DETAILED ACTION
Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . Claims 1-2 and 5-9 are currently pending in this application.
Priority
2. Receipt is acknowledged of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file.
Information Disclosure Statement
3. No information disclosure statement (IDS) was submitted in this application.
Drawings
4. The drawings submitted on 12/12/2023 are in compliance with 37 CFR § 1.81 and 37 CFR § 1.83 and have been accepted by the examiner.
Claim Rejections - 35 USC § 101 Non-Statutory
5. 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.
6. Claims 6-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Specifically, representative Claim 6 recites:
6. A vision-based enhanced omni-directional defect detection method, performed using a defect detection apparatus comprising a conveyor belt, a lift lever disposed on the conveyor belt, the lift lever being provided with a six-degree of freedom mechanism and a complementary metal oxide semiconductor (CMOS) camera connected to a tail end of the six degree of freedom mechanism and a plurality of LED lights disposed along longitudinal movement direction of the conveyer belt the method comprising:
step 1, performing posture adjustment on the vision based enhanced omnidirectional defect detection apparatus and changing an angle of the CMOS camera through the six degree of freedom mechanism and a transmission speed of the conveyor belt;
step 2, searching for a source of a suspected defect and performing focus detection, comprising collecting information of a multi-angle detection picture by the CMOS camera, preliminarily identifying the multi-angle detection picture by using a YOLOv5 defect fast identification technology, and in a case where a confidence value is less than 0.6, continuously sending a signal to change the angle of the CMOS camera through the six degree of freedom mechanism and acquiring the multi-angle detection picture at different angles;
step 3, after the multi-angle detection picture is acquired, segmenting a defective region within an identification box in the multi-angle detection picture by using a grayscale threshold, and for the defective region, extracting feature information of a defect in the defective region based on OpenCV, wherein the feature information comprises area, perimeter, a pixel mean value, and pixel variance information;
step 4, extracting a light value of the detection picture based on OpenCV, increasing a brightness difference by actively adjusting an intensity of a the plurality of LED lights, and enhancing a contrast between the defect and a background in the detection picture, wherein extraction of the light value comprises: converting the detection picture from a red-green-blue (RGB) color space to a hue-saturation-value (HSV) space, extracting brightness V values and calculating a mean value of the brightness V values, and wherein the detection picture has n non-zero pixels, with a non-zero pixels inside the identification box and b non-zero pixels outside the box, HSV values of the non-zero pixels are (h1,S1,V1,(h2,S2,V2),…,(hn, Sn, Vn) respectively, and a pixel mean V value is calculated as:
PNG
media_image1.png
78
112
media_image1.png
Greyscale
wherein Vn and Vb are brightness mean values inside the identification box and outside the identification box respectively, and a difference is dmax = |Va-Vb|
step 5, performing accurate identification on the defective region, wherein the defect in the defective region is extracted based on OpenCV, data preprocessing is performed firstly, and an image quality is improved through a normalization operation, wherein a normalization formula is
PNG
media_image2.png
80
172
media_image2.png
Greyscale
wherein X denotes a pixel value of an input image, Xo,1-n denotes a pixel value of an output image, Xmax denotes a maximum pixel value of the input image, Xmin denotes a minimum pixel value of the input image, and image pixels are adjusted to a range of [0, I] after normalization;
a defect contour is detected by using Canny edges, geometric area information S of the defect is acquired by using a function of cv2.contourArea() in an OpenCV library, at the same time, geometric perimeter information L of the defect is extracted by using a function of cv2.arcLength() in the OpenCV library, a slenderness ratio M and an area occupancy degree N of the defective region are acquired,
the slenderness ratio is obtained by M=w/h,
wherein h and W are a length value and a width value of the defective rectangular region, and the area occupancy degree is obtained by
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media_image3.png
74
96
media_image3.png
Greyscale
and
step 6, acquiring, based on multi-feature information of the detection picture, multi-feature average data of the defect, using the multi-feature average data as input, and achieving defect category differentiation through a decision tree classification model.
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements.”
Under Step 1 of the analysis, claim 6 does belong to a statutory category, namely it is a process claim.
Under Step 2A, prong 1, claim 1 is found to include at least one judicial exception, that being a mathematical process. This can be seen in the claim limitation of step 1, performing posture adjustment on the vision based enhanced omnidirectional defect detection apparatus and changing an angle of the CMOS camera through the six degree of freedom mechanism and a transmission speed of the conveyor belt;
step 3, after the multi-angle detection picture is acquired, segmenting a defective region within an identification box in the multi-angle detection picture by using a grayscale threshold, and for the defective region, extracting feature information of a defect in the defective region based on OpenCV, wherein the feature information comprises area, perimeter, a pixel mean value, and pixel variance information;
step 4, extracting a light value of the detection picture based on OpenCV, increasing a brightness difference by actively adjusting an intensity of the plurality of LED lights, and enhancing a contrast between the defect and a background in the detection picture, wherein extraction of the light value comprises: converting the detection picture from a red-green-blue (RGB) color space to a hue-saturation-value (HSV) space, extracting brightness V values and calculating a mean value of the brightness V values, and wherein the detection picture has n non-zero pixels, with a non-zero pixels inside the identification box and b non-zero pixels outside the box, HSV values of the non-zero pixels are (h1,S1,V1,(h2,S2,V2),…,(hn, Sn, Vn) respectively, and a pixel mean V value is calculated as:
PNG
media_image1.png
78
112
media_image1.png
Greyscale
wherein Vn and Vb are brightness mean values inside the identification box and outside the identification box respectively, and a difference is dmax = |Va-Vb|
step 5, performing accurate identification on the defective region, wherein the defect in the defective region is extracted based on OpenCV, data preprocessing is performed firstly, and an image quality is improved through a normalization operation, wherein a normalization formula is
PNG
media_image2.png
80
172
media_image2.png
Greyscale
wherein X denotes a pixel value of an input image, Xo,1-n denotes a pixel value of an output image, Xmax denotes a maximum pixel value of the input image, Xmin denotes a minimum pixel value of the input image, and image pixels are adjusted to a range of [0, I] after normalization;
a defect contour is detected by using Canny edges, geometric area information S of the defect is acquired by using a function of cv2.contourArea() in an OpenCV library, at the same time, geometric perimeter information L of the defect is extracted by using a function of cv2.arcLength() in the OpenCV library, a slenderness ratio M and an area occupancy degree N of the defective region are acquired, the slenderness ratio is obtained by M=w/h,
wherein h and W are a length value and a width value of the defective rectangular region, and the area occupancy degree is obtained by
PNG
media_image3.png
74
96
media_image3.png
Greyscale
and
step 6, acquiring, based on multi-feature information of the detection picture, multi-feature average data of the defect, using the multi-feature average data as input, and achieving defect category differentiation through a decision tree classification model.
, which is the judicial exception of a mental process and/or a mathematical concept because it is merely a data evaluation including calculations, and/or judgements capable of being performed mentally.
Step 2A, prong 2 of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception(s) into a practical application of the exception. This evaluation is performed by (a) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (b) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application.
In addition to the abstract ideas recited in claim 6, the claimed method recites additional elements including step 2, searching for a source of a suspected defect and performing focus detection, comprising collecting information of a multi-angle detection picture, preliminarily identifying the multi-angle detection picture by using a YOLOv5 defect fast identification technology, and in a case where a confidence value is less than 0.6, continuously sending a signal to change the equipment angle and acquiring the multi-angle detection picture; which is merely a data gathering step recited at a high level of generality and therefore merely amount to “insignificant extra-solution” activity(ies). See MPEP 2106.05(g) “Insignificant Extra-Solution Activity,”.
The generic data gathering, processing, and output steps, and other elements, are recited so generically (no details whatsoever are provided ) that it represents no more than mere instructions to apply the judicial exceptions on a computer. It can also be viewed as nothing more than an attempt to generally link the use of the judicial exceptions to the technological environment of a computer. Noting MPEP 2106.04(d)(I): “It is notable that mere physicality or tangibility of an additional element or elements is not a relevant consideration in Step 2A Prong Two. As the Supreme Court explained in Alice Corp., mere physical or tangible implementation of an exception does not guarantee eligibility. Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 224, 110 USPQ2d 1976, 1983-84 (2014) ("The fact that a computer ‘necessarily exist[s] in the physical, rather than purely conceptual, realm,’ is beside the point")”.
Thus, under Step 2A, prong 2 of the analysis, even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application and the claim is directed to the judicial exception. No specific practical application is associated with the claimed system. For instance, nothing is done with the output from the first model.
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as described above with respect to Step 2A Prong 2, merely amount to a general purpose computer system that attempts to apply the abstract idea in a technological environment, limiting the abstract idea to a particular field of use, and/or merely insignificant extra-solution activity (claims 1, 8, and 15). Such insignificant extra-solution activity, e.g. data gathering and output, when re-evaluated under Step 2B is further found to be well-understood, routine, and conventional as evidenced by MPEP 2106.05(d)(II) (describing conventional activities that include transmitting and receiving data over a network, electronic recordkeeping, storing and retrieving information from memory, and electronically scanning or extracting data from a physical document).
With regards to the dependent claims, claims 7-9, merely further expand upon the algorithm/abstract idea and do not set forth further additional elements therefore these claims are found ineligible for the reasons described for independent claim 6
See Supreme court decision in Alice Corporation Pty. Ltd. V. CLS Bank International, et al.
Response to Arguments
Applicant's arguments filed 4/27/2026 have been fully considered but they are not persuasive with regards to the USC 101 rejection. The rejections in view of the USC 102 rejections have been withdrawn.
In this instance applicant argues that the physical acts performed on real world machinery are not mental process merely because they are informed by computed data.
Applicant appears to argue that the method being tied to the specific defect detection apparatus makes the claims eligible. Applicant also states that by performing posture adjustment which applicant considers concrete machine control operations the claims are eligible.
Noting MPEP 2106.04(d)(I): “It is notable that mere physicality or tangibility of an additional element or elements is not a relevant consideration in Step 2A Prong Two. As the Supreme Court explained in Alice Corp., mere physical or tangible implementation of an exception does not guarantee eligibility. Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 224, 110 USPQ2d 1976, 1983-84 (2014) ("The fact that a computer ‘necessarily exist[s] in the physical, rather than purely conceptual, realm,’ is beside the point")”. Merely using a machine to perform a method does not render the claim eligible. For example, if one would claim a method of cutting the grass using a lawn mower. The claim would not be eligible just because the method is being performed by a human using a machine . In the instant application posture adjustments are capable of being done by a human. Therefore, the rejection is deemed proper as merely adding components does not change the abstract idea.
Applicant goes on to argue that collecting information… is not a mental process.
It is directed to insignificant extra-solution activity, e.g. data gathering and output, when re-evaluated under Step 2B is further found to be well-understood, routine, and conventional as evidenced by MPEP 2106.05(d)(II) (describing conventional activities that include transmitting and receiving data over a network, electronic recordkeeping, storing and retrieving information from memory, and electronically scanning or extracting data from a physical document).
Applicant states that actively adjusting the intensity of the plurality of LED lights is not a mathematical concept or cannot be performed by the human mind.
A human is capable of adjusting a light(s). Therefore, this argument is not persuasive as the human would physically perform the adjustment using mental process i.e. the mind would instruct the human on how to adjust the light.
Applicant goes on to argue that the judicial exception is integrated in t a practical application in several ways. i.e. tied to a machine, improves a technical field, step 2 is not insignificant extra solution activity as recites a feedback loop, the output is used to control subsequent machine operation
Again noting MPEP 2106.04(d)(I): “It is notable that mere physicality or tangibility of an additional element or elements is not a relevant consideration in Step 2A Prong Two. As the Supreme Court explained in Alice Corp., mere physical or tangible implementation of an exception does not guarantee eligibility. The feedback loop maybe interpreted as constant data gathering. The control of the machine maybe eligible if it is based on the analysis (abstract idea) in the claim. However, Examiner could not find this particular limitation explicitly recited in the claims.
For these reasons the rejection is deemed proper and has been maintained.
Conclusion
7. THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
8. 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.
9. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ADITYA S BHAT whose telephone number is (571)272-2270. The examiner can normally be reached on Monday-Friday 8 am-6pm.
10. 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.
11. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shelby Turner can be reached on 571-272-6334. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ADITYA S BHAT/Primary Examiner, Art Unit 2857 July 11, 2026
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