DETAILED ACTION
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 FINAL and is in response to the claims filed August 19, 2026. Claims 1-13, 15, 21, 23, 26, 27, 30, and 33 are currently pending, of which claims 1 and 13 are currently amended. Claims 14, 16-20, 22, 24, 25, 28, 29, 31, 32, and 34-37 were previously canceled. Claims 26, 27, 30, and 33 are withdrawn from further consideration pursuant to 37 CFR 1.142(b).
Response to Arguments
Claim Objections
Applicant argues that the language of claim 8, stating “wherein the preset range is a scalar value” is intentional and that a range can be a single value. See Remarks 7; see also Specification para. [0010]. Therefore, the previous objection has been withdrawn. Examiner notes that the broadest reasonable interpretation for this language is now merely a single value. Regarding claim 13, Applicant has amended the claim at issue and the objection has therefore been withdrawn.
Prior Art Rejections
Applicant’s arguments regarding the previously cited art have been fully considered. Specifically, Applicant has amended the claims at issue and argues that the digital image of Kitchen is “not an image of the formed layer” and that in Kitchen “at the moment of image capture, the formed layer has been physically withdrawn from the imaged build zone, and therefore what is images is the residual slurry.” See Remarks 8. Applicant’s arguments do not address Kottilingam, the other cited reference in the Action. Nevertheless, even if the arguments were to address, Kottilingam explicitly captures images of the build process, including in real-time, to ascertain characteristics of the structure being printed. Thermographic scans of the build platform area and parts 140 being built. See Kottilingam Fig. 6 and paras. [0019] and [0033-38].
It is for at least these reasons, and the reasons cited below, that the claims remain rejected in this Action.
Examiner’s Note
The prior art rejections below cite particular paragraphs, columns, and/or line numbers in the references for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art.
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.
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.
Claim(s) 1-13, 15, 21, and 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kitchen et al. (U.S. 2021/0170676; hereinafter “Kitchen”), and further in view of Kottilingam (U.S. Publication No. 2018/0104742; hereinafter “Kottilingam”).
As per claim 1, Kitchen teaches a method for assessing a quality of a formed layer of a build structure being fabricated by an additive manufacturing machine, comprising steps of:
obtaining a digital image of at least a portion of the formed layer of the build structure within a first build layer, [the obtained digital image depicting the portion of the formed layer within the first build layer] (See Kitchen para. [0083]: capturing image of slurry volume in the build zone after the first layer has been formed);
separating, via one or more computer processors, first region image data including data corresponding to a first region of the formed layer from second region image data including data corresponding to either one of or both a second region of the formed layer or a first region of the build layer outside of the formed layer based on a layer image template (See Kitchen paras. [0091], [0098], [0102], and [0105]: samples are masked out from a 3D model. Layer slice imagery used when masking out areas).
analyzing, via the one or more computer processors, a first subset of first region image intensity data corresponding to a first subset of the first region image data to determine a first region characteristic value based on the analysis (See Kitchen paras. [0102] and [0104-105]: “The binary void image and binary displacement image for each layer formed in the additive manufacturing process are then compared S570 to a binary expected image for that layer.” “When comparing the corrected void image to the binary expected image from the computer generated model, a percentage of coverage in the corrected void image can be quantified based on a pixel-level comparison within each contiguous region. The presence (or absence) of a manufacturing defect can be indicated based on a percentage of coverage below (or above) a threshold void image value in a portion of the additive manufacturing product corresponding to the contiguous region of the corrected void image”); and
sending, via the one or more computer processors, an alert including a readable communication when the first region characteristic value deviates from a preset range (See Kitchen Figs. 17A-17D and paras. [0105] and [0108]: fail indications when manufacturing defect “based on a percentage of coverage below (or above) a threshold void image value in a portion of the additive manufacturing product corresponding to the contiguous region of the corrected void image”).
However, while Kitchen teaches layer imaging, Kitchen does not explicitly teach imaging the portion of the formed layer within the first build layer.
Kottilingam teaches the obtained digital image depicting the portion of the formed layer within the first build layer (See Kottilingam Fig. 6 and paras. [0019] and [0033-38]: capturing images of the build process, including in real-time, to ascertain characteristics of the structure being printed. This includes determining layer characteristics such as thickness. Thermographic scans of the build platform area and parts 140 being built; para. [0051]: “The method and system of the present invention not only aims at evaluating and modifying the 3D manufacturing apparatus, but is also designed to evaluate each 3D printed part/structure in real time and after the build is completed”).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine, with a reasonable expectation of success, the image capturing of Kitchen with the thermal camera of Kottilingam. One would have been motivated to combine these references because both references disclose imaging in an additive manufacturing environment. Kottilingam enhances the imaging of Kitchen by increasing the efficiency and variety of the inspections performed, in a non-intrusive and high-speed way. Furthermore, performing the imaging in real-time as the parts are being built, can increase efficiency in determining defects as they relate to machining or design problems (See Kottilingam paras. [0002] and [0021]).
As per claim 2, Kitchen/Kottilingam further teaches the method of claim 1, further comprising a step of comparing, via the one or more computer processors, the first region characteristic value to the preset range prior to the sending step (See Kitchen paras. [0104-105]: “The presence (or absence) of a manufacturing defect can be indicated based on a percentage of coverage below (or above) a threshold void image value in a portion of the additive manufacturing product corresponding to the contiguous region of the corrected void image”).
As per claim 3, Kitchen/Kottilingam further teaches the method of claim 1, wherein the first region image intensity data includes matrix locations of a matrix (See Kitchen para. [0103]: “The tiled method breaks the expected part into square tiles, which enables the detection of partially adhered areas or smaller defects”).
As per claim 4, Kitchen/Kottilingam further teaches the method of claim 1, wherein the layer image template is a virtual model, and further comprising preparing a virtual model of the formed layer (See Kitchen para. [0098]: 3D model used for samples, including image “slices” defining the layer geometry).
As per claim 5, Kitchen/Kottilingam further teaches the method of claim 4, wherein the first region image intensity data includes a grey scale level of pixels of the virtual model of the formed layer (See Kitchen para. [0095]: grey-scale values of the uncured resin in the image).
As per claim 6, Kitchen/Kottilingam further teaches the method of claim 4, further comprising a step of corresponding a preset portion of the layer image template with a portion of the virtual model of the formed layer such that the layer image template outlines at least a section of the virtual model of the formed layer (See Kitchen para. [0098]: samples taken of cropped image and “are masked out using the expected geometry from the digital model data from, for example, a 3D model or another electronic data source such as a computer-aided design (CAD) model…” This includes image “slices”. “This image is overlaid onto the captured image to mask out areas which are expected to be printed, and areas which are not”).
As per claim 7, Kitchen/Kottilingam further teaches the method of claim 6, wherein the corresponding step includes any one or any combination of rescaling, translating, and rotating either one of or both the layer image template and the virtual model of the formed layer to align at least one location of the layer image template with at least one respective location of the virtual model of the formed layer (See Kitchen paras. [0069] and [0098]: “models provide image “slices” defining the layer geometry the printer is commanded to print, which can be converted into a binary image. This image is overlaid onto the captured image to mask out areas which are expected to be printed, and areas which are not. The pixel values from the masked out areas are converted to value frequency diagrams using a binning method.” “This image is overlaid onto the captured image to mask out areas which are expected to be printed, and areas which are not”).
As per claim 8, Kitchen/Kottilingam further teaches the method of claim 4, wherein the first region characteristic value corresponds to a quantity of virtual spots identified in a virtual first region of the virtual model of the formed layer corresponding to the first region of the formed layer, and wherein the preset range is a scalar value (See Kitchen paras. [095-96], [0098], and [0105]: number of pixels from each sample, where thresholding classifies pixels on a regional basis. “The thresholding process determines whether each pixel's data value (generally from 0-255) lies in a particular range”).
As per claim 9, Kitchen/Kottilingam further teaches the method of claim 4, wherein the first region characteristic value corresponds to a quantity of adjacent virtual spots identified in a virtual first region of the virtual model of the formed layer corresponding to the first region of the formed layer having an image intensity value greater than a preset image intensity value, and wherein the preset range is a scalar value (See Kitchen paras. [095-96], [0098], and [0105]: number of pixels from each sample, where thresholding classifies pixels on a regional basis based on nearest neighbors. This includes displacement images, where “displaced resin appears as either darker or brighter than the uncured resin slurry, which has a very uniform appearance.” Furthermore, “[t]he thresholding process determines whether each pixel's data value (generally from 0-255) lies in a particular range”).
As per claim 10, Kitchen/Kottilingam further teaches the method of claim 9, further comprising a step of identifying and thereby counting each individual virtual spot from a respective single pixel of the obtained digital image, wherein adjacent virtual spots correspond to pixels of the obtained digital image less than a preset distance from each other (See Kitchen paras. [0095-96]: threshold level applied on a regional basis based on nearest neighbors. There is a pixel-by-pixel basis for threshold boundary conditions).
As per claim 11, Kitchen/Kottilingam further teaches the method of claim 9, further comprising a step of identifying and thereby counting each individual virtual spot from a respective single pixel of the obtained digital image, wherein adjacent virtual spots correspond to abutting pixels of the obtained digital image (See Kitchen paras. [0095-96]: threshold level applied on a regional basis based on nearest neighbors. There is a pixel-by-pixel basis for threshold boundary conditions).
As per claim 12, Kitchen/Kottilingam further teaches the method of claim 8, wherein individual virtual spots correspond to respective single pixels of the obtained digital image, further comprising a step of identifying and thereby counting each individual virtual spot (See Kitchen paras. [095-96], [0098], and [0105]: number of pixels from each sample, where thresholding classifies pixels on a regional basis based on nearest neighbors).
As per claim 13, Kitchen/Kottilingam further teaches the method of claim 8, wherein the quantity of virtual spots is less than the scalar value (See Kitchen paras. [095-96], [0098], and [0105]: number of pixels from each sample, where thresholding classifies pixels on a regional basis. “The thresholding process determines whether each pixel's data value (generally from 0-255) lies in a particular range”. Moreover, “the threshold displacement image value is 97% and a percentage of coverage less than 97% correlates to the presence of a manufacturing defect and a percentage of coverage equal to or greater than 97% correlates to the absence of a manufacturing defect”).
As per claim 15, Kitchen/Kottilingam further teaches the method of claim 1, wherein the first region characteristic value is a measure of central tendency, a measure of variability, or a sum of the measure of central tendency and the measure of variability (See Kitchen paras. [0095-96] and [0098]: pixel differences used in measuring difference from the mean).
As per claim 21, Kitchen/Kottilingam further teaches the method of claim 1, wherein the obtained digital image includes at least a portion of the first region of the build layer outside of the formed layer (See Kitchen para. [0075]: “surfaces of the mesa 400 and of the channel 402 are at different distances relative to the transporting film 222. Thus, the thickness of the layer 220 slurry between the surfaces of the mesa 400 and of the channel 402 are different. Because of the differences in the surfaces (such as distance (d1, d2)), the different areas A1 and A2 of the slurry have different visual appearances when viewed through the transporting film 222”).
As per claim 23, Kitchen teaches the method of claim 1. However, while Kitchen teaches “a high resolution camera using CCD, CMOS or hyperspectral imaging technology” (See Kitchen para. [0067]), Kitchen does not explicitly teach a thermal camera.
Kottilingam teaches wherein the digital image is obtained via a thermographic camera (See Kottilingam paras. [0019-21]: thermal camera to inspect composite materials).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine Kitchen with the teachings of Kottilingam for at least the same reasons as discussed above in claim 1.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Nicholas Klicos whose telephone number is (571)270-5889. The examiner can normally be reached Mon-Fri 9:00 AM-5:00 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Scott Baderman can be reached at (571) 272-3644. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NICHOLAS KLICOS/Primary Examiner, Art Unit 2118
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