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 Amendment
In response to applicant’s amendment received on 4/23/26, all requested changes to the claims have been entered. Claims 1-20 were previously pending. Claims 5 and 15 have been cancelled. Claims 1-4, 6-14 and 16-20 are currently pending. The amendments raise new issues with regards to112(f) interpretations, prior art rejections, and 112(b/d) rejections discussed further below.
Response to Arguments
Regarding independent claim 1, it’s noted that no specific arguments are presented regarding the prior art rejection of claims 1 and previously pending claim 5 now partially incorporated into claim 1. It’s also noted that none of the limitations are considered to invoke 112(f) and therefore do not incorporate the same structure previously indicated as making the prior version of claim 11 allowable. For example, the limitation “obtaining a defect eigenvector of an image to be detected by using an encoding module of the program codes” is disclosed in the claim as being carried out by the structure “defect detection device that comprises a storage medium storing program codes and a processor configured to execute the program codes”. Therefore, 112(f) is not invoked and the limitation would not incorporate the algorithm discussed in the specification (i.e. figure 3 and paragraphs 94-117) as was the case with the prior version of claim 11. In other words, claim 1 has not added any allowable subject matter and the rejection is updated below.
Regarding the previous interpretation of limitations in claims 11 and 13 under 112(f), the amendments filed on 4/23/26 have now resolved/avoid those interpretations which are herein withdrawn. Those claims now recite sufficient structure for performing the functions and are therefore now no longer limited by the structure/algorithm disclosed in the specification, which did include a processor programmed with the entire algorithm for performing the functions claimed (i.e. they were considered computer-implemented means plus function limitations as discussed in MPEP 2181(II)(B)). As such, the claims are no longer considered to incorporate the algorithms disclosed in figure 3 and paragraphs 94-11 associated with the encoding and decoding modules which had placed them into condition for allowance. Thus the previous indicated allowability of claims 11-14 and 17 is herein withdrawn and an updated rejection, necessitated by the amendment, is provided below.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 19 and 20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 19 depends from claim 1, and therefore incorporates all the limitations of claim 1, which already states “a method for defect detection, performed by a defect detection device that comprises a storage medium storing program codes and a processor configured to execute the program codes”. Therefore it’s unclear if the apparatus with processor and memory discussed in claim 19 are the same as the defect device with processor and storage medium discussed in claim 1, or something different? If it is meant for the apparatus with processor and memory discussed in claim 19 to replace the defect device with processor and storage medium discussed in claim 1, then claim 19 would raise issue under 112(d) discussed below.
Claim 20 depends from claim 1, and therefore incorporates all the limitations of claim 1, which already states “a method for defect detection, performed by a defect detection device that comprises a storage medium storing program codes and a processor configured to execute the program codes”. Therefore it’s unclear if the computer and storage medium discussed in claim 20 are the same as the defect device with processor and storage medium discussed in claim 1, or something different? If it is meant for computer and storage medium discussed in claim 20 to replace the defect device with processor and storage medium discussed in claim 1, then claim 20 would raise issue under 112(d) discussed below.
Claim Rejections - 35 USC § 112(d)
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 19 and 20 are rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
Claim 19 depends from claim 1, and therefore incorporates all the limitations of claim 1, which already states “a method for defect detection, performed by a defect detection device that comprises a storage medium storing program codes and a processor configured to execute the program codes”. It would appear that perhaps the apparatus with processor and memory discussed in claim 19 are meant to replace the defect device with processor and storage medium discussed in claim 1. If that is the case, claim 19 would fail to include all the limitations of the claim upon which it depends.
Claim 20 depends from claim 1, and therefore incorporates all the limitations of claim 1, which already states “a method for defect detection, performed by a defect detection device that comprises a storage medium storing program codes and a processor configured to execute the program codes”. It would appear that perhaps the computer and storage medium discussed in claim 20 are meant to replace the defect device with processor and storage medium discussed in claim 1. If that is the case, claim 20 would fail to include all the limitations of the claim upon which it depends.
Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Claim Rejections - 35 USC § 102
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.
Claims 1-4, 7, 11-14, 17, 19 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 2020/0294222 to Wen et al. (“Wen”).
Regarding claim 1, Wen discloses a method for defect detection (Fig. 2), performed by a defect detection device that comprises a storage medium storing program codes and a processor configured to execute the program codes (Fig. 6; paragraphs 86-90), the method comprising:
obtaining a defect eigenvector of an image to be detected by using an encoding module of the program codes; (paragraphs 37, 46-48, wherein features are extracted from the subimage and used to obtain a target/defect eigenvector, wherein the deep CNN, implemented via program codes (Fig. 6; paragraphs 86-90), corresponds to an “encoding module” configured to create/obtain the target/defect eigenvector from extracted features);
calculating a similarity score of the image to be detected for each known defect type according to the defect eigenvector (paragraphs 37, 46-48, wherein, using a deep CNN defect classification model, the target/defect eigenvector is compared to each eigenvector of known defects in the table to determine a known eigenvector that are identical or similar, corresponding to calculating a similarity score); and
performing defect classification on the image to be detected according to the similarity score (paragraph 49-50, wherein at least one defect category for the subimage is obtained based on the calculated similarities, corresponding to performing defect classification on the image).
Regarding claim 2, Wen discloses the method according to claim 1, wherein calculating the similarity score of the image to be detected for each known defect type according to the defect eigenvector comprises:
mapping the defect eigenvector into a trained eigenvector space, wherein the trained eigenvector space comprises a distribution position of a defect eigenvector of a known defect type (paragraphs 37, 46-48, wherein mapping/comparing the target/defect eigenvector to known defect eigenvectors in the table/space is done by a trained Deep CNN classification model (Fig. 4)); and
calculating a distance between the defect eigenvector and the defect eigenvector of the known defect type, so as to obtain the similarity score of the image to be detected for each known defect type (paragraphs 37, 46-48, wherein, as is known in the art, the trained Deep CNN calculates a distance/difference between the target/defect eigenvector and each of the known defect eigenvectors so as to obtain a similarity score used to identify the most appropriate defect category).
Regarding claim 3, Wen discloses the method according to claim 2, wherein the performing defect classification on the image to be detected according to the similarity scores comprises:
when a maximum similarity score in similarity scores is not less than a similarity threshold, outputting a defect type corresponding to the maximum similarity score (paragraph 48, wherein if the compared eigenvectors are identical or similar (i.e. not less than a similarity threshold) then the corresponding defect type is output);
OR
when the similarity scores are all less than the similarity threshold, outputting an unknown defect type (alternative limitation).
Regarding claim 4, Wen discloses the method according to claim 3, further comprising:
outputting the similarity score corresponding to the defect type (paragraphs 37 and 48, wherein as is known in the art, a deep CNN provides outputs a correlation/similarity score associated with the categories/patterns it is trained to recognize).
Regarding claim 7, Wen discloses he method according to claim 1, further comprising:
obtaining defect position information of the image to be detected by using a decoding module of the program codes (paragraphs 37 and 48, wherein the deep CNN, implemented via program codes (Fig. 6; paragraphs 86-90), corresponds to an “decoding module” configured to “decode” an image to obtain the position of defects).
Regarding claim 11, Wen discloses a device for defect detection, comprising:
a computer-readable storage medium configured to store program codes, and a processor configured to execute the program codes (Fig. 6; paragraphs 86-90);
wherein by executing the program codes, the processor is configured to:
obtain a defect eigenvector of an image to be detected by using an encoding module of the program codes (paragraphs 37, 46-48, wherein features are extracted from the subimage and used to obtain a target/defect eigenvector, wherein the deep CNN, implemented via program codes (Fig. 6; paragraphs 86-90), corresponds to an “encoding module” configured to create/obtain the target/defect eigenvector from extracted features);
calculate a similarity score of the image to be detected for each known defect type according to the defect eigenvector (paragraphs 37, 46-48, wherein, using a deep CNN defect classification model, the target/defect eigenvector is compared to each eigenvector of known defects in the table to determine a known eigenvector that are identical or similar, corresponding to calculating a similarity score); and
perform defect classification on the image to be detected according to the similarity score (paragraph 49-50, wherein at least one defect category for the subimage is obtained based on the calculated similarities, corresponding to performing defect classification on the image).
Regarding claim 12-14 and 17, please refer to the rejections of claims 2-4 and 7, respectively, above.
Regarding claim 19, Wen discloses an apparatus for defect detection, comprising a processor and a memory, wherein the memory is configured to store a program, and the processor is configured to call the program from the memory and run the program to perform the method for defect detection according to claim 1 (Figs. 6 and paragraphs 86-90).
Regarding claim 20, Wen discloses a non-transitory computer-readable storage medium, storing a computer program, wherein the computer program, when run on a computer, causes the computer to perform the method for defect detection according to claim 1 (Figs. 6 and paragraphs 86-90).
Allowable Subject Matter
Claims 6, 8-10, 16 and 18 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
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 AARON W CARTER whose telephone number is (571)272-7445. The examiner can normally be reached 8am - 5pm (Mon - Fri).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, John Villecco can be reached at (571) 272-7319. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/AARON W CARTER/Primary Examiner, Art Unit 2661
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