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 .
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.
Claim(s) 22 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claim 22 does not fall within at least one of the four categories of patent eligible subject matter because claim 22 is directed to a “computer program” without being stored on a non-transitory computer-readable medium. The software computer program per se does not fall within a statutory category.
The examiner suggests amending the claim(s) to recite a “non-transitory computer-readable medium” storing a computer program or equivalent. Any amendment to the claims should be commensurate with its corresponding disclosure.
Claim Rejections - 35 USC § 102
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)(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.
Claim(s) 1, 3-19, 21-22 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Adler (US 2021/0010953).
As to claim 1, Adler discloses a method for performing quality control of objects in an apparatus which produces the objects in continuous cycle (para. 0005), the method comprising the following steps:
feeding the objects individually to an inspecting station (para. 0042, 0044, 0048);
for each object positioned in the inspecting station, capturing an image (para. 0029, 0033);
for each image, applying a first processing step, for attributing the image and the corresponding object to one of the two following categories: defective objects category and non-defective objects category (para. 0038);
if the image is attributed to the defective objects category, applying to image data related to that image a second processing step, and further classifying, based on a plurality of identification features extracted from the image data, the image and the corresponding object according to a plurality of defect categories (para.0038, 0045).
As to claim 3, Adler discloses the method according to claim 1, comprising a step of storing in a database the images which are attributed to the defective objects category (para. 0056).
As to claim 4, Adler discloses the method according to claim 3 wherein an unsupervised clustering is used in the second processing step, wherein during the unsupervised clustering, a working space having multiple dimensions is defined, wherein each dimension corresponds to a feature of the plurality of identification features extracted from the image data of each image, wherein values of said identification features extracted for each image define the location of the image data of each image in the workspace, so that each image data is represented as a data point in the working space and the defect categories are generated by grouping data points that have similar locations in the working space (para. 0101).
As to claim 5, Adler discloses the method according to claim 3 wherein the images attributed to the non-defective objects category are excluded from the storing step (para. 0056).
As to claim 6, Adler discloses the method according to claim 1, wherein during the first processing step position information related to the position of a defect in each defective object is obtained (para. 0082).
As to claim 7, Adler discloses the method according to claim 6, wherein the position information is fed to the second processing step (para. 0082).
As to claim 8, Adler discloses the method according to claim 7, wherein the plurality of identification features includes at least one feature representative of the position information (para. 0082).
As to claim 9, Adler discloses the method according to claim 1, wherein the first processing step is carried out through a machine-learned model trained to attribute each image to the defective objects category or to the non-defective objects category, wherein the machine-learned model is trained based on training data including only images of non-defective objects (para. 0082, 0108).
As to claim 10, Adler discloses the method according to claims 1, wherein the first processing step includes, for each image, extracting diagnostic markers from the image data and applying predetermined diagnostic rules (para. 0045).
As to claim 11, Adler discloses the method according to claim 1, wherein the first processing step includes: a machine-learned model trained to attribute each image to the defective objects category or to the non-defective objects category, and for each image, extracting diagnostic markers from the image data and applying predetermined diagnostic rules, at a second stage, wherein both the outcome of the first stage and the outcome of the second stage of the first processing step are taken into consideration for attributing the image and the corresponding object to the defective objects category or to the non-defective objects category (para. 0045).
As to claim 12, Adler discloses the method according to claim 11, wherein, during the first processing step, one of the following conditions occurs: i) both the first stage and the second stage are applied to the image data taken from each object; ii) according to predetermined criteria, the image data of each object is divided into a first subset and a second subset, wherein, for each object, the first stage is applied to the first subset and the second stage is applied to the second subset; iii) the first processing step includes a plurality of predetermined tasks, providing a corresponding plurality of conditions to be met according to a predetermined sequence, wherein a first group of tasks of the plurality of tasks is performed by the machine-learned model, and a second group of tasks of the plurality of tasks is performed by extracting diagnostic markers from the image data and applying predetermined diagnostic rules (para. 0045).
As to claims 13-19, 21-22, these claims recite features similar to those discussed above. Therefore, they are rejected for reasons similar to those discussed above.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 2, 20 are is/are rejected under 35 U.S.C. 103 as being unpatentable over in view of Wu et al. [“Classification of Solder Joint Using Feature Selection Based on Bayes and Support Vector Machine”] hereinafter, Wu.
As to claim 1, Adler does not disclose the method according to claim 1, wherein the optical device illuminates the object in the inspecting station with light in the spectrum of visible light, or IR or UV, and includes a camera, wherein the camera views the object and takes the image of the object.
Wu teaches the optical device that illuminates the object in the inspecting station with light in the spectrum of visible light, or IR or UV, and includes a camera, wherein the camera views the object and takes the image of the object (Figs. 1, 2, Section II. IMAGE ACQUIREMENT AND SOLDER
CLASS DEFINITION).
It would have been obvious to one of ordinary skill in the art to incorporate Wu’s teachings into Adler since doing so would merely combine prior art elements according to known methods to yield predictable results, and expand processing capability.
As to claim 20, the claim recites features similar to those discussed above. Therefore, claim 20 is rejected for reasons similar to those discussed above.
Claim Rejections - 35 USC § 102
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)(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.
Claim(s) 1-2, 13, 20-22 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wu et al. [“Classification of Solder Joint Using Feature Selection Based on Bayes and Support Vector Machine”] hereinafter, Wu.
As to claim 1, Wu discloses a method for performing quality control of objects in an apparatus which produces the objects in continuous cycle (abstract), the method comprising the following steps:
feeding the objects individually to an inspecting station (Figs. 1, 2, Section II. IMAGE ACQUIREMENT AND SOLDER CLASS DEFINITION);
for each object positioned in the inspecting station, capturing an image (Figs. 1, 2, Section II. IMAGE ACQUIREMENT AND SOLDER CLASS DEFINITION);
for each image, applying a first processing step, for attributing the image and the corresponding object to one of the two following categories: defective objects category and non-defective objects category (Fig. 4, page 519, Section VI. CLASSIFICATION);
if the image is attributed to the defective objects category, applying to image data related to that image a second processing step, and further classifying, based on a plurality of identification features extracted from the image data, the image and the corresponding object according to a plurality of defect categories (Fig. 4, page 519, Section VI. CLASSIFICATION).
As to claim 2, Wu discloses the method according to claim 1, wherein the optical device illuminates the object in the inspecting station with light in the spectrum of visible light, or IR or UV, and includes a camera, wherein the camera views the object and takes the image of the object (Figs. 1, 2, Section II. IMAGE ACQUIREMENT AND SOLDER CLASS DEFINITION).
As to claims 13, 20-22, these claims recite features similar to those discussed above. Therefore, they are rejected for reasons similar to those discussed above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
OKAZAKI discloses a method for performing defect inspection by using a learning model generated by machine learning
Bakhshmand et al. disclose systems and methods for automated visual inspection of a manufactured article using artificial intelligence.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PHUOC TRAN whose telephone number is (571)272-7399. The examiner can normally be reached 9am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Vu Le can be reached at 571-272-7332. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PHUOC TRAN/Primary Examiner, Art Unit 2668
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