DETAILED ACTION
This Non-Final Office Action is in response to the arguments and amendments filed May 12, 2026 and Request for Continued Examination filed June 12, 2026.
Claims 1, 7, and 10 have been amended.
Claim 6 has been cancelled.
Claims 1-5 and 7-10 are currently pending and have been considered below.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on May 12 and June 12, 2026 has been entered.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. Examiner notes this acknowledgement is specifically for FOR application PCT/IB2022/054796 filed May 23, 2022 which claims priority to Indian Patent Application No 202123026879 filed June 16, 2021.
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 120 as follows:
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed application, Application No. 17/799,128, fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application. Claim 1 (and similarly claim 10) of the pending application ‘074 is directed towards the specific limitation, “a memory (102) configured to store a machine learning model trained using image data of a plurality of said products […] and said machine learning model is trained to identify a product based on a design detected in said image data” and “said product identification module (108) further configured to cooperate with said memory (102) to execute said machine learning model on the detected design to identify said products based on said detected design”. Application ‘128 provides aspects of machine learning, however, ‘128 does not provide 35 USC 112(a) support for training the machine learning model and identifying products based on the trained model. As such, Application 17/799128 does not provide sufficient or adequate support or enablement with respect to the pending application. Therefore, claims 1-10 has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 120 as an identified Continuation-in-Part continuity.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-10 are rejected under 35 U.S.C. 103 as being unpatentable over Caton et al [2015/0302421], hereafter Caton, in view of Tscherepanow et al [2019/0325183], hereafter Tscherepanow, further in view of Kandregula [2016/0048604].
Regarding claim 1, Caton discloses a system (100) to authenticate a product, said system (100) comprising: a product identification module (108) configured to detect a design printed on the packaging of said products, said design comprising at least one of an artwork, a text in and around a visual code, a brand logo, and a relative position and size of said visual code and said artwork, text, or brand logo (Fig 1, 4A, 7A-D, and paragraphs [ and 175]; Caton discloses a character/symbol arrangement design that includes a code with text and other elements around the QR code for document (product) identification.),
said product identification module (108) further configured to cooperate with said memory (102) to identify said products based on said detected design (Fig 1, 4A, and paragraphs [140-149]; Caton discloses mapping rules based on the image data to identify the document with the hidden/overt security features.);
a scanning module (104) comprising a camera and one or more processors configured to facilitate at least one user to scan said visual codes printed on said products, each visual code including an embedded information comprising a unique identification number (UID) of associated product and a verification URL, said scanning module (104) further configured to extract said UIDs and said verification URLs from said scanned visual code (Fig 1, 4A, and paragraphs [185-186, 212-218, and 230-238]; Caton discloses authentication links to website elements and extracting the elements through the QR code (visual code) to produce the identification/authentication information. Fig 1 provides the camera and processors to scan and identify the elements.);
a first re-direction module (106) configured to cooperate with said scanning module (104) and said product identification module (108) to receive said extracted UID and said verification URL upon successful identification of said product as a registered product by the product identification module (108), and further configured to screen said extracted URL to identify said product's authenticity and: re-direct said user to a verification server (114) at said extracted URL for product verification if said URL is found to be authentic; or append said extracted contents of said visual code (UID and verification URL) to a pre-stored URL of said verification server (114) if said extracted URL is found to be spurious and forcefully re-direct said user to said genuine verification server (114) at said appended URL for product verification (Fig 1, 16B, and paragraphs [139-147, 215-219, and 228-232]; Caton discloses providing verification to the authentication website server based on the captured image of the security elements within the code This includes user identity and other aspects that include access control and re-direct to an authentication display based on the authentication of the security elements.); and
a second re-direction module (110) configured to cooperate with said scanning module (104) to receive said extracted UID and said verification URL, and further configured to cooperate with said product identification module (108) to re-direct said user to a third-party verification platform at an embedded URL for product verification if said product is not identified as a registered product by the product identification module (108) (Fig 1, 4A, and paragraphs [93-97, 140-149 and 228-231]; Caton discloses elements of providing third party services and authentication elements based on counterfeit verification for the product. This includes elements of re-direction and other aspects including providing user session information and the code within the counterfeit image.).
Caton discloses the above-enclosed limitations of the QR code and product authentication system based on the information within the image, however, the combination does not specifically teach using machine learning model to identify the product;
Tscherepanow teaches a memory (102) configured to store a machine learning model trained using image data of a plurality of said products, wherein said image data comprises designs of products that are registered with the system (100), and said machine learning model is trained to identify a product based on a design detected in said image data; said product identification module (108) further configured to cooperate with said memory (102) to execute said machine learning model on the detected design to identify said products based on said detected design (Paragraphs [50-59 and 105-110]; Tscherepanow teaches a similar QR code matching system that specifically provides a trained machine learning model to provide identification for the codes. The combination is that Caton provides the product authentication and identification based on matching QR code and other logo elements and Tscherepanow teaches a similar QR code system specifically to provide identification through a trained ML model.).
Caton discloses a QR code system to provide product authentication based on the detected design and other features, however, Caton does not specifically teach machine learning models to provide the identification;
Tscherepanow teaches a similar QR code system that specifically provides a trained machine learning model to provide identification of the code.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention for the QR code system to provide product authentication based on the detected design and other features of Caton the ability to include a similar QR code system that specifically provides a trained machine learning model to provide identification of the code as taught by Tscherepanow since the claimed invention is merely a combination of prior art elements and in the combination each element would have performed the same function as it did separately and one of ordinary skill in the art would have recognized the results of the combination as predictable.
The combination teaches the above-enclosed limitations of the QR code system for product authentication, however, the combination does not explicitly state URL for the QR code redirection;
Kandregula teaches a similar QR code system that specifically provides the mapping table that redirects to a URL (Fig 4 and paragraphs [65-68]). This would be in combination with the website server and other mapping tables that are discussed and disclosed within Caton to provide the product authentication through the website server and Kandregula teaches the specific URL redirection as discussed above.
The combination teaches the above-enclosed limitations of the QR code system for product authentication, however, the combination does not explicitly state URL for the QR code redirection;
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention for the QR code system to provide product authentication with elements includes a website server and redirection elements to include a similar QR code system that specifically provides a mapping table for URL as taught by Kandregula since the claimed invention is merely a combination of prior art elements and in the combination each element would have performed the same function as it did separately and one of ordinary skill in the art would have recognized the results of the combination as predictable.
Regarding claim 2, the combination teaches the above-enclosed limitations of the system (100) as claimed in claim 1,
Caton further discloses wherein said verification server (114) comprises: a first repository (116) configured to store a list of registered products and UIDs associated with each of said products; and a verification module (118) configured to receive said extracted UID from said first re-direction module (106), and crawl through said list stored in said first repository (116) to identify whether or not said UID exists in said list, i. if said UID is identified in said list, said verification module (118) configured to generate a verification successful message and optionally delete/kill said identified UID from said list; and ii. if said UID is not identified in said list, said verification module (118) configured to generate a verification failure message (Fig 1, 4A-C, and paragraphs [139-145]; Caton discloses the use of verification messages based on the authentic/counterfeit document code provided by the scanned information. This includes a counterfeit message if counterfeit and an authentication message based on authentication information. Further, Caton discloses [225-230] the removal of ID based on single use authentication for the code that provides the authentication message then removes the code from the mapping database to send non-verified message for any subsequent attempt.).
Regarding claim 3, the combination teaches the above-enclosed limitations of the system (100) as claimed in claim 1,
Caton further discloses wherein said product identification module (108), said scanning module (104), said first re-direction module (106) and said second re-direction module (110) are configured in a user device (112) and implemented using one or more processor(s) of said user device (112) (Fig 1-3, 16A, 16B, and paragraphs [72-74 and 93-97]; Caton discloses the user device and other system elements to provide the modules and configuration elements.).
Regarding claim 4, the combination teaches the above-enclosed limitations of the system (100) as claimed in claim 3,
Caton further discloses wherein either of said verification failure message or said verification successful message is displayed on said user device (112) indicating whether or not said product is a verified product (Fig 1, 4A-C, and paragraphs [139-145]; Caton discloses the use of verification messages displayed on the user device.).
Regarding claim 5, the combination teaches the above-enclosed limitations of the system (100) as claimed in claim 1,
Caton further discloses wherein said scanning module (104) is configured in a user device (112), said product identification module (108), said first re-direction module (106) and said second re-direction module (110) are configured in said verification server (114) and implemented using one or more processor(s) of said verification server (114) (Fig 1-3, 16A, 16B, and paragraphs [72-74 and 93-97]; Caton discloses the user device and other system elements to provide the modules and configuration elements.).
Regarding claim 7, the combination teaches the above-enclosed limitations of the system (100) as claimed in claim 1,
Caton further discloses wherein said product identification module (108) includes a flag generator (120) configured to cooperate with said memory (102) to receive said detected design, recognize said products using said machine learning model, and generate a flag signal on successful recognition of said products, said flag generator (120) further configured to transmit said flag signal to said first re-direction module (106) for appending URL for forceful redirection (Fig 1, 4A-C, and paragraphs [139-145]; Caton discloses the use of verification messages based on the authentic/counterfeit document code provided by the scanned information. This includes a counterfeit message if counterfeit and an authentication message based on authentication information. Within the combination, Tscherepanow teaches the machine learning identification.).
Regarding claim 8, the combination teaches the above-enclosed limitations of the system (100) as claimed in claim 1,
Caton further discloses wherein said first re-direction module (106) includes: a receiving unit (128) configured to cooperate with said scanning module (104) and said product identification module (108) to receive said extracted UID and said verification URL if said product is identified; a screening unit (130) comprising: i. a parser (132) configured to parse through said extracted URL and generate at least one token; and ii. an authenticator (134) configured to cooperate with said parser to receive said generated token and compare said received token with a standard pre-stored string to determine whether or not said URL authentic, a forwarding unit (136) configured to cooperate with said screening unit to re-direct said user to said verification server (114) at said extracted URL for product verification if said URL is found to be authentic when said generated token is same as said standard pre-stored string (Paragraphs [135-148 and 154-157]; Caton discloses the system to OCR and provide the authentication determination based on the id and other information extracted from the code. This includes redirected to the authentication determination of authentic or counterfeit.); and
an appending unit (138) configured to append said extracted contents of said visual code (UID and verification URL) to said a pre-stored URL of said verification server (114) if said extracted URL is found to be spurious, when said generated token is not same as said standard pre-stored string, said appending unit (138) further configured to forcefully re-direct said user to said genuine verification server (114) at said appended URL for product verification (Paragraphs [135-148 and 154-157]; Caton discloses the system to OCR and provide the authentication determination based on the id and other information extracted from the code. This includes redirected to the authentication determination of authentic or counterfeit. When the code is counterfeit the system redirects to provide the counterfeit message and provides the ID and other information to third party services and directs the user to the authenticated indicator. This is further shown in paragraphs [225-230].).
Regarding claim 9, the combination teaches the above-enclosed limitations of the system (100) as claimed in claim 1,
Caton further discloses wherein said product identification module (108) is configured to first detect said visual code printed on product packaging, and then use the detected visual code's location and scale as a reference for detecting and identifying said design printed on product packaging for product identification (Figs 7A-7D, 9A-9D, and paragraphs [132-139, 160-171, 181-184, and 254-256]; Caton discloses the design printed and identified based on reference to location and other elements to detect the code and hidden features.).
Regarding claim 10, Caton discloses a method (200) for authenticating a product, said method (200) comprising the following steps: detecting (204), by a product identification module (108), comprising a second identification unit (126) that includes one or more processors, a design printed on the packaging of said products, said design comprising at least one of an artwork, a text in and around a visual code, a brand logo, and a relative position and size of said visual code and said artwork, text, or brand logo (Fig 1, 4A, 7A-D, and paragraphs [140-149 and 175]; Caton discloses a character/symbol arrangement design that includes a code with text and other elements around the QR code for document (product) identification. Figure 1 provides and discloses the processor and unit elements to perform the identification.);
identifying (206), by said product identification module (108), said products based on said detected design (Fig 1, 4A, and paragraphs [140-149]; Caton discloses mapping rules based on the image data to identify the document with the hidden/overt security features.);
facilitating (208), by a scanning module (104) comprising a camera and one or more processors, at least one user to scan said visual codes printed on said products, each visual code including an embedded information comprising a unique identification number (UID) of associated product and a verification URL; extracting (210), by said scanning module (104), said UIDs and said verification URLs from said scanned visual code (Fig 1, 4A, and paragraphs [185-186, 212-218, and 230-238]; Caton discloses authentication links to website elements and extracting the elements through the QR code (visual code) to produce the identification/authentication information. Further, Caton provides the camera and processor elements to provide the scanning and analysis.);
receiving (212), by a first re-direction module (106), said extracted UID and said verification URL upon successful identification of said product as a registered product by the product identification module (108); screening (214), by said first re-direction module (106), said extracted URL to identify its authenticity by comparing one or more tokens parsed from the extracted URL against a standard pre-stored string; re-directing (216), by said first re-direction module (106), said user to a verification server at said extracted URL for product verification if said URL is found to be authentic; or appending (218), by said first re-direction module (106), said extracted UID to said extracted URL if said URL is found to be spurious and forcefully re- directing said user to said verification server at said appended URL for product verification (Fig 1, 16B, and paragraphs [139-147, 215-219, and 228-232]; Caton discloses providing verification to the authentication website server based on the captured image of the security elements within the code This includes user identity and other aspects that include access control and re-direct to an authentication display based on the authentication of the security elements. Caton further provides and describes comparing the authentication for the website using verified source and other authentication. This is further described as a comparison [238-240] for the authentication using image graphic analysis [218-219].);
receiving (220), by a second re-direction module (110), said extracted UID and said verification URL; and re-directing (222), by said second re-direction module (110), said user to a third-party verification platform at said embedded URL for product verification if said product is not identified as a registered product by the product identification module (108) (Fig 1, 4A, and paragraphs [93-97, 140-149 and 228-231]; Caton discloses elements of providing third party services and authentication elements based on counterfeit verification for the product. This includes elements of re-direction and other aspects including providing user session information and the code within the counterfeit image (interpreted as registered).).
Caton discloses the above-enclosed limitations of the QR code and product authentication system based on the information within the image, however, the combination does not specifically teach using machine learning model to identify the product;
Tscherepanow teaches storing (202), in a memory (102), a machine learning model trained using image data of a plurality of said products,; identifying (206), by said product identification module (108), said products based on executing said machine learning model on said detected design (Paragraphs [50-59 and 105-110]; Tscherepanow teaches a similar QR code matching system that specifically provides a trained machine learning model to provide identification for the codes. The combination is that Caton provides the product authentication and identification based on matching QR code and other logo elements and Tscherepanow teaches a similar QR code system specifically to provide identification through a trained ML model.).
wherein said image data comprises designs of products that are registered with the system (100), and said machine learning model is trained to identify a product based on a design detected in said image data (Paragraphs [50-59 and 105-110]; Tscherepanow teaches a similar QR code matching system that specifically provides a trained machine learning model to provide identification for the codes. The combination is that Caton provides the system elements including the received design and identification and Tscherepanow teaches a similar QR code system specifically to provide identification through a trained ML model
Caton discloses a QR code system to provide product authentication based on the detected design and other features, however, Caton does not specifically teach machine learning models to provide the identification;
Tscherepanow teaches a similar QR code system that specifically provides a trained machine learning model to provide identification of the code.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention for the QR code system to provide product authentication based on the detected design and other features of Caton the ability to include a similar QR code system that specifically provides a trained machine learning model to provide identification of the code as taught by Tscherepanow since the claimed invention is merely a combination of prior art elements and in the combination each element would have performed the same function as it did separately and one of ordinary skill in the art would have recognized the results of the combination as predictable.
The combination teaches the above-enclosed limitations of the QR code system for product authentication, however, the combination does not explicitly state URL for the QR code redirection;
Kandregula teaches a similar QR code system that specifically provides the mapping table that redirects to a URL (Fig 4 and paragraphs [65-68]). This would be in combination with the website server and other mapping tables that are discussed and disclosed within Caton to provide the product authentication through the website server and Kandregula teaches the specific URL redirection as discussed above.
The combination teaches the above-enclosed limitations of the QR code system for product authentication, however, the combination does not explicitly state URL for the QR code redirection;
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention for the QR code system to provide product authentication with elements includes a website server and redirection elements to include a similar QR code system that specifically provides a mapping table for URL as taught by Kandregula since the claimed invention is merely a combination of prior art elements and in the combination each element would have performed the same function as it did separately and one of ordinary skill in the art would have recognized the results of the combination as predictable.
Response to Arguments
In response to the arguments filed June 12, 2026 on page 7-8 regarding the priority under 35 USC 120, specifically that the parent application provides 35 USC 112(a) support for the priority status.
Examiner respectfully disagrees.
The arguments allege that the CIP application 17/799128 provides 35 USC 112(a) support for machine learning in paragraphs [23-24 and 43-48]. Based on the consideration, the passages are not providing proper 35 USC 112 written description support for the trained machine learning model. The considered passages provide aspects of machine learning with regards to image recognition, however, the specific claim limitations in the pending application is directed towards a specific trained model and storing a machine learning model trained using image data. The ‘128 application does not provide written description support for the machine learning model with respect to the specificity as required in the claims. One of ordinary skill would not recognize that the specification of the parent claim has written description to support. While the arguments continue to allege that the specification provides inherent support, that is not properly supported or provided based on the written description and requirements under 35 USC 112 and priority under 35 USC 120. As such, the priority is maintained as previously considered.
In response to the arguments filed June 12, 2026 on page 8 regarding the priority under 35 USC 112(f) interpretation, specifically that the amended claim language removes the interpretation. Examiner agrees. The amended claim language provides structural elements that remove the interpretation under 35 USC 112(f).
In response to the arguments filed June 12, 2026 on page 9-15 regarding the 35 USC 103 rejection, specifically that the amended claim limitations are not taught by the cited prior art.
Examiner respectfully disagrees.
The arguments allege that the Caton reference does not teach design elements to matching with respect to the ML model and providing extracting the URL to screen the authenticity. In terms of the combination, Caton provides authenticating a website based on the information within the image analysis. In terms of the ML, the combination provided in Tscherepanow provides QR code validation utilizing machine learning. The combination provides the design features within a QR code within the Caton reference that teaches image analysis to authenticate and provide information based on the image analysis. Within the combination, Kandregula teaches the specific instance of a URL that in combination with Caton, specifically, that teaches a website authentication and redirection based on a mapped URL. As such, the combination of elements teaches the claims, as considered above in light of the amended claim limitations.
In terms of the unexpected result, the claims are providing a URL redirection based on authentication information. The arguments are outlining elements that are not present in the claims by providing more specific instances and description than is required in the claims, as currently written. As such, the combination teaches the claim limitations with respect to product authentication utilizing image analysis that is obvious with respect to the 35 USC 103 rejection. Lacking any further arguments, claims 1 and 10 are maintaining the 35 USC 103 rejection, as considered above in light of the amended claim limitations.
Lacking any further arguments, claims 1-5 and 7-10 are maintaining the 35 USC 103 rejection, as considered above in light of the amended claim limitations.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Ibrahimbegovic et al [2013/0320100] (secure barcodes);
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDREW CHASE LAKHANI whose telephone number is (571)272-5687. The examiner can normally be reached M-F 730am - 5pm (EST).
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/ANDREW CHASE LAKHANI/Primary Examiner, Art Unit 3629
