DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Applicant claims the benefit of US Provisional Application No. 63/493,123, filed 03/30/2023. Claims 1-24 have been afforded the benefit of this filing date.
Receipt is acknowledged that application is a Continuation-in-part of application 17/811,433, filed 07/08/2022. To be afforded the benefit of this filing date, the later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed application, Application No. 17/811,433, fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for claims 1-24 of this application.
Claim Objections
Claims 3 and 15 are objected to because of the following informalities: “generate first alert indicating the fiber inconsistencies; generate a second alert indicating the material inconsistencies” should read “generate an alert indicating the fiber inconsistencies; generate an alert indicating the material inconsistencies”. Since only one action, at minimum, is required by claim 3, an alert indicating material inconsistencies may not be a second alert. Appropriate correction is required.
Claims 12 and 24 are objected to because of the following informalities: “nInconsistencyBoth is a first number of the sections in images that had fiber inconsistencies” should read “nInconsistencyBoth is a first number of the sections in images that had fiber inconsistencies and material inconsistencies”. At present, the claim language (as well as para 403 in the specification) is inconsistent with the term “both” and the equation on pg. 11 of the specification in the provisional application. Appropriate correction is required.
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 1-2, 4-14, and 16-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Under Step 1, claim 1 is a machine (system) claim. Under Step 2A Prong One, all claims recite abstract ideas, specifically mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III). These mental processes are more particularly recited in claim 1 as:
detect fiber inconsistencies in sections of fibers input into a composite material manufacturing system in real time during operation of the composite material manufacturing system; detect material inconsistencies in an unconsolidated composite material in real time as the unconsolidated composite material is being manufactured by the composite material manufacturing system using the fibers; determine whether the material inconsistencies are present in a number of the sections in which the fiber inconsistencies are detected in real time as the unconsolidated composite material is being manufactured by the composite material manufacturing system; and track the fiber inconsistencies in the number of the sections in which the fibers with fiber inconsistencies resulted in the material inconsistencies in the number of the sections in the unconsolidated composite material.
Dependent claims 2 and 4-12 provide additional limitations that are further part of the abstract idea of detecting fiber/material inconsistencies introduced in independent claim 1. It is noted that the above analysis is according to the 2019 Revised Patent Subject Matter Eligibility Guidance published in the Federal Register (84 FR 50) on January 7, 2019 and MPEP 2106.04(a)(2)(III). Consider also that “If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea” as per MPEP 2106.04(a)(2)(III)(B). See also footnotes 14 and 15 of the Federal Register Notice. As detailed above, the steps for detecting fiber/material inconsistencies may be practically performed in the human mind with or without the use of a physical aid such as a pen and paper.
Under Step 2A Prong Two, for claims 1-2 and 4-12, this judicial exception is not integrated into a practical application because each of the claims do not recite additional elements that integrate the exception into a practical application. The additional elements of performing actions in claim 2, displaying images and graphical indicators in claims 4-5, and generating images in claims 6-7 add insignificant extra-solution activity, which is not indicative of integration into a practical application, as per MPEP 2106.05(g). The additional elements of the computer system of claim 1 and machine learning models of claim 6 are recited at a high level of generality and merely equate to “apply it” or otherwise merely uses a generic computer as a tool to perform an abstract idea which is not indicative of integration into a practical application as per MPEP 2106.05(f). See also MPEP 2106.04(a)(2)(III) with respect to Mental Processes: “Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer”. See also MPEP 2106.04(a)(2)(III)(C)(3) “Using a computer as tool to perform a mental process” and MPEP 2106.04(a)(2)(III)(D), as well as the case law cited therein. Claims 8-12 recite elements that are further part of the abstract idea.
Regarding claim 3, the claims recite steps performed by the system that either halt production or indicate a potential need to halt production of the composite material. These details are explicitly aimed at improving the manufacturing process and ease of use for an operator, and thus integrate the judicial exception into a practical application.
Under Step 2B, each of claims 1-2 and 4-12 do not recite additional elements that are indicative of an inventive concept. The additional elements are simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception as per MPEP 2106.05(d) and 2106.07(a)III. In other words, the additional elements do not amount to significantly more than the judicial exception.
Regarding claims 2, 4-5, and 6-7, the generation and display of images is well-known extra-solution activity, and thus does not amount to significantly more (see MPEP 2106.05(g)). Regarding the additional limitations of claim 6, use of a machine learning model to perform an abstract idea amounts to merely an instruction to apply the abstract idea using generic computer elements, and does not integrate the judicial exception into a practical application (see MPEP 2106.05(f), MPEP 2106.05(I)(A)). Regarding claims 8-9 and 10-12, additional limitations are directed to utilizing mathematical formulas and calculations (see MPEP 2106.04(a)(2)) to perform the abstract idea of detecting and quantifying a number of fiber/material inconsistencies. The addition of further judicial exceptions does not amount to significantly more (see MPEP 2106.05(I)).
The system of claim 1 corresponds to the method of claim 13 which performs the same steps disclosed in claim 1. Regarding independent claim 13 and dependent claims 14 and 16-24, the rationale provided in the rejection of claim 1, and corresponding dependent claims, is incorporated herein. For all of the above reasons, taken alone or in combination, claims 1-2, 4-14, and 16-24 recite a non-statutory mental process.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-2, 10-11, 13-14 and 22-23 are rejected under 35 U.S.C. 102(a)(1) and (a)(2) as being anticipated by Green et al. (U.S. Patent No. 2019/0072500 A1), hereinafter Green.
Regarding claim 1, Green teaches an inconsistency detection system (Green, computer and defect detection systems in abstract, para 24, and para 32) comprising:
a computer system (Green, para 24: “main computer/electronics assembly 102”); and
an inconsistency analyzer located in the computer system (Green, defect detection systems of para 32), wherein the inconsistency analyzer is configured to:
detect fiber inconsistencies in sections of fibers input into a composite material manufacturing system in real time during operation of the composite material manufacturing system (Green, para 32: “the substantially non-conductive structure 112 may comprise a non-conductive fiber preform that has not yet been impregnated with a slurry. The substantially non-conductive structure 112 may, for example, be impregnated with the slurry subsequent to examination using the defect detection system 100”; real time detection, para 61: “continuous and real time detection of defects in one or more non-conductive structure(s) (e.g., the non-conductive structures 112, 212)”);
detect material inconsistencies in an unconsolidated composite material in real time as the unconsolidated composite material is being manufactured by the composite material manufacturing system using the fibers (Green, para 32: “the resulting prepreg structure may optionally be subjected to examination to identify any defects (e.g., conductive-debris-based defects) therein using another defect detection system substantially similar to and downstream of the defect detection system 100”);
determine whether the material inconsistencies are present in a number of the sections in which the fiber inconsistencies are detected in real time as the unconsolidated composite material is being manufactured by the composite material manufacturing system (Green, determined by if defects are acted upon by both 1) the defect detection system, corresponding to the fibers, and 2) the another defect detection system, corresponding to the prepreg, para 40: “main computer/electronics assembly 102 may employ to identify and act upon (e.g., by way of the marking assembly 110) defects (e.g., conductive debris) within the substantially non-conductive structure 112”; para 56: “The marking assembly 110 may physically mark and/or may digitally mark the substantially non-conductive structure 112 at the location(s) of defects (e.g., conductive debris) sensed by the sensor assembly 108”); and
track the fiber inconsistencies in the number of the sections in which the fibers with fiber inconsistencies resulted in the material inconsistencies in the number of the sections in the unconsolidated composite material (Green, tracked by the marking assembly where both defect detection systems mark the structure at different manufacture points, para 56: “The marking assembly 110 may, for example, interact with (e.g., by one or more software operations) with the main computer/electronics assembly 102 to digitally store (e.g., by way of one or more memory devices) the location(s) of the defects within the substantially non-conductive structure 112…If employed, the digital mark(s) may alleviate the need to physically alter the substantially non-conductive structure 112 prior to subsequent operations (e.g., defect removal operations)”).
Regarding claim 2 (dependent on claim 1), Green teaches wherein the inconsistency analyzer is configured to: perform a number of actions in response to the material inconsistencies being present in the number of the sections in which the fiber inconsistencies were detected (Green, para 13: “The defect detection system may further include a material removal device to remove the marked locations of the non-conductive structure, along with the defects (e.g., embedded conductive debris) associated therewith”; para 56: “selectively remove (e.g., cut out) defects from the substantially non-conductive structure 112”).
Regarding claim 10 (dependent on claim 1), wherein the fiber inconsistencies are selected from at least one of a gap between the fibers, a loose fiber, or a foreign object in the fibers (Green, debris is a foreign object, see embedded conductive debris in para 13).
Regarding claim 11 (dependent on claim 1), Green teaches wherein the inconsistency analyzer is configured to: determine an amount of fiber inconsistencies that affects the material inconsistencies (Green, the extent of fiber defects affecting material defects is reflected by the marking assembly by the resulting sections where both defect detection systems marked the structure at different manufacture points, see para 56 citation in claim 1 rejection).
Regarding claim 13, all claim limitations are met by Green because the method steps of claim 13 are the same as that of the corresponding system in claim 1.
Regarding claim 14 (dependent on claim 13), all claim limitations are met by Green because the method steps of claim 14 are the same as that of the corresponding system in claim 2.
Regarding claim 22 (dependent on claim 13), all claim limitations are met by Green because the method steps of claim 22 are the same as that of the corresponding system in claim 10.
Regarding claim 23 (dependent on claim 13), all claim limitations are met by Green because the method steps of claim 23 are the same as that of the corresponding system in claim 11.
Claim Rejections - 35 USC § 103
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 3-7 and 15-19 are rejected under 35 U.S.C. 103 as being unpatentable over Green in view of Marcoe et al. (U.S. Patent No. 2018/0311914 A1), hereinafter Marcoe.
Regarding claim 3 (dependent on claim 2), Green fails to explicitly teach wherein the number of actions is selected from at least one of: generate an alert indicating the fiber inconsistencies; generate an alert indicating the material inconsistencies; predict material inconsistencies prior to the material inconsistencies occurring; halt production of the unconsolidated composite material in response to the material inconsistencies being present in a number of the sections in which the fiber inconsistencies were detected; and select new fibers for input into the composite material manufacturing system.
However, Marcoe teaches a similar system for the detection of inconsistencies during composite material manufacturing (Marcoe, abstract), including generate an alert indicating the material inconsistencies (Marcoe, FIG. 8; para 103: “display inconsistencies in composite material 210 within image data 212 of FIG. 2. Method 800 may be implemented within manufacturing environment 300 using vision system 308 and display 310 of FIG. 3”; composite material may be prepreg, see para 33). It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have combined the displayed alert, taught by Marcoe, with the system of Green in order to indicate to the user that material defects are detected and where they are located (Marcoe, para 8: “computer system identifies inconsistencies in the composite material visible within the image data in real-time. The image data is displayed on a display in real-time with a width and a length superimposed over each of the inconsistencies that is visible within the image data on the display”).
Regarding claim 4 (dependent on claim 1), Green fails to explicitly teach wherein the inconsistency analyzer is configured to: display an image of the fibers with a fiber inconsistency; and display a graphical indicator in association with the fiber inconsistency in the image.
However, Marcoe teaches a similar system for the detection of inconsistencies during composite material manufacturing (Marcoe, abstract), including display an image of the fibers with a fiber inconsistency; and display a graphical indicator in association with the fiber inconsistency in the image (Marcoe, FIG. 8; para 103: “display inconsistencies in composite material 210 within image data 212 of FIG. 2. Method 800 may be implemented within manufacturing environment 300 using vision system 308 and display 310 of FIG. 3”; composite material may be dry fibers, see para 33). It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have combined the display, taught by Marcoe, with the system of Green in order to indicate to the user that material defects are detected and where they are located (Marcoe, para 8: “computer system identifies inconsistencies in the composite material visible within the image data in real-time. The image data is displayed on a display in real-time with a width and a length superimposed over each of the inconsistencies that is visible within the image data on the display”).
Furthermore, Green discloses the collection of sensor data using a radar device (Green, para 13), which the defect detection system utilizes to detect inconsistencies. However, the system of Green does not teach the generation of images. The system of Marcoe utilizes cameras to create image data (Marcoe, para 34) for the detection of inconsistencies. Thus, Green and Marcoe each disclose a type of sensor to generate data which defect information can be derived from. A person of ordinary skill in the art, before the effective filing date of the claimed invention, would have recognized that the radar device of Green could have been substituted for the camera of Marcoe because both serve the purpose of collecting representative data of composite fibers/materials to detect defects. Furthermore, a person of ordinary skill in the art would have been able to carry out the substitution. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to substitute the radar device of Green for the camera of Marcoe according to known methods to yield the predictable result of improving computer vision tasks using high-resolution imaging, as opposed to electromagnetic data.
Regarding claim 5 (dependent on claim 1), Green fails to explicitly teach wherein the inconsistency analyzer is configured to: display an image of the unconsolidated composite material with a material inconsistency; and display a graphical indicator in association with the material inconsistency in the image.
However, Marcoe teaches a similar system for the detection of inconsistencies during composite material manufacturing (abstract), including display an image of the unconsolidated composite material with a material inconsistency; and display a graphical indicator in association with the material inconsistency in the image (Marcoe, FIG. 8; para 103: “display inconsistencies in composite material 210 within image data 212 of FIG. 2. Method 800 may be implemented within manufacturing environment 300 using vision system 308 and display 310 of FIG. 3”; composite material may be prepreg, see para 33). It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have combined the display, taught by Marcoe, with the system of Green in order to indicate to the user that material defects are detected and where they are located (Marcoe, para 8: “computer system identifies inconsistencies in the composite material visible within the image data in real-time. The image data is displayed on a display in real-time with a width and a length superimposed over each of the inconsistencies that is visible within the image data on the display”).
Furthermore, Green discloses the collection of sensor data using a radar device (Green, para 13), which the defect detection system utilizes to detect inconsistencies. However, the system of Green does not teach the generation of images. The system of Marcoe utilizes cameras to create image data (Marcoe, para 34) for the detection of inconsistencies. Thus, Green and Marcoe each disclose a type of sensor to generate data which defect information can be derived from. A person of ordinary skill in the art, before the effective filing date of the claimed invention, would have recognized that the radar device of Green could have been substituted for the camera of Marcoe because both serve the purpose of collecting representative data of composite fibers/materials to detect defects. Furthermore, a person of ordinary skill in the art would have been able to carry out the substitution. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to substitute the radar device of Green for the camera of Marcoe according to known methods to yield the predictable result of improving computer vision tasks using high-resolution imaging, as opposed to electromagnetic data.
Regarding claim 6 (dependent on claim 1), Green teaches two defect detection systems that generate sensor data of the fibers input into the composite material manufacturing system in real time during operation of the composite material manufacturing system and of the unconsolidated composite material in real time as the unconsolidated composite material is being manufactured by the composite material manufacturing system (Green, para 5: “The sensor assembly comprises at least one radar device configured and positioned to detect conductive debris in the substantially non-conductive structure as portions of the substantially non-conductive structure move therepast”; see para 32 in claim 1 wherein there are two defect detection systems that are substantially similar), but fails to teach wherein images are generated, and thus fails to teach wherein in detecting the fiber inconsistencies, the inconsistency analyzer is configured to: generate fiber images of the fibers input into the composite material manufacturing system in real time during operation of the composite material manufacturing system; detect the fiber inconsistencies in the sections of the fibers input into the composite material manufacturing system in real time during operation of the composite material manufacturing system using the fiber images and a first machine learning model trained to detect the fiber inconsistencies in the fiber images of the fibers; and
wherein in detecting the material inconsistencies, the inconsistency analyzer is configured to: generate material images of the unconsolidated composite material in real time as the unconsolidated composite material is being manufactured by the composite material manufacturing system; and detect the material inconsistencies in the unconsolidated composite material in real time as the unconsolidated composite material is being manufactured by the composite material manufacturing system using the material images and a second machine learning model trained to detect the material inconsistencies in the material images of the unconsolidated composite material.
However, Marcoe teaches a similar system for the detection of inconsistencies during composite material manufacturing (Marcoe, abstract), including generate fiber images of the fibers input into the composite material manufacturing system in real time during operation of the composite material manufacturing system (Marcoe, para 87: “Method 700 automatically images a composite material, during or after laying down the composite material, using a vision system to form image data (operation 702). Method 700 identifies, by a computer system, inconsistencies in the composite material visible within the image data in real-time (operation 704)”; composite material may be dry fibers, see para 33); detect the fiber inconsistencies in the sections of the fibers input into the composite material manufacturing system in real time during operation of the composite material manufacturing system using the fiber images and a first machine learning model trained to detect the fiber inconsistencies in the fiber images of the fibers (Marcoe, para 107: “method 700 may further comprise storing data for the inconsistencies in a database, building machine learning datasets and probabilistic information using the database, and using the machine learning datasets and probabilistic information to forecast a quality of a portion of a component containing the composite material”); and
wherein in detecting the material inconsistencies, the inconsistency analyzer is configured to: generate material images of the unconsolidated composite material in real time as the unconsolidated composite material is being manufactured by the composite material manufacturing system (Marcoe, para 87: “Method 700 automatically images a composite material, during or after laying down the composite material, using a vision system to form image data (operation 702). Method 700 identifies, by a computer system, inconsistencies in the composite material visible within the image data in real-time (operation 704)”; composite material may be prepreg, see para 33); and detect the material inconsistencies in the unconsolidated composite material in real time as the unconsolidated composite material is being manufactured by the composite material manufacturing system using the material images and a machine learning model trained to detect the material inconsistencies in the material images of the unconsolidated composite material (Marcoe, para 107: “method 700 may further comprise storing data for the inconsistencies in a database, building machine learning datasets and probabilistic information using the database, and using the machine learning datasets and probabilistic information to forecast a quality of a portion of a component containing the composite material”).
While Marcoe doesn’t explicitly teach a second machine learning model, in the combination of Green in view of Marcoe, implementing a machine learning model to detect defects in each of the two defect detection systems results in a second machine learning model. It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have combined the machine learning models, taught by Marcoe, with the two defect detection systems of Green in order to improve defect detection by training models based on historical and probabilistic data (Marcoe, see para 107 citation above).
Furthermore, Green discloses the collection of sensor data using a radar device (Green, para 13), which the defect detection system utilizes to detect inconsistencies. However, the system of Green does not teach the generation of images. The system of Marcoe utilizes cameras to create image data (Marcoe, para 34) for the detection of inconsistencies. Thus, Green and Marcoe each disclose a type of sensor to generate data which defect information can be derived from. A person of ordinary skill in the art, before the effective filing date of the claimed invention, would have recognized that the radar device of Green could have been substituted for the camera of Marcoe because both serve the purpose of collecting representative data of composite fibers/materials to detect defects. Furthermore, a person of ordinary skill in the art would have been able to carry out the substitution. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to substitute the radar device of Green for the camera of Marcoe according to known methods to yield the predictable result of improving computer vision tasks using high-resolution imaging, as opposed to electromagnetic data.
Regarding claim 7 (dependent on claim 6), Green in view of Marcoe teaches further comprising: a first camera system positioned to generate the fiber images of fibers in a location in which the fibers are input into the composite material manufacturing system (See para 32 of Green wherein fibers are examined before impregnation); and a second camera system positioned to generate the material images in a location prior to the unconsolidated composite material being wound onto a spool (In the combination of Green in view of Marcoe, camera systems are taught by Marcoe in the two defect detection systems of Green – see claim 6 rejection above; prepreg is pulled from a first reel, then examined according to para 32, before the second reel – see para 7 and two reels in FIG. 1).
Regarding claim 15, all claim limitations are met and rendered obvious by Green in view of Marcoe because the method steps of claim 15 are the same as that of the corresponding system in claim 3.
Regarding claim 16, all claim limitations are met and rendered obvious by Green in view of Marcoe because the method steps of claim 16 are the same as that of the corresponding system in claim 4.
Regarding claim 17, all claim limitations are met and rendered obvious by Green in view of Marcoe because the method steps of claim 17 are the same as that of the corresponding system in claim 5.
Regarding claim 18, all claim limitations are met and rendered obvious by Green in view of Marcoe because the method steps of claim 18 are the same as that of the corresponding system in claim 6.
Regarding claim 19, all claim limitations are met and rendered obvious by Green in view of Marcoe because the method steps of claim 19 are the same as that of the corresponding system in claim 7.
Allowable Subject Matter
Claims 8-9, 12, 20-21, and 24 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten to overcome outstanding claim objections, 35 U.S.C. 101 rejections, and in independent form including all of the limitations of the base claim and any intervening claims. The following is a statement of reasons for the indication of allowable subject matter:
Regarding claim 8, Green and Marcoe both fail to teach tracking fiber inconsistencies using a Kalman filter. Green relies on a marking apparatus to track inconsistencies in inconsistent fiber and material sections. The use of Kalman filtering in the field of flaw detection in composite materials is known in the art (See Nash and Zhou, for example, in conclusion below). However, Nash and Zhou fail to remedy the deficiencies of Green in reasonable combination. Therefore, the prior art fails to teach as a whole wherein in tracking the fiber inconsistencies, the inconsistency analyzer is configured to: track the fiber inconsistencies in the number of the sections in which the fibers with fiber inconsistencies resulted in the material inconsistencies in the number of the sections in the unconsolidated composite material using a Kalman filter. Claim 9 is similarly objected to due to its dependence on claim 8. Corresponding methods claims 20-21 are similarly objected to.
Regarding claim 12, Green relies on the marking apparatus to physically or digitally indicate flaw detection in inconsistent fiber and material sections. Green does not explicitly teach a numerical comparison between the number of inconsistent sections of fiber, material, and/or both. Similar prior art, such as Harik (See citation in conclusion below), teaches how fiber inconsistencies, such as gaps, can cause subsequent composite material defects, while Nakatani quantifies the number of defects in the material (See citation in conclusion below). However, the prior art fails to remedy the deficiencies of Green in claim 12. Therefore, the prior art fails to teach as a whole:
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…wherein nInconsistencyBoth is a first number of the sections in images that had fiber inconsistencies and material inconsistencies (Note the claim objection above) and nInconsistencyMaterial is a second number of the sections in the images that had the material inconsistencies.
Corresponding method claims 24 is similarly objected to.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Nash et al. (Nash, C., Karve, P., Adams, D., & Mahadevan, S. (2021). Flaw detection and localization in curing fiber-reinforced polymer composites using infrared thermography and Kalman filtering: a simulation study. Journal of Nondestructive Evaluation, 40(3), 78.) teaches a method for flaw detection in composite material using Kalman filtering (abstract: “Specifically, a methodology that compares a metric of the time-history of Kalman filter corrections at different spatial locations to identify anomalous curing behavior was developed”).
Zhou et al. (Zhou, Y., Yu, H., Simmons, J., Przybyla, C. P., & Wang, S. (2016). Large-scale fiber tracking through sparsely sampled image sequences of composite materials. IEEE Transactions on Image Processing, 25(10), 4931-4942.) teaches a method for tracking composite material fibers using Kalman filtering (abstract: “In particular, the problem is formulated as multi-target tracking, and the Kalman filters are applied to track each fiber along the image sequence”).
Koshi et al. (WO 2021141015 A1) teaches a similar system wherein foreign objects are detected in both fibers before impregnation and in the prepreg (para 26: “A: Mechanism for detecting foreign objects accompanying the reinforced fiber bundle before it is supplied to the resin impregnation section. B: Mechanism for detecting foreign objects within the resin impregnation section. C: Mechanism for detecting defects in the appearance of the molded sheet-like prepreg”).
Minamida et al. (US 20140299253 A1) teaches two optical devices for detecting prepreg defects at two locations in the manufacturing process (para 150-151 and FIG. 3).
Sundstrom et al. (US 20210311440 A1) teaches a manufacturing monitoring system wherein a component is tracked over multiple steps using multiple machine learning models (abstract; see first and second ML models in claim 1).
Harik et al. (Harik, R., Saidy, C., Williams, S. J., Gurdal, Z., & Grimsley, B. (2018). Automated fiber placement defect identity cards: cause, anticipation, existence, significance, and progression (No. NF1676L-29045).) discusses the relationship between fiber and material defects.
Nakatani et al. (JP 2006266847 A) teaches a defect detection method in composite material manufacturing that determines the number of defects detected (para 7: “the number of detected foreign matter or defects”).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EMMA E DRYDEN whose telephone number is (571)272-1179. The examiner can normally be reached M-F 9-5 EST.
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/EMMA E DRYDEN/Examiner, Art Unit 2677
/ANDREW W BEE/Supervisory Patent Examiner, Art Unit 2677