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 .
Response to Arguments
Claim Rejections – 35 U.S.C. § 112
The rejection of claims 1-13 and 15-16 has been withdrawn.
Claim Rejections – 35 U.S.C. § 103
Applicant argues that Bennett fails to teach "detecting in the first image alone possibly occurring defects in a powder bed that surrounds the part, wherein the detecting is solely based on the first spatial resolution of the first image without further input." As an initial matter, the limitation of "wherein the detecting is solely based on the first spatial resolution of the first image without further input." is no longer present in claim as since it was deleted in the amendment of March 2, 2026. As to the limitation “detecting in the first image alone possibly occurring defects in a powder bed that surrounds the part,” it appears Applicant considers Bennett's enhancing of the camera data with profilometer data cannot correspond to "first image alone" i.e. that the first image must be e.g., raw image data which cannot be modified or supplemented. This does not appear to be supported by the disclosure and conflicts with e.g. claims 2 and 3. Claim 2 describes a sequence of images being combined and then being referred to as a first image. Claim 3 describes changing the contrast of the image and is termed the first image. As such, it does not appear to be unreasonable to refer to Bennett's defect detection in the enhanced image as "detecting in the first image alone possibly occurring defects…"
Applicant's argument against Alles et al. and Singh et al. are not found pertinent to the limitation in question above since they were not relied upon for the teaching of the limitation.
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.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
processor in claims 11 and 12;
computer program in claim 13. See MPEP 2181(V).
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 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 and 15-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bennett et al. (2020/0384693) in view of Alles et al. (US 2008/0304056) and Singh et al. ("A Comprehensive Review of Convolutional Neural Network based Image Enhancement Techniques"; IDS of 05/26/2023).
Bennett shows a system for monitoring powder bed fusion using images comprising:
a) receiving a first image (combined data from profilometer and camera is received; the claimed "first image" is not taken to be raw image data but can be any image including a combined image or images that have been enhanced, etc. See claim 2 where the first image is a combination of a sequence of images. Alternatively, see para. [0004]: “An image of the second powder bed is captured with the camera. The image of the second powder bed is compared to the set of training data. A set of deviations from a nominal model of the first powder bed is determined based on comparison of the image of the second powder bed to the set of training data. A deviation from the set of deviations that is greater than a numerical threshold is labelled. The deviation that is greater than the numerical threshold is identified as a defect.”) of a construction space of the 3D printer, wherein the first image has a first spatial resolution (inherent that an image has a resolution), and wherein the construction space comprises a 3D printed part that is shown in the first image (para. [0027]: “Profilometer data 36 is communicated from profilometer 24 to computer 28 and is input into machine learning system 34. Camera data 38 is communicated from camera 26 to computer 28 and is input into machine learning system 34.”);
d1) detecting in the first image alone possibly occurring defects in a powder bed that surrounds the part, wherein the detecting is solely based on the first spatial resolution of the first image without further input (the combined image data is used to identify a type of defect. Bennet does not show any additional input is required. Para. [0029]: "The relative height data from profilometer 24 (e.g., profilometer data 36) is then combined with camera data 38 so as to classify different regions in an image of powder bed 14 to be nominal or as having a specific type of defect (e.g. streaking, debris, incomplete spreading, etc.)" Alternatively, see para. [0004]: “An image of the second powder bed is captured with the camera. The image of the second powder bed is compared to the set of training data. A set of deviations from a nominal model of the first powder bed is determined based on comparison of the image of the second powder bed to the set of training data. A deviation from the set of deviations that is greater than a numerical threshold is labelled. The deviation that is greater than the numerical threshold is identified as a defect.”).
As indicated by the strikeout above, Bennett does not show the generation of a second image of higher resolution out of the first image using a spatial resolution increasing artificial neural network.
Alles shows the use of both low and high resolution images to identify defects and classify defects with high sensitivity (e.g. para. [0024]). Before the effective filing date of the claimed invention, it would have been obvious to also obtain high resolution images in the system of Bennett in order to improve the sensitivity of identifying and classifying defects.
Bennett and Alles does not show the use of a spatial resolution increasing artificial neural network to obtain the high resolution images. Singh shows the use of a spatial resolution increasing artificial neural network to generate a second image of higher resolution out of the first image (Abstract).
Before the effective filing date of the claimed invention, it would have been obvious to use a spatial resolution increasing artificial neural network to generate a second image of higher resolution out of the first image in order to avoid the cost of a high resolution camera (Abstract).
With respect to claim 2, Bennett, Alles, and Singh show all the steps as discussed for claim 1 above but Bennett does not show the use of a sequence of images being combined into the first image. Official notice is taken that combining multiple images into a single image was well known. Alles also teaches the use of “one or more images” (para. [0024]) at low resolution to detect defects. Before the effective filing date of the claimed invention, it would have been obvious take a sequence of images and combine them into a single image in order to remove noise, thereby improving the identification of defects.
3. The method according to claim further comprising: a2) increasing the contrast of the first image by using a contrast increasing artificial neural network, wherein in step b) the first image with the increased contrast is used. (Singh shows the neural network enhances the contrast at Part D. Before the effective filing date of the claimed invention, it would have been obvious to enhance the contrast in order to better distinguish between objects in the images).
4. The method according to claim 1 further comprising: c) increasing the contrast of the second image by using a contrast increasing artificial neural network. (Singh shows the neural network enhances the contrast at Part D. Before the effective filing date of the claimed invention, it would have been obvious to enhance the contrast in all the images analyzed in order to better distinguish between objects in the images)
5. The method according to claim 3 wherein the contrast increasing artificial neural network has an input layer and an output layer (Singh: “two convolutional layers,”pg. 3, col. 1) and is trained by providing a first set of images with a low contrast and a second set of corresponding images with a high contrast and by adapting the weights of the contrast (Singh: “chromatic contrast weights,”pg. 3, col. 2 - pg. 4, col. 1) increasing artificial neural network such that when the images of the first set are respectively taken as the input layer, each histogram of the output layer approximates the histogram of the corresponding image of the second set.
6. The method according to claim further comprising: d2) detecting in the second image possibly occurring defects in the part and/or in the powder bed that surrounds the part. Alles shows the use of both low and high resolution images to identify defects and classify defects with high sensitivity (e.g. para. [0024])
7. The method according to claim 6 wherein in step d1) and/or step d2) the defects are detected by an image processing method and/or by a machine learning method. (see discussions above)
8. The method according to claim 1, further comprising: e) classifying the defects. (see discussion for claim 1)
9. The method according to claim 8, wherein step a) and step b) and optionally step d1), and/or step e) are performed during manufacturing of the part (Bennet shows the image analysis is done during manufacturing. See Abstract).
10. The method according to claim 1 wherein step a) and step b) and optionally step d1) are performed after manufacturing of the part (Bennet shows the image analysis is done during manufacturing. See Abstract).
11. A data processing apparatus, comprising:
a spatial resolution increasing artificial neural network (see discussion for claim 1); and
a processor adapted to perform the steps of the method according to claim 1. (As interpreted by the Examiner, Bennett shows a computer 28)
12. A 3D printer comprising:
the data processing apparatus according to the data processing apparatus according to claim 11,
a construction space (10) and a camera (26) adapted to capture a first image and/or a sequence of images.
13. A non-transitory computer readable medium (As interpreted by the Examiner, computer chip or memory, para. [0027]), comprising:
a computer program stored thereon comprising instructions which, when the program is executed by a data processing apparatus comprising a spatial resolution increasing artificial neural network (see discussion for claim 1) the data processing apparatus to carry out the steps of the method according to claim 1.
15. The method according to claim 9, wherein at least one of step dl) and step e) is performed during manufacturing of the part (see claim 9).
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) 10 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bennett, Alles, and Singh as applied to claim 1 above, and further in view of Official notice.
Bennett, Alles, and Singh show all the steps as recited in claim 1 but do not show the steps being performed after manufacturing the part. Official notice is taken that detecting defects in a part after the part is manufactured was well known. Before the effective filing date of the claimed invention, it would have been obvious to analyze the image after the part is manufactured.
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 Hwa Andrew S Lee whose telephone number is (571)272-2419. The examiner can normally be reached Mon-Fri 9am-5:30pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michelle Iacoletti can be reached at (571) 270-5789. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Hwa Andrew Lee/Primary Examiner, Art Unit 2877
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