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 § 112
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 3-6 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 3 recites the limitation "the generating of the machine learning dataset " in lines 1-2. There is insufficient antecedent basis for this limitation in the claim. It appears that claim 3 should depend from claim 2 instead of claim 1.
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) 1-8, 11-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tanniru et al. [“Online Fake Logo Detection System”], hereafter Tanniru in view of Mossoba (US 2021/0056564).
As to claim 1, Tanniru discloses a brand logo illegal use detection method (Fig. 6) comprising:
acquiring a logo image and logo text that are targets of illegal use detection (page 6, Section III-B. Proposed System);
generating image illegal use information indicating whether an illegal use suspect image in which the logo is suspected to be illegally used includes the logo image by using a logo identification model (Page 6, Section III-B. Proposed System);
generating illegal use detection information on the illegal use suspect image based on the image illegal use information (page 6, Section III-B. Proposed System).
Although Tanniru teaches using the system to recognize text, logos (page, 7, right column, Section III- C. Context-Dependent Similarity Algorithm), Tanniru is silent regarding performing text analysis on the illegal use suspect image and generating text illegal use information indicating whether the logo text is illegally used; and generating illegal use detection information on the illegal use suspect image based on the text illegal use information.
Mossoba teaches performing text analysis on the illegal use suspect image and generating text illegal use information indicating whether the logo text is illegally used (para. 0046, 0047); and generating illegal use detection information on the illegal use suspect image based on the text illegal use information (para. 0046, 0047).
It would have been obvious to one of ordinary skill in the art to incorporate Mossoba’s teachings into Tanniru since doing so would merely combine prior art elements according to known methods to yield predictable results, and would improve illegal use detection by including text illegal use information.
As to claim 2, the combination of Tanniru and Mossoba discloses the brand logo illegal use detection method of claim 1, further comprising:
prior to the generating of the image illegal use information,
generating logo transformation images including transformed forms of the logo image to generate a machine learning dataset (Tanniru, page 8, Section IV-C. Model Development); and
performing learning based on the machine learning dataset to generate the logo identification model (Tanniru, page 8, Section IV-C. Model Development).
As to claim 3, the combination of Tanniru and Mossoba discloses the brand logo illegal use detection method of claim 1, wherein the generating of the machine learning dataset comprises:
generating the transformed forms based on at least one of rotation, transition, enlargement, reduction, proportion change, color change, brightness adjustment, transparency adjustment, and partial removal of the logo image (Tanniru, Page 6, Section III-B. Proposed System, page 8, Section IV-C. Model Development).; and
applying a plurality of background images to the transformed forms to generate the logo transformation images (Tanniru, Page 6, Section III-B. Proposed System, page 8, Section IV-C. Model Development).
As to claim 4, the combination of Tanniru and Mossoba discloses the brand logo illegal use detection method of claim 3, wherein the generating of the machine learning dataset further comprises adding noise text and a noise image to the logo transformation images (Tanniru, Page 6, Section III-B. Proposed System, page 8, Section IV-C. Model Development) .
As to claim 5, the combination of Tanniru and Mossoba discloses the brand logo illegal use detection method of claim 3, wherein the illegal use suspect image includes a product description image uploaded to an online shopping mall and a phishing image uploaded to a phishing site (Tanniru, Page 6, Section III-B. Proposed System, page 8, Section IV-C. Model Development).
As to claim 6, the combination of Tanniru and Mossoba discloses the brand logo illegal use detection method of claim 3, wherein the plurality of background images include a plurality of product description images uploaded to an online shopping mall and a plurality of public institution images used as a public institution description material (Tanniru, Page 6, Section III-B. Proposed System, page 8, Section IV-C. Model Development).
As to claim 7, the combination of Tanniru and Mossoba discloses the brand logo illegal use detection method of claim 1, wherein the image illegal use information includes a probability that the illegal use suspect image includes the logo image (Tanniru, Page 6, Section III-B. Proposed System) , and
the text illegal use information includes a probability that the illegal use suspect image includes the logo text (Mossoba, para. 0046, 0047).
As to claim 8, the combination of Tanniru and Mossoba discloses the brand logo illegal use detection method of claim 7, wherein the generating of the text illegal use information comprises:
performing optical character recognition (OCR) on the illegal use suspect image to extract illegal use suspect text (Mossoba, para. 0044, 0052); and
calculating a probability that the illegal use suspect image includes the logo text based on a similarity discrimination result between the illegal use suspect text and the logo text (Mossoba, para. 0046, 0047).
As to claim 11, the combination of Tanniru and Mossoba discloses the brand logo illegal use detection method of claim 8, wherein the generating of the text illegal use information comprises:
when the illegal use suspect text is not extracted through the OCR, estimating that the illegal use suspect image includes a designed text (Tanniru, Page 6, Section III-B. Proposed System, page, 7, right column, Section III- C. Context-Dependent Similarity Algorithm); and
calculating a similarity between the designed text and the logo image using the logo identification model to calculate the probability that the illegal use suspect image includes the logo text (Tanniru, Page 6, Section III-B. Proposed System, page, 7, right column, Section III- C. Context-Dependent Similarity Algorithm; Mossoba, para. 0046, 0047).
As to claims 12-13, these claims recite features similar to those discussed above. Therefore, they are rejected for reasons similar to those discussed above.
Allowable Subject Matter
Claims 9-10 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.
The following is a statement of reasons for the indication of allowable subject matter: None of the prior art discloses the combination of features required by claim 9.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
UNVER discloses a method for detecting a package containing a counterfeit product.
Cunningham discloses a system for scanning text and a logo image from a document including a solicitation to donate, comparing the scanned text to a database including legitimate and fraudulent solicitations.
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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