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 Arguments
The present action is responsive to communications that was filed on 08/05/2026. Claims 1, 6, 9, 12, 15, and 18 are amended. Claims 5, 7, 11, 13, 17, 19, are 21-23 were previously cancelled. Claim 24 has been added. Claims 1, 6, 8, 9, 12, 14, 15, 18, 20, and 24 are currently pending.
Applicant’s arguments and amendments, with respect to claims 1, 6, 8, 9, 12, 14, 20, 21, 22, and 23 under 35 U.S.C. 101 rejection, as seen on pages 10-17, have been fully considered and persuasive. Therefore, the rejection under 35 U.S.C. 101 has been withdrawn.
Applicant’s arguments and amendments, with respect to claims 1, 6, 8, 9, 12, 14, 15, 18, 20, 21, 22, and 23 under 35 U.S.C. 112 rejection, as seen on pages 14-15, have been fully considered but not fully persuasive.
With regards to applicant’s argument regarding the rejections of the claims under 35 U.S.C. 112 on pages 14-15:
Independent claim 1 is amended herein to recite: "...receiving, by a camera application of a mobile device, an image, including a QR code and a surrounding area adjacent the QR code and within the image, by a mobile device; identifying, by the camera application, a location of the QR code within the image and generating a bounding box surrounding the QR code; determining, from the bounding box, the surrounding area outside the bounding box; converting the image to grayscale; detecting edges of the QR code and one or more objects in the surrounding area using a computer vision algorithm; determining, based on the detected edges, whether an object is located underneath the QR code; determining a first contrast level of the QR code and a second contrast level of the surrounding area; comparing the first contrast level and the second contrast level; determining that the QR code includes an indication of tampering when an occurrence occurs that is selected from a group consisting of the object is located underneath the QR code, and the first contrast level differs from the second contrast level by a significant amount; and responsive to determining the indication of tampering, generating an alert on the mobile device requesting physical inspection of the QR code." Therefore, claim 1 should be allowed. Independent claims 9 and 15 include similar features and should be allowed for the same reasons. Additionally, any claim that depends, either directly or indirectly, on claims 1, 9 or 15 should also be allowed for at least the same reasons. Therefore, Applicant respectfully requests that the rejections of claims 1, 6, 8, 9, 12, 14, 15, 18,20, 21, 22, and 23 under 35 USC § 112 be withdrawn.
The argument is not persuasive.
The following limitations do not remedy the 35 USC § 112 rejection:
The limitation of “detecting edges of the QR code” . The Examiner asserts that though the specification does reference using Open CV (e.g., Canny, Sobel) are generally discussed detecting edges of the QR code and surrounding objects. However just simply amending the limitation “using a computer vision algorithm” does not remedy the 35 U.S.C. 112(a) rejection because the specifications does not disclose any concrete algorithm, parameter values (such as threshold settings), region of interest, or decision criteria for how the invention uses the “computer vison algorithm” to detect edges. Additionally, the limitation does not remedy the 35 U.S.C. 112(b) rejection as being indefinite. As previously disclosed while QR code are generally known to have structural features, the claim does not specify what constitute the “edges” of the QR code. It is unclear whether this refers to the outer boundary of the QR code as a whole, the edges of individual QR code modules, boundaries between the QR code and the quiet zone, or other high-contrast transition within or around the QR code. Therefore, the precision scope of “edges of the QR code” remains ambiguous and not clearly defined, lacking objective boundaries.
The limitation of “determining a first contrast level of the QR code and second contrast level of the surrounding area” Examiner further asserts “determining a first contrast level of the QR code and second contrast level of the surrounding area” does not remedy 35 U.S.C. 112(a) rejection because the specifications does not reasonably convey to those skilled in that the inventors in possession of the full scope of this limitation at the time filing. Examiner does acknowledge the ¶0026-0027 of the specification disclosing the use of packages in OpenCV can be used to contrast and compare the QR code with general contrast levels, but the application fails both convey any structure or implementation details for how the contrast levels are determined or classified. Further the specification does not disclose what type of packages are being used to determine the contrast level based on the QR code and the surrounding area. Additionally, the limitation does not remedy the 35 U.S.C. 112(b) rejection as being indefinite. As previously disclosed, the claim does not recite what the contrast level (e.g., score, numerical value, RMS contrast, Michelson contrast, Weber contrast, edge density, or another measure), now it is derived, or from what inputs it is calculated. Functional limitations must be supported by structure in the specification, but the application fails to disclose any algorithms, flow charts, pseudo-code, etc. that offer structural support for this application.
The newly amended limitations in view of 35 USC § 112 had been fully considered but raises new §112(a) written description rejection and §112(b) indefiniteness concerns which is described below.
Applicant’s arguments and amendments, with respect to claims 1, 6, 8, 9, 12, 14, 15, 18, 20 under 35 U.S.C. 103 rejection, as seen on pages 15-16, have been fully considered and fully persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new grounds of rejection of is made in view of …
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 6, 8, 9, 12, 14, 15, 18, 20, 21, 22, and 23 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding claims 1, 9, and 15:
“identifying, by the application of a mobile device, a location of the QR code within the image ” is recited in claims 1, 9, and 15. The specifications fails to demonstrate that the applicant possessed the steps of identifying a location of the QR code, as seen in MPEP §2161.01. The applicant suggests that the identifying is performed by an undisclosed/proprietary external black box mechanism. Thus, the desired result (locating the QR code within the image) is not sufficiently described and should be rejected under 35 U.S.C. 112(a) Written Description. The specification references a "visual inspection module" and generically refers to use of known image-processing and machine-learning techniques (e.g., OpenCV routines, ML classification). However, the specification does not disclose a concrete, stepwise algorithm or processing pipeline that ties inputs to outputs for the full breadth of "identifying" The specification provides higher-level descriptions and examples of possible inspection activities but does not supply algorithmic steps, flowchart logic, or equations that define how the visual inspection is to be performed in all the ways claimed. Thus, while narrower support exists for individual CV steps recited elsewhere, the specification does not convey possession of the full functional genus “identifying” as broadly claimed. In the end, the specification must disclose the computer and the algorithm, steps, or procedure for performing the claimed function in sufficient detail to show possession of that functionality. See Ariad Pharm., Inc. v. Eli Lilly & Co., 598 F.3d 1336, 1351 (Fed. Cir. 2010); LizardTech, Inc. v. Earth Res.Mapping, Inc., 424 F.3d 1336, 1345-46 (Fed. Cir. 2005); Vasudevan Software, Inc. v. MicroStrategy, Inc., 782 F.3d 671, 681-83 (Fed. Cir. 2015); Finisar Corp. v. DirecTV Grp., 523 F.3d 1323, 1340 (Fed. Cir. 2008).
“generating a bounding box surrounding the QR code…determining, from the bounding box, the surrounding area outside the bounding box” is recited in claims 1, 9, and 15. The applicant suggests that the identifying is performed by an undisclosed/proprietary external black box mechanism. Although, the specification references wherein the camera application provision of a bounding box and “understand” surrounding areas of the QR code. However, the specification fails to demonstrate that the applicant possesses the step of generating a bounding box surrounding a QR code and determining from the bounding box the surrounding area outside the bounding box by a camera application. As explained MPEP §2161.01, the written description requirements are separate and distinct from enablement, and requires that the specification describe the claimed invention in sufficient detail that one of ordinary skill in the art can reasonably conclude that the inventor had possession of the claimed invention at the time of filing. This requirement applies to computer-implemented inventions recited in functional terms, and simply restating the claimed function or desired results is not sufficient. There is not definition, no rule, and no algorithm within the specification disclosed for how the bounding box is generated and how the surrounding area outside of bounding box is determined from the bounding box .
“converting the image to grayscale” is recited in claims 1, 9, and 15. The specification do not reasonably convey skill in the art that inventor in possession in of the full scope of this limitation at the time of filing. Although, the specifications ford explain the definition of grayscale, but the specifications does not disclose but the specification does not disclose the possession of how the image is being converted into grayscale . The specification must disclose the computer algorithm, steps, or procedure for performing the claimed function in sufficient detail to show possession of that functionality. See Ariad Pharm., Inc. v. Eli Lilly & Co., 598 F.3d 1336, 1351 (Fed. Cir. 2010); LizardTech, Inc. v. Earth Res. Mapping, Inc., 424 F.3d 1336, 1345-46 (Fed. Cir. 2005); Vasudevan Software, Inc. v. MicroStrategy, Inc., 782 F.3d 671, 681-83 (Fed. Cir. 2015); Finisar Corp. v. DirecTV Grp., 523 F.3d 1323, 1340 (Fed. Cir. 2008).
“detecting edges of the QR code and one or more objects in surrounding area using computer vision algorithm” is recited in claims 1, 9, and 15. The specification references edge detection using OpenCV (e.g., Canny, Sobel) and generally discusses detecting edges of the QR code and surrounding objects. However, it does not disclose a concrete algorithm, parameter values (such as threshold settings), region of interest, or decision criteria for how “edge detection” is to be performed in the context of QR code tampering detection. The disclosure is limited to generic statements that edge detection algorithms “can be used,” without specifying which algorithm, how it is configured, or how its output is interpreted for the claimed purpose. As such, the specification does not reasonably convey possession of the full scope of “detecting edges” as claimed, especially since the claim reads on any method of edge detection. Further the specification does not disclose a concrete algorithm, parameter values (such as threshold settings), region of interest, or decision criteria of one or more objects in surrounding area are detected. The specification references a "visual inspection module" and generically refers to use of known image-processing and machine-learning techniques (e.g., OpenCV routines, ML classification). However, the specification does not disclose a concrete, stepwise algorithm or processing pipeline that ties inputs to outputs for the full breadth of "detecting" The specification provides higher-level descriptions and examples of possible inspection activities but does not supply algorithmic steps, flowchart logic, or equations that define how the visual inspection is to be performed in all the ways claimed. Thus, while narrower support exists for individual CV steps recited elsewhere, the specification does not convey possession of the full functional genus “detecting” as broadly claimed. In the end, the specification must disclose the computer and the algorithm, steps, or procedure for performing the claimed function in sufficient detail to show possession of that functionality. See Ariad Pharm., Inc. v. Eli Lilly & Co., 598 F.3d 1336, 1351 (Fed. Cir. 2010); LizardTech, Inc. v. Earth Res.Mapping, Inc., 424 F.3d 1336, 1345-46 (Fed. Cir. 2005); Vasudevan Software, Inc. v. MicroStrategy, Inc., 782 F.3d 671, 681-83 (Fed. Cir. 2015); Finisar Corp. v. DirecTV Grp., 523 F.3d 1323, 1340 (Fed. Cir. 2008).
“based on the detected edges, whether an object is located underneath the QR code” is recited in claims 1, 9, and 15. The specification suggests that underlay/overlay detection may be inferred from “edge detection,” “shadows,” and identification of other shapes located within the surrounding area in the image , but it does not disclose algorithmic criteria, feature extraction steps, morphological or statistical tests, or thresholds that operationalize an “object under” determination across varying surfaces, lighting, occlusion, and image quality. No feature vector composition, decision rule, or explicit test (e.g., specific edge density threshold or shadow profile metric) is provided that would enable a POSITA to practice the full genus of methods for making that determination.
“…determining a first contrast level of the QR code and a second contrast level of the surrounding area…” is recited in claims 1, 9, and 15. The specifications do not reasonably convey to those skilled in the art that the inventors in possession of the full scope of this limitation at the time of the filing. Although, the specifications in ¶0026-0027 discloses “Additionally, or alternatively, contrast levels between the QR code and objects in the background and surrounding areas can be compared. Packages in OpenCV can be used to contrast and compare the QR code with general background contrast levels. If contrast levels between the QR code and the background vary greatly, then it can indicate that the QR code is “new” and may be a scam. The user can be alerted to double-check the QR code. “ . The application fails to both convey any structure or implementation details for how the contrast levels are determined or classified. Further, specification does not mention what type of packages are used to determine the contrast level based on the QR code and the surrounding area.
“determining that the QR code include an indication of tampering when an occurrence occurs what is selected from a group consisting of the object is located underneath the QR code, and the first contrast level differs level by a significant amount” is recited in claims 1, 9, and 15. The specification do not reasonably convey skill in the art that inventor in possession in of the full scope of this limitation at the time of filing. Although, the specifications in ¶0050 and Figure 5 states, “..wherein another operation 408 is to determine whether the first contrast level and the second contrast level differ, and possibly different to a significant degree…” The specification must disclose the computer algorithm, steps, or procedure for performing the claimed function in sufficient detail to show possession of that functionality. See Ariad Pharm., Inc. v. Eli Lilly & Co., 598 F.3d 1336, 1351 (Fed. Cir. 2010); LizardTech, Inc. v. Earth Res.Mapping, Inc., 424 F.3d 1336, 1345-46 (Fed. Cir. 2005); Vasudevan Software, Inc. v. MicroStrategy, Inc., 782 F.3d 671, 681-83 (Fed. Cir. 2015); Finisar Corp. v. DirecTV Grp., 523 F.3d 1323, 1340 (Fed. Cir. 2008). The specification does not disclose specifically what it is mean by a significant amount, the parameters or the criteria of the selection process, nor how the contrast levels and objects are selected into a group. Under MPEP §2161.01 and cited case law, identifying relevant inputs or factors does not by itself provide adequate written description support for a functionality claimed determination unless that specification also explains how the factors are actually used to perform the determination.
Claims 6, 8, 12, 14, 18, 20, 21, 22, 23, 24 do not overcome the rejections of their respective base claims that have been rejected above, and therefore rejected under the same grounds provided to claims 1, 9, and 15.
Regarding claims 8, 14, and 20:
“determine that the identified text includes one or more misspelled words, wherein a presence of misspelled words indicates tampering.” is recited in claims 8, 14, and 20. The specification references checking text in the picture or surrounding area for misspellings (e.g., via OpenCV text detection) but does not disclose an OCR/text‑recognition pipeline, tokenization rules, dictionary sources or language/locale handling, error‑tolerance criteria, whitelist/blacklist handling, or the decision rules for when an OCR result constitutes a misspelling sufficient to indicate tampering. The broad claim entitlement to any method of detecting misspellings is unsupported by the disclosed examples.
Regarding claim 24 :
“providing the image to a machine learning model trained using labeled images of both safe QR codes and tampered QR codes; and receiving from the machine learning model a confidence score indicating whether the QR code is safe” is recited in claim 24, but the specification does not provide any concrete algorithmic detail (e.g., model choice, features, training procedure, etc.) beyond high-level functional descriptions. Although the specifications and claim state the use of labels and preloaded image. This is still very high level. The specification never explains what features are extracted from the images, how those features are encoded, what parameters are, nor how QR code is constituted as safe. Further the “machine learning model” is recited at the level of a broad functional genus that, under a broadest reasonably interpretation, encompasses essentially all types of machine learning techniques capable of being configured to perform the recited functions. This specification does not disclose any representative set of specific machine-learning species (e.g., particular model families, feature sets, or training schemes) nor any common structural characteristics that the inventor had possession of the full scope of the machine-learning genus at the time filing. Instead, the application provides only results-oriented statements that such machine learning model is performed, without any example implementation or description of how any particular machine learning technique is configured to carry out the analysis , or how such machine learning technique is adapted to produce the output of a “indicating whether the QR code is safe” as further recited in the claim. As MPEP § 2161.01 explains, original claims may lack written description when they define the invention in functional language specifying a desired result but the specification does not sufficiently describe how the function is performed or the result is achieved. This is the same type of deficiency identified by the Patent Trial and Appeal Board (PTAB) in previous instances (see Ex parte Corville Allen et al., 2020-005211), where claims reciting computer-based analysis (e.g., classification, scoring, or determination steps) were found unsupported because the specification only described the desired result and general factors, but failed to disclose how the determination was actually performed (e.g., how scores were generated, how comparisons were made, or what specific logic governed the outcome).
Further it is recited within claim 24 the receiving of a confidence score. The specification fails
to demonstrate that the applicant possessed the step of calculating a confidence score . As explained in MPEP §2161.01, the written description requirement is separate and distinct from enablement, and requires that the specification describe the claimed invention in sufficient detail that one of ordinary skill in the art can reasonably conclude that the inventor had possession of the claimed invention at the time of filing. This requirement applies to computer-implemented inventions recited in functional terms, and simply restating the claimed function or a desired result is not sufficient. The specification must disclose the computer and the algorithm, steps, or procedure for performing the claimed function in sufficient detail to show possession of that functionality. See Ariad Pharm., Inc. v. Eli Lilly & Co., 598 F.3d 1336, 1351 (Fed. Cir. 2010); LizardTech, Inc. v. Earth Res. Mapping, Inc., 424 F.3d 1336, 1345-46 (Fed. Cir. 2005); Vasudevan Software, Inc. v. MicroStrategy, Inc., 782 F.3d 671, 681-83 (Fed. Cir. 2015); Finisar Corp. v. DirecTV Grp., Inc., 523 F.3d 1323, 1340 (Fed. Cir. 2008). The specification does identify some clues. It says the confidence score is calculated by the visual inspection module. Those paragraphs support that the risk score is being calculated, but the specification does not disclose the possession of how the confidence score is being calculated nor what inputs the confidence score is calculated from. Under MPEP §2161.01 and the cited case law, identifying relevant inputs or factors does not by itself provide adequate written description support for a functionally claimed determination unless the specification also explains how those factors are actually used to perform the determination.
Claims 6 and 8 depend on claim 1 and thereby inherit all the deficiencies of claim 1. Claims 12 and 14 depend on claim 9 and thereby inherit all the deficiencies of claim 9. Claims 18 and 20 depend on claim 15 and thereby inherit all the deficiencies of claim 15. Therefore, claims 6, 8, 12, 14, 18, and 20 are also rejected under 35 U.S.C. 112(a) for lack of written description.
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 1, 6, 8, 9, 12, 14, 15, 18, 20, and 24 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.
Regarding claims 1, 9, and 15:
“identifying, by the camera application, a location of the QR code within the image…determining, from the bounding box, the surrounding area outside bounding box”
The limitation “visually inspecting the image of the QR code and the surrounding area” is indefinite because it lacks objective boundaries as to both who performs the inspection and what the inspection consists of when implemented by software. The claim provides no definition of what “identifying” or “determining” entails in algorithmic terms—i.e., whether it includes any of decoding/locating the QR code, segmentation, edge detection (e.g., Canny/Sobel), contrast computation (which metric/channel/window), shadow/crease detection, color analysis (which color space/distance metric), OCR/text detection, and/or ML classification; in what sequence; with what parameter ranges; over what regions of interest (how the “surrounding area” is defined); and what measurable outputs or decision criteria signal completion of the step or trigger subsequent actions. This dual ambiguity—regarding the actor and the substantive operations—permits multiple reasonable interpretations and prevents a POSITA from determining the metes and bounds of the limitation with reasonable certainty.
“edges of the QR code”
While QR codes are generally known to have structural features, the claim does not specify what constitutes the “edges” of the QR code. It is unclear whether this refers to the outer boundary of the QR code as a whole, the edges of individual QR code modules, boundaries between the QR code and the quiet zone, or other high-contrast transitions within or around the QR code. Therefore, the precise scope of “edges of the QR code” is ambiguous and not clearly defined, lacking objective boundaries.
“one or more objects located in the surrounding area in the image” and “object is located underneath QR code”
This limitation fails to particularly point out the scope of the claimed invention because neither “object” nor “under the QR code” is defined with objective boundaries. The term “object” is entirely open-ended and may reasonably encompass any detectable feature in an image, including another QR code, a sticker, a substrate, a background surface, a shadow, or an image artifact. The claim does not specify any characteristics that distinguish the object from background elements or from the QR code itself.
“…determining the first contrast level of the QR code and a second contrast level of the surrounding area…”
Indefinite because the claim does not recite what the contrast level represents (e.g., score, numerical value, RMS contrast, Michelson contrast, Weber contrast, edge density, or another measure), how it is derived, or from what inputs it is calculated Functional limitations must be supported by structure in the specification, but the application fails to disclose any algorithms, flow charts, pseudo-code, etc. that offer structural support for this limitation.
“…the first contrast level differs from the second contrast level by a significant amount…”
Indefinite because the term of “significant amount” provides no objective boundaries or metrics by which a person having ordinary skill in the art can determine or apply “a significant amount”, nor it clear whether this refers to beyond a threshold or something else.
Claims 6, 8, 12, 14, 18, 20, 21, 22, 23, 24 do not overcome the rejections of their respective base claims that have been rejected above, and therefore rejected under the same grounds provided to claims 1, 9, and 15.
Regarding claim 24:
“receiving from the machine learning model a confidence score indicating whether the QR code is safe”
Indefinite because the claim does not define what a person having ordinary skill in the art would understand how the confidence score is derived or from what inputs it is calculated. Functional limitations must be supported by structure in the specification, but the application fails to disclose any algorithms, flowcharts, pseudo-code, etc. that offer structural support for this limitation.
Claim Rejections - 35 USC § 103
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.
Claims 1, 6, 8, 9, 12, 14, 15, 18,and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Todasco et al. (US PGPub No.20220083660-A1) in view of O’Herlihy et al. (US PGPub Nol. 20220391796-A1), Yu et al. (US Pat No. 9626577-B1), Adams Jr. (US Pat No. 9378405-B2), Simkin et al. (Ubic: Bridging the gap between digital cryptography and the physical world. In European Symposium on Research in Computer Security (pp. 56-75) Cham: Springer International Publishing. (Year: 2014)),Cooperman et al. (US Pat No. 5784487-A), Fleisher et al. (US PGPub No. 20130121529-A1), and Faulkner et al. (US Pat No. 11457271-B1).
Todasco in view of O’Herlihy, Yu, Adams Jr., Simkin, Cooperman, Fleisher, and Faulkner
With respect to claim 1, Todasco teaches computer-implemented method (CIM) comprising: (¶0082-0083: In various embodiments of the present disclosure, execution of instruction sequences to practice the present disclosure may be performed by computer system ) receiving, by a camera application of a mobile device, an image, including a QR code and a surrounding area adjacent the QR code and within the image, by a mobile device; (¶0030-0033: User device 110 of Figure 1A contains a camera 120, a code processing application 130, a payment application 112, other applications 114, a database 116, and a communication module 118. Code processing application 130, payment application 112, and other applications correspond to execute processes, procedures, and/or applications with associated hardware. Camera 120 may interface with one or more applications of user device 110 for the purposes of capturing images, videos, and/or other visual data of the user associated with user device 110, for example display data of the user associated with user device 110, for example during display of an augmented reality determined by code processing application 130 based on valid or fraudulent code detection (QR code) and placement in a real-world environment (surrounding area adjacent the QR code) , as discussed herein. );
identifying, by the camera application, a location of the QR code within the image and (¶0033-0034: When capturing the capture data, additional data may also be detected, which may correspond to any metadata and/or contextual data of code output object 140. For example, a geo-location or other location data may be detected (identifying by camera application, a location of the QR code within the image) , including a device orientation, cardinal direction, and/or gyroscope/accelerometer-based data that may be used to determine where the data is captured. );
responsive to determining the indication of tampering, generating an alert on the mobile device requesting physical inspection of the QR code. (¶0021-022: If the capture data indicates fraud, the user's computing device may delay and/or refuse processing and may further notify the user of the fraudulent code/wireless device. The user may be notified through a message displayed with the malicious or fraudulent capture data on the user's device. The message may indicate that the code/wireless device is suspicious or fraudulent and may provide further information on removal of the code/wireless device or where a valid or trustworthy code/wireless device is located. An administrator or authority figure associated with the location, such as a merchant, location owner, vending machine owner/operator, authority enforcement officer, or other entity, may be alerted of the fraudulent code/wireless device and the location of such code/wireless device for removal and/or determination of the malicious actor (physical inspection of the QR code).);
Todasco does not disclose:
generating a bounding box surrounding the QR code;
However, O’Herlihy teaches generating a bounding box surrounding the QR code; (¶0130: Once trained, the Yolo_v5 network is configured to receive a video frame Fr(τ); and in response thereto, to output three vectors, as follows: the coordinates of the centre of a bounding box encompassing a QR code detected in the received video frame (generating a bounding box surrounding the QR code), together with the width and height of the bounding box, wherein the width and height are normalized relative to the width and height respectively of the video frame (determining from bounding box the surrounding the bounding box));
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention utilize the teachings of O’Herlihy with regards to generating a bounding box surrounding the QR code to the method of Todasco in order to better identify the QR code and distinguish the QR code from its setting (O’Herlihy ¶0127-0130).
Todasco in view of O’Herlihy does not disclose:
determining, from the bounding box, the surrounding area outside the bounding box;
However, Yu teaches determining, from the bounding box, the surrounding area outside the bounding box; (¶0001: Mobile electronic devices such as tablet computers and smart phones commonly include cameras. Software on mobile device may process captured images to identify image to identify and recognize text and glyph symbols ¶0030: In the preprocessor, a bounding box may be generated that bounds a line of text. The bounding box may bound the glyph region. Value(s) for the image/region suitability metric(s) may be calculated for the portion of the image outside bounding box (surrounding are outside bounding box) . The value(s) for inside the bounding box. The value(s) for inside the bounding box may then be compared to value(s) outside the boundary box to make another determination on the suitability of the image. This determination may also use a classifier.);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention utilize the teachings of Yu et al. (US Pat No. 9626577-B1) with regards to determining the surrounding area outside the bounding box within the image by the mobile device to the method of Todasco in view of O’Herlihy in order to better analyze image (Yu et al. (US Pat No. 9626577-B1) ¶0009-0010).
Todasco in view of O’Herlihy and Yu does not disclose:
converting the image to grayscale;
However, Adams Jr. teaches converting the image to grayscale; (¶0006: In Figure 1, shows the normalized image block 105 produces the normalized image 110 by first converting the document 100 to a grayscale image and then performing a full-scale histogram stretch on the grayscale image. The conversion to grayscale image operation combines the color component of the document 100 to produce a gray document at least location in the document 100.);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention utilizes the teachings of Adams Jr. with regards to converting the image to grayscale to the method of Todasco in view of O’Herlihy and Yu in order to better detect the features of the QR code (Adams Jr ¶0003).
Todasco in view O’Herlihy, Yu, and Adams Jr does not disclose:
detecting edges of the QR code and one or more objects in the surrounding area using a computer vision algorithm;
However, Simkin teaches detecting edges of the QR code and one or more objects in the surrounding area using a computer vision algorithm; (6 The VeriDoc Interface: Page 68-69: Fig. 10 Vision subsystem to assist the user in working with VeriDoc: The user points the front-facing camera roughly at the VeriDoc document, the system detects the four corners of the first content block and snaps the locations of the brackets to them, and the system unwarps and extracts the content of that block…. To assist the user in scanning Veri Doc document content we provide a refinement procedure that allows the user to roughly indicate relevant text blocks, but still provide the required accuracy for the computer vision processing pipeline (see Figure 10). On the very left, a typical user interaction is depicted showing a coarse alignment of the brackets with the first text block. We proceed by a corner detection algorithm and snap the locations of the brackets provided by the user to the closest corners. We use the Harris corner measure M to robustly detect corners. The second image at the top shows a visualization of the closest corner and the box spanned by them in green. Under the assumption of a pinhole camera model as well as a planar target (documents in our case), we can compute a homography H ∈ R3×3 in order to undo the perspective transformation under which the content is viewed. The third image from the left in Figure 10 shows the content after unwarping and cropping. Using the information on the ratio between text and code contained in the header, we now split the content area into text and the associated QR code. In a last step, the QR code is decoded, the signature extracted, and the text area is further processed using OCR in combination with the font and language information from the header code);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention utilizes the teachings of Simkin with regards to detecting the edges of the QR code and one or more one objects in the surrounding are using a computer vision algorithm to the method of Todasco in view of O’Herlihy, Yu, and Adams Jr in order to better detect fraudulent data being transmitted to the user and better security (Simkin Pages 56-57).
Todasco in view of O’Herlihy, Yu, Adams Jr, and Simkin do not disclose:
determining, based on the detected edges, whether an object is located underneath the QR code;
However, Cooperman teaches determining, based on the detected edges, whether an object is located underneath the QR code; (¶0037: Having described general operation of the document layout analysis, attention is now turned to Figures 4-7, where further details of various operations are described in detail. Step 306 preferably includes a two-pass approach to identifying segments as headers and footers. In the first pass, the data for each section is considered, witch particular attention being paid attention: (a) whether there any text regions (header) or below (footer) the section under consideration (i.e., based on the detected edges (header and footer) whether text (object is located underneath)), (b) the height the region or section under consideration, (c) the number of text lines in the region (d) the distance of the region from the edge of the image (or document), and (e) the prevalent font size of the text in the region, relative to the font size of the other regions on the page.);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention utilizes the teachings of Cooperman with regards to converting the image to grayscale to the method of Todasco in view of O’Herlihy, Yu, Adams Jr., and Simkin in order to increase efficiency when determining features of an image such as a QR code (Cooperman ¶0009-0014).
Todasco in view of O’Herlihy, Yu, Adams Jr., Simkin, and Cooperman does not disclose:
determining a first contrast level of the QR code and a second contrast level of the surrounding area; comparing the first contrast level and the second contrast level;
However, Fleisher teaches determining a first contrast level of the QR code and a second contrast level of the surrounding area; comparing the first contrast level and the second contrast level; (¶0071: In a scenario, the server 210 may compare the contrast level of the content to the contrast level of an image to determine if a threshold is met or exceeded. If the threshold is met or exceeded (i.e., the difference in contrast levels is larger), the server 210 may refrain from modifying the content. Conversely, if the threshold is not met or exceeded (i.e., the difference in contrast levels is smaller), the server 210 may determine to modify the content. In particular, if the content is textual content, the server 210 may apply a bold formatting to the text, adjust the color or contrast of the text, or facilitate another modification.);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention utilize the teachings of Fleisher with regards to comparing the contrast levels between selected area, such as a QR code, and surrounding area to indicate tampering to the method of Todasco in view of O’Herlihy, Yu, Adams Jr., Simkin, and Cooperman in order to improve the accuracy of monitoring (Fleisher ¶0012 & ¶0071 ).
Todasco in view of O’Herlihy, Yu, Adams Jr., Simkin, Cooperman, and Fleisher does not disclose:
determining that the QR code includes an indication of tampering when an occurrence occurs that is selected from a group consisting of the object is located underneath the QR code, and the first contrast level differs from the second contrast level by a significant amount; and
However, Faulkner teaches determining that the QR code includes an indication of tampering when an occurrence occurs that is selected from a group consisting of the object is located underneath the QR code, and the first contrast level differs from the second contrast level by a significant amount; and (¶0050-0051: Thus, a client computing device detection that nay parameter e.g., a resolution level, contrast level, brightness level, or contrast level, of video data 151 exceeds a threshold (by a significant amount), or it is not within a predetermined range, the client computing device detections the presence of lighting of color anomalies (indication of tampering) and can invoke one or more adjustments. The image calibration 152 can define contrast thresholds for various aspects of an image. For instance, a first contrast threshold can measure a contrast between an object and a background of an image (first and second contrast level). A contrast level can be measured with respect to an average brightness level of an object versus an average level of brightness of the background. The contrast can also compare a brightness level of selected portions of an object, such as an area around a person's eyes, and compare that brightness level to a brightness level of a specific part (the object located) of a background.);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention utilize the teachings of Faulkner with regards to determining an indication of tampering to the method of Todasco in view of O’Herlihy, Yu, Adams Jr., Simkin, Cooperman, and Fleisher in order to better detect and efficiently anomalies (Faulkner ¶0002-0003).
With respect to claim 6, the combination of Todasco in view of O’Herlihy, Yu, Adams Jr., Simkin, Cooperman, Fleisher, and Falkner teaches method of claim 1 (see rejection of claim 1 above) further comprising: comparing the received image (Todasco ¶0062 :As seen in Figure 1A and Figure 2B, user device 110 captures image 1100 to determine whether the additional data within image 1100 or detected when image 1100 is captured indicates that a displayable code in image 1100 is potentially fraudulent and includes malicious data.) to a plurality of images of QR codes that both include and do not include tampering, (Todasco: ¶0062-0063: Menu 1102 may have been captured through one or more past images by user device 110 and/or another device, which may have previously been used to determine whether code 1116 is fraudulent or valid, or may have been used to validate code 1116 or add code 1116 to a blacklist (e.g., based on computing attack attempt or success)).
wherein the plurality of images of QR codes (Todasco: ¶0062: Such data may be compared to stored data or expected, learned, or registered locations of valid codes, as well as a blacklist of invalid or fraudulent codes. This allows user device 110 to determine whether code 1116 is fraudulent in the merchant environment.) were stored in a computer; and (Todasco: ¶0050: As seen in Figure 1A, the transaction processor 150 includes database 156. Database 156 may also store one or more blacklists of invalid and fraudulent codes or devices, which may be used to determine if code output object 140 is fraudulent & ¶0047 Transaction processing application 152 may correspond to one or more processes to execute modules and associated specialized hardware of transaction processor 150 to process a transaction.)
determining that the received image is similar to at least one of the plurality of images of QR codes stored in the computer. (Todasco: ¶0062-0063: Based on the layout of the previous images of menu 1102 and/or registered information of placement of code 1116 on menu 1102 or within a merchant location corresponding to image 1100, a risk and/or fraud score of a likelihood of fraud or validity of code 1116 may be determined).
With respect to claim 8, the combination of Todasco in view of O’Herlihy, Yu, Adams Jr., Simkin, Cooperman, Fleisher, and Falkner teaches method of claim 1 (see rejection of claim 1 above) further comprising: identifying any text in the image; and determining that the identified text includes one or more misspelled words, wherein a presence of misspelled words indicates tampering. (Todasco: ¶0064: As seen in Figure 2B, additional data in image 1100, such as font, color, fading or age, misspelling or of nuanced textual/image data, or other characteristics of the displayed information on menu 1102 may be used to determine if menu 1102 appears as expected or may be fraudulently placed with malicious data for code 1116.).
With respect to claim 9, Todasco teaches a system comprising: (¶0078-0079: As seen, in Figure 5 is a block diagram of a computer system suitable for implementing one or more components in Figure 1A, according to an embodiment.) a memory; and a processor communicatively coupled to the memory, wherein the processor is configured to perform a method comprising: (¶0079-0080: Shown in Figure 5, the computer system 500 includes a memory 514 and processor 512. Wherein computer system perform specific operations by the processor 512 in system memory component 514. Logic may be encoded in a computer medium which may refer to any medium that participates in providing instructions to processor 512 for execution)
receive an image, including a QR code and a surrounding area adjacent the QR code and within the image, by a mobile device; (¶0016-0017: When capturing (receiving) an image or broadcast of the code (QR code), additional data may also be captured at a time corresponding to the capture data. The image may also directly include the additional data, such as background objects within the image and/or nearby the code within the image. Thus, the additional data may correspond to any contextual data that assists in further describing the capture data of the code or wireless device.);
inspect the QR code and the surrounding area within the image by the mobile device; (¶0033-0035: As seen in Figure 1A, once the capture data of code data 142 from code output object 140 is detected, code processing application 130 may process the additional data to determine whether the code is fraudulent, or the device is transmitting fraudulent data. Code processing application 130 may also process a layout, placement, or detection (visually inspecting) of additional objects within the capture data to determine if the code/wireless device is placed at an expected or valid location.);
determine presence of any objects located within the surrounding area in the image; (¶0057: Figure 2A is exemplary real-world environment where a user may capture a QR code that is analyzed to determine if it has been fraudulently placed in the real-world environment. Environment 200a of Figure 2A includes user device 110 discussed in system 100a of Figure 1A. In particular, user 102 may utilize user device 110 to capture an image 1002 of stand 1008 having QR code 1004 and additional objects 1006 in proximity to QR code 1004, such as located on stand 1008. Similarly, the placement and/or proximity of QR code 1004 and/or wireless device 1010 to additional objects 1006, stand 1008, and/or menu 1012 may be used to determine if QR code 1004 and/or wireless device 1010 is expected by located in that area. Thus, the layout of merchant location 1000 and registered or expected codes/devices at the location may be utilized in the determination of fraudulent codes or devices.);
when tampering is indicated, alert a user of the mobile device to physically check the QR code and the surrounding area for tampering. (¶0021: If the capture data indicates fraud, the user's computing device may delay and/or refuse processing and may further notify the user of the fraudulent code/wireless device. The user may be notified through a message displayed with the malicious or fraudulent capture data on the user's device. The message may indicate that the code/wireless device is suspicious or fraudulent and may provide further information on removal of the code/wireless device or where a valid or trustworthy code/wireless device is located.);
Todasco does not disclose:
provide a bounding box around edges of the QR code within the image by the mobile device;
However, Yu teaches provide a bounding box around edges of the QR code within the image by the mobile device; (¶0020-0033: In some embodiments, the data extraction system may generate a bounding box for each group characters identified in the image. The user device 110, in one embodiment, includes a user interface (UI) application 112 (e.g., a web browser, a mobile payment application, etc.), which may be utilized by the user 140 to interact with the merchant server 120 and/or the service provider server 130 over the network 160.Further in ¶0080: As seen in Figure 6, in some embodiments, at least a portion of processing 600 may be performed by data extraction module 132. The process 600 begins by processing (at step 605) an image to determine bounding boxes in the image. For example, the image processing module 204 may use one or more OCR techniques to identify characters in the image. The image processing module 204 may also translate any code (e.g., bar codes, QR codes) in the image into characters. ).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention utilize the teachings of Yu with regards to providing a bounding box around the QR code by mobile device determining the area surrounding the QR code by mobile device to the method of Todasco in order to better analyze images and identify relationships within an image (Yu ¶0002-0016).
Todasco in view of Yu does not disclose:
determine the surrounding area outside the bounding box within the image by the mobile device;
However, Yu et al. (US Pat No. 9626577-B1) teaches determine the surrounding area outside the bounding box within the image by the mobile device; (¶0001: Mobile electronic devices such as tablet computers and smart phones commonly include cameras. Software on mobile device may process captured images to identify image to identify and recognize text and glyph symbols ¶0030: In the preprocessor, a bounding box may be generated that bounds a line of text. The bounding box may bound the glyph region. Value(s) for the image/region suitability metric(s) may be calculated for the portion of the image outside bounding box (surrounding are outside bounding box) . The value(s) for inside the bounding box. The value(s) for inside the bounding box may then be compared to value(s) outside the boundary box to make another determination on the suitability of the image. This determination may also use a classifier.);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention utilize the teachings of Yu et al. (US Pat No. 9626577-B1) with regards to determining the surrounding area outside the bounding box within the image by the mobile device to the method of Todasco in view of Yu in order to better analyze image (Yu et al. (US Pat No. 9626577-B1) ¶0009-0010).
Todasco in view of Yu and Yu et al. (US Pat No. 9626577-B1) does not disclose:
detect the edges of the QR code within the image by the mobile device;
detect contrast sensitivity of the image by the mobile device;
Todasco does teach detecting the QR code via mobile device, Todasco does not disclose the detecting the edges of the QR code. However, Wei teaches detect the edges of the QR code within the image by the mobile device; (¶0043-0047: Illustrated in Figure 2, step S201, wherein the method includes identifying edges of the QR code. According to the edges of the QR code obtained at step S201, the area within the edges where the QR code is located according to the coordinate information.);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention utilize the teachings of Wei with regards to detecting edges of the QR Code to the method of Todasco in view of Yu and Yu et al. (US Pat No. 9626577-B1) in order to determine the presence and locate the QR code on a product or picture (Wei ¶0035).
Todasco in view of Yu, Yu et al. (US Pat No. 9626577-B1), and Wei does not disclose:
detecting contrast sensitivity of the image by the mobile device;
Todasco does teach detecting an image via mobile device, but Todasco does not disclose detecting contrast sensitivity of the image. However, Smith teaches detect contrast sensitivity of the image by the mobile device; (¶0042: More specifically, in an embodiment, method 300 comprises obtaining an image of each of the two codes (one of the codes include QR code ¶0017) and determining, for each image, a contrast ratio between one or more elements of the code and the background of the code).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention utilize the teachings of Smith with regards to detecting contrast sensitivity of the image to the method of Todasco in view of Yu, Yu et al. (US Pat No. 9626577-B1), and Wei in order to determine the code on the product is authentic and ensures that the product is not reused or a counterfeit . (Smith ¶0002 & 0014).
Todasco in view of Yu, Yu et al. (US Pat No. 9626577-B1), Wei, and Smith does not disclose:
determine a first contrast level of the QR code and a second contrast level of the surrounding area within the image; determine that the first contrast level and the second contrast level differ, wherein a difference indicates tampering; and
However, Fleisher teaches determine a first contrast level of the QR code and a second contrast level of the surrounding area within the image; determining that the first contrast level and the second contrast level differ, wherein a difference indicates tampering; and (¶0071: As example and referring to Figure 6, the characteristics of test-image features of test data corresponding to image portion 144 from test image 140 may be compared to characteristics of reference-image features of reference data corresponding to image portion 146 of reference image 142. In this simple example of comparing two intensity images, it is seen that an area of interest (e.g., QR code) that shows up as a bright area in the test image is given a higher level of contrast to the surrounding body areas (surrounding area), indicating that there is an anomaly in this area. The difference image or more generally the FOM can then be compared to the known statistics or images for objects, or pixels patterns and features characteristic of known objects or otherwise analyzed to determine the presence or absence of an object, and if present the likely type of object that it is. Additionally or alternatively, the suspect area may be identified in a display to an operator to alert her or him to examine the test subject to determine what the object is);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention utilize the teachings of Fleisher with regards to comparing the contrast levels between selected area, such as a QR code, and surrounding area to indicate tampering to the method of Todasco in view of Yu and Yu et al. (US Pat No. 9626577-B1), Wei, and Smith in order to improve the accuracy of monitoring (Fleisher ¶0012 & ¶0071 ).
With respect to claim 9, teaches system comprising: a memory; and a processor communicatively coupled to the memory, wherein the processor is configured to perform a method comprising: (¶0080: As seen in Figure 5, components of computer system 500 also include a system memory component 514 (e.g., RAM), a static storage component 516 (e.g., ROM), and/or a disk drive 517. Computer system 500 performs specific operations by processor(s) 512 and other components by executing one or more sequences of instructions contained in system memory component 514.);
receive, by a camera application of a mobile device, an image, including a QR code and a surrounding area adjacent the QR code and within the image, by a mobile device; (¶0030-0033: User device 110 of Figure 1A contains a camera 120, a code processing application 130, a payment application 112, other applications 114, a database 116, and a communication module 118. Code processing application 130, payment application 112, and other applications correspond to execute processes, procedures, and/or applications with associated hardware. Camera 120 may interface with one or more applications of user device 110 for the purposes of capturing images, videos, and/or other visual data of the user associated with user device 110, for example display data of the user associated with user device 110, for example during display of an augmented reality determined by code processing application 130 based on valid or fraudulent code detection (QR code) and placement in a real-world environment (surrounding area adjacent the QR code) , as discussed herein. );
identify, by the camera application, a location of the QR code within the image and (¶0033-0034: When capturing the capture data, additional data may also be detected, which may correspond to any metadata and/or contextual data of code output object 140. For example, a geo-location or other location data may be detected (identifying by camera application, a location of the QR code within the image) , including a device orientation, cardinal direction, and/or gyroscope/accelerometer-based data that may be used to determine where the data is captured. );
responsive to determining the indication of tampering, generating an alert on the mobile device requesting physical inspection of the QR code. (¶0021-022: If the capture data indicates fraud, the user's computing device may delay and/or refuse processing and may further notify the user of the fraudulent code/wireless device. The user may be notified through a message displayed with the malicious or fraudulent capture data on the user's device. The message may indicate that the code/wireless device is suspicious or fraudulent and may provide further information on removal of the code/wireless device or where a valid or trustworthy code/wireless device is located. An administrator or authority figure associated with the location, such as a merchant, location owner, vending machine owner/operator, authority enforcement officer, or other entity, may be alerted of the fraudulent code/wireless device and the location of such code/wireless device for removal and/or determination of the malicious actor (physical inspection of the QR code).);
Todasco does not disclose:
generate a bounding box surrounding the QR code within the image by the mobile device;
However, O’Herlihy teaches generate a bounding box surrounding the QR code within the image by the mobile device; (¶0130: Once trained, the Yolo_v5 network is configured to receive a video frame Fr(τ); and in response thereto, to output three vectors, as follows: the coordinates of the centre of a bounding box encompassing a QR code detected in the received video frame (generating a bounding box surrounding the QR code), together with the width and height of the bounding box, wherein the width and height are normalized relative to the width and height respectively of the video frame (determining from bounding box the surrounding the bounding box));
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention utilize the teachings of O’Herlihy with regards to generating a bounding box surrounding the QR code to the method of Todasco in order to better identify the QR code and distinguish the QR code from its setting (O’Herlihy ¶0127-0130).
Todasco in view of O’Herlihy does not disclose:
determine, from the bounding box, the surrounding area outside the bounding box;
However, Yu teaches determine, from the bounding box, the surrounding area outside the bounding box; (¶0001: Mobile electronic devices such as tablet computers and smart phones commonly include cameras. Software on mobile device may process captured images to identify image to identify and recognize text and glyph symbols ¶0030: In the preprocessor, a bounding box may be generated that bounds a line of text. The bounding box may bound the glyph region. Value(s) for the image/region suitability metric(s) may be calculated for the portion of the image outside bounding box (surrounding are outside bounding box) . The value(s) for inside the bounding box. The value(s) for inside the bounding box may then be compared to value(s) outside the boundary box to make another determination on the suitability of the image. This determination may also use a classifier.);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention utilize the teachings of Yu et al. (US Pat No. 9626577-B1) with regards to determining the surrounding area outside the bounding box within the image by the mobile device to the method of Todasco in view of O’Herlihy in order to better analyze image (Yu et al. (US Pat No. 9626577-B1) ¶0009-0010).
Todasco in view of O’Herlihy and Yu does not disclose:
convert the image to grayscale;
However, Adams Jr. teaches convert the image to grayscale; (¶0006: In Figure 1, shows the normalized image block 105 produces the normalized image 110 by first converting the document 100 to a grayscale image and then performing a full-scale histogram stretch on the grayscale image. The conversion to grayscale image operation combines the color component of the document 100 to produce a gray document at least location in the document 100.);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention utilizes the teachings of Adams Jr. with regards to converting the image to grayscale to the method of Todasco in view of O’Herlihy and Yu in order to better detect the features of the QR code (Adams Jr ¶0003).
Todasco in view O’Herlihy, Yu, and Adams Jr does not disclose:
detect edges of the QR code and one or more objects in the surrounding area using a computer vision algorithm determine,
However, Simkin teaches detect edges of the QR code and one or more objects in the surrounding area using a computer vision algorithm determine, (6 The VeriDoc Interface: Page 68-69: Fig. 10 Vision subsystem to assist the user in working with VeriDoc: The user points the front-facing camera roughly at the VeriDoc document, the system detects the four corners of the first content block and snaps the locations of the brackets to them, and the system unwarps and extracts the content of that block…. To assist the user in scanning Veri Doc document content we provide a refinement procedure that allows the user to roughly indicate relevant text blocks, but still provide the required accuracy for the computer vision processing pipeline (see Figure 10). On the very left, a typical user interaction is depicted showing a coarse alignment of the brackets with the first text block. We proceed by a corner detection algorithm and snap the locations of the brackets provided by the user to the closest corners. We use the Harris corner measure M to robustly detect corners. The second image at the top shows a visualization of the closest corner and the box spanned by them in green. Under the assumption of a pinhole camera model as well as a planar target (documents in our case), we can compute a homography H ∈ R3×3 in order to undo the perspective transformation under which the content is viewed. The third image from the left in Figure 10 shows the content after unwarping and cropping. Using the information on the ratio between text and code contained in the header, we now split the content area into text and the associated QR code. In a last step, the QR code is decoded, the signature extracted, and the text area is further processed using OCR in combination with the font and language information from the header code);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention utilizes the teachings of Simkin with regards to detecting the edges of the QR code and one or more one objects in the surrounding are using a computer vision algorithm to the method of Todasco in view of O’Herlihy, Yu, and Adams Jr in order to better detect fraudulent data being transmitted to the user and better security (Simkin Pages 56-57).
Todasco in view of O’Herlihy, Yu, Adams Jr, and Simkin do not disclose:
based on the detected edges, whether an object is located underneath the QR code;
However, Cooperman teaches based on the detected edges, whether an object is located underneath the QR code; (¶0037: Having described general operation of the document layout analysis, attention is now turned to Figures 4-7, where further details of various operations are described in detail. Step 306 preferably includes a two-pass approach to identifying segments as headers and footers. In the first pass, the data for each section is considered, witch particular attention being paid attention: (a) whether there any text regions (header) or below (footer) the section under consideration (i.e., based on the detected edges (header and footer) whether text (object is located underneath)), (b) the height the region or section under consideration, (c) the number of text lines in the region (d) the distance of the region from the edge of the image (or document), and (e) the prevalent font size of the text in the region, relative to the font size of the other regions on the page.);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention utilizes the teachings of Cooperman with regards to converting the image to grayscale to the method of Todasco in view of O’Herlihy, Yu, Adams Jr., and Simkin in order to increase efficiency when determining features of an image such as a QR code (Cooperman ¶0009-0014).
Todasco in view of O’Herlihy, Yu, Adams Jr., Simkin, and Cooperman does not disclose:
determine a first contrast level of the QR code and a second contrast level of the surrounding area;
However, Fleisher teaches determine a first contrast level of the QR code and a second contrast level of the surrounding area; (¶0071: In a scenario, the server 210 may compare the contrast level of the content to the contrast level of an image to determine if a threshold is met or exceeded. If the threshold is met or exceeded (i.e., the difference in contrast levels is larger), the server 210 may refrain from modifying the content. Conversely, if the threshold is not met or exceeded (i.e., the difference in contrast levels is smaller), the server 210 may determine to modify the content. In particular, if the content is textual content, the server 210 may apply a bold formatting to the text, adjust the color or contrast of the text, or facilitate another modification.);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention utilize the teachings of Fleisher with regards to comparing the contrast levels between selected area, such as a QR code, and surrounding area to indicate tampering to the method of Todasco in view of O’Herlihy, Yu, Adams Jr., Simkin, and Cooperman in order to improve the accuracy of monitoring (Fleisher ¶0012 & ¶0071 ).
Todasco in view of O’Herlihy, Yu, Adams Jr., Simkin, Cooperman, and Fleisher does not disclose:
determine that the QR code includes an indication of tampering when an occurrence occurs that is selected from a group consisting of the object is located underneath the QR code, and the first contrast level differs from the second contrast level by a significant amount; and
However, Faulkner teaches determine that the QR code includes an indication of tampering when an occurrence occurs that is selected from a group consisting of the object is located underneath the QR code, and the first contrast level differs from the second contrast level by a significant amount; and (¶0050-0051: Thus, a client computing device detection that nay parameter e.g., a resolution level, contrast level, brightness level, or contrast level, of video data 151 exceeds a threshold (by a significant amount), or it is not within a predetermined range, the client computing device detections the presence of lighting of color anomalies (indication of tampering) and can invoke one or more adjustments. The image calibration 152 can define contrast thresholds for various aspects of an image. For instance, a first contrast threshold can measure a contrast between an object and a background of an image (first and second contrast level). A contrast level can be measured with respect to an average brightness level of an object versus an average level of brightness of the background. The contrast can also compare a brightness level of selected portions of an object, such as an area around a person's eyes, and compare that brightness level to a brightness level of a specific part (the object located) of a background.);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention utilize the teachings of Faulkner with regards to determining an indication of tampering to the method of Todasco in view of O’Herlihy, Yu, Adams Jr., Simkin, Cooperman, and Fleisher in order to better detect and efficiently anomalies (Faulkner ¶0002-0003).
With respect to claim 12, the combination of Todasco in view of O’Herlihy, Yu, Adams Jr., Simkin, Cooperman, Fleisher, and Faulkner teaches the system of claim 9 (see rejection of claim 9 above) wherein the processor is further configured to perform the method further comprising: compare the received image (Todasco: ¶0062 :As seen in Figure 1A and Figure 2B, user device 110 captures image 1100 to determine whether the additional data within image 1100 or detected when image 1100 is captured indicates that a displayable code in image 1100 is potentially fraudulent and includes malicious data.) to a plurality of images of QR codes that both include and do not include tampering, (Todasco: ¶0062-0063: Menu 1102 may have been captured through one or more past images by user device 110 and/or another device, which may have previously been used to determine whether code 1116 is fraudulent or valid, or may have been used to validate code 1116 or add code 1116 to a blacklist (e.g., based on computing attack attempt or success))
wherein the plurality of images of QR codes (Todasco: ¶0062: Such data may be compared to stored data or expected, learned, or registered locations of valid codes, as well as a blacklist of invalid or fraudulent codes. This allows user device 110 to determine whether code 1116 is fraudulent in the merchant environment.) were stored in a computer; (Todasco: ¶0050: As seen in Figure 1A, the transaction processor 150 includes database 156. Database 156 may also store one or more blacklists of invalid and fraudulent codes or devices, which may be used to determine if code output object 140 is fraudulent & ¶0047 Transaction processing application 152 may correspond to one or more processes to execute modules and associated specialized hardware of transaction processor 150 to process a transaction.)
and determine that the received image is similar to at least one of the plurality of images of QR codes stored in the computer. (Todasco: ¶0062-0063: Based on the layout of the previous images of menu 1102 and/or registered information of placement of code 1116 on menu 1102 or within a merchant location corresponding to image 1100, a risk and/or fraud score of a likelihood of fraud or validity of code 1116 may be determined).
With respect to claim 14, the combination of Todasco in view of O’Herlihy, Yu, Adams Jr., Simkin, Cooperman, Fleisher, and Faulkner teaches the system of claim 9 (see rejection of claim 9 above) wherein the processor is further configured to perform the method further comprising: identify any text in the image; and determine that the identified text includes one or more misspelled words, wherein a presence of misspelled words indicates tampering. (Todasco: ¶0064: As seen in Figure 2B, additional data in image 1100, such as font, color, fading or age, misspelling or of nuanced textual/image data, or other characteristics of the displayed information on menu 1102 may be used to determine if menu 1102 appears as expected or may be fraudulently placed with malicious data for code 1116.).
With respect to claim 15, Todasco teaches a computer program product (¶0083: Where applicable, various embodiments provided by the present disclosure may be implemented using hardware, software, or combinations of hardware and software) comprising a computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed by a processor, causes the processor to perform a method comprising: (¶0084: Software, in accordance with the present disclosure, such as program code and/or data, may be stored on one or more computer readable mediums. It is also contemplated that software identified herein may be implemented using one or more general purpose or specific purpose computers and/or computer systems, networked and/or otherwise.);
receive, by a camera application of a mobile device, an image, including a QR code and a surrounding area adjacent the QR code and within the image, by a mobile device; (¶0030-0033: User device 110 of Figure 1A contains a camera 120, a code processing application 130, a payment application 112, other applications 114, a database 116, and a communication module 118. Code processing application 130, payment application 112, and other applications correspond to execute processes, procedures, and/or applications with associated hardware. Camera 120 may interface with one or more applications of user device 110 for the purposes of capturing images, videos, and/or other visual data of the user associated with user device 110, for example display data of the user associated with user device 110, for example during display of an augmented reality determined by code processing application 130 based on valid or fraudulent code detection (QR code) and placement in a real-world environment (surrounding area adjacent the QR code) , as discussed herein. );
identify, by the camera application, a location of the QR code within the image and (¶0033-0034: When capturing the capture data, additional data may also be detected, which may correspond to any metadata and/or contextual data of code output object 140. For example, a geo-location or other location data may be detected (identifying by camera application, a location of the QR code within the image) , including a device orientation, cardinal direction, and/or gyroscope/accelerometer-based data that may be used to determine where the data is captured. )
responsive to determining the indication of tampering, generating an alert on the mobile device requesting physical inspection of the QR code. (¶0021-022: If the capture data indicates fraud, the user's computing device may delay and/or refuse processing and may further notify the user of the fraudulent code/wireless device. The user may be notified through a message displayed with the malicious or fraudulent capture data on the user's device. The message may indicate that the code/wireless device is suspicious or fraudulent and may provide further information on removal of the code/wireless device or where a valid or trustworthy code/wireless device is located. An administrator or authority figure associated with the location, such as a merchant, location owner, vending machine owner/operator, authority enforcement officer, or other entity, may be alerted of the fraudulent code/wireless device and the location of such code/wireless device for removal and/or determination of the malicious actor (physical inspection of the QR code).);
Todasco does not disclose:
generate bounding box surrounding the QR code within the image by the mobile device;
However, O’Herlihy generate bounding box surrounding the QR code within the image by the mobile device; (¶0130: Once trained, the Yolo_v5 network is configured to receive a video frame Fr(τ); and in response thereto, to output three vectors, as follows: the coordinates of the centre of a bounding box encompassing a QR code detected in the received video frame (generating a bounding box surrounding the QR code), together with the width and height of the bounding box, wherein the width and height are normalized relative to the width and height respectively of the video frame (determining from bounding box the surrounding the bounding box));
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention utilize the teachings of O’Herlihy with regards to generating a bounding box surrounding the QR code to the method of Todasco in order to better identify the QR code and distinguish the QR code from its setting (O’Herlihy ¶0127-0130).
Todasco in view of O’Herlihy does not disclose:
determine, from the bounding box, the surrounding area outside the bounding box
However, Yu teaches determine, from the bounding box, the surrounding area outside the bounding box (¶0001: Mobile electronic devices such as tablet computers and smart phones commonly include cameras. Software on mobile device may process captured images to identify image to identify and recognize text and glyph symbols ¶0030: In the preprocessor, a bounding box may be generated that bounds a line of text. The bounding box may bound the glyph region. Value(s) for the image/region suitability metric(s) may be calculated for the portion of the image outside bounding box (surrounding are outside bounding box) . The value(s) for inside the bounding box. The value(s) for inside the bounding box may then be compared to value(s) outside the boundary box to make another determination on the suitability of the image. This determination may also use a classifier.);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention utilize the teachings of Yu et al. (US Pat No. 9626577-B1) with regards to determining the surrounding area outside the bounding box within the image by the mobile device to the method of Todasco in view of O’Herlihy in order to better analyze image (Yu et al. (US Pat No. 9626577-B1) ¶0009-0010).
Todasco in view of O’Herlihy and Yu does not disclose:
convert the image to grayscale;
However, Adams Jr. teaches convert the image to grayscale; (¶0006: In Figure 1, shows the normalized image block 105 produces the normalized image 110 by first converting the document 100 to a grayscale image and then performing a full-scale histogram stretch on the grayscale image. The conversion to grayscale image operation combines the color component of the document 100 to produce a gray document at least location in the document 100.);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention utilizes the teachings of Adams Jr. with regards to converting the image to grayscale to the method of Todasco in view of O’Herlihy and Yu in order to better detect the features of the QR code (Adams Jr ¶0003).
Todasco in view O’Herlihy, Yu, and Adams Jr does not disclose:
detect edges of the QR code and one or more objects in the surrounding area using a computer vision algorithm
However, Simkin teaches detecting edges of the QR code and one or more objects in the surrounding area using a computer vision algorithm; (6 The VeriDoc Interface: Page 68-69: Fig. 10 Vision subsystem to assist the user in working with VeriDoc: The user points the front-facing camera roughly at the VeriDoc document, the system detects the four corners of the first content block and snaps the locations of the brackets to them, and the system unwarps and extracts the content of that block…. To assist the user in scanning Veri Doc document content we provide a refinement procedure that allows the user to roughly indicate relevant text blocks, but still provide the required accuracy for the computer vision processing pipeline (see Figure 10). On the very left, a typical user interaction is depicted showing a coarse alignment of the brackets with the first text block. We proceed by a corner detection algorithm and snap the locations of the brackets provided by the user to the closest corners. We use the Harris corner measure M to robustly detect corners. The second image at the top shows a visualization of the closest corner and the box spanned by them in green. Under the assumption of a pinhole camera model as well as a planar target (documents in our case), we can compute a homography H ∈ R3×3 in order to undo the perspective transformation under which the content is viewed. The third image from the left in Figure 10 shows the content after unwarping and cropping. Using the information on the ratio between text and code contained in the header, we now split the content area into text and the associated QR code. In a last step, the QR code is decoded, the signature extracted, and the text area is further processed using OCR in combination with the font and language information from the header code);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention utilizes the teachings of Simkin with regards to detecting the edges of the QR code and one or more one objects in the surrounding are using a computer vision algorithm to the method of Todasco in view of O’Herlihy, Yu, and Adams Jr in order to better detect fraudulent data being transmitted to the user and better security (Simkin Pages 56-57).
Todasco in view of O’Herlihy, Yu, Adams Jr, and Simkin do not disclose:
determine, based on the detected edges, whether an object is located underneath the QR code;
However, Cooperman teaches determine, based on the detected edges, whether an object is located underneath the QR code; (¶0037: Having described general operation of the document layout analysis, attention is now turned to Figures 4-7, where further details of various operations are described in detail. Step 306 preferably includes a two-pass approach to identifying segments as headers and footers. In the first pass, the data for each section is considered, witch particular attention being paid attention: (a) whether there any text regions (header) or below (footer) the section under consideration (i.e., based on the detected edges (header and footer) whether text (object is located underneath)), (b) the height the region or section under consideration, (c) the number of text lines in the region (d) the distance of the region from the edge of the image (or document), and (e) the prevalent font size of the text in the region, relative to the font size of the other regions on the page.);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention utilizes the teachings of Cooperman with regards to converting the image to grayscale to the method of Todasco in view of O’Herlihy, Yu, Adams Jr., and Simkin in order to increase efficiency when determining features of an image such as a QR code (Cooperman ¶0009-0014).
Todasco in view of O’Herlihy, Yu, Adams Jr., Simkin, and Cooperman does not disclose:
determine a first contrast level of the QR code and a second contrast level of the surrounding area;
However, Fleisher teaches determine a first contrast level of the QR code and a second contrast level of the surrounding area; (¶0071: In a scenario, the server 210 may compare the contrast level of the content to the contrast level of an image to determine if a threshold is met or exceeded. If the threshold is met or exceeded (i.e., the difference in contrast levels is larger), the server 210 may refrain from modifying the content. Conversely, if the threshold is not met or exceeded (i.e., the difference in contrast levels is smaller), the server 210 may determine to modify the content. In particular, if the content is textual content, the server 210 may apply a bold formatting to the text, adjust the color or contrast of the text, or facilitate another modification.);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention utilize the teachings of Fleisher with regards to comparing the contrast levels between selected area, such as a QR code, and surrounding area to indicate tampering to the method of Todasco in view of O’Herlihy, Yu, Adams Jr., Simkin, and Cooperman in order to improve the accuracy of monitoring (Fleisher ¶0012 & ¶0071 ).
Todasco in view of O’Herlihy, Yu, Adams Jr., Simkin, Cooperman, and Fleisher does not disclose:
determine that the QR code includes an indication of tampering when an occurrence occurs that is selected from a group consisting of the object is located underneath the QR code, and the first contrast level differs from the second contrast level by a significant amount; and
However, Faulkner teaches determine that the QR code includes an indication of tampering when an occurrence occurs that is selected from a group consisting of the object is located underneath the QR code, and the first contrast level differs from the second contrast level by a significant amount; and (¶0050-0051: Thus, a client computing device detection that nay parameter e.g., a resolution level, contrast level, brightness level, or contrast level, of video data 151 exceeds a threshold (by a significant amount), or it is not within a predetermined range, the client computing device detections the presence of lighting of color anomalies (indication of tampering) and can invoke one or more adjustments. The image calibration 152 can define contrast thresholds for various aspects of an image. For instance, a first contrast threshold can measure a contrast between an object and a background of an image (first and second contrast level). A contrast level can be measured with respect to an average brightness level of an object versus an average level of brightness of the background. The contrast can also compare a brightness level of selected portions of an object, such as an area around a person's eyes, and compare that brightness level to a brightness level of a specific part (the object located) of a background.);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention utilize the teachings of Faulkner with regards to determining an indication of tampering to the method of Todasco in view of O’Herlihy, Yu, Adams Jr., Simkin, Cooperman, and Fleisher in order to better detect and efficiently anomalies (Faulkner ¶0002-0003).
With respect to claim 18, the combination of Todasco in view of O’Herlihy, Yu, Adams Jr., Simkin, Cooperman, Fleisher, and Faulkner teaches the computer program product of claim 15 (see rejection of claim 15 above) wherein the computer readable program causes the processor to: compare the received image of the QR code (Todasco: ¶0062 :As seen in Figure 1A and Figure 2B, user device 110 captures image 1100 to determine whether the additional data within image 1100 or detected when image 1100 is captured indicates that a displayable code in image 1100 is potentially fraudulent and includes malicious data.) to a plurality of images of QR codes that both include and do not include tampering, (Todasco: ¶0062-0063: Menu 1102 may have been captured through one or more past images by user device 110 and/or another device, which may have previously been used to determine whether code 1116 is fraudulent or valid, or may have been used to validate code 1116 or add code 1116 to a blacklist (e.g., based on computing attack attempt or success)).
wherein the plurality of images of QR codes (Todasco: ¶0062: Such data may be compared to stored data or expected, learned, or registered locations of valid codes, as well as a blacklist of invalid or fraudulent codes. This allows user device 110 to determine whether code 1116 is fraudulent in the merchant environment.) were stored in a computer; (Todasco: ¶0050: As seen in Figure 1A, the transaction processor 150 includes database 156. Database 156 may also store one or more blacklists of invalid and fraudulent codes or devices, which may be used to determine if code output object 140 is fraudulent & ¶0047 Transaction processing application 152 may correspond to one or more processes to execute modules and associated specialized hardware of transaction processor 150 to process a transaction.)
and determine that the received image of the QR code is similar to at least one of the plurality of images of QR codes stored in the computer. (Todasco: ¶0062-0063: Based on the layout of the previous images of menu 1102 and/or registered information of placement of code 1116 on menu 1102 or within a merchant location corresponding to image 1100, a risk and/or fraud score of a likelihood of fraud or validity of code 1116 may be determined).
With respect to claim 20, the combination of Todasco in view of O’Herlihy, Yu, Adams Jr., Simkin, Cooperman, Fleisher, and Faulkner teaches the computer program product of claim 15 (see rejection of claim 15 above) wherein the computer readable program causes the processor to: identify any text in the image; and determine that the identified text includes one or more misspelled words, wherein a presence of misspelled words indicates tampering. (Todasco: ¶0064: As seen in Figure 2B, additional data in image 1100, such as font, color, fading or age, misspelling or of nuanced textual/image data, or other characteristics of the displayed information on menu 1102 may be used to determine if menu 1102 appears as expected or may be fraudulently placed with malicious data for code 1116.).
Claims 24 is rejected under 35 U.S.C. 103 as being unpatentable over Todasco et al. (US PGPub No.20220083660-A1) in view of O’Herlihy et al. (US PGPub Nol. 20220391796-A1), Yu et al. (US Pat No. 9626577-B1), Adams Jr. (US Pat No. 9378405-B2), Simkin et al. (Ubic: Bridging the gap between digital cryptography and the physical world. In European Symposium on Research in Computer Security (pp. 56-75) Cham: Springer International Publishing. (Year: 2014)),Cooperman et al. (US Pat No. 5784487-A), Fleisher et al. (US PGPub No. 20130121529-A1), Faulkner et al. (US Pat No. 11457271-B1), and Seifert et al. (US PGPub No. 20210141897-A1).
With respect to claim 24, the combination Todasco in view of O’Herlihy, Yu, Adams Jr., Simkin, Cooperman, and Fleisher teaches the method of claim 1 (see rejection of claim 1 above), but does not disclose, wherein the determining whether the QR code includes an indication of tampering comprises: providing the image to a machine learning model trained using labeled images of both safe QR codes and tampered QR codes; and receiving from the machine learning model a confidence score indicating whether the QR code is safe.
However, Seifert teaches wherein the determining whether the QR code includes an indication of tampering comprises: providing the image to a machine learning model trained using labeled images of both safe QR codes and tampered QR codes; and (¶0042: : The labeled data 213 includes files or other object samples that are tagged or indicated with labels or classifications for training in a machine learning system. For instance the labeled data 213 can include multiple files where the files have been tagged according to particular malicious code or file families (and/or sub-families) and/or labeled benign (or families/sub-families) of benign files. For example, the labeled data 213 can include several iterations or sub-families (the sub-family is the label) of files that have been infected with Rootkit malware, as well as other families of malicious code. In this way, a machine learning model can be trained to identify patterns or associations indicated in the labeled data 213 for prediction purposes, as described in more detail herein. The unknown data 215 includes files or other content for which there is no predetermined label or classification. For example, the unknown data 215 can be any incoming file (e.g., a test file) that is analyzed after a machine learning model has been deployed, or trained or tested using the labeled data 213. );
receiving from the machine learning model a confidence score indicating whether the QR code is safe. (¶0109-0110: In some embodiments, at least partially responsive to determining whether the indication of the computer object is within the threshold distance (e.g., as described with respect to the similarity score of block 709), an indicator indicating whether the computer object contains malicious content (a score indicating whether it is safe) is provided to a user interface of a computing device.);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention utilize the teachings of Seifert with regards to the provisioning of a machine learning model to indicate if an object is safe to the method of Todasco in view of O’Herlihy, Yu, Adams Jr., Simkin, Cooperman, and Fleisher in order to better detect malicious content and improve accuracy of detecting malicious content (Seifert ¶0004-0006).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Caton et al. (US PGPub No. 20140105449-A1) discloses a method for hidden security feature detection, analysis, and authentication using a smart device, and wherein the smart device can detect and analyze hidden security features using the camera capability of the smart device to capture a digital image, and then analyze and authenticate that image using security service including a database of security feature data, processing rules, and other information.
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/T.P.V./ Examiner, Art Unit 2437
/MENG LI/ Primary Examiner, Art Unit 2437
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