic A scan data” and “ultrasonic S scan data”, nothing in the claim element precludes the process from practically being performed in the mind.
Note, 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. See CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). See also to MPEP 2106.04(a)(2).III
Furthermore, the limitation of “determining a type of defect of the steel object among one or more of crack, pore, slag inclusion, incomplete penetration, or non-fusion by performing a classification task based on a result of the feature evaluation and screening” is considered an extended portion of the abstract idea. Performing a “determination” based on information that has been obtained and calculated is treated as a mental process, e.g. data evaluation or judgment, and/or mathematical concept, as it specifically determines the defect according to the various mathematical calculations/algorithm (e.g. see claim 4 describing “the step 4 comprises:…”). Therefore, the recited “determination” is not an additional element that could be considered a meaningful limit on the abstract idea.
In particular, the limitation of “performing a classification task based on a result of the feature evaluation and screening” is recited at high level of generality. Under its BRI, it encompasses a mental that can be performed in human mind using mental steps/critical thinking.
The limitation of step 5 further details the types of defects of the steel object including “crack, pore, slag inclusion, incomplete penetration, or non-fusion” which is considered merely data characterization and/or description of the information being determined/observed. For example, a human being is capable of determining the specific type of defect in a steel object through observation and data evaluation. At most, the listed defects of the steel object describe the type of data being analyzed which could be considered a field of use limitation; See MPEP 2106.05(h): “For instance, a data gathering step that is limited to a particular data source (such as the Internet) or a particular type of data (such as power grid data or XML tags) could be considered to be both insignificant extra-solution activity and a field of use limitation.”
The newly added wherein clause recites: “the pre-processing in step 2 comprises noise reduction of the S scan data and noise reduction of the A scan data, the noise reduction of the S scan data including: median filtering the detect image to obtain a filtered image, graying the filtered image, and extracting the image features using the feature extraction method after the graying the image: and the noise reduction of ultrasonic A scan data including: performing the noise reduction for the ultrasonic A scan data, based on a sym7 wavelet basis function, to obtain noise reduction data, and normalizing the noise reduction data to be (0, 1)”. Under its BRI, this limitation encompasses mathematical concepts or relationships (see Spec. paragraphs 51-54) that are seen as fundamental tools of science and are not eligible for patent protection on their own. The claim does not recite any related additional limitations that are “significantly more” such that the math-based “noise reduction” is applied in a practical way through an inventive and concrete application, such as a specific machine or process, rather than just being claimed on its own.
As such, under its BRI, the bolded portion of claim 1 constitutes a judicial exception that falls within the combination of the “Mathematical Concepts” and “Mental Process” Groupings of abstract Ideas defined by the 2019 PEG.
2A - Prong 2: Integrated into a Practical Application?
No.
The claim as a whole does not integrate the abstract idea into a practical application.
The limitation of “step 1, obtaining steel defect data by machine scanning a steel object, the steel defect data including ultrasonic A scan data and ultrasonic S scan data capable of characterizing defect information on the basis of data obtained by machine scanning a defect, wherein the ultrasonic A scan data is ultrasound ultrasonic sequence data, and the ultrasonic S scan data is sector image data” reads on merely a process of gathering the data/information necessary for performing the abstract idea identified above in 2A - Prong 1. Although the claim recites that data is “obtained” by “a machine scanning a steel object”, this is still recited at a high level of generality, it is a generic scanning machine without any particular details, and is used for scanning any steel object. The “obtaining” does not appear to be done in any particular manner and the limitation merely describes the type of data obtained, not how it is obtained in any meaningful way. Therefore, the limitation of “step 1” represents merely a pre-solution activity of data gathering. See MPEP 2106.05(g)(3): … that were described as mere data gathering in conjunction with a law of nature or abstract idea. See also Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 13863, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering).
Moreover, acts such as “obtaining data, and selecting ultrasonic A scan data and ultrasonic S scan data capable of characterizing defect information on the basis of data obtained by machine scanning a defect, wherein the ultrasonic A scan data is ultrasound ultrasonic sequence data, and the ultrasonic S scan data is sector image data” are deemed well-understood, routine, conventional activities (see the prior art cited in section 6 below, in particular, WO 2008000940 A1). None of these elements is considered to be qualified for a significant or meaningful limitation to integrate the judicial exception into a practical application because they do not impose any meaningful limits on practicing the claimed abstract idea.
In general, the claim as a whole does not meet any of the following criteria to integrate the abstract idea into a practical application:
An additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other 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.
Various considerations are used to determine whether the additional elements are sufficient to integrate the abstract idea into a practical application. However, in all of these respects, the claim fails to recite additional elements which might possibly integrate the claim into a particular practical application. Instead, based on the above considerations, the claim would tend to monopolize the algorithm across a wide range of applications.
2B: Claim provides an Inventive Concept?
No.
It is deemed that the focus of the claim is on an algorithm of data analysis and manipulation for feature-level ultrasonic image data fusion as input for defect classification/recognition. The additional elements such as “obtaining steel defect data by machine scanning a steel object, the steel defect data including ultrasonic A scan data and ultrasonic S scan data capable of characterizing defect information on the basis of data obtained by machine scanning a defect, wherein the ultrasonic A scan data is ultrasound ultrasonic sequence data, and the ultrasonic S scan data is sector image data”, under the BRI, are all well-understood, routine, conventional in the art (see the prior art references cited in section 7 below in this office action). They do not provide any inventive concepts or reflect a qualified improvement. See MPEP 2106.05.
The amended claim recites the limitation of “median filtering the detect image to obtain a filtered image, graying the filtered image,”. It is held that a “median filter”, as a non-linear digital filtering technique, is commonly used in image processing to remove noise while preserving edges, and is a well-known pre-processing step to improve the quality of an image for later analysis. As such, the generic recitation of “median filtering the detect image to obtain a filtered image, graying the filtered image” does not provide any inventive concepts or reflect a qualified improvement.
The dependent claims 3-5 inherit attributes of the independent claim 1, but do not add anything which would render the claimed invention a patent eligible application of the abstract idea. These claims merely extend (or narrow) the abstract idea which do not amount for "significant more" because they merely add details to the algorithm which forms the abstract idea as discussed above.
In particular, under its BRI, the limitation of “a BP neural network model based on a full connection layer” recited in claim 5 encompasses mathematical concepts and/or specific mathematical calculations to compute the neural network parameters (see Spec. paragraphs 79-80).
Hence the claims 1 and 3-5 are treated as ineligible subject matter under 35 U.S.C. § 101.
Examiner’s Note
6. While there are related references that discuss feature level fusion technique, wherein extracted features from multiple sources are combined to create more comprehensive feature sets which are then used for further processing like defect classification, the prior art of record do not specifically provide teachings for the claimed limitations including:
“step 3, extracting feature information from the pre-processed ultrasonic A scan data and ultrasonic S scan data, and taking the feature information as an index for distinguishing the defects, and using the feature information respectively extracted from the ultrasonic A scan data and ultrasonic S scan data as an input variable for classification, comprising:
step 3.1, extracting ultrasonic A scan data features, comprising extracting time-domain features and geometric features of a feature waveform; when extracting the time-domain features, firstly decomposing the A scan pulse into a fourth layer by wavelet packet decomposition, and taking a ratio of the energy of each node of first three nodes of 16 nodes in the fourth layer to total energy of the A scan pulse as one time-domain feature; wherein the calculation formula is Efi=E / Ei，i=1,2,3; Efi represents the time-domain feature value calculated by an ith node of the fourth layer after decomposition, E represents the total energy of the A scan pulse, and Ei represents the energy value on the ith node of the fourth layer; if first three nodes are selected in total, three time-domain features are obtained; in the step 3.1, the geometric features of the feature waveform comprise an envelope length, an area enclosed by the envelope and a horizontal axis, envelope gradient features, two groups of 1-D LBP feature values, a wave root width and a kurtosis;
the specific extraction method comprises:
(1) calculating the envelope length and the area enclosed by the envelope and the horizontal axis by calculating the length of the envelope and the area enclosed by the envelope and the horizontal axis by means of differentiation of discrete data, wherein the length of the envelope is calculated by means of differentiation and integration; according to a sampling step length h = 1 of the pulse signal and a pulse amplitude corresponding to each sampling point, the calculation formula of the longitudinal axis distance between adjacent sampling points is ∆y=f(x+h)-f(x), where x represents a value of the horizontal axis, ∆y is a distance of the longitudinal axis between two adjacent sampling points, and f () represents an amplitude value of the pulse at a certain point, i.e., a value of the longitudinal axis; then, the calculation formula of envelope length is
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where b represents a sampling length of the feature interval, L represents the length feature, and ∑0b() is a summation formula; the method for calculating the area enclosed by the envelope and the horizontal axis includes, firstly, calculating ∆y=f(x+h)-f(x); on this basis, the area enclosed by the envelope and the horizontal axis is calculated by
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where S represents an area feature value;
(2) calculating of the envelope gradient features, comprising: if an amplitude value of an initial sampling point in the feature interval is set to y0 and an amplitude value of a next adjacent sampling point is set to y1, setting the difference value between the two to Δy= y1- y0; if Δy is less than 0, recording Δy; if Δy is greater than 0, not recording; then moving backward to the next sampling point, calculating Δy between the second and third sampling points, successively calculating sampling points of the feature interval, and summing all the recorded values to obtain an envelope gradient feature value;
(3) the envelope 1-D LBP feature extraction method, comprising: taking any point in the feature interval as an intermediate point, and respectively selecting three points before and after the intermediate point or four points before and after the intermediate point; when the three points before and after the intermediate point are selected, calculating a difference value between each point of the selected three points before and after the intermediate point and the intermediate point respectively; if the difference value is greater than or equal to 0, assigning the sampling point as 1; if less than 0, assigning the sampling point as 0; then combining six sampling points except the intermediate point into a group of binary numbers and converting the binary numbers into decimal numbers; circularly taking each sampling point as the intermediate point and sequentially calculating the corresponding decimal value of each sampling point, wherein since three points are taken before and after the intermediate point, the decimal points after the conversion are all within (0, 63), this interval is divided into 7 parts equally, and the size of each interval is 9; calculating the number of occurrence times of these decimal points in (0, 7) and (54, 63) as the 1-D LBP feature value; when the four points are taken before and after the intermediate point, dividing (0, 255) into 10 parts equally; calculating the number of occurrence times in the (0, 25.5) and (229.5, 255) as the feature value;
(4) taking the wave root width as a width between the peak starting position and a decline end position of the feature wave, wherein the wave root width is measured by the number of sampling points in the interval;
(5) the calculation formula of kurtosis is as follows:
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where A(k) represents amplitude value information of each sampling point; Aa represents a mean value, Kurt represents a kurtosis, and n represents the number of sampling points; M4, m2 are transition parameters”.
It is these limitations, as they are claimed in the combination recited in independent claim 1, that would make the pending claims 1 and 3-5 of the present application distinguish over the prior art of record.
Citation of Relevant Prior Art
7. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
BISIAUX BERNARD et al. (WO 2008000940 A1, machine translation) is considered the closest prior art of record which discloses a technique of non-destructive inspection by ultrasound of foundry products. BISIAUX BERNARD teaches: collecting and selecting ultrasonic A scan data (e.g., ultrasonic time sequence data) and ultrasonic S scan data (e.g., scan data of pipe cross sections) capable of characterizing defect information on the basis of data obtained by machine scanning a defect; pre-processing the ultrasonic scans; extracting feature information from the ultrasonic scans as an index for distinguishing defects, and using the extracted feature information respectively as an input variable for defect classification, etc. (see Figs. 8, 11-12 and related text).
Jack (US 20210349058 A1) discloses a system and method for real-time visualization of a material during ultrasonic non-destructive testing, which includes feature level fusion of multiple types of scan data (e.g., para. 0098) and applying the results of the feature level fusion to identify defects (para. 0039-0041).
Ke et al. (US 20240252226) discloses a technique of processing ultrasonic scan data with an AI algorithm to identify defects, wherein said processing includes feature level fusion (para. 0040, 0042, 0143, 0157, 0194); wherein said processing ultrasonic scan data comprises noise reduction based on filtering (para. 0212).
Fu (CN 117400002 A, machine translation) discloses a technique of detect identification based on ultrasonic inspection and neural network model (see Fig. 4 and related discussions).
Contact Information
8. Any inquiry concerning this communication or earlier communications from the examiner should be directed to XIUQIN SUN whose telephone number is (571)272-2280. The examiner can normally be reached 9:30am-6:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shelby A. Turner can be reached on (571) 272-6334. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/X.S/ Examiner, Art Unit 2857
/SHELBY A TURNER/Supervisory Patent Examiner, Art Unit 2857