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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 5/25/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
The information disclosure statement filed 4/2/2024 fails to comply with 37 CFR 1.98(a)(3)(i) because it does not include a concise explanation of the relevance, as it is presently understood by the individual designated in 37 CFR 1.56(c) most knowledgeable about the content of the information, of each reference listed that is not in the English language. It has been placed in the application file, but a reference referred to therein has not been considered.
Claim Objections
Claims 10-13 and 17 are objected to because of the following informalities:
Claim 10 recites “the neural network” in line 1. For clarity of the claim, it should be recited as “the trained neural network” since it is previously recited as such.
Claim 11 recites “the training” in line 1. For clarity of the claim, it should be recited as “training” since it is the first recitation of the limitation.
Claim 12 recites “the training” in line 1. For clarity of the claim, it should be recited as “training” since it is the first recitation of the limitation.
Claim 12 recites “the training set” in line 1-2. For clarity of the claim, it should be recited as “a training set” since it is the first recitation of the limitation.
Claim 13 recites “a first portion of a first material” in line 3. For clarity of the claim, it should be recited as “the first portion of the first material” since it is previously recitation in claim 1, line 3.
Claim 13 recites “a second portion of a second material” in line 3. For clarity of the claim, it should be recited as “the second portion of the second material” since it is previously recitation in claim 1, line 3.
Claim 13 recites “a stator” in line 4. For clarity of the claim, it should be recited as “the stator” since it is previously recitation in line 2.
Claim 13 recites “a joining point” in line 6. For clarity of the claim, it should be recited as “the second portion of the second material” since it is previously recitation in claim 1, line 12.
Claim 17 recites “the materials” in line 6. For clarity of the claim, it should be recited as “the first and the second material” since it is previously recitation in line 2.
Appropriate correction is required.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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)(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 17-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Ziegler et al. US 20250033145.
Regarding claim 17, Ziegler discloses: A system for joining materials (Abstract: method of joining partners), comprising:
a holder (Fig 3a: 12) adapted to hold at least a first and a second material (Fig 3b: 20a and 20b), such that a first portion of the first material is placed adjacent to a second portion of the second material (Par 29: 20a and 20b are placed adjacent to one another);
a camera (Fig 4: camera 18) adapted to take digital images of the first and second portions (Par 29: a real image is captured);
an image processing computer adapted to process the digital images into a segmentation mask using a neural network (NN) (Par 21: real image is being fed into a AI deep convolutional neural network and a false color image of the real image is output by AI), and
to determine joining points of the materials (Par 18: a movement path or the position of a predetermined movement path can be specified); and
a joining apparatus adapted to join the first and second material at the joining points (Par 32: Welding together 20a and 20b).
Regarding claim 18, Ziegler as modified by Sabato in the rejection of claim 17, where Ziegler teaches:
wherein the joining apparatus is a welding apparatus, brazing apparatus, or soldering apparatus (Par 33: Hairpins 20a and 20b are welded).
Regarding claim 19, Ziegler as modified by Sabato in the rejection of claim 17, where Ziegler teaches:
wherein the welding apparatus is a gas welding apparatus, arc welding apparatus, resistance welding apparatus, energy beam welding apparatus, or ultrasonic welding apparatus (Par 33: Hairpins 20a and 20b are welded by laser beam which is energy beam welding).
Regarding claim 20, Ziegler as modified by Sabato in the rejection of claim 17, where Ziegler teaches:
wherein the holder is a stator with holes that hold electrically conductive hairpins parallel to one another such that portions of adjacent hairpins are placed adjacent to each other, and the joining apparatus is a hairpin welding apparatus (Fig 3a: 12 holds the hairpins 20a and 20b within parallel and adjacent to one another).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-11, 13-14, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Ziegler et al. US 20250033145 in view of Sabato et al. US 20220383525.
Regarding claim 1, Ziegler discloses: A method for joining materials (Abstract: method of joining partners), comprising:
providing two materials (Fig 3b: 20a and 20b);
placing a first portion of a first material adjacent to a second portion of a second material (Par 29: 20a and 20b are placed adjacent to one another);
taking a digital image of the first and second portions (Par 29: a real image is captured) by an imaging sensor (Fig 4: camera 18);
entering the image into a trained neural network (NN) (Par 21: real image is being fed into a AI deep convolutional neural network);
outputting a segmentation mask by the NN (Par 21: a false color image of the real image is output by AI),
determining a joining point using the segmentation mask (Par 18: a movement path or the position of a predetermined movement path can be specified); and
joining the first and second material at the joining point (Par 32: Welding together 20a and 20b).
However, Ziegler is silent as to:
converting the digital image into a tensor,
the tensor comprising at least first, second, and third dimensions,
wherein the first dimension comprises a height of the digital image,
the second dimension comprises a width of the digital image, and
the third dimension comprises a number of digital channels of the imaging sensor,
entering the tensor into a trained neural network (NN).
From the same field of endeavor, Sabato teaches:
converting the digital image into a tensor (Par 45: Image features are extracted to a three dimensional tensor),
the tensor comprising at least first, second, and third dimensions (Par 45: three dimensional tensor),
wherein the first dimension comprises a height of the digital image (Par 45: Height dimension H),
the second dimension comprises a width of the digital image (Par 45: Width dimension W), and
the third dimension comprises a number of digital channels of the imaging sensor (Par 45: Channel dimension C).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified the image of Ziegler to have the three dimensions of the tensor as taught by Sabato inputted into the Neural Network of Ziegler to improve the accuracy and performance of the image and object detection algorithms (Par 41) by using the image feature in the neural networks (Par 45). The combination would result in: entering the tensor into a trained neural network (NN).
Regarding claim 2, Ziegler as modified by Sabato in the rejection of claim 1, where Ziegler teaches:
wherein the first and/or second materials comprise a metal (Par 22 and 33: 20a and 20b are hairpins which are known to be copper metal).
Regarding claim 3, Ziegler as modified by Sabato in the rejection of claim 1, where Ziegler teaches:
wherein the joining comprises welding, brazing, and/or soldering (Par 33: Hairpins 20a and 20b are welded).
Regarding claim 4, Ziegler as modified by Sabato in the rejection of claim 1, where Ziegler teaches:
wherein the welding comprises gas welding, arc welding, resistance welding, energy beam welding, or ultrasonic welding (Par 33: Hairpins 20a and 20b are welded by laser beam which is energy beam welding).
Regarding claim 5, Ziegler as modified by Sabato in the rejection of claim 1, where Ziegler teaches:
wherein the first and/or second material comprises a wire (Par 22 and 33: Hairpins 20a and 20 are wires).
Regarding claim 6, Ziegler as modified by Sabato in the rejection of claim 1, where Ziegler teaches:
wherein the joining point is determined by thresholding, connected- components, contours, morphological segmentation, or Gaussian mixture (Par 36: Imagine is converted into black and white so that the hairpins stand out; this is the description of thresholding based on objects).
Regarding claim 7, Ziegler as modified by Sabato in the rejection of claim 1, where Ziegler teaches:
wherein the thresholding comprises histogram shape-based thresholding, clustering-based thresholding, entropy-based thresholding, object attribute-based thresholding, and spatial thresholding (Par 36: Imagine is converted into black and white so that the hairpins stand out; this is the description of thresholding based on objects).
Regarding claim 8, Ziegler as modified by Sabato in the rejection of claim 1, where Ziegler teaches:
wherein the digital image is segmented by at least one of semantic segmentation, panoptic segmentation, and instance segmentation (Par 21: a false color image is produced which each pixel is assigned into a class which is semantic segmentation).
Regarding claim 9, Ziegler as modified by Sabato in the rejection of claim 1, where Ziegler teaches:
wherein the joining point is determined by determining at least one of a distance (gap), a convexity, a boundary contour, and a centroid (center point) of the first and second portions (Par 33: the counter of the hairpins is relevant for the welding; therefore the weld is determined by a boundary contour).
Regarding claim 10, Ziegler as modified by Sabato in the rejection of claim 1, where Ziegler teaches:
wherein the neural network is trained using deep learning (Par 21: real image is being fed into a AI deep convolutional neural network).
Regarding claim 11, Ziegler as modified by Sabato in the rejection of claim 1, where Ziegler teaches:
wherein the training of the NN comprises training at least one of a convolutional NN, a Fully Convolutional Neural Network (FCN), a Vision Transformer (ViT), and a SNN (Par 21: real image is being fed into a AI deep convolutional neural network).
Regarding claim 13, Ziegler as modified by Sabato in the rejection of claim 1, where Ziegler teaches:
wherein the first and second material are electrically conductive hairpins, and the joining comprises hairpin welding for manufacturing a stator (Par 33: 20a and 20b are hairpins to make a stator), wherein:
placing a first portion of a first material adjacent to a second portion of a second material comprises introducing the hairpins into a stator such that portions of adjacent hairpins are placed adjacent to each other (Par 29 and 33: 20a and 20b are placed adjacent to one another);
determining a joining point comprises determining a shape and an orientation of the portions placed adjacent to each other from the segmentation mask (Par 21 and 33: a false color image of the real image is output by AI which determines the position and shape of 20a and 20b); and
joining the first and second material comprises welding the joining points to form a stator winding from the welded hairpins (Par 33: Hairpins are joined together for winding technology for stators).
Regarding claim 14, Ziegler as modified by Sabato in the rejection of claim 1, where Ziegler teaches:
wherein the digital images are taken from cross sections of the portions placed adjacent to each other (Fig 3a shows the cross sections of 20a and 20b adjacent to one another).
Regarding claim 16, Ziegler as modified by Sabato in the rejection of claim 1, where Ziegler teaches:
wherein the NN is an artificial NN or a spiking neural network (Par 21: an artificial intelligence deep convolutional neural network).
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Ziegler et al. US 20250033145 and Sabato et al. US 20220383525 as applied to claim 1 above, and further in view of Newton et al. US 20230038435.
Regarding claim 12, Ziegler as modified by Sabato in the rejection of claim 1, where they are silent as to:
wherein the training comprises reducing an error associated with the training set.
From the same field of endeavor, Newton teaches:
wherein the training comprises reducing an error associated with the training set (Par 54: The desired result of this is known for each training data record, so that a deviation between the actual and the desired result can be determined. This deviation can be expressed as an error function and the goal of the training is to reduce usage of this function).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified the neural network of Ziegler to be trained under an error function as taught by Newton to show the desired response, even to unknown data records and also the trained neural network has the ability to transfer information, or to generalize (Par 54).
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Ziegler et al. US 20250033145 and Sabato et al. US 20220383525 as applied to claim 1 above, and further in view of Cicchitti US 20220339729.
Regarding claim 15, Ziegler as modified by Sabato in the rejection of claim 1, where they are silent as to:
preprocessing the digital image by normalizing cropping, and/or scaling the digital image.
From the same field of endeavor, Cicchitti teaches:
preprocessing the digital image by normalizing cropping, and/or scaling the digital image (Par 53: A camera acquires an image and the processing unit can crop the image).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified the camera and processor of Ziegler’s so that if the camera image contains a large area that the processor can crop the image as taught by Cicchitti so that the image only contains the welding area for better processing (Par 53 and 59).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Huet et al. US 12541850 discloses similar image processing as applicant. An et al. US 20250245808 discloses a similar welding method using NN however, is not considered prior art due to a later filing. Pour et al. US 12118475 and Ozaki et al. US 20230264285 discloses using AI to determine welding errors. Rout et al. “Advances in weld seam tracking techniques for robotic welding: A review”, Yanbiao et al. “Real-time seam tracking control system based on line laser visions”, and Liu et al. “Automatic seam detection of welding robots using deep learning” all discloses similar AI uses in welding robots.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Andrew J Marien whose telephone number is (469)295-9159. The examiner can normally be reached 9:00 am- 6:00 pm CST, Monday through Friday.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Courtney Heinle can be reached at (571) 270-3508. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/Andrew J Marien/Primary Examiner, Art Unit 3745