DETAILED ACTION
Contents
I. Notice of Pre-AIA or AIA Status 4
II. Continued Examination Under 37 CFR 1.114 4
III. Priority 4
IV. Pertinent Prosecution History 5
V. Claim Status 7
VI. Reissue Requirements 7
VII. Reissue Oath/Declaration 9
VIII. Claim Objections 9
IX. Claim Interpretation 11
A. Lexicographic Definitions 11
B. 35 U.S.C § 112 6th Paragraph 11
(2) Functional Phrase – “Program Code I” 12
(3) Functional Phrase – “Program Code II” 17
X. Claim Rejections – 35 U.S.C. § 112 22
A. 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph 22
(1) New Matter 22
B. 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph 24
XI. Claim Rejections – 35 U.S.C. § 251 29
A. Oath/Declaration 29
B. New Matter 29
C. Original Patent Requirement 30
XII. Claim Rejections – 35 USC § 103 32
A. Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Ladron et al. (International Publication No. WO 2017/207064 A1) (“Ladron”) in view of Rothberg et al. (U.S. Publication No. 2017/0360412) (“Rothberg”) and Das et al. (U.S. Patent No. 10,962,939) (“Das”). 33
B. Claims 13 and 14 are rejected under 35 U.S.C. 103(a) as being unpatentable over Ladron et al. (International Publication No. WO 2017/207064 A1) (“Ladron”) in view of Rothberg et al. (U.S. Publication No. 2017/0360412) (“Rothberg”) and Das et al. (U.S. Patent No. 10,962,939) (“Das”) as applied to claims 1-20 above, and further in view of Sliz et al. (U.S. Patent No. 10,587,796)(“Sliz”). 91
XIII. Response to Arguments 93
A. Oath/Declaration Issue 93
B. Claim Objection(s) 94
C. Claim Interpretation - 35 U.S.C. § 112, Sixth Paragraph, Invocation 94
(1) Instructions/Program Code 94
D. 35 U.S.C. § 112 Rejections 97
(1) 35 U.S.C.§ 112(b) Rejections 97
(2) 35 U.S.C.§ 112(d) Rejections 98
E. 35 U.S.C. § 251 Rejections 98
(1) Oath/Declaration Issue 98
(2) Original Patent Requirement 99
F. 35 U.S.C. § 103 Rejections 99
(1) Claims 1-21, 24-26, 29, 30 and 33-35 99
(2) Claims 13, 14, 23, 24, 28, 32 and 33 103
XIV. Conclusion 105
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 . In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03 September 2024 has been entered.
Priority
Applicant filed the instant reissue application 17/528,668 (“‘668 Reissue Application”) on 17 November 2021 for U.S. Application No. 16/553,388 (“‘388 Application"), filed 28 August 2019, now U.S. Patent No. 10,482,343 (“‘343 Patent”), issued 19 November 2019, which is a continuation of U.S. Application No. 16/283,157 (“‘157 Application"), filed 22 February 2019, now U.S. Patent No. 10,445,609 (“‘609 Patent”), issued 15 October 2019, which is a continuation in part of U.S. Application No. 16/150,772 (“‘772 Application"), filed 03 October 2018, now U.S. Patent No. 10,242,283 (“‘283 Patent”), issued 26 March 2019.
Thus, the Examiner concludes that for examination purposes the instant ‘668 Reissue Application claims a priority date of 3 October 2018.
Pertinent Prosecution History
As set forth supra, Applicant filed the application for the instant ‘668 Reissue Application on 17 November 2021. The Examiner finds that the instant ‘668 Reissue Application included a preliminary amendment (“Nov 2021 Preliminary Amendment”) to the claims (“Nov 2021 Claim Amendment”). The Nov 2021 Claim Amendment includes an amendment: providing amended original claims 1, 6, 8, 10-12, 14, 16-18 and 20; original claims 2-5, 7, 13, 15 and 19; and adding new claims 21-34.
The Office issued a Non-Final Office action on 03 November 2023 (“Nov 2023 Non-Final Office Action”). In particular, the June 2023 Non-Final Office Action provided rejections for claims 1-34 (“Rejected Claims”) under 35 U.S.C. §§ 112(a), 112(b), 102(a)(1-2), 103, 251, and non-statutory Double Patenting. 1
On 03 April 2024, Applicant filed a Response to Non-Final Office Action (“April 2024 Applicant Response”). The April 2024 Applicant Response contained: “Remarks,” a “Terminal Disclaimer” (“April 2024 TD”), “Amendments to the Drawing” (“April 2024 Drawings Amendment”), “Amendments to the Specification” (“April 2024 Spec Amendment”), and “Amendments to the Claims” (“April 2024 Claim Amendment”) including: twice2 amended original claims 1, 6, 8, 10, 11, 14 and 16-18; once3 amended original claims 3, 9, 15 and 20; original claims 2, 4, 5, 7, 12, 13 and 19; once4 amended new claims 24, 26-28 and 33; and new5 claims 21-23, 25, 29-32, 34 and 35.
The Office issued a Final Office action on 03 May 2024 (“May 2024 Final Office Action”). In particular, the May 2024 Final Office Action provided rejections for claims 1-35 (“Rejected Claims”) under 35 U.S.C. §§ 112(b), 112(d), 103 and 251. 6
On 31 July 2024, the Office and Applicant had an interview (“July 2024 Interview”) discussing the May 2024 Final Office Action (see Interview Summary mailed 06 August 2024 (“Aug 2024 Int Summary”).
On 03 September 2024, Applicant filed a Response to Final Office Action (“Sept 2024 Applicant Response”). The Sept 2024 Applicant Response contained: “Remarks,” and “Amendments to the Claims” (“Sept 2024 Claim Amendment”) including: thrice7 amended original claims 1, 10, 16 and 18; twice8 amended original claims 6, 8, 11, 14 and 17; once9 amended original claims 3, 9, 15 and 20; original claims 2, 4, 5, 7, 12, 13 and 19; and canceled10 new claims 21-35.
Claim Status
The Examiner finds that the claim status in the instant ‘668 Reissue Application is as follows:
Claim(s) 1, 10, 16 and 18 (Original and thrice amended)
Claim(s) 6, 8, 11, 14 and 17 (Original and twice amended)
Claim(s) 3, 9, 15 and 20 (Original and once amended)
Claim(s) 2, 4, 5, 7, 12, 13 and 19 (Original)
Claim(s) 21-35 (New and canceled)
Thus, the Examiner concludes that claims 1-20 are pending in the instant ‘668 Reissue Application. Claims 1-20 are examined (“Examined Claims”).
Reissue Requirements
For reissue applications filed before September 16, 2012, all references to 35 U.S.C. 251 and 37 CFR 1.172, 1.175, and 3.73 are to the law and rules in effect on September 15, 2012. Where specifically designated, these are “pre-AIA ” provisions.
For reissue applications filed on or after September 16, 2012, all references to 35 U.S.C. 251 and 37 CFR 1.172, 1.175, and 3.73 are to the current provisions.
Applicant is reminded of the continuing obligation under 37 CFR 1.178(b), to timely apprise the Office of any prior or concurrent proceed-ing in which the ‘343 Patent is or was involved. These proceedings would include interferences, reissues, reexaminations, post-grant proceedings and litigation.
Applicant is further reminded of the continuing obligation under 37 CFR 1.56, to timely apprise the Office of any information which is mate-rial to patentability of the claims under consideration in this reissue appli-cation.
These obligations rest with each individual associated with the filing and prosecution of this application for reissue. See also MPEP §§ 1404, 1442.01 and 1442.04.
The Examiner notes that Amendment practice for Reissue Applications is NOT the same as for non-provisional applications. See MPEP §§ 1413 and 1453. Reissue application amendments must comply with 37 CFR 1.173, while non-provisional application amendments must comply with 37 CFR 1.121. Particularly,
Manner of making amendments under 37 CFR 1.173:
All markings (underlining and bracketing) are made relative to the original patent text, 37 CFR 1.173(g) (and not relative to the prior amendment).
For amendments to the abstract, specification and claims, the deleted matter must be enclosed in brackets, and the added matter must be underlined. See 37 CFR 1.173(d).
For amendments to the drawings, any changes to a patent drawing must be submitted as a replacement sheet of drawings which shall be an attachment to the amendment document. Any replacement sheet of drawings must be in compliance with § 1.84 and shall include all of the figures appearing on the original version of the sheet, even if only one figure is amended. Amended figures must be identified as "Amended," and any added figure must be identified as "New." In the event that a figure is canceled, the figure must be surrounded by brackets and identified as "Canceled." All changes to the drawing(s) shall be explained, in detail, beginning on a separate sheet accompanying the papers including the amendment to the drawings. See 37 CFR 1.173(d)(3).
The Examiner further notes that all amendments to the instant ‘668 Reissue Application must comply with 37 CFR 1.173(b)-(g).
Reissue Oath/Declaration
The Examiner finds that the Declaration filed by Applicant on 17 November 2021 (“Nov 2021 Oath/Declaration”) is defective because of the following:
The Examiner finds that the Nov 2021 Oath/Declaration filed by Applicant states,
Patentee believes the issued patent to be partially inoperative by reason of the patentee claiming less than they had a right to claim. Amended claims 1, 10, and 18 are broader than issued claims 1, 10, and 18 at least in that the feature "identification card" has been replaced by "object". Thus, this reissue seeks to broaden at least claims 1, 10, and 18 of the issued patent. All errors corrected by this reissue arose without deceptive intent.
(Nov 2021 Oath/Declaration; emphasis added). The Examiner finds that the Nov 2021 Oath/Declaration does identify a specific claim that the instant ‘668 Reissue Application seeks to broaden. However, in light of the April 2024 Claim Amendment (i.e., amendment from “object” back to original claim requirement “identification card),” the Nov 2021 Oath/Declaration does not identify a single word, phrase, or expression in the specification or in an original claim that the instant ‘668 Reissue Application seeks to broaden; nor identify how it renders the original patent wholly or partly inoperative or invalid. See 37 CFR 1.175(b) and MPEP § 1414.II.
Claim Objections
MPEP § 1453 states,
pursuant to 37 CFR 1.173(c), each claim amendment must be accompanied by an explanation of the support in the disclosure of the patent for the amendment (i.e., support for all changes made in the claim(s), whether insertions or deletions). The failure to submit an explanation will generally result in a notification to applicant that the amendment before final rejection is not completely responsive (see 37 CFR 1.135(c)).
(MPEP § 1453; emphasis added). The Examiner finds that Applicant has not provided sufficient explanation of support for at least the amendments to claims 1, 10 and 18 instantly provided in the Sept 2024 Claim Amendment, as set forth in 37 CFR 1.173(c). (Id.) While the Sept 2024 Applicant Response provides direction for the Examiner to find support for the claim amendment, the Examiner finds that the direction is not sufficient. Specifically, while the Sept 2024 Applicant Response cites to Figures and sections of the ‘343 Patent which are directed to “types of identification cards,” the Examiner finds that the citations do not provide support for images utilized for training the “trained neural network” being images representing “acceptable and unacceptable types of identification cards” as well a “determining that the at least a first image is of an acceptable type of identification card and unusable (i.e., for the later emphasis on both determinations being made). (Sep 2024 Applicant Response at 4, 16 and 18; emphasis added).
Claim Interpretation
During examination, claims are given the broadest reasonable interpretation consistent with the specification and limitations in the specification are not read into the claims. See MPEP § 2111, MPEP § 2111.01 and In re Yamamoto et al., 222 USPQ 934 (Fed. Cir. 1984). Under a broadest reasonable interpretation, words of the claim must be given their plain meaning, unless such meaning is inconsistent with the specification. See MPEP § 2111.01(I). It is further noted it is improper to import claim limitations from the specification, i.e., a particular embodiment appearing in the written description may not be read into a claim when the claim language is broader than the embodiment. See MPEP § 2111.01(II). Therefore, unless one of the exceptions applies below, Examiners will interpret the limitations of the pending and examined claims using the broadest reasonable interpretation.
Lexicographic Definitions
A first exception to the prohibition of reading limitations from the specification into the claims is when the Applicant for patent has provided a lexicographic definition for the term. (See MPEP § 2111.01(IV)). Following an independent review of the claims in view of the specification herein, Examiners find that Applicant has not provided any lexicographic definitions related to claim terms with any reasonable clarity, deliberateness and precision.
35 U.S.C § 112 6th Paragraph
A second exception to the prohibition of reading limitations from the specification into the claims is when the claimed feature is written as a means-plus-function or a step-plus-function. See 35 U.S.C. § 112(6th ¶) and MPEP §§ 2181-2183. As noted in MPEP § 2181, a three prong test is used to determine the scope of a means-plus-function or step-plus-function limitation in a claim:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as "configured to" or "so that"
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
The Examiner finds herein that claims 10-17 include one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. §112 (6th ¶) because the claim limitations use a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Each such limitation will be discussed in turn as follows:
Functional Phrase – “Program Code I”11
A first means-plus-function phrase is recited in claim 10 (and included in each of dependent claims 11-17) which recites “program code …” or hereinafter “Functional Phrase 2” or “FP2.” The Examiner determines herein that FP2 does meet the three prong test and thus will be interpreted as a means-plus-function limitation under 35 U.S.C. §112(6th ¶).
The Examiner finds that claim 10 expressly recites:
program code that, when executed by the one or more processors, cause the system to determine, using the trained neural network, whether at least a first image of the one or more images is of an acceptable type of identification card and unusable by the remote post-validation platform based at least in part on one or more analyzed quality features of the first image
[emphasis added]; and claim 16 expressly recites:
wherein the one or more processors are further configured to, when executing the program code, cause the system to determine whether at least a second image of the one or more captured images is usable by the remote post-validation platform
[emphasis added];
i. 3-Prong Analysis: Prong (A)
FP2 meets invocation prong (A) because "means ... for" type language is recited. The Examiner first finds that “program code” is a generic placeholder or nonce term equivalent to “means” because the term “program code” does nothing more than simply define a generic structure, i.e., means. The Examiner further notes that the specification of the ‘343 Patent does not specifically define “program code” and thus the specification of the ‘343 Patent does not impart or disclose any structure for the phrase. Rather, the Examiner finds that the ‘343 Patent uses this same phrase to describe several program codes.
Furthermore, the Examiner finds there is no disclosure or suggestion from the prior art that program code is a sufficient and definite structure to perform the functions recited in FP2. For example, U.S. Patent No. 10,962,939 illustrates program code for restricting images having different program code (i.e., implemented blocks) and distinct operation from any of the program code of the ‘343 Patent. Similarly, U.S. Patent No. 10,587,796 illustrates program code for determining if an image is unacceptable/acceptable having different program code (i.e., implemented blocks) and distinct operation from any of the program code of the ‘343 Patent.
Accordingly, the Examiner finds nothing in the specification, prosecution history or the prior art to construe “program code …” in FP2 as the name of a sufficiently definite structure for performing the functions recited in FP2 so as to take the overall claim limitation out of the ambit of §112(6th ¶). See Williamson v. Citrix Online, L.L.C., 115 USPQ2d 1105, 1112 (Fed. Cir. 2015).
In light of the above, the Examiner concludes that the term “program code …” is a generic placeholder having no specific structure associated therewith. Because “program code …” is merely a generic placeholder having no specific structure associated therewith, the Examiner concludes that FP2 meets invocation Prong (A).
ii. 3-Prong Analysis: Prong (B)
Based upon a review of FP2, the Examiner finds that claimed function(s) are:
[D]etermin[ing], using the trained neural network, whether at least a first image of the one or more images is of an acceptable type of identification card and unusable by the remote post-validation platform based at least in part on one or more analyzed quality features of the first image
D]etermin[ing] whether a second image of the one or more captured images is usable by the remote post-validation platform
Because FP2 recites the above recited functions, the Examiner concludes that FP2 meets Invocation Prong (B).
iii. 3-Prong Analysis: Prong (C)
Based upon a review of the entire Functional Phrase 2, the Examiner finds that Functional Phrase 2 does not contain sufficient structure for performing the entire claimed function that is set forth within Functional Phrase 2. Specifically, the Examiner finds that FP2 recites further program code including: using the trained neural network. In fact, the Examiner finds that FP2 recites insufficient structure and/or algorithms for performing the claimed function, rather the Examiner finds that claim 10 recites only the underlying function of using the trained neural network. From the perspective, the Examiner finds that Functional Phrase 2 does not recite sufficient structure for performing the claimed function.
Because Functional Phrase 2 does not contain sufficient structure for performing the entire claimed function, the Examiner concludes that Functional Phrase 2 meets invocation Prong (C).
Because Functional Phrase 2 does meet the 3-prong analysis as set forth in MPEP § 2181 I., the Examiner concludes that Functional Phrase 2 does invoke 35 U.S.C § 112 6th paragraph.
Corresponding structure for Functional Phrase 2
Once a claimed phrase invokes 35 U.S.C. § 112 6th paragraph, the next step is to determine the corresponding structure. (MPEP § 2181 II). In order to satisfy the requirements of 35 U.S.C. § 112, second paragraph, there must be identified in the applications’ disclosure a single structure and/or algorithm which performs the function of FP2.
The Examiner has carefully reviewed the original disclosure to determine the corresponding structure for FP2. In reviewing the original disclosure, the Examiner finds that the ‘343 Patent utilizes a good/bad determination based upon the output of the “special image processing 412 and/or image analysis 414 modules and/or software in communication with a trained neural network 415.” (‘343 Patent at c.10, ll.47-56). Furthermore, in examination of annotated Figure 4 of the ‘343 Patent above, again, while one of ordinary skill in the art would recognize that the exemplary process for validating an image, the Examiner finds that the ‘343 Patent provides a “good/bad” categorization being determined by black boxes. As noted in the MPEP, “merely referencing [] a black box designed to perform their cited function, will not be sufficient because there must be some explanation of how the computer or the computer component performs the claimed function. [Emphasis added.] ” (MPEP § 2181 II. B citing Blackboard). In lieu of the black box, the Examiner [AltContent: textbox (Figure 4 of '343 Patent (Annotated))]
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finds it unclear to what the exact criteria the ‘343 Patent utilizes to ascertain good or bad. (See id. at c.11, l.65 – c. 12, l.15). Moreover, in further examination of Figure 4 above, while one of ordinary skill in the art would recognize that if the processor/system determines an image to be “good” (i.e., acceptable), the Examiner finds that no further processing is provide to determine if the ”good” (i.e., acceptable) image is unusable, only to the “good” (i.e., acceptable) image being provide to a “3rd Party Validation Service” for further processing.” Thus, the Examiner concludes that the functions and ‘343 Patent fail to clearly link and associate corresponding structure to FP2.12
In light of the finding that Functional Phrase 2 invokes 35 U.S.C. § 112 6th paragraph, Functional Phrase 2 is construed to cover the corresponding structure described in the specification and equivalents thereof. From this perspective, and to advance prosecution by providing art rejections infra, the Examiner construes the structure for performing the claimed function as program code, that can be executed by at least one processor, using the output of the trained neural network model, to determine whether the output parameter of detected feature categorizes the input image as confirmed13 and unusable, or its equivalent.
Functional Phrase – “Program Code II”14
A second means-plus-function phrase is recited in claim 10 (and included in each of dependent claims 11-17) which recites “program code …” or hereinafter “Functional Phrase 3” or “FP3.” The Examiner determines herein that FP3 does meet the three prong test and thus will be interpreted as a means-plus-function limitation under 35 U.S.C. §112(6th ¶).
The Examiner finds that claim 10 expressly recites:
wherein the one or more processors, when executing the program code, are configured to output feedback instructions to capture one or more new images for determining whether the one or more new images are unusable by the remote post-validation platform
[emphasis added];
i. 3-Prong Analysis: Prong (A)
FP3 meets invocation prong (A) because "means ... for" type language is recited. The Examiner first finds that “program code” is a generic placeholder or nonce term equivalent to “means” because the term “program code” does not convey any particular structure. The Examiner further notes that the specification of the ‘343 Patent does not specifically define “program code” and thus the specification of the ‘343 Patent does not impart or disclose any structure for the phrase. Rather, the Examiner finds that the ‘343 Patent uses this same phrase to describe several program codes. (See § IX.B.(2).i, supra).
Additionally, the Examiner has reviewed the prosecution history and the relevant prior art of record herein, and performed a prior art search for the phrase “program code,” and finds that “program code” as used in the claims does not provide an art-recognized structure to perform the claimed function. This is evidenced by the analysis applied to the use of this phrase in Functional Phrase 2 above. (See § IX.B.(2).i, supra). Thus, rather more than a simple program code would be required to perform the function recited in FP3.
Accordingly, the Examiner finds nothing in the specification, prosecution history or the prior art to construe “program code …” in FP3 as the name of a sufficiently definite structure for performing the functions recited in FP3 so as to take the overall claim limitation out of the ambit of §112(6th ¶). See Williamson v. Citrix Online, L.L.C., 115 USPQ2d 1105, 1112 (Fed. Cir. 2015).
In light of the above, the Examiner concludes that the term “program code …” is a generic placeholder having no specific structure associated therewith. Because “program code …” is merely a generic placeholder having no specific structure associated therewith, the Examiner concludes that FP3 meets invocation Prong (A).
ii. 3-Prong Analysis: Prong (B)
Based upon a review of FP3, the Examiner finds that claimed function(s) are:
[O]utput[ting] feedback instructions to capture one or more new images for determining whether the one or more new images are unusable by the remote post-validation platform
Because FP3 recites the above recited functions, the Examiner concludes that FP3 meets Invocation Prong (B).
iii. 3-Prong Analysis: Prong (C)
Based upon a review of the entire Functional Phrase 3, the Examiner finds that Functional Phrase 3 does not contain sufficient structure for performing the entire claimed function that is set forth within Functional Phrase 3.15 In fact, the Examiner finds that the Functional Phrase 3 recites very little structure (beyond some generically recited structure) for performing the claimed function.
Because Functional Phrase 3 does not contain sufficient structure for performing the entire claimed function, the Examiner concludes that Functional Phrase 3 meets invocation Prong (C).
Because Functional Phrase 3 does meet the 3-prong analysis as set forth in MPEP § 2181 I., the Examiner concludes that Functional Phrase 3 does invoke 35 U.S.C § 112 6th paragraph.
Corresponding structure for Functional Phrase 3
Once a claimed phrase invokes 35 U.S.C. § 112 6th paragraph, the next step is to determine the corresponding structure. (MPEP § 2181 II). In order to satisfy the requirements of 35 U.S.C. § 112, second paragraph, there must be identified in the applications’ disclosure a single structure and/or algorithm which performs the function of FP3.
The Examiner has carefully reviewed the original disclosure to determine the corresponding structure for FP3. In reviewing the original disclosure, the Examiner finds that the ‘343 Patent utilizes various sensors and/or image processing to provide parameters for determining keystone or other issues of captured images for outputting feedback instructions. (c.5, l.58 – c.6, ll.9; c.6, ll.45-59; c.12, l.7 – c.13, l.23; see Figures 4-7). Furthermore, in examination of annotated Figure 4 of the ‘343 Patent above, again, while one of ordinary skill in the art would recognize that the exemplary process of performing the various image processing tasks, the Examiner finds that the ‘343 Patent provides such processing being performed by black boxes. As noted in the MPEP, “merely referencing [] a black box designed to perform their cited function, will not be sufficient because there must be some explanation of how the computer or the computer component performs the claimed function. [Emphasis added.] ” (MPEP § 2181 II. B citing Blackboard). In lieu of the black box, while one of ordinary skill in the art would recognize that various distance, focus, etc. parameters may be obtained by the sensors, the Examiner finds that the image processing and the correlation to the various sensor parameters being measured would require more than simple and/or generic image processing techniques, as is evidenced by Figure 5-7. To support the Examiner’ position, the Examiner finds that the ‘343 Patent states,
[t]he program instructions or program code may include specially designed and constructed instructions or code. For example, the disclosed embodiments may execute high-level and/or low-level software instructions, such as machine code (e.g., such as that produced by a compiler) and/or high-level code that can be executed by a processor using an interpreter.
(‘343 Patent at c.10, ll.9-15; emphasis added). One of ordinary skill in the art would recognize that the ‘343 Patent provides disclosure to broad and undefined image processing software. From this perspective, the Examiner finds that the ‘343 Patent fails to disclose or discuss any exact, unique and separate specific algorithms to perform image processing to attain the keystone and or other issues of captured images. Thus, the Examiner concludes that the functions and ‘343 Patent fails to clearly link and associate corresponding structure to FP3. Thus, the Examiner concludes that the ‘343 Patent fails to clearly link and associate corresponding structure to FP3.16
In light of the finding that Functional Phrase 3 invokes 35 U.S.C. § 112 6th paragraph, Functional Phrase 3 is construed to cover the corresponding structure described in the specification and equivalents thereof. From this perspective, and to advance prosecution by providing art rejections infra, the Examiner construes the structure for performing the claimed function as program code, that can be executed by at least one processor, outputting feedback instructions to a user for corrective action, or its equivalent.
Claim Rejections – 35 U.S.C. § 112
35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph
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.
New Matter
Claims 1-20 are 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 pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
With respect to the limitations of claims 1, 10 and 18, the Examiner finds that claims 1, 10 and 18 recite:
the trained neural network having been trained using a dataset including images that have been rejected by the remote post-validation platform and images representing acceptable and unacceptable types of identification cards;
(Sept 2024 Claim Amendment at claims 1, 10 and 18 emphasis added). The Examiner finds that the recitation to “the trained neural network having been trained using a dataset including … images representing acceptable and unacceptable types of identification card” is not sufficiently described in the ‘343 Patent. First, the Examiner finds insufficient support for the claim requirements in the ‘343 Patent as indicated by Applicant. (See Sept 2024 Applicant Response at 14, 16, 18). Second, to support the Examiner’s position, the Examiner finds that the ‘343 Patent explicitly discloses the trained neural network being trained with “a history of acceptances and/or rejections of previously submitted ID images.” (‘343 Patent at c.10, ll.52-53). Specifically, the Examiner finds that the “trained neural network” is “trained using a dataset including images that have been accepted by a post-validation platform and images that have been rejected by the post-validation platform.” (Id. at Abstract; c.2, ll.20-23; c.3, ll.48-51; c.4, ll.23-27; c.13, ll.35-38). To the contrary, the Examiner finds that the ‘343 Patent only discloses an “unacceptable or wrong type of ID card” being provided to the system for the determination process, not at the training process. (Id. at c.6, ll.7-9; c.12, ll.7-15). While one of ordinary skill in the art would recognize that rejected images are based upon parameters and/or characteristics of the rejected images, the Examiner finds that the ‘343 Patent provides insufficient disclosure to the parameter and/or characteristic being an “unacceptable type of identification card,” only to features of the rejected image being insufficient. Moreover, while the ‘343 Patent does disclose an embodiment in which the neural network is pre-trained for particular subsets of ID card images for each state in the United States and utilizes the trained neural network to provide a match for a particular state type of ID (id. at c.11, ll.44-54), again, and light of the above disclosure from the ‘343 Patent, the Examiner finds that these images utilized to train the neural network are deemed acceptable and rejectable based upon a process that previously occurred at and by a post-validation platform and not explicitly disclosed as being acceptable or unacceptable types of identification images. (Emphasis added). In addition, the Examiner finds that the acceptable images utilized to train the neural network are specifically from a post-validation platform and not broadly without that post-validation platform process being utilized to determine the rejected/accepted images. Hence, even though the ‘343 Patent provides disclosure to the trained neural network having been trained using a dataset including images that have been rejected and accepted by a post-validation platform, the Examiner finds that neither the ‘343 Patent, nor its prosecution history, establish that the ‘343 Patent was concerned with trained images being acceptable/unacceptable based upon the type of identification card, nor the trained images not coming from a post-validation platform.
Thus, as such, the Examiner concludes that there is insufficient indication in the specification that Applicant had possession of a system, method or computer readable media (“CRM”) for pre-validation of identification images comprising “the trained neural network having been trained using a dataset including images that have been rejected by the remote post-validation platform and images representing acceptable and unacceptable types of identification cards,” as recited.
Claims 2-9, 11-17, 19 and 20 are similarly rejected based on their dependency from independent claims 1, 10 and 18.
35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph
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 10-17 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 pre-AIA the applicant regards as the invention.
With respect to claim 10, the claim element “program code”(i.e., FP2 and FP3) is a limitation that invoke 35 U.S.C. 112, sixth paragraph. However, the written description fails to clearly link or associate the disclosed structures, materials, or acts to the claimed functions such that one of ordinary skill in the art would recognize what structures, materials, or acts perform the claimed function.
With respect to the “program code,” the phrases are indefinite because–to one of ordinary skill in this art–the metes and bounds of the phrases cannot be reasonably determined. The Examiner finds that the ‘343 Patent’s written description fails to disclose the corresponding structures, materials, or acts for the claimed function. (See § IX.B.(2)-(3)17 for explanation supra).
Therefore, the claims are indefinite and are rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112, sixth paragraph; or
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the claimed function without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claims 11-17 are similarly rejected based on their dependency from independent claim 10.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
With respect to the limitations of claim 1, the Examiner finds that claim 1 recites the limitation,
responsive to the determining that the at least a first image is of an acceptable type of identification card and unusable
(Sept 2024 Claim Amendment at claim 1; emphasis added). The Examiner finds that the claim previously recites “determining, … , whether at least a first image of the one or more captured images is unusable by a remote post-validation platform.” The Examiner finds it unclear and indefinite to how the “responsive” step is based on a previous determination step to whether the image is of an acceptable type of identification card when there is no antecedent basis for such a determination. Further clarification is required to either provide proper antecedent basis or further differentiate the limitations.
Claims 2-9 are similarly rejected based on their dependency from independent claim 1.
With respect to the limitations of claim 10, the Examiner finds that claim 10 recites the limitation,
determine, … , whether at least a first image of the one or more images is of an acceptable type of identification card and unusable by a remote post-validation platform
(Sept 2024 Claim Amendment at claim 10; emphasis added). The Examiner finds that the claim further recites “determining whether the one or more images are unusable by a remote post-validation platform.” The Examiner finds it unclear and indefinite to: (1) how many different determination steps are required to satisfy the claim requirements; and/or (2) to why the “second” determination step does not have the requirement of determining whether the one or more images are of an acceptable type of identification card, like the previous determination step. Further clarification is required to either provide proper antecedent basis or further differentiate the limitations.
Claims 11-17 are similarly rejected based on their dependency from independent claim 10.
With respect to the limitations of claim 16, the Examiner finds that claim 16 recites the limitation,
wherein the one or more processors are further configured to, when executing the program code, cause the system to determine whether at least a second image of the one or more captured images is usable by the remote post-validation platform.
(Sept 2024 Claim Amendment at claim 10; emphasis added). The Examiner finds that the preceding claim recites “determin[ing] … , whether at least a first image of the one or more images is of an acceptable type of identification card and unusable by a remote post-validation platform.” The Examiner finds it unclear and indefinite to: (1) how many different determination steps are required to satisfy the claim requirements; (2) to why the “second” determination step does not have the requirement of determining whether the one or more images are of an acceptable type of identification card, like the previous determination step; and/or (3) what is the difference between determining usability” versus” unusability.” Further clarification is required to either provide proper antecedent basis or further differentiate the limitations.
With respect to the limitations of claim 18, the Examiner finds that claim 18 recites the limitation,
determining, … , whether at least a first image of the one or more captured images is unusable by a remote post-validation platform
(Sept 2024 Claim Amendment at claim 18; emphasis added). The Examiner finds that the claim further recites “determining whether the one or more images are of an acceptable type of identification card and unusable by a remote post-validation platform.” The Examiner finds it unclear and indefinite to: (1) how many different determination steps are required to satisfy the claim requirements; and/or (2) to why the “second” determination step does have the requirement of determining whether the one or more images are of an acceptable type of identification card, whereas the previous determination step does not. Further clarification is required to either provide proper antecedent basis or further differentiate the limitations.
Claims 11-17 are similarly rejected based on their dependency from independent claim 10.
Claim Rejections – 35 U.S.C. § 251
Oath/Declaration
Claims 1-20 are rejected as being based upon a defective reissue declaration under 35 U.S.C. 251 as set forth above. See 37 CFR 1.175.
The nature of the defect(s) in the declaration is set forth in the discussion above in this Office action. (See § VII supra).
New Matter
Claims 1-20 are rejected under 35 U.S.C. 251 as being based upon new matter added to the patent for which reissue is sought. The added material which is not supported by the prior patent is as follows:
The Examiner finds that there is insufficient indication in the specification that Applicant had possession of at least a system, method or computer readable media (“CRM”) for pre-validation of identification images including a trained neural network having been trained using a dataset including images representing acceptable and unacceptable types of identification, in addition to images that have been rejected by the remote post-validation platform cards. (See § X.A.(1) supra).
Original Patent Requirement
Claims 1, 2, 8, 9 and 16-22 are rejected under 35 U.S.C. 251 as being in violation of the original patent requirement.
Section 251 requires that reissue is for “the invention disclosed in the original patent.” In order to satisfy the original patent requirement, “[i]t must appear from the face of the instrument that what is covered by the reissue was intended to have been covered and secured by the original.” U.S. Indus. Chems., Inc. v. Carbide & Carbon Chems. Corp., 315 U.S. 668, 676 (1942). Furthermore, “it is not enough that an invention might have been claimed in the original patent because it was suggested or indicated in the specification.” Id. In other words, the original patent “must clearly and unequivocally disclose the newly claimed invention as a separate invention.” Antares Pharma, Inc. v. Medac Pharma Inc., 771 F.3d 1354, 1362 (Fed. Cir. 2014).
In the instant case, it does not appear from the face of the original patent that Applicant intended to a system, method or CRM for pre-validation of identification images in which the determination step performs a determination of both whether the first image is of an acceptable type of identification card and unusable. 18
The Technical Problem makes clear that the invention is drawn to a system, method or CRM for pre-validation of identification images in which images are captured and analyzed, utilizing a trained neural network, to determine whether an image captured is unusable/usable is a remote post-validation process. (‘343 Patent at c.2, ll.16-20). The Examiner finds the problem is solved by a particular system, method and CRM comprising:
[a] mobile computing device captures image(s) of an ID card. In response, a pre-validation system in communication with the mobile computing device analyzes one or more quality features of the image(s), which includes determining, utilizing a trained neural network (trained using a dataset including images that have been accepted by a post-validation platform and images that have been rejected by the post-validation platform) and based on the one or more quality features, whether at least a first image of the captured image(s) is usable by a remote post-validation process.
(Id. at Abstract; c.2, ll.19-28; emphasis added)19. First, it is readily apparent that the entire point of the ‘343 Patent was to solve a problem of providing a system, method and CRM capable of prevalidating an image captured for a post-validation process in order to determine whether the captured image is unusable/usable. (Id. at c.2, ll.16-20). The claims as filed and during prosecution of the were always drawn to a system, method and CRM comprising performing multiple steps, including ““determining, … , whether at least a first image of the one or more captured images is unusable by a remote post-validation platform. (Id. at Abstract; c.1, ll.42-47; c.2, ll.16-20; c.12, ll.7-20; c.12, l.35 – c.13,l.23; emphasis added on “unusable”). Moreover, the claims in the ‘343 Patent are with respect to the inventive feature of what must occur if the determination is based upon the first image being “unusable” as opposed to “usable.” This is evidenced by comparing the claims filed in the ‘388 Application of the ‘343 Patent with the claims filed and patented in both ‘609 and ‘283 Parent Patents. (Compare ‘388 Application Claims with both the ‘609 and ‘283 Parent Patent Claims).
This situation is also somewhat analogous to the recent Federal Circuit decision in Forum US, Inc. v. Flow Valve, LLC, 926 F.3d 1346 (Fed. Cir. 2019). In Forum US, the original patent claims were drawn to a workpiece having a body member and a plurality of arbors (arbors circled):
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267
600
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Forum US, 926 F.3d at 1348-49. In reissue, patentee broadened the claims to remove the requirement as to arbors. Id. at 1349. The Federal Circuit determined that the new claims did not comply with the original patent requirement of section 251 because the face of the patent did not disclose any arbor-less embodiment, and the abstract, summary of invention, and all disclosed embodiments including arbors. Id. at 1352. The Court concluded that the specification did not clearly and unequivocally disclose an embodiment without arbors, thus the original patent requirement was violated by broadening the claims to no longer require arbors. Id. Similarly, the patent here does not clearly and unequivocally disclose any embodiment that includes
a system, method and CRM comprising performing steps, including: determining whether the first image is both of an acceptable type of identification card and unusable. Thus, to broaden the claims to permit such an embodiment runs afoul of the original patent requirement.
Claims 2-9, 11-17, 19 and 20 are similarly rejected based on their dependency from independent claims 1, 10 and 18, respectively.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Ladron et al. (International Publication No. WO 2017/207064 A1) (“Ladron”) in view of Rothberg et al. (U.S. Publication No. 2017/0360412) (“Rothberg”) and Das et al. (U.S. Patent No. 10,962,939) (“Das”).
With respect to the limitations of claim 1, and
[1] [a] computer-implemented method for pre-validation of identification images, the method comprising:
In this regard, the Examiner finds that Ladron discloses a method for authenticating a document from, obtained images of the document. (Ladron at Abstract. p.1, ll.5-7; p.4, l.26 – p.2, l.12; p.3, l.26 – p.4, l.23; p.5, ll.16 -20; p.6, l.17 – p.7, l.27; p.8, ll.1-27; p.12, l.13 – p.13, l.21; p.14, l.19 – p.15, l.8; ; p.15, ll.25-28; p.16, ll.3-5, 16-20; p.18, l.27 – p.19, l.16; p.25, ll.11-34; see Figure 1, 4). In the method disclosed by Ladron, the Examiner finds that Ladron discloses a pre-validation processing including the capturing of images of ID documents, in real-time, and utilizing an image processing method based upon artificial intelligence indicating whether a complete view of the ID is depicted. (Id. at p.12, l.13 – p.13, l.21).
capturing, by a digital camera of a mobile computing device, one or more images of an identification card;
In this regard, the Examiner finds that Ladron discloses a mobile computing device with a camera (i.e., mobile phone, camera, tablet, smart watch, etc.) capturing images of a document (i.e., ID card, driving license, credit, debit, business, medical insurance card, passport, tickets, etc.). (Id. at p.8, ll.1-27). The Examiner finds that Ladron discloses the mobile computing device having a pre-validation processing, thereon, which includes the capturing of images of ID documents, in real-time, and utilizing an image processing method based upon artificial intelligence indicating whether a complete view of the ID is depicted. (Id. at p.12, l.13 – p.13, l.21).
analyzing, by a processor of the mobile computing device, one or more quality features of the one or more images, wherein the analyzing comprises:
determining, utilizing an application, local to the mobile computing device, having a trained neural network and based on the one or more quality features, whether at least a first image of the one or more captured images is unusable by a remote post-validation platform, the trained neural network having been trained using a dataset including images that have been rejected by the remote post-validation platform and images representing acceptable and unacceptable types of identification cards; and
In this regard, the Examiner finds that the claim requirement of “the trained neural network having been trained using a dataset including images that have been rejected by the remote post-validation platform” is NOT a product-by-process limitation. (See May 2024 Final Office Action at § XII.K.(1).(a)). Thus, the Examiner construes “the trained neural network” as being a neural network having been trained with some images that have been rejected and now also including images representing acceptable and unacceptable types of images.
From this perspective, the Examiner finds that Ladron discloses the mobile computing device comprising a computing system having computer code program residing on a carrier that performs the method of pre-validation. (Ladron at p.6, l.25 – p.7, l.19; p.8, l.1-27). The Examiner finds that Ladron discloses the mobile computing device utilizing an image processing method based upon artificial intelligence indicating whether a complete view of the ID is depicted, which is inherently equivalent to whether an image of the captured ID document is unusable or usable and providing the user information to correct the orientation issue. (Id. at p.12, l.13 – p.13, l.13). The Examiner finds that Ladron further teaches the utilization of an image processing detection method to determine one or more parameters of detected elements of an ID document in which one or more neural networks are trained with set of images using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected. (Id. at p.14, l.19 – p.15, l.8; ; p.15, ll.25-28; p.16, ll.3-5, 16-20; p.18, l.27 – p.19, l.16; p.25, ll.11-34; emphasis added).
While Ladron discloses all the limitations as set forth above including utilizing an image processing method based upon artificial intelligence to indicating whether a complete view of the ID is depicted; and including utilizing an image processing method based upon one or more neural networks which are trained with set of image using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected, Ladron is silent to utilizing the image processing method based upon one or more neural networks which are trained with set of images, including: (1) some images that have been rejected by the remote post-validation platform; and (2) images representing acceptable and unacceptable types of identification cards, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected to specifically indicate whether a complete view of the ID is depicted.
However, utilizing an image processing method based upon one or more neural networks which are trained with set of images, including: (1) some images that have been rejected by the remote post-validation platform; and (2) images representing acceptable and unacceptable types of identification cards, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected to specifically indicate whether a complete view of an object is being depicted is known in the art. The Examiner finds that Rothberg, for example, teaches a portable handheld computing device 104, 1502, 1804 for capturing images in which a pre-validation process is performed on the captured images before further processing is performed on the captured images. (Rothberg at Abstract; ¶¶ 0005, 0009, 0011-0013, 0106, 0127, 0132, 0144-0147, 0149-0151, 0181-0193, 0227-0234, 0254, 0259-270; see Figures 1, 9, 14, 18A-18E). The Examiner finds that Rothberg teaches an automated image analysis which utilizes neural network model analysis to determine whether a particular image is in the correct view (i.e., unusable or usable) and, based upon the neural network model analysis, provide a user direction on how to correct the orientation to capture the correct view (i.e., usable) of the feature. (Id.) The Examiner finds that Rothberg teaches the training image set data being selected having both “standard/good” image data as well as “non-ideal” image data by a specialist on remote system. (Id. at ¶¶ 0261-0262).
Moreover, the Examiner finds that Das teaches an automated image analysis which utilizes a neural network model analysis to determine whether a particular image is in a restricted or unrestricted image category. (Das at Abstract; c.2, ll.21-25; c.5, ll.22-29, 38-60; c.7, ll.12-56; c.8, l.13 – c.10, l.43; c.27, ll.6-27, 36-38, 45-46; c.29, l.1 – c.30, l.18; see Figures 2, 3). The Examiner finds that Das further teaches the neural network model including a convolutional neural network (“CNN”) which may include training images that belong to certain categories and images that do not belong to certain categories. (Id. at c.9, ll.15-25; emphasis on images belonging/not belong to certain categories being equivalent to images being acceptable/unacceptable types of images).
The Examiner finds that it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate an image processing method based upon one or more neural networks which are trained with set of images, including (1) some images that have been rejected and accepted by the remote post-validation platform, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected to specifically indicate whether a complete view of an object is being depicted as described in Rothberg to the computer-implemented method of pre-validation of ID documents images of Ladron.
A person of ordinary skill in the art would be motivated to incorporate an image processing method based upon one or more neural networks which are trained with set of images, including (1) some images that have been rejected and accepted by the remote post-validation platform, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected to specifically indicate whether a complete view of an object is being depicted, since it provides a mechanism to: utilize state-of-the-art image processing technology; allow users, who are not trained to identify relevant information of an object in a captured image, to provide correct and proper images for further and future processing (id. at ¶¶ 0141-0142, 0145). In other words, such a modification would provide an apparatus and method for pre-validation of an ID document image which can easily and dynamically determine whether a target image is acceptable, thereby increasing the overall efficiency of the apparatus and method. (Id.)
This combination of references also satisfies at least rationale C identified by the Supreme Court in KSR International Co. v. Teleflex Inc., 550 U.S. 398, 82 USPQ2d 1385, 1395-97 (2007): "Use of known technique to improve similar devices (methods, or products) in the same way." (See MPEP 2143.) The elements of the Graham factual inquiry for supporting a finding of obviousness based on this rationale are provided below:
(1) A finding that the prior art (Ladron) contained a “base” device (a mobile device for pre-validation of ID identification images) upon which the claimed invention can be seen as an “improvement” for including an image processing method based upon one or more neural networks which are trained with set of images, including some images that have been rejected and accepted by the remote post-validation platform, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected to specifically indicate whether a complete view of an object is being depicted in order to provide an apparatus and method for pre-validation of an ID document image which can easily and dynamically determine whether a target image is acceptable.
(2) A finding that the prior art (Rothberg) contained a "comparable" device (a mobile device for pre-validation of object images) that has been improved in the same way as the claimed invention, i.e. the Rothberg mobile device for pre-validation of object images utilizes an image processing method based upon one or more neural networks which are trained with set of images, including (1) some images that have been rejected and accepted by the remote post-validation platform, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected to specifically indicate whether a complete view of an object is being depicted in order to: utilize state-of-the-art image processing technology; allow users, who are not trained to identify relevant information of an object in a captured image, and provide correct and proper images for further and future processing.
(3) A finding that one of ordinary skill in the art could have applied the known “improvement” technique in the same way to the “base” device (the Ladron mobile device for pre-validation of ID identification images) and the results would have been predictable to one of ordinary skill in the art. Here, because Ladron indicates that the mobile device can be utilized for pre-validation of ID document images utilizing image processing methods based upon artificial intelligence and Rothberg teaches a manner for improving this, the results would be predictable. In other words, the Rothberg successful implementation or providing an image processing method based upon one or more neural networks which are trained with set of images, including (1) some images that have been rejected and accepted by the remote post-validation platform, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected to specifically indicate whether a complete view of an object is being depicted proves that the implementation is both successful and entirely predictable. In Ladron, the mobile device for pre-validation of ID document images modified according to Rothberg would be capable of incorporating an image processing method based upon one or more neural networks which are trained with set of images, including (1) some images that have been rejected and accepted by the remote post-validation platform, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected to specifically indicate whether a complete view of an object is being depicted to carry out the functions of: utilizing state-of-the-art image processing technology; allowing users, who are not trained to identify relevant information of an object in a captured image, and providing correct and proper images for further and future processing.
In that regard, the Examiner asserts the use of known technique to improve similar devices in the same way is obvious to one of ordinary skill in the art. That is, the manner of enhancing a particular device (providing utilizing state-of-the-art image processing technology; allowing users, who are not trained to identify relevant information of an object in a captured image, and providing correct and proper images for further and future processing including an image processing method based upon one or more neural networks which are trained with set of images, including (1) some images that have been rejected and accepted by the remote post-validation platform, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected to specifically indicate whether a complete view of an object is being depicted) was made part of the ordinary capabilities of one skilled in the art based upon the teaching of such improvement in Rothberg. Accordingly, one of ordinary skill in the art would have been capable of applying this known “improvement” technique in the same manner to the prior art mobile device for pre-validation of object images of Ladron and the results would have been predictable to one of ordinary skill in the art, namely, one skilled in the art would have readily recognized that providing utilizing state-of-the-art image processing technology; allowing users, who are not trained to identify relevant information of an object in a captured image, and providing correct and proper images for further and future processing including an image processing method based upon one or more neural networks which are trained with set of images, including (1) some images that have been rejected and accepted by the remote post-validation platform, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected to specifically indicate whether a complete view of an object is being depicted in Ladron would positively provide a means to carry out, in addition to utilizing image processing methods based upon artificial intelligence, a new functions of: utilizing state-of-the-art image processing technology; allowing users, who are not trained to identify relevant information of an object in a captured image, and providing correct and proper images for further and future processing that provides for easier and more dynamic image capturing for consistency purposes, since such functionality is taught to be highly desirable by Rothberg as set forth above.
Thus, the rationale to support a conclusion that the claim would have been obvious is that a method of enhancing a particular class of devices (methods, or products) has been made part of the ordinary capabilities of one skilled in the art based upon the teaching of such improvement in other situations. One of ordinary skill in the art would have been capable of applying this known method of enhancement to a “base” device (method, or product) in the prior art and the results would have been predictable to one of ordinary skill in the art. The Supreme Court in KSR noted that if the actual application of the technique would have been beyond the skill of one of ordinary skill in the art, then using the technique would not have been obvious. KSR, 550 U.S. at 398, 82 USPQ2d at 1396. If any of these findings cannot be made, then this rationale cannot be used to support a conclusion that the claim would have been obvious to one of ordinary skill in the art.
Similarly, the Examiner finds that it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate an image processing method based upon one or more neural networks which are trained with set of images, including (2) images representing acceptable and unacceptable types of images20, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected as described in Das to the computer-implemented method of pre-validation of ID documents images of Ladron and Rothberg.
A person of ordinary skill in the art would be motivated to incorporate an image processing method based upon one or more neural networks which are trained with set of images, including (2) images representing acceptable and unacceptable types of images21, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected, since it provides a mechanism to utilize the “hard negative mining” technique to further distinguish between whether an image belongs to a certain category or not. (Das at c.9, ll.15-25). In other words, such a modification would provide an apparatus and method for pre-validation of an ID document image which can easily and dynamically determine whether a target image is acceptable, thereby increasing the overall efficiency of the apparatus and method. (Id.)
Again, and similarly, this combination of references also satisfies at least rationale C identified by the Supreme Court in KSR International Co. v. Teleflex Inc., 550 U.S. 398, 82 USPQ2d 1385, 1395-97 (2007): "Use of known technique to improve similar devices (methods, or products) in the same way." (See MPEP 2143.) The elements of the Graham factual inquiry for supporting a finding of obviousness based on this rationale are provided below:
(1) A finding that the prior art (Ladron and Rothberg) contained a “base” device (a mobile device for pre-validation of ID identification images) upon which the claimed invention can be seen as an “improvement” for including an image processing method based upon one or more neural networks which are trained with set of images, including (2) images representing acceptable and unacceptable types of images, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected in order to provide an apparatus and method for pre-validation of an ID document image which can easily and dynamically determine whether a target image is acceptable.
(2) A finding that the prior art (Das) contained a "comparable" device (a mobile device for pre-validation of object images) that has been improved in the same way as the claimed invention, i.e. the Das mobile device for pre-validation of object images utilizes an image processing method based upon one or more neural networks which are trained with set of images, including (2) images representing acceptable and unacceptable types of images, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected to specifically indicate whether a complete view of an object is being depicted in order to utilize the “hard negative mining” technique to further distinguish between whether an image belongs to a certain category or not.
(3) A finding that one of ordinary skill in the art could have applied the known “improvement” technique in the same way to the “base” device (the Ladron and Rothberg mobile device for pre-validation of ID identification images) and the results would have been predictable to one of ordinary skill in the art. Here, because Ladron indicates that the mobile device can be utilized for pre-validation of ID document images utilizing image processing methods based upon artificial intelligence and Das teaches a manner for improving this, the results would be predictable. In other words, the Das successful implementation or providing an image processing method based upon one or more neural networks which are trained with set of images, including (2) images representing acceptable and unacceptable types of images, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected proves that the implementation is both successful and entirely predictable. In Ladron and Rothberg, the mobile device for pre-validation of ID document images modified according to Das would be capable of incorporating an image processing method based upon one or more neural networks which are trained with set of images, including (2) images representing acceptable and unacceptable types of images, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected to carry out the function of utilizing the “hard negative mining” technique to further distinguish between whether an image belongs to a certain category or not that provides for easier and more dynamic image capturing for consistency purposes, as evidenced by the success in the Das mobile device.
In that regard, the Examiner asserts the use of known technique to improve similar devices in the same way is obvious to one of ordinary skill in the art. That is, the manner of enhancing a particular device (providing utilizing the “hard negative mining” technique to further distinguish between whether an image belongs to a certain category or not including an image processing method based upon one or more neural networks which are trained with set of images, including (2) images representing acceptable and unacceptable types of images, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected) was made part of the ordinary capabilities of one skilled in the art based upon the teaching of such improvement in Das. Accordingly, one of ordinary skill in the art would have been capable of applying this known “improvement” technique in the same manner to the prior art mobile device for pre-validation of object images of Ladron and the results would have been predictable to one of ordinary skill in the art, namely, one skilled in the art would have readily recognized that providing utilizing the “hard negative mining” technique to further distinguish between whether an image belongs to a certain category or not including an image processing method based upon one or more neural networks which are trained with set of images, including some images that have been rejected and accepted by the remote post-validation platform, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected to specifically indicate whether a complete view of an object is being depicted in Ladron would positively provide a means to carry out, in addition to utilizing image processing methods based upon artificial intelligence, a new functions of: utilizing state-of-the-art image processing technology; allowing users, who are not trained to identify relevant information of an object in a captured image, to provide correct and proper images for further and future processing; and utilizing the “hard negative mining” technique to further distinguish between whether an image belongs to a certain category or not that provides for easier and more dynamic image capturing for consistency purposes, since such functionality is taught to be highly desirable by Rothberg, as evidenced by Das, as set forth above.
Thus, the rationale to support a conclusion that the claim would have been obvious is that a method of enhancing a particular class of devices (methods, or products) has been made part of the ordinary capabilities of one skilled in the art based upon the teaching of such improvement in other situations. One of ordinary skill in the art would have been capable of applying this known method of enhancement to a “base” device (method, or product) in the prior art and the results would have been predictable to one of ordinary skill in the art. The Supreme Court in KSR noted that if the actual application of the technique would have been beyond the skill of one of ordinary skill in the art, then using the technique would not have been obvious. KSR, 550 U.S. at 398, 82 USPQ2d at 1396. If any of these findings cannot be made, then this rationale cannot be used to support a conclusion that the claim would have been obvious to one of ordinary skill in the art.
responsive to the determining that the at least a first image is of an acceptable type of identification card and unusable, outputting, by the mobile computing device, feedback instructions to capture one or more new images for determining whether the one or more new images are unusable by the remote post-validation platform.
As set forth above, the Examiner finds that the “determining” step including both the determination of an acceptable type of identification card and unusability as indefinite. (See § X.B. supra). Thus, for examination purposes, the “determining” step is simply the determination the identification card is confirmed and deemed unusable.
From this perspective, the Examiner finds that Ladron discloses the mobile computing device comprising a computing system having computer code program residing on a carrier that performs the method of pre-validation. (Ladron at p.6, l.25 – p.7, l.19; p.8, l.1-27). The Examiner finds that Ladron discloses the mobile computing device having the pre-validation processing, thereon, which includes the capturing of images of objects, in real-time, and utilizing an image processing method based upon artificial intelligence indicating whether a complete view of the ID is depicted. (Id. at p.12, l.13 – p.13, l.21). The Examiner finds that Ladron discloses the mobile computing device utilizing an image processing method based upon artificial intelligence indicating whether a complete view of the ID is depicted, which is inherently equivalent to whether an image of the captured ID document is unusable or usable and providing the user information to correct the orientation issue. (Id. at p.12, l.13 – p.13, l.13; p.13, ll.23-26). The Examiner finds that Ladron discloses the mobile device providing notification to the user to change the orientation of the device via an image and/or sound and/or vibration means. (Id. at p.13, 11.15-21).
The Examiner finds that Rothberg further teaches the computing device 104, 1502 outputting image capture framing feedback information to a user via a display screen 106, 1508 of the computing device 104, 1502. (Rothberg at ¶¶ 0005, 0144-0151, 0154, 0185, 0027-0234, 0254; see Figures 1, 9, 14, 18A-18E).
As set forth above, the Examiner finds that Rothberg teaches and/or renders obvious utilizing an image processing method based upon one or more neural networks which are trained with set of images, including some images that have been rejected and accepted by the remote post-validation platform, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected to specifically indicate whether a complete view of an object is being depicted. (See § XII.A.(1).b, supra).
In addition, the Examiner finds that Rothberg additionally teaches the computing device 104, 1502 determining that the image contains the target view and provides an output that is either a confirmation of proper positioning or instructions to move the device in a particular direction. (Rothberg at ¶ 0230). The Examiner finds that the latter would include that the target view is confirmed as being within the image and unusable since instructions are required to attain a proper image. (Id.)
Thus, and in addition, the Examiner finds that Rothberg and Das teaches and/or renders obvious the determining that the at least a first image is of an acceptable type of identification card and unusable. (See § XII.A.(1).b, supra).
With respect to the limitations of claim 2, Ladron, Rothberg and Das teaches and/or renders obvious
[2] wherein the application is a native application configured to execute on the mobile computing device, the native application comprising a pre-trained machine vision model configured to process the one or more captured images
In this regard, Ladron teaches the application being configured to run on the mobile computing device. (Ladron at p.8, ll.1-27p.12, l.16 – p.13, l.21). Moreover, as set forth above, the Examiner finds that Rothberg teaches and/or renders obvious utilizing an image processing method based upon one or more neural networks which are trained with set of images, including some images that have been rejected by the remote post-validation platform, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected to specifically indicate whether a complete view of an object is being depicted. (See § XII.A.(1).b, supra).
The Examiner finds that Rothberg further teaches the neural network application including a pre-trained vision model. (Rothberg at ¶¶ 0146; 0259-0269; see Figure 14).
Thus, the Examiner finds that Rothberg teaches and/or renders obvious the application being a native application configured to execute on the mobile computing device, the native application comprising a pre-trained machine vision model configured to process the one or more captured images. (See § XII.A.(1).b, supra).
With respect to the limitations of claim 3, Ladron, Rothberg and Das teaches and/or renders obvious
[3] wherein the pre-trained machine vision model is configured to evaluate one or more features of the one or more captured images based on pre-trained features of the pre-trained machine vision model
In this regard, Ladron teaches the utilization of an image processing detection method to determine one or more parameters of detected elements of an ID document in which one or more neural networks are trained with set of images using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected. (Ladron at p.14, l.19 – p.15, l.8; p.15, ll.25-28; p.16, ll.3-5, 16-20; p.18, l.27 – p.19, l.16; p.25, ll.11-34; emphasis added).
The Examiner finds that it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the pre-trained machine vision model being configured to evaluate one or more features of the one or more captured images based on pre-trained features of the pre-trained machine vision model as described in Ladron to the computer-implemented method of pre-validation of object images of Ladron, Rothberg and Das.
A person of ordinary skill in the art would be motivated to incorporate the pre-trained machine vision model being configured to evaluate one or more features of the one or more captured images based on pre-trained features of the pre-trained machine vision model, since it provides a mechanism to provide an authenticity indicator of the object/document in question. (Ladron at Abstract; p.3, ll.9-24; p.4, ll.11-14). In other words, such a modification would provide an apparatus and method for pre-validation of object image which is based upon a safer and more robust and secure process, thereby increasing the overall efficiency of the apparatus and method. (Id.)
With respect to the limitations of claim 4, Ladron, Rothberg and Das teaches and/or renders obvious
[4] wherein the pre-trained machine vision model occupies less than 50 Mbytes of memory on the mobile computing device.
As set forth above, the Examiner finds that Rothberg teaches and/or renders obvious utilizing an image processing method based upon one or more neural networks which are trained with set of images, including some images that have been rejected by the remote post-validation platform, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected to specifically indicate whether a complete view of an object is being depicted. (See § XII.A.(1).b, supra). The Examiner finds that Rothberg further teaches the application including a pre-trained vision model. (Rothberg at ¶¶ 0146; 0259-0269; see Figure 14).
From this perspective, Ladron teaches the application being configured to run on the mobile computing device with a camera (i.e., mobile phone, camera, tablet, smart watch, etc.). (Ladron at p.8, ll.1-27; p.12, l.16 – p.13, l.21).
The Examiner finds that Rothberg further teaches the neural network application including a pre-trained vision model that is performed on the computing device having memory thereon. (Rothberg at ¶¶ 0146-0147; 0259-0269; emphasis at ¶ 0246, the trained neural network being deployed to the computing device; see Figures 1, 14, 18A-18E).
Ladron, Rothberg and Das discloses all the limitations, as set forth above, except for specifically calling for the pre-trained machine vision model to occupy less than 50 Mbytes of memory on the mobile computing device.
The Examiner finds it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the pre-trained machine vision model to occupy less than 50 Mbytes of memory on the mobile computing device, since it has been held that where general conditions of a claim are disclosed in the prior art, discovering the optimum or workable ranges involves only routine skill in the art. In re Aller, 220 F.2d 454, 456, 105 USPQ 233, 235 (CCPA 1955). The Examiner finds that the ‘343 Patent has not disclosed any criticality for the claim limitation. (See the ‘343 Patent at c.17, ll.40-45).
With respect to the limitations of claim 5, Ladron, Rothberg and Das teaches and/or renders obvious
[5] further comprising: determining, utilizing the application, whether at least the first image of the one or more captured images is usable by the remote post-validation platform
In this regard, as set forth above, the Examiner finds that Ladron discloses the mobile computing device utilizing an image processing method based upon artificial intelligence indicating whether a complete view of the ID is depicted, which is inherently equivalent to whether an image of the captured ID document is unusable or usable and providing the user information to correct the orientation issue. (Ladron at p.12, l.13 – p.13, l.13).
Moreover, as set forth above, the Examiner finds that Rothberg teaches and/or renders obvious utilizing an image processing method based upon one or more neural networks which are trained with set of images, including some images that have been rejected by the remote post-validation platform, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected to specifically indicate whether a complete view of an object is being depicted. (See § XII.A.(1).b, supra).
Thus, the Examiner finds that Rothberg teaches and/or renders obvious determining, utilizing the application, whether at least the first image of the one or more captured images is usable by the remote post-validation platform. (Id.)
With respect to the limitations of claim 6, Ladron, Rothberg and Das teaches and/or renders obvious
[6] wherein the identification card comprises: a government-issued identification card, a credit card, a debit card, a loyalty card, an access card, or a stored value card.
In this regard, the Examiner finds that Ladron discloses a mobile computing device with a camera (i.e., mobile phone, camera, tablet, smart watch, etc.) capturing images of a document (i.e., ID card, driving license, credit, debit, business, medical insurance card, passport, tickets, etc.). (Ladron at p.8, ll.1-27).
With respect to the limitations of claim 7, Ladron, Rothberg and Das teaches and/or renders obvious
[7] further comprising outputting, to a display of the mobile computing device, image capture framing feedback information related to the one or more analyzed quality features.
From this perspective, Ladron teaches the application being configured to run on the mobile computing device with a camera (i.e., mobile phone, camera, tablet, smart watch, etc.). (Ladron at p.8, ll.1-27; p.12, l.16 – p.13, l.21).
Moreover, as set forth above, the Examiner finds that Rothberg teaches and/or renders obvious utilizing an image processing method based upon one or more neural networks which are trained with set of images, including some images that have been rejected by the remote post-validation platform, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected to specifically indicate whether a complete view of an object is being depicted. (See § XII.A.(1).b, supra).
The Examiner finds that Rothberg further teaches the computing device 104, 1502 outputting image capture framing feedback information to a user via a display screen 106, 1508 of the computing device 104, 1502. (Rothberg at ¶¶ 0005, 0144-0151, 0154, 0185, 0027-0234, 0254; see Figures 1, 9, 14, 18A-18E).
The Examiner finds that it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate outputting, to a display of the mobile computing device, image capture framing feedback information related to the one or more analyzed quality features as described in Rothberg to the computer-implemented method of pre-validation of object images of Ladron, Rothberg and Das.
A person of ordinary skill in the art would be motivated to incorporate Rothberg, since it provides a mechanism to: utilize state-of-the-art image processing technology; and allow users, who are not trained to identify relevant information of an object in a captured image, to provide correct and proper images for further and future processing. (Id. at ¶¶ 0141-0142, 0145). In other words, such a modification would provide an apparatus and method for pre-validation of object image which can easily and dynamically determine whether a target image is acceptable, thereby increasing the overall efficiency of the apparatus and method. (Id.)
Thus, the Examiner finds that Rothberg teaches and/or renders obvious the outputting, to a display of the mobile computing device, image capture framing feedback information related to the one or more analyzed quality features. (See § XII.A.(1).b, supra).
With respect to the limitations of claim 8, Ladron, Rothberg and Das teaches and/or renders obvious
[8] wherein the one or more quality features comprise: fill ratio, blur, framing, rotation, keystone, sharpness, brightness, contrast, color, presence of an image of a human face, or whether the one or more images were captured live by the mobile computing device
In this regard, Ladron teaches the utilization of an image processing detection method to determine one or more parameters of detected elements of an ID document in which one or more neural networks are trained with set of images using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected. (Ladron at p.14, l.19 – p.15, l.8; p.15, ll.25-28; p.16, ll.3-5, 16-20; p.18, l.27 – p.19, l.16; p.25, ll.11-34; emphasis added). The Examiner finds that Ladron teaches the parameters including, as an example, alignment, spacing, width, height, colo[u]r, texture… etc.) (Id. at p.14, l.19 – p.15, l.8; p.15, ll.25-28; p.16, ll.3-5, 16-20).
The Examiner finds that it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the one or more quality features comprising: fill ratio, blur, framing, rotation, keystone, sharpness, brightness, contrast, color, presence of an image of a human face, or whether the one or more images were captured live by the mobile computing device as described in Ladron to the computer-implemented method of pre-validation of object images of Ladron, Rothberg and Das.
A person of ordinary skill in the art would be motivated to incorporate the one or more quality features comprising: fill ratio, blur, framing, rotation, keystone, sharpness, brightness, contrast, color, presence of an image of a human face, or whether the one or more images were captured live by the mobile computing device, since it provides a mechanism to provide an authenticity indicator of the object/document in question. (Ladron at Abstract; p.3, ll.9-24; p.4, ll.11-14). In other words, such a modification would provide an apparatus and method for pre-validation of object image which is based upon a safer and more robust and secure process, thereby increasing the overall efficiency of the apparatus and method. (Id.)
With respect to the limitations of claim 9, Ladron, Rothberg and Das teaches and/or renders obvious
[9] wherein the determining, utilizing the application, whether at least the first image of the one or more captured images is unusable by the remote post-validation platform further comprises analyzing the first image for a presence of one or more indicia
In this regard, as set forth above, the Examiner finds that Ladron discloses the mobile computing device utilizing an image processing method based upon artificial intelligence indicating whether a complete view of the ID is depicted, which is inherently equivalent to whether an image of the captured ID document is unusable or usable and providing the user information to correct the orientation issue. (Id. at p.12, l.13 – p.13, l.13).
Moreover, as set forth above, the Examiner finds that Rothberg teaches and/or renders obvious utilizing an image processing method based upon one or more neural networks which are trained with set of images, including some images that have been rejected by the remote post-validation platform, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected to specifically indicate whether a complete view of an object is being depicted. (See § XII.A.(1).b, supra).
The Examiner finds that Ladron further teaches a neural network application for ID document verification including detecting parameters of elements in which the elements of the ID document include indicia. (Ladron at p.8, l.29 – p.12, l.11),
The Examiner finds that it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the determining whether at least a first image of the one or more captured images is unusable by the remote post-validation platform further comprising analyzing the first image for a presence of one or more indicia as described in Ladron to the computer-implemented method of pre-validation of object images of Ladron, Rothberg and Das.
A person of ordinary skill in the art would be motivated to incorporate the determining whether at least a first image of the one or more captured images is unusable by the remote post-validation platform further comprising analyzing the first image for a presence of one or more indicia , since it provides a mechanism to provide an authenticity indicator of the object/document in question. (Ladron at Abstract; p.3, ll.9-24; p.4, ll.11-14). In other words, such a modification would provide an apparatus and method for pre-validation of object image which is based upon a safer and more robust and secure process, thereby increasing the overall efficiency of the apparatus and method. (Id.)
With respect to the limitations of claim 10, and
[10] [a] system for pre-validation of identification card images, the system comprising:
In this regard, the Examiner finds that Ladron discloses a system and method for authenticating a document from, obtained images of the document that is performed on a computing system. (Abstract. p.1, ll.5-7; p.4, l.26 – p.2, l.12; p.3, l.26 – p.4, l.23; p.5, ll.16 -20; p.6, l.17 – p.7, l.27; p.8, ll.1-27; p.12, l.13 – p.13, l.21; p.14, l.19 – p.15, l.8; ; p.15, ll.25-28; p.16, ll.3-5, 16-20; p.18, l.27 – p.19, l.16; p.25, ll.11-34; see Figure 1, 4). In the system and disclosed by Ladron, the Examiner finds that Ladron discloses a pre-validation processing including the capturing of images of objects, in real-time, and utilizing an image processing method based upon artificial intelligence indicating whether a complete view of the ID is depicted. (Id. at p.12, l.13 – p.13, l.21).
an image capture component,
one or more processors;
a memory in communication with the one or more processors,
In this regard, the Examiner finds that Ladron disclose the mobile device, on which the pre-validation processing is performed, comprising a camera, instructions stored in memory, and processor to execute the instructions,. (Id. at p.8, ll.15-24; p.12, ll.13-37; claims 35-38).
wherein the memory comprises:
a trained neural network, comprising a trained model, and configured to analyze one or more quality features associated with one or more images, stored in memory, of an identification card captured by the image capture component, the trained neural network having been previously trained using a dataset including images that have been rejected by a remote post-validation platform and images representing acceptable and unacceptable types of identification cards; and
In this regard, the Examiner finds that the claim requirement of “the trained neural network having been previously trained using a dataset including images that have been rejected by the remote post-validation platform” is NOT a product-by-process limitation. (See May 2024 Final Office Action at § XII.K.(1).(a)). Thus, the Examiner construes “the trained neural network” as being a neural network having been trained some images that have been rejected and now also including images representing acceptable and unacceptable types of identification cards.
From this perspective, the Examiner finds that Ladron discloses a mobile computing device with a camera (i.e., mobile phone, camera, tablet, smart watch, etc.) capturing images of a document (i.e., ID card, driving license, credit, debit, business, medical insurance card, passport, tickets, etc.). (Id. at p.8, ll.1-27). The Examiner finds that Ladron discloses the mobile computing device having a pre-validation processing, thereon, which includes the capturing of images of objects, in real-time, and utilizing an image processing method based upon artificial intelligence indicating whether a complete view of the ID is depicted. (Id. at p.12, l.13 – p.13, l.21). The Examiner finds that Ladron discloses the mobile computing device comprising a computing system having computer code program residing on a carrier that performs the method of pre-validation. (Ladron at p.6, l.25 – p.7, l.19; p.8, l.1-27). The Examiner finds that Ladron discloses the mobile computing device utilizing an image processing method based upon artificial intelligence indicating whether a complete view of the ID is depicted, which is inherently equivalent to whether an image of the captured ID document is unusable or usable and providing the user information to correct the orientation issue. (Id. at p.12, l.13 – p.13, l.13). The Examiner finds that Ladron further teaches the utilization of an image processing detection method to determine one or more parameters of detected elements of an ID document in which one or more neural networks are trained with set of images using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected. (Id. at p.14, l.19 – p.15, l.8; p.15, ll.25-28; p.16, ll.3-5, 16-20; p.18, l.27 – p.19, l.16; p.25, ll.11-34; emphasis added).
While Ladron discloses all the limitations as set forth above including utilizing an image processing method based upon artificial intelligence to indicating whether a complete view of the ID is depicted; and including utilizing an image processing method based upon one or more neural networks which are trained with set of image using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected, Ladron is silent to utilizing the image processing method based upon one or more neural networks which are trained with set of images, including: (1) some images that have been rejected by the remote post-validation platform; and (2) images representing acceptable and unacceptable types of identification cards, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected to specifically indicate whether a complete view of the ID is depicted.
However, utilizing an image processing method based upon one or more neural networks which are trained with set of images, including: (1) some images that have been rejected by the remote post-validation platform; and (2) images representing acceptable and unacceptable types of identification cards, machine learning techniques in such a way that output of the networks represent the one or more parameters being detected to specifically indicate whether a complete view of an object is being depicted is known in the art. The Examiner finds that Rothberg, for example, teaches a portable handheld computing device 104, 1502 comprising one or more processors 1510 and memory 1512 to control the acquisition and pre-validation of input images. (Id. at ¶¶ 0307-0316; see Figures 1, 15B. 18A-18E). The Examiner finds that Rothberg, for example, teaches the portable handheld computing device 104, 1502 capturing images in which a pre-validation process is performed on the captured images before further processing is performed on the captured images. (Rothberg at Abstract; ¶¶ 0005, 0009, 0011-0013, 0106, 0127, 0132, 0144-0147, 0149-0151, 0181-0193, 0227-0234, 0254, 0259-270; see Figures 1, 9, 14, 18A-18E). The Examiner finds that Rothberg teaches an automated image analysis which utilizes neural network model analysis to determine whether a particular image is in the correct view (i.e., unusable or usable) and, based upon the neural network model analysis, provide a user direction on how to correct the orientation to capture the correct view (i.e., usable) of the feature. (Id.) The Examiner finds that Rothberg teaches the training image set data being selected having both “standard/good” image data as well as “non-ideal” image data by a specialist on remote system. (Id. at ¶¶ 0261-0262).
Moreover, the Examiner finds that Das teaches an automated image analysis which utilizes a neural network model analysis to determine whether a particular image is in a restricted or unrestricted image category. (Das at Abstract; c.2, ll.21-25; c.5, ll.22-29, 38-60; c.7, ll.12-56; c.8, l.13 – c.10, l.43; c.27, ll.6-27, 36-38, 45-46; c.29, l.1 – c.30, l.18; see Figures 2, 3). The Examiner finds that Das further teaches the neural network model including a convolutional neural network (“CNN”) which may include training images that belong to certain categories and images that do not belong to certain categories. (Id. at c.9, ll.15-25; emphasis on images belonging/not belong to certain categories being equivalent to images being acceptable/unacceptable types of images).
The Examiner finds that it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate an image processing method based upon one or more neural networks which are trained with set of images, including (1) some images that have been rejected and accepted by the remote post-validation platform, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected to specifically indicate whether a complete view of an object is being depicted as described in Rothberg to the computer-implemented method of pre-validation of object images of Ladron.
A person of ordinary skill in the art would be motivated to incorporate an image processing method based upon one or more neural networks which are trained with set of images, including (1) some images that have been rejected and accepted by the remote post-validation platform, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected to specifically indicate whether a complete view of an object is being depicted, since it provides a mechanism to: utilize state-of-the-art image processing technology; and allow users, who are not trained to identify relevant information of an object in a captured image, to provide correct and proper images for further and future processing. (Id. at ¶¶ 0141-0142, 0145). In other words, such a modification would provide an apparatus and method for pre-validation of object image which can easily and dynamically determine whether a target image is acceptable, thereby increasing the overall efficiency of the apparatus and method. (Id.)
This combination of references also satisfies at least rationale C identified by the Supreme Court in KSR International Co. v. Teleflex Inc., 550 U.S. 398, 82 USPQ2d 1385, 1395-97 (2007): "Use of known technique to improve similar devices (methods, or products) in the same way." (See MPEP 2143.) The elements of the Graham factual inquiry for supporting a finding of obviousness based on this rationale are provided below:
(1) A finding that the prior art (Ladron) contained a “base” device (a mobile device for pre-validation of ID identification images) upon which the claimed invention can be seen as an “improvement” for including an image processing method based upon one or more neural networks which are trained with set of images, including some images that have been rejected and accepted by the remote post-validation platform, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected to specifically indicate whether a complete view of an object is being depicted in order to provide an apparatus and method for pre-validation of an ID document image which can easily and dynamically determine whether a target image is acceptable.
(2) A finding that the prior art (Rothberg) contained a "comparable" device (a mobile device for pre-validation of object images) that has been improved in the same way as the claimed invention, i.e. the Rothberg mobile device for pre-validation of object images utilizes an image processing method based upon one or more neural networks which are trained with set of images, including (1) some images that have been rejected and accepted by the remote post-validation platform, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected to specifically indicate whether a complete view of an object is being depicted in order to: utilize state-of-the-art image processing technology; allow users, who are not trained to identify relevant information of an object in a captured image, and provide correct and proper images for further and future processing.
(3) A finding that one of ordinary skill in the art could have applied the known “improvement” technique in the same way to the “base” device (the Ladron mobile device for pre-validation of ID identification images) and the results would have been predictable to one of ordinary skill in the art. Here, because Ladron indicates that the mobile device can be utilized for pre-validation of ID document images utilizing image processing methods based upon artificial intelligence and Rothberg teaches a manner for improving this, the results would be predictable. In other words, the Rothberg successful implementation or providing an image processing method based upon one or more neural networks which are trained with set of images, including (1) some images that have been rejected and accepted by the remote post-validation platform, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected to specifically indicate whether a complete view of an object is being depicted proves that the implementation is both successful and entirely predictable. In Ladron, the mobile device for pre-validation of ID document images modified according to Rothberg would be capable of incorporating an image processing method based upon one or more neural networks which are trained with set of images, including (1) some images that have been rejected and accepted by the remote post-validation platform, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected to specifically indicate whether a complete view of an object is being depicted to carry out the functions of: utilizing state-of-the-art image processing technology; allowing users, who are not trained to identify relevant information of an object in a captured image, and providing correct and proper images for further and future processing.
In that regard, the Examiner asserts the use of known technique to improve similar devices in the same way is obvious to one of ordinary skill in the art. That is, the manner of enhancing a particular device (providing utilizing state-of-the-art image processing technology; allowing users, who are not trained to identify relevant information of an object in a captured image, and providing correct and proper images for further and future processing including an image processing method based upon one or more neural networks which are trained with set of images, including (1) some images that have been rejected and accepted by the remote post-validation platform, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected to specifically indicate whether a complete view of an object is being depicted) was made part of the ordinary capabilities of one skilled in the art based upon the teaching of such improvement in Rothberg. Accordingly, one of ordinary skill in the art would have been capable of applying this known “improvement” technique in the same manner to the prior art mobile device for pre-validation of object images of Ladron and the results would have been predictable to one of ordinary skill in the art, namely, one skilled in the art would have readily recognized that providing utilizing state-of-the-art image processing technology; allowing users, who are not trained to identify relevant information of an object in a captured image, and providing correct and proper images for further and future processing including an image processing method based upon one or more neural networks which are trained with set of images, including (1) some images that have been rejected and accepted by the remote post-validation platform, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected to specifically indicate whether a complete view of an object is being depicted in Ladron would positively provide a means to carry out, in addition to utilizing image processing methods based upon artificial intelligence, a new functions of: utilizing state-of-the-art image processing technology; allowing users, who are not trained to identify relevant information of an object in a captured image, and providing correct and proper images for further and future processing that provides for easier and more dynamic image capturing for consistency purposes, since such functionality is taught to be highly desirable by Rothberg as set forth above.
Thus, the rationale to support a conclusion that the claim would have been obvious is that a method of enhancing a particular class of devices (methods, or products) has been made part of the ordinary capabilities of one skilled in the art based upon the teaching of such improvement in other situations. One of ordinary skill in the art would have been capable of applying this known method of enhancement to a “base” device (method, or product) in the prior art and the results would have been predictable to one of ordinary skill in the art. The Supreme Court in KSR noted that if the actual application of the technique would have been beyond the skill of one of ordinary skill in the art, then using the technique would not have been obvious. KSR, 550 U.S. at 398, 82 USPQ2d at 1396. If any of these findings cannot be made, then this rationale cannot be used to support a conclusion that the claim would have been obvious to one of ordinary skill in the art.
Similarly, the Examiner finds that it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate an image processing method based upon one or more neural networks which are trained with set of images, including (2) images representing acceptable and unacceptable types of images22, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected as described in Das to the computer-implemented method of pre-validation of ID documents images of Ladron and Rothberg.
A person of ordinary skill in the art would be motivated to incorporate an image processing method based upon one or more neural networks which are trained with set of images, including (2) images representing acceptable and unacceptable types of images23, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected, since it provides a mechanism to utilize the “hard negative mining” technique to further distinguish between whether an image belongs to a certain category or not. (Das at c.9, ll.15-25). In other words, such a modification would provide an apparatus and method for pre-validation of an ID document image which can easily and dynamically determine whether a target image is acceptable, thereby increasing the overall efficiency of the apparatus and method. (Id.)
Again, and similarly, this combination of references also satisfies at least rationale C identified by the Supreme Court in KSR International Co. v. Teleflex Inc., 550 U.S. 398, 82 USPQ2d 1385, 1395-97 (2007): "Use of known technique to improve similar devices (methods, or products) in the same way." (See MPEP 2143.) The elements of the Graham factual inquiry for supporting a finding of obviousness based on this rationale are provided below:
(1) A finding that the prior art (Ladron and Rothberg) contained a “base” device (a mobile device for pre-validation of ID identification images) upon which the claimed invention can be seen as an “improvement” for including an image processing method based upon one or more neural networks which are trained with set of images, including (2) images representing acceptable and unacceptable types of images, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected in order to provide an apparatus and method for pre-validation of an ID document image which can easily and dynamically determine whether a target image is acceptable.
(2) A finding that the prior art (Das) contained a "comparable" device (a mobile device for pre-validation of object images) that has been improved in the same way as the claimed invention, i.e. the Das mobile device for pre-validation of object images utilizes an image processing method based upon one or more neural networks which are trained with set of images, including (2) images representing acceptable and unacceptable types of images, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected to specifically indicate whether a complete view of an object is being depicted in order to utilize the “hard negative mining” technique to further distinguish between whether an image belongs to a certain category or not.
(3) A finding that one of ordinary skill in the art could have applied the known “improvement” technique in the same way to the “base” device (the Ladron and Rothberg mobile device for pre-validation of ID identification images) and the results would have been predictable to one of ordinary skill in the art. Here, because Ladron indicates that the mobile device can be utilized for pre-validation of ID document images utilizing image processing methods based upon artificial intelligence and Das teaches a manner for improving this, the results would be predictable. In other words, the Das successful implementation or providing an image processing method based upon one or more neural networks which are trained with set of images, including (2) images representing acceptable and unacceptable types of images, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected proves that the implementation is both successful and entirely predictable. In Ladron and Rothberg, the mobile device for pre-validation of ID document images modified according to Das would be capable of incorporating an image processing method based upon one or more neural networks which are trained with set of images, including (2) images representing acceptable and unacceptable types of images, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected to carry out the function of utilizing the “hard negative mining” technique to further distinguish between whether an image belongs to a certain category or not that provides for easier and more dynamic image capturing for consistency purposes, as evidenced by the success in the Das mobile device.
In that regard, the Examiner asserts the use of known technique to improve similar devices in the same way is obvious to one of ordinary skill in the art. That is, the manner of enhancing a particular device (providing utilizing the “hard negative mining” technique to further distinguish between whether an image belongs to a certain category or not including an image processing method based upon one or more neural networks which are trained with set of images, including (2) images representing acceptable and unacceptable types of images, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected) was made part of the ordinary capabilities of one skilled in the art based upon the teaching of such improvement in Das. Accordingly, one of ordinary skill in the art would have been capable of applying this known “improvement” technique in the same manner to the prior art mobile device for pre-validation of object images of Ladron and the results would have been predictable to one of ordinary skill in the art, namely, one skilled in the art would have readily recognized that providing utilizing the “hard negative mining” technique to further distinguish between whether an image belongs to a certain category or not including an image processing method based upon one or more neural networks which are trained with set of images, including some images that have been rejected and accepted by the remote post-validation platform, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected to specifically indicate whether a complete view of an object is being depicted in Ladron would positively provide a means to carry out, in addition to utilizing image processing methods based upon artificial intelligence, a new functions of: utilizing state-of-the-art image processing technology; allowing users, who are not trained to identify relevant information of an object in a captured image, to provide correct and proper images for further and future processing; and utilizing the “hard negative mining” technique to further distinguish between whether an image belongs to a certain category or not that provides for easier and more dynamic image capturing for consistency purposes, since such functionality is taught to be highly desirable by Rothberg, as evidenced by Das, as set forth above.
Thus, the rationale to support a conclusion that the claim would have been obvious is that a method of enhancing a particular class of devices (methods, or products) has been made part of the ordinary capabilities of one skilled in the art based upon the teaching of such improvement in other situations. One of ordinary skill in the art would have been capable of applying this known method of enhancement to a “base” device (method, or product) in the prior art and the results would have been predictable to one of ordinary skill in the art. The Supreme Court in KSR noted that if the actual application of the technique would have been beyond the skill of one of ordinary skill in the art, then using the technique would not have been obvious. KSR, 550 U.S. at 398, 82 USPQ2d at 1396. If any of these findings cannot be made, then this rationale cannot be used to support a conclusion that the claim would have been obvious to one of ordinary skill in the art.
program code that, when executed by the one or more processors, cause the system to determine, using the trained neural network, whether at least a first image of the one or more images is of an acceptable type of identification card and unusable by the remote post-validation platform based at least in part on one or more analyzed quality features of the first image
As set forth supra and with respect to claim 10, the Examiner finds that Functional Phrase 2 does invoke 35 U.S.C. §112, 6th paragraph. (See § IX.B.(2) supra). In addition, the Examiner finds that Functional Phrase 2 as recited in claim 10 is indefinite. (See § X.B supra). In this light and to advance prosecution by providing art rejections even though this claim is indefinite, the Examiner construes the ‘instructions …’ as program code, that can be executed by at least one processor, using the output of the trained neural network, to determine whether the output parameter of detected feature categories the input image as confirmed and unusable, or its equivalent.
From this perspective, as set forth above, the Examiner finds that Ladron discloses the mobile computing device having a pre-validation processing, thereon, which includes the capturing of images of objects, in real-time, and utilizing an image processing method based upon artificial intelligence indicating whether a complete view of the ID is depicted. (Id. at p.12, l.13 – p.13, l.21). The Examiner finds that Ladron discloses the mobile computing device comprising a computing system having computer code program residing on a carrier that performs the method of pre-validation. (Ladron at p.6, l.25 – p.7, l.19; p.8, l.1-27).
In addition, the Examiner finds that Rothberg teaches the portable handheld computing device 104, 1502 comprising one or more processors 1510 and memory 1512 to control the acquisition and pre-validation of input images. (Rothberg at ¶¶ 0307-0316; see Figures 1, 15B). The Examiner finds that Rothberg, for example, teaches the portable handheld computing device 104, 1502 capturing images in which a pre-validation process is performed on the captured images before further processing is performed on the captured images. (Rothberg at Abstract; ¶¶ 0005, 0009, 0011-0013, 0106, 0127, 0132, 0144-0147, 0149-0151, 0181-0193, 0227-0234, 0254, 0259-270; see Figures 1, 9, 14, 18A-18E).
Moreover, as set forth above, the Examiner finds that Rothberg teaches and/or renders obvious utilizing an image processing method based upon one or more neural networks which are trained with set of images, including some images that have been rejected and accepted by the remote post-validation platform, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected to specifically indicate whether a complete view of an object is being depicted. (See § XII.A.(2).d, supra).
Thus, the Examiner finds that Rothberg teaches and/or renders obvious instructions, that can be executed by at least one processor, using the output of the trained neural network, to determine whether the output parameter of detected feature categories the input image as unusable, or its equivalent. (Id.)
wherein the one or more processors, when executing the program code, are configured to output feedback instructions to capture one or more new images for determining whether the one or more new images are unusable by the remote post-validation platform.
As set forth supra and with respect to claim 10, the Examiner finds that Functional Phrase 3 does invoke 35 U.S.C. §112, 6th paragraph. (See § IX.B.(3) supra). In addition, the Examiner finds that Functional Phrase 3 as recited in claim 10 is indefinite. (See § X.B supra). In this light and to advance prosecution by providing art rejections even though this claim is indefinite, the Examiner construes the ‘instructions …’ as program code, that can be executed by at least one processor, using the output of the trained neural network, outputting feedback instructions to a user for corrective action, or its equivalent.
From this perspective, the Examiner finds that Ladron discloses the mobile computing device comprising a computing system having computer code program residing on a carrier that performs the method of pre-validation. (Ladron at p.6, l.25 – p.7, l.19; p.8, l.1-27). The Examiner finds that Ladron discloses the mobile computing device having the pre-validation processing, thereon, which includes the capturing of images of objects, in real-time, and utilizing an image processing method based upon artificial intelligence indicating whether a complete view of the ID is depicted. (Id. at p.12, l.13 – p.13, l.21). The Examiner finds that Ladron discloses the mobile computing device utilizing an image processing method based upon artificial intelligence indicating whether a complete view of the ID is depicted, which is inherently equivalent to whether an image of the captured ID document is unusable or usable and providing the user information to correct the orientation issue. (Id. at p.12, l.13 – p.13, l.13; p.13, ll.23-26). The Examiner finds that Ladron discloses the mobile device providing notification to the user to change the orientation of the device via an image and/or sound and/or vibration means. (Id. at p.13, 11.15-21).
The Examiner finds that Rothberg further teaches the computing device 104, 1502 outputting image capture framing feedback information to a user via a display screen 106, 1508 of the computing device 104, 1502. (Rothberg at ¶¶ 0005, 0144-0151, 0154, 0185, 0027-0234, 0254; see Figures 1, 9, 14, 18A-18E).
As set forth above, the Examiner finds that Rothberg teaches and/or renders obvious utilizing an image processing method based upon one or more neural networks which are trained with set of images, including some images that have been rejected and accepted by the remote post-validation platform, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected to specifically indicate whether a complete view of an object is being depicted. (See § XII.A.(2).d, supra).
With respect to the limitations of claim 11, Ladron, Rothberg and Das teaches and/or renders obvious
[11] wherein the one or more quality features comprise: distance between an object and the image capture component, fill ratio, blur, framing, rotation, keystone, sharpness, brightness, contrast, or color.
In this regard, Ladron teaches the utilization of an image processing detection method to determine one or more parameters of detected elements of an ID document in which one or more neural networks are trained with set of images using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected. (Ladron at p.14, l.19 – p.15, l.8; p.15, ll.25-28; p.16, ll.3-5, 16-20; p.18, l.27 – p.19, l.16; p.25, ll.11-34; emphasis added). The Examiner finds that Ladron teaches the parameters including, as an example, alignment, spacing, width, height, colo[u]r, texture… etc.) (Id. at p.14, l.19 – p.15, l.8; p.15, ll.25-28; p.16, ll.3-5, 16-20).
The Examiner finds that it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the one or more quality features comprising: distance between an object and the image capture component, fill ratio, blur, framing, rotation, keystone, sharpness, brightness, contrast, or color as described in Ladron to the computer-implemented method of pre-validation of object images of Ladron, Rothberg and Das.
A person of ordinary skill in the art would be motivated to incorporate the one or more quality features comprising: distance between an object and the image capture component, fill ratio, blur, framing, rotation, keystone, sharpness, brightness, contrast, or color, since it provides a mechanism to provide an authenticity indicator of the object/document in question. (Ladron at Abstract; p.3, ll.9-24; p.4, ll.11-14). In other words, such a modification would provide an apparatus and method for pre-validation of object image which is based upon a safer and more robust and secure process, thereby increasing the overall efficiency of the apparatus and method. (Id.)
With respect to the limitations of claim 12, Ladron, Rothberg and Das teaches and/or renders obvious
[12] wherein the one or more quality features comprise a presence of an image of a human face on the identification card
In this regard, Ladron teaches the utilization of an image processing detection method to determine one or more parameters of detected elements of an ID document in which one or more neural networks are trained with set of images using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected. (Ladron at p.14, l.19 – p.15, l.8; ; p.15, ll.25-28; p.16, ll.3-5, 16-20; p.18, l.27 – p.19, l.16; p.25, ll.11-34; emphasis added). The Examiner finds that Ladron teaches the parameters including, as an example, alignment, spacing, width, height, colo[u]r, texture… etc. of a complete face view of a person in the ID document. (Id. at p.14, l.19 – p.15, l.8; p.15, ll.25-28; p.16, ll.3-5, 16-20. p.25, l.11 – p.26, l.7; see Figure 4).
The Examiner finds that it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the one or more quality features comprising a presence of an image of a human face on the object as described in Ladron to the computer-implemented method of pre-validation of object images of Ladron, Rothberg and Das.
A person of ordinary skill in the art would be motivated to incorporate the one or more quality features comprising a presence of an image of a human face on the object, since it provides a mechanism to provide an authenticity indicator of the object/document in question. (Ladron at Abstract; p.3, ll.9-24; p.4, ll.11-14). In other words, such a modification would provide an apparatus and method for pre-validation of object image which is based upon a safer and more robust and secure process, thereby increasing the overall efficiency of the apparatus and method. (Id.)
With respect to the limitations of claim 13, Ladron, Rothberg and Das teaches and/or renders obvious
[13] wherein the one or more quality features comprise an indication that the one or more images were captured by the image capture component.
In this regard, Ladron teaches the utilization of an image processing detection method to determine one or more parameters of detected elements of an ID document in which one or more neural networks are trained with set of images using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected. (Ladron at p.14, l.19 – p.15, l.8; p.15, ll.25-28; p.16, ll.3-5, 16-20; p.18, l.27 – p.19, l.16; p.25, ll.11-34; emphasis added). The Examiner finds that Ladron teaches the parameters including, as an example, alignment, spacing, width, height, colo[u]r, texture… etc. or a complete face view of a person in the ID document. (Id. at p.14, l.19 – p.15, l.8; p.15, ll.25-28; p.16, ll.3-5, 16-20. p.25, l.11 – p.26, l.7; see Figure 4). The Examiner finds that Ladron further teaches comparing several features of almost simultaneously taken image ID document photos to avoid fraud which indicates that the photo is no other than what was instantly provided by the mobile device. (Id. at p.13, l.35 – p.14, l.10).
The Examiner finds that it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the one or more quality features comprising an indication that the one or more images were captured by the image capture component as described in Ladron to the computer-implemented method of pre-validation of object images of Ladron, Rothberg and Das.
A person of ordinary skill in the art would be motivated to incorporate the one or more quality features comprising an indication that the one or more images were captured by the image capture component, since it provides a mechanism to provide an authenticity indicator of the object/document in question. (Ladron at Abstract; p.3, ll.9-24; p.4, ll.11-14). In other words, such a modification would provide an apparatus and method for pre-validation of object image which is based upon a safer and more robust and secure process, thereby increasing the overall efficiency of the apparatus and method. (Id.)
With respect to the limitations of claim 14, Ladron, Rothberg and Das teaches and/or renders obvious
[14] wherein the one or more processors are configured to determine a distance between the image capture component and the identification card.
In this regard, the Examiner finds that Ladron discloses a mobile computing device with a camera (i.e., mobile phone, camera, tablet, smart watch, etc.) capturing images of a document (i.e., ID card, driving license, credit, debit, business, medical insurance card, passport, tickets, etc.). (Ladron at p.8, ll.1-27). The Examiner finds that Ladron disclose the mobile device, on which the pre-validation processing is performed, comprising a camera, instructions stored in memory, and processor to execute the instructions,. (Id. at p.8, ll.15-24; p.12, ll.13-37; claims 35-38). In addition, the Examiner finds that Ladron discloses automatically adjusting the focus and illumination of the camera unit and tracking it via image processing. (Id. at p.13, ll.5-13; p.25, ll.18-22).
With respect to the limitations of claim 15, Ladron, Rothberg and Das teaches and/or renders obvious
[15] wherein the trained neural network occupies less than 500 Mbytes of the memory
As set forth above, the Examiner finds that Rothberg teaches and/or renders obvious utilizing an image processing method based upon one or more neural networks which are trained with set of images, including some images that have been rejected and accepted by the remote post-validation platform, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected to specifically indicate whether a complete view of an object is being depicted. (See § XII.A.(2).d, supra). The Examiner finds that Rothberg further teaches the application including a pre-trained vision model. (Rothberg at ¶¶ 0146; 0259-0269; see Figure 14).
From this perspective, Ladron teaches the application being configured to run on the mobile computing device with a camera (i.e., mobile phone, camera, tablet, smart watch, etc.). (Ladron at p.8, ll.1-27; p.12, l.16 – p.13, l.21).
The Examiner finds that Rothberg further teaches the neural network application including a pre-trained vision model that is performed on the computing device having memory thereon. (Rothberg at ¶¶ 0146-0147; 0259-0269; emphasis at ¶ 0246, the trained neural network being deployed to the computing device; see Figures 1, 14, 18A-18E).
Ladron, Rothberg and Das discloses all the limitations, as set forth above, except for specifically calling for the trained machine vision model to occupy less than 500 Mbytes of memory.
The Examiner finds it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the pre- the trained machine vision model to occupy less than 500 Mbytes of memory, since it has been held that where general conditions of a claim are disclosed in the prior art, discovering the optimum or workable ranges involves only routine skill in the art. In re Aller, 220 F.2d 454, 456, 105 USPQ 233, 235 (CCPA 1955). The Examiner finds that the ‘343 Patent has not disclosed any criticality for the claim limitation. (See the ‘343 Patent at c.17, ll.40-45).
With respect to the limitations of claim 16, Ladron, Rothberg and Das teaches and/or renders obvious
[16] wherein the one or processors are further configured to, when executing the program code, cause the system to determine whether at least a second image of the one or more captured images is usable by the remote post-validation platform
As set forth supra and with respect to claim 16, the Examiner finds that Functional Phrase 2 does invoke 35 U.S.C. §112, 6th paragraph. (See § IX.B.(2) supra). In addition, the Examiner finds that Functional Phrase 2 as recited in claim 16 is indefinite. (See § X.B supra). In this light and to advance prosecution by providing art rejections even though this claim is indefinite, the Examiner construes the ‘instructions …’ as instructions, that can be executed by at least one processor, using the output of the trained neural network, to determine whether the output parameter of detected feature categories the input image as unusable, or its equivalent.
From this perspective, as set forth above, the Examiner finds that Ladron discloses the mobile computing device utilizing an image processing method based upon artificial intelligence indicating whether a complete view of the ID is depicted, which is inherently equivalent to whether an image of the captured ID document is unusable or usable and providing the user information to correct the orientation issue. (Id. at p.12, l.13 – p.13, l.13).
Moreover, as set forth above, the Examiner finds that Rothberg teaches and/or renders obvious utilizing an image processing method based upon one or more neural networks which are trained with set of images, including some images that have been rejected and accepted by the remote post-validation platform, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected to specifically indicate whether a complete view of an object is being depicted. (See § XII.A.(2).d, supra).
Thus, the Examiner finds that Rothberg teaches and/or renders obvious the instructions being further configured to determine whether at least the first image of the one or more captured images is usable by the remote post-validation platform. (Id.)
With respect to the limitations of claim 17, Ladron, Rothberg and Das teaches and/or renders obvious
[17] wherein the object comprises: a government-issued identification card, a credit card, a debit card, a loyalty card, an access card, or a stored value card
In this regard, the Examiner finds that Ladron discloses a mobile computing device with a camera (i.e., mobile phone, camera, tablet, smart watch, etc.) capturing images of a document (i.e., ID card, driving license, credit, debit, business, medical insurance card, passport, tickets, etc.). (Ladron at p.8, ll.1-27).
With respect to the limitations of claim 18, and
[18] [a] non-transitory computer-readable media comprising computer- executable instructions that, when executed by one or more processors, configure the one or more processors to perform a method for pre-validation of identification card images, the method comprising the steps of:
In this regard, the Examiner finds that Ladron discloses a method for authenticating a document from, obtained images of the document. (Ladron at Abstract. p.1, ll.5-7; p.4, l.26 – p.2, l.12; p.3, l.26 – p.4, l.23; p.5, ll.16 -20; p.6, l.17 – p.7, l.27; p.8, ll.1-27; p.12, l.13 – p.13, l.21; p.14, l.19 – p.15, l.8; ; p.15, ll.25-28; p.16, ll.3-5, 16-20; p.18, l.27 – p.19, l.16; p.25, ll.11-34; see Figure 1, 4). The Examiner finds that Ladron discloses the method being implemented as computer program product embodied on a storage medium. (Id. at p.6, l.31 – p.7, l.9). In the method disclosed by Ladron, the Examiner finds that Ladron discloses a pre-validation processing including the capturing of images of ID documents, in real-time, and utilizing an image processing method based upon artificial intelligence indicating whether a complete view of the ID is depicted. (Id. at p.12, l.13 – p.13, l.21).
capturing, by a digital camera, one or more images of an identification card;
In this regard, the Examiner finds that Ladron discloses a mobile computing device with a camera (i.e., mobile phone, camera, tablet, smart watch, etc.) capturing images of a document (i.e., ID card, driving license, credit, debit, business, medical insurance card, passport, tickets, etc.). (Id. at p.8, ll.1-27). The Examiner finds that Ladron discloses the mobile computing device having a pre-validation processing, thereon, which includes the capturing of images of ID documents, in real-time, and utilizing an image processing method based upon artificial intelligence indicating whether a complete view of the ID is depicted. (Id. at p.12, l.13 – p.13, l.21).
analyzing, by the one or more processors, one or more quality features of the one or more images, wherein the analyzing comprises:
determining, utilizing an application, local to the one or more processors, having a trained neural network and based on the one or more quality features, whether at least a first image of the one or more captured images is unusable by a remote post-validation platform, the trained neural network having been trained using a dataset including images that have been rejected by the remote post-validation platform and images representing acceptable and unacceptable types of identification cards; and
In this regard, the Examiner finds that the claim requirement of “the trained neural network having been trained using a dataset including images that have been rejected by the remote post-validation platform” is NOT a product-by-process limitation. (See May 2024 Final Office Action at § XII.K.(1).(a)). Thus, the Examiner construes “the trained neural network” as being a neural network having been trained some images that have been rejected and now also including images representing acceptable and unacceptable types of images.
From this perspective, the Examiner finds that Ladron discloses the mobile computing device comprising a computing system having computer code program residing on a carrier that performs the method of pre-validation. (Ladron at p.6, l.25 – p.7, l.19; p.8, l.1-27). The Examiner finds that Ladron discloses the mobile computing device utilizing an image processing method based upon artificial intelligence indicating whether a complete view of the ID is depicted, which is inherently equivalent to whether an image of the captured ID document is unusable or usable and providing the user information to correct the orientation issue. (Id. at p.12, l.13 – p.13, l.13). The Examiner finds that Ladron further teaches the utilization of an image processing detection method to determine one or more parameters of detected elements of an ID document in which one or more neural networks are trained with set of images using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected. (Id. at p.14, l.19 – p.15, l.8; p.15, ll.25-28; p.16, ll.3-5, 16-20; p.18, l.27 – p.19, l.16; p.25, ll.11-34; emphasis added).
While Ladron discloses all the limitations as set forth above including utilizing an image processing method based upon artificial intelligence to indicating whether a complete view of the ID is depicted; and including utilizing an image processing method based upon one or more neural networks which are trained with set of image using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected, Ladron is silent to utilizing the image processing method based upon one or more neural networks which are trained with set of images, including: (1) some images that have been rejected by the remote post-validation platform; and (2) images representing acceptable and unacceptable types of identification cards, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected to specifically indicate whether a complete view of the ID is depicted.
However, utilizing an image processing method based upon one or more neural networks which are trained with set of images, including: (1) some images that have been rejected by the remote post-validation platform; and (2) images representing acceptable and unacceptable types of identification cards, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected to specifically indicate whether a complete view of an object is being depicted is known in the art. The Examiner finds that Rothberg, for example, teaches a portable handheld computing device 104, 1502, 1804 for capturing images in which a pre-validation process is performed on the captured images before further processing is performed on the captured images. (Rothberg at Abstract; ¶¶ 0005, 0009, 0011-0013, 0106, 0127, 0132, 0144-0147, 0149-0151, 0181-0193, 0227-0234, 0254, 0259-270; see Figures 1, 9, 14, 18A-18E). The Examiner finds that Rothberg teaches an automated image analysis which utilizes neural network model analysis to determine whether a particular image is in the correct view (i.e., unusable or usable) and, based upon the neural network model analysis, provide a user direction on how to correct the orientation to capture the correct view (i.e., usable) of the feature. (Id.) The Examiner finds that Rothberg teaches the training image set data being selected having both “standard/good” image data as well as “non-ideal” image data by a specialist on remote system. (Id. at ¶¶ 0261-0262).
Moreover, the Examiner finds that Das teaches an automated image analysis which utilizes a neural network model analysis to determine whether a particular image is in a restricted or unrestricted image category. (Das at Abstract; c.2, ll.21-25; c.5, ll.22-29, 38-60; c.7, ll.12-56; c.8, l.13 – c.10, l.43; c.27, ll.6-27, 36-38, 45-46; c.29, l.1 – c.30, l.18; see Figures 2, 3). The Examiner finds that Das further teaches the neural network model including a convolutional neural network (“CNN”) which may include training images that belong to certain categories and images that do not belong to certain categories. (Id. at c.9, ll.15-25; emphasis on images belonging/not belong to certain categories being equivalent to images being acceptable/unacceptable types of images).
The Examiner finds that it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate an image processing method based upon one or more neural networks which are trained with set of images, including (1) some images that have been rejected and accepted by the remote post-validation platform, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected to specifically indicate whether a complete view of an object is being depicted as described in Rothberg to the computer-implemented method of pre-validation of ID documents images of Ladron.
A person of ordinary skill in the art would be motivated to incorporate an image processing method based upon one or more neural networks which are trained with set of images, including (1) some images that have been rejected and accepted by the remote post-validation platform, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected to specifically indicate whether a complete view of an object is being depicted, since it provides a mechanism to: utilize state-of-the-art image processing technology; allow users, who are not trained to identify relevant information of an object in a captured image, to provide correct and proper images for further and future processing (id. at ¶¶ 0141-0142, 0145). In other words, such a modification would provide an apparatus and method for pre-validation of an ID document image which can easily and dynamically determine whether a target image is acceptable, thereby increasing the overall efficiency of the apparatus and method. (Id.)
This combination of references also satisfies at least rationale C identified by the Supreme Court in KSR International Co. v. Teleflex Inc., 550 U.S. 398, 82 USPQ2d 1385, 1395-97 (2007): "Use of known technique to improve similar devices (methods, or products) in the same way." (See MPEP 2143.) The elements of the Graham factual inquiry for supporting a finding of obviousness based on this rationale are provided below:
(1) A finding that the prior art (Ladron) contained a “base” device (a mobile device for pre-validation of ID identification images) upon which the claimed invention can be seen as an “improvement” for including an image processing method based upon one or more neural networks which are trained with set of images, including some images that have been rejected and accepted by the remote post-validation platform, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected to specifically indicate whether a complete view of an object is being depicted in order to provide an apparatus and method for pre-validation of an ID document image which can easily and dynamically determine whether a target image is acceptable.
(2) A finding that the prior art (Rothberg) contained a "comparable" device (a mobile device for pre-validation of object images) that has been improved in the same way as the claimed invention, i.e. the Rothberg mobile device for pre-validation of object images utilizes an image processing method based upon one or more neural networks which are trained with set of images, including (1) some images that have been rejected and accepted by the remote post-validation platform, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected to specifically indicate whether a complete view of an object is being depicted in order to: utilize state-of-the-art image processing technology; allow users, who are not trained to identify relevant information of an object in a captured image, and provide correct and proper images for further and future processing.
(3) A finding that one of ordinary skill in the art could have applied the known “improvement” technique in the same way to the “base” device (the Ladron mobile device for pre-validation of ID identification images) and the results would have been predictable to one of ordinary skill in the art. Here, because Ladron indicates that the mobile device can be utilized for pre-validation of ID document images utilizing image processing methods based upon artificial intelligence and Rothberg teaches a manner for improving this, the results would be predictable. In other words, the Rothberg successful implementation or providing an image processing method based upon one or more neural networks which are trained with set of images, including (1) some images that have been rejected and accepted by the remote post-validation platform, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected to specifically indicate whether a complete view of an object is being depicted proves that the implementation is both successful and entirely predictable. In Ladron, the mobile device for pre-validation of ID document images modified according to Rothberg would be capable of incorporating an image processing method based upon one or more neural networks which are trained with set of images, including (1) some images that have been rejected and accepted by the remote post-validation platform, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected to specifically indicate whether a complete view of an object is being depicted to carry out the functions of: utilizing state-of-the-art image processing technology; allowing users, who are not trained to identify relevant information of an object in a captured image, and providing correct and proper images for further and future processing.
In that regard, the Examiner asserts the use of known technique to improve similar devices in the same way is obvious to one of ordinary skill in the art. That is, the manner of enhancing a particular device (providing utilizing state-of-the-art image processing technology; allowing users, who are not trained to identify relevant information of an object in a captured image, and providing correct and proper images for further and future processing including an image processing method based upon one or more neural networks which are trained with set of images, including (1) some images that have been rejected and accepted by the remote post-validation platform, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected to specifically indicate whether a complete view of an object is being depicted) was made part of the ordinary capabilities of one skilled in the art based upon the teaching of such improvement in Rothberg. Accordingly, one of ordinary skill in the art would have been capable of applying this known “improvement” technique in the same manner to the prior art mobile device for pre-validation of object images of Ladron and the results would have been predictable to one of ordinary skill in the art, namely, one skilled in the art would have readily recognized that providing utilizing state-of-the-art image processing technology; allowing users, who are not trained to identify relevant information of an object in a captured image, and providing correct and proper images for further and future processing including an image processing method based upon one or more neural networks which are trained with set of images, including (1) some images that have been rejected and accepted by the remote post-validation platform, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected to specifically indicate whether a complete view of an object is being depicted in Ladron would positively provide a means to carry out, in addition to utilizing image processing methods based upon artificial intelligence, a new functions of: utilizing state-of-the-art image processing technology; allowing users, who are not trained to identify relevant information of an object in a captured image, and providing correct and proper images for further and future processing that provides for easier and more dynamic image capturing for consistency purposes, since such functionality is taught to be highly desirable by Rothberg as set forth above.
Thus, the rationale to support a conclusion that the claim would have been obvious is that a method of enhancing a particular class of devices (methods, or products) has been made part of the ordinary capabilities of one skilled in the art based upon the teaching of such improvement in other situations. One of ordinary skill in the art would have been capable of applying this known method of enhancement to a “base” device (method, or product) in the prior art and the results would have been predictable to one of ordinary skill in the art. The Supreme Court in KSR noted that if the actual application of the technique would have been beyond the skill of one of ordinary skill in the art, then using the technique would not have been obvious. KSR, 550 U.S. at 398, 82 USPQ2d at 1396. If any of these findings cannot be made, then this rationale cannot be used to support a conclusion that the claim would have been obvious to one of ordinary skill in the art.
Similarly, the Examiner finds that it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate an image processing method based upon one or more neural networks which are trained with set of images, including (2) images representing acceptable and unacceptable types of images24, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected as described in Das to the computer-implemented method of pre-validation of ID documents images of Ladron and Rothberg.
A person of ordinary skill in the art would be motivated to incorporate an image processing method based upon one or more neural networks which are trained with set of images, including (2) images representing acceptable and unacceptable types of images25, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected, since it provides a mechanism to utilize the “hard negative mining” technique to further distinguish between whether an image belongs to a certain category or not. (Das at c.9, ll.15-25). In other words, such a modification would provide an apparatus and method for pre-validation of an ID document image which can easily and dynamically determine whether a target image is acceptable, thereby increasing the overall efficiency of the apparatus and method. (Id.)
Again, and similarly, this combination of references also satisfies at least rationale C identified by the Supreme Court in KSR International Co. v. Teleflex Inc., 550 U.S. 398, 82 USPQ2d 1385, 1395-97 (2007): "Use of known technique to improve similar devices (methods, or products) in the same way." (See MPEP 2143.) The elements of the Graham factual inquiry for supporting a finding of obviousness based on this rationale are provided below:
(1) A finding that the prior art (Ladron and Rothberg) contained a “base” device (a mobile device for pre-validation of ID identification images) upon which the claimed invention can be seen as an “improvement” for including an image processing method based upon one or more neural networks which are trained with set of images, including (2) images representing acceptable and unacceptable types of images, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected in order to provide an apparatus and method for pre-validation of an ID document image which can easily and dynamically determine whether a target image is acceptable.
(2) A finding that the prior art (Das) contained a "comparable" device (a mobile device for pre-validation of object images) that has been improved in the same way as the claimed invention, i.e. the Das mobile device for pre-validation of object images utilizes an image processing method based upon one or more neural networks which are trained with set of images, including (2) images representing acceptable and unacceptable types of images, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected to specifically indicate whether a complete view of an object is being depicted in order to utilize the “hard negative mining” technique to further distinguish between whether an image belongs to a certain category or not.
(3) A finding that one of ordinary skill in the art could have applied the known “improvement” technique in the same way to the “base” device (the Ladron and Rothberg mobile device for pre-validation of ID identification images) and the results would have been predictable to one of ordinary skill in the art. Here, because Ladron indicates that the mobile device can be utilized for pre-validation of ID document images utilizing image processing methods based upon artificial intelligence and Das teaches a manner for improving this, the results would be predictable. In other words, the Das successful implementation or providing an image processing method based upon one or more neural networks which are trained with set of images, including (2) images representing acceptable and unacceptable types of images, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected proves that the implementation is both successful and entirely predictable. In Ladron and Rothberg, the mobile device for pre-validation of ID document images modified according to Das would be capable of incorporating an image processing method based upon one or more neural networks which are trained with set of images, including (2) images representing acceptable and unacceptable types of images, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected to carry out the function of utilizing the “hard negative mining” technique to further distinguish between whether an image belongs to a certain category or not that provides for easier and more dynamic image capturing for consistency purposes, as evidenced by the success in the Das mobile device.
In that regard, the Examiner asserts the use of known technique to improve similar devices in the same way is obvious to one of ordinary skill in the art. That is, the manner of enhancing a particular device (providing utilizing the “hard negative mining” technique to further distinguish between whether an image belongs to a certain category or not including an image processing method based upon one or more neural networks which are trained with set of images, including (2) images representing acceptable and unacceptable types of images, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected) was made part of the ordinary capabilities of one skilled in the art based upon the teaching of such improvement in Das. Accordingly, one of ordinary skill in the art would have been capable of applying this known “improvement” technique in the same manner to the prior art mobile device for pre-validation of object images of Ladron and the results would have been predictable to one of ordinary skill in the art, namely, one skilled in the art would have readily recognized that providing utilizing the “hard negative mining” technique to further distinguish between whether an image belongs to a certain category or not including an image processing method based upon one or more neural networks which are trained with set of images, including some images that have been rejected and accepted by the remote post-validation platform, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected to specifically indicate whether a complete view of an object is being depicted in Ladron would positively provide a means to carry out, in addition to utilizing image processing methods based upon artificial intelligence, a new functions of: utilizing state-of-the-art image processing technology; allowing users, who are not trained to identify relevant information of an object in a captured image, to provide correct and proper images for further and future processing; and utilizing the “hard negative mining” technique to further distinguish between whether an image belongs to a certain category or not that provides for easier and more dynamic image capturing for consistency purposes, since such functionality is taught to be highly desirable by Rothberg, as evidenced by Das, as set forth above.
Thus, the rationale to support a conclusion that the claim would have been obvious is that a method of enhancing a particular class of devices (methods, or products) has been made part of the ordinary capabilities of one skilled in the art based upon the teaching of such improvement in other situations. One of ordinary skill in the art would have been capable of applying this known method of enhancement to a “base” device (method, or product) in the prior art and the results would have been predictable to one of ordinary skill in the art. The Supreme Court in KSR noted that if the actual application of the technique would have been beyond the skill of one of ordinary skill in the art, then using the technique would not have been obvious. KSR, 550 U.S. at 398, 82 USPQ2d at 1396. If any of these findings cannot be made, then this rationale cannot be used to support a conclusion that the claim would have been obvious to one of ordinary skill in the art.
responsive to the determining that the at least a first image is unusable, outputting, by the mobile computing device, feedback instructions to capture one or more new images for determining whether the one or more new images are of an acceptable type of identification card and unusable by the remote post-validation platform.
As set forth above, the Examiner finds that the “determining” step including both the determination of an acceptable type of identification card and unusability as indefinite. (See § X.B. supra). Thus, for examination purposes, the “determining” step is simply the determination the identification card is confirmed and deemed unusable.
From this perspective, the Examiner finds that Ladron discloses the mobile computing device comprising a computing system having computer code program residing on a carrier that performs the method of pre-validation. (Ladron at p.6, l.25 – p.7, l.19; p.8, l.1-27). The Examiner finds that Ladron discloses the mobile computing device having the pre-validation processing, thereon, which includes the capturing of images of objects, in real-time, and utilizing an image processing method based upon artificial intelligence indicating whether a complete view of the ID is depicted. (Id. at p.12, l.13 – p.13, l.21). The Examiner finds that Ladron discloses the mobile computing device utilizing an image processing method based upon artificial intelligence indicating whether a complete view of the ID is depicted, which is inherently equivalent to whether an image of the captured ID document is unusable or usable and providing the user information to correct the orientation issue. (Id. at p.12, l.13 – p.13, l.13; p.13, ll.23-26). The Examiner finds that Ladron discloses the mobile device providing notification to the user to change the orientation of the device via an image and/or sound and/or vibration means. (Id. at p.13, 11.15-21).
The Examiner finds that Rothberg further teaches the computing device 104, 1502 outputting image capture framing feedback information to a user via a display screen 106, 1508 of the computing device 104, 1502. (Rothberg at ¶¶ 0005, 0144-0151, 0154, 0185, 0027-0234, 0254; see Figures 1, 9, 14, 18A-18E).
As set forth above, the Examiner finds that Rothberg teaches and/or renders obvious utilizing an image processing method based upon one or more neural networks which are trained with set of images, including some images that have been rejected and accepted by the remote post-validation platform, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected to specifically indicate whether a complete view of an object is being depicted. (See § XII.A.(3).b, supra).
In addition, the Examiner finds that Rothberg additionally teaches the computing device 104, 1502 determining that the image contains the target view and provides an output that is either a confirmation of proper positioning or instructions to move the device in a particular direction. (Rothberg at ¶ 0230). The Examiner finds that the latter would include that the target view is confirmed as being within the image and unusable since instructions are required to attain a proper image. (Id.)
Thus, and in addition, the Examiner finds that Rothberg and Das teaches and/or renders obvious the determining that the at least a first image is of an acceptable type of identification card and unusable. (See § XII.A.(3).b, supra).
With respect to the limitations of claim 19, Ladron, Rothberg and Das teaches and/or renders obvious
[19] wherein the application comprises a pre-trained machine vision model configured to process the one or more captured images.
In this regard, Ladron teaches the application being configured to run on the mobile computing device. (Ladron at p.8, ll.1-27p.12, l.16 – p.13, l.21). Moreover, as set forth above, the Examiner finds that Rothberg teaches and/or renders obvious utilizing an image processing method based upon one or more neural networks which are trained with set of images, including some images that have been rejected and accepted by the remote post-validation platform, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected to specifically indicate whether a complete view of an object is being depicted. (See § XII.A.(3).b, supra).
The Examiner finds that Rothberg further teaches the neural network application including a pre-trained vision model. (Rothberg at ¶¶ 0146; 0259-0269; see Figure 14).
Thus, the Examiner finds that Rothberg teaches and/or renders obvious the application being a native application configured to execute on the mobile computing device, the native application comprising a pre-trained machine vision model configured to process the one or more captured images. (See § XII.A.(3).b, supra).
With respect to the limitations of claim 20, Ladron, Rothberg and Das teaches and/or renders obvious
[20] wherein the pre-trained machine vision model is configured to evaluate the one or more quality features of the one or more captured images based on pre-trained features of the pre-trained machine vision model
In this regard, Ladron teaches the utilization of an image processing detection method to determine one or more parameters of detected elements of an ID document in which one or more neural networks are trained with set of images using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected. (Ladron at p.14, l.19 – p.15, l.8; p.15, ll.25-28; p.16, ll.3-5, 16-20; p.18, l.27 – p.19, l.16; p.25, ll.11-34; emphasis added).
The Examiner finds that it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the pre-trained machine vision model being configured to evaluate one or more features of the one or more captured images based on pre-trained features of the model as described in Ladron to the computer-implemented method of pre-validation of object images of Ladron, Rothberg and Das.
A person of ordinary skill in the art would be motivated to incorporate the pre-trained machine vision model being configured to evaluate one or more features of the one or more captured images based on pre-trained features of the model, since it provides a mechanism to provide an authenticity indicator of the object/document in question. (Ladron at Abstract; p.3, ll.9-24; p.4, ll.11-14). In other words, such a modification would provide an apparatus and method for pre-validation of object image which is based upon a safer and more robust and secure process, thereby increasing the overall efficiency of the apparatus and method. (Id.)
Claims 13 and 14 are rejected under 35 U.S.C. 103(a) as being unpatentable over Ladron et al. (International Publication No. WO 2017/207064 A1) (“Ladron”) in view of Rothberg et al. (U.S. Publication No. 2017/0360412) (“Rothberg”) and Das et al. (U.S. Patent No. 10,962,939) (“Das”) as applied to claims 1-20 above, and further in view of Sliz et al. (U.S. Patent No. 10,587,796)(“Sliz”).26
With respect to the limitations of claims 13 and 14, and
[13] wherein the one or more quality features comprise an indication that the one or more images were captured by the image capture component (claim 13);
[14] wherein the one or more processors are configured to determine a distance between the image capture component and the identification card (claim 14);
To the degree a reviewing body finds that Ladron does not teach and/or render obvious “wherein the one or more quality features comprise an indication that the one or more images were captured by the image capture component,” the following alternative to this feature is provided as set forth below:
In this regard, Ladron, Rothberg and Das discloses all the limitations, as previously set forth, except for specifically calling for the one or more quality features comprising an indication that the one or more images were captured by the image capture component; and the analyzing determining distance between the image capturing device of the mobile computing device and the object.
However, an artificial intelligence processing utilizing contrast information as a classified feature is known in the art. The Examiner finds that Sliz, for example, teaches the utilization of an image processing, machine learning process technique, calculation, algorithm, and or the like to analyze the image data to extract a set of image capture parameters. (Sliz at c.5, ll.49 – c.6, l.2; c.6, l. 53 – c.7, l.12; c.7, ll.30-49; see Figures 1, 2, 5, 6). The Examiner finds that Sliz teaches several parameters including type of image capture component, and distance from the object(s) depicted in the image data. (Id.)
The Examiner finds that it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the one or more quality features comprising an indication that the one or more images were captured by the image capture component, and determining distance between the image capturing device of the mobile computing device and the object as described in Sliz in the computer-implemented method of pre-validation of object images of Ladron, Rothberg and Das.
A person of ordinary skill in the art would be motivated to provide the one or more quality features comprising contrast information, an indication that the one or more images were captured by the image capture component, and determining distance between the image capturing device of the mobile computing device and the object, since it provides a mechanism to utilize the conditions or environment associated with the capture of an image to classify an object. (id. at c.6, ll.63-66). In other words, such a modification would increase the accuracy of classifying an object, thereby inherently increasing the operational efficiency.
Response to Arguments
Oath/Declaration Issue
With respect to the defective Nov 2021 Oath/Declaration, the Examiner finds that no new Oath/Declaration has been filed. Applicant provides potential Oath/Declaration language to overcome the Oath/Declaration issues (i.e., compliance under declaration under 35 U.S.C. 251). (See Sept 2024 Applicant Response at 22). However, since new claim 35 is canceled in the instant Sept 2024 Claim Amendment, the Examiner finds the broadening of the claim 35 provided by Applicant in the potential Oath/Declaration is no longer present in the instant ‘668 Reissue Application. (See § XI.A, supra).
Thus, In light of the discussion above with respect to the Oath/Declaration, the Examiner finds that there is an outstanding 35 U.S.C. 251 rejection issue still present. (See § VII, supra).
Claim Objection(s)
With respect to the Claim Objections, the Sept 2024 Applicant Response, including the Sept 2024 Claim Amendment and “Remarks,” has been fully considered and is persuasive. (See Sept 2024 Applicant Response at 20).
Claim Interpretation - 35 U.S.C. § 112, Sixth Paragraph, Invocation
Instructions/Program Code
Applicant contends that “instructions” is not a generic placeholder and is supported by sufficient structure for performing the entire claimed function within the functional phrases. (See Sept 2024 Applicant Response at 20-22). Applicant further contends that the Sept 2024 Claim Amendment, replacing “instructions with “program code,” removes the claims from the invocation of 35 U.S.C. § 112, sixth paragraph.” (Id. at 22).
The Examiner respectfully disagrees. The Examiner finds that the specification of the ‘343 Patent does not specifically define “instructions” nor “program code” and thus the specification of the ‘343 Patent does not impart or disclose any structure for the phrase. Rather, the Examiner finds that the ‘343 Patent uses this same phrase to describe several instructions. This is evidence by the recitation to “the memory comprises program code that, when executed by the one or more processors, cause the system to determine, …, whether at least a first image of the one or more images is of an acceptable type of identification card and unusable; and to output feedback instructions to capture one or more images….” (Sept 2024 Claim Amendment at claim 10).
While one of ordinary skill in the art would agree that off-the-shelf “instructions” and “program code” (e.g. an operating system) are structure, the question is not whether these terms are structure but whether these terms are “sufficient structure” as defined by the Federal Circuit. For example, for FP2-FP3, what is clear from the express claimed language is that “program code,” that is comprised within the “memory” (in e.g., claim 10), performs at least two separate functions (see: Functions of FP2-FP3) discussed above. Moreover, in the Prong A analysis performed by the Office in regards to whether Functional Phrases 2-3 (i.e., Program Code I and Program Code II) invoke 35 U.S.C § 112 6th Paragraph, the Examiner found there is no disclosure or suggestion from the prior art that program code is a sufficient and definite structure to perform the functions recited in FP2-FP3. Specifically, the Examiner cited to prior art (i.e., U.S. Patent No. 10,962,939 and U.S. Patent No. 10,587,796) illustrating program code for: creating a trained neural network from a plurality of captured images; utilizing the trained neural network to determine whether a captured image is consistent with baseline parameters; and in response thereto, providing either instructions for recapture or certain other permitted actions having different program code (i.e., implemented blocks) and distinct operations from any of the instructions of the ‘343 Patent.
Moreover, it is the Examiner’s position that without the application of 35 U.S.C § 112 6th Paragraph to the Functional Phrases, Applicant would be obtaining a right to exclude others for all ways and methods for performing the Functions of FP2-FP3. For example, without the invocation of § 35 U.S.C § 112 6th Paragraph, FP2 utilizes pure functional language since such claims, as currently drafted, will cover all methods and manner of performing the Functions of FP2. Yet the Federal Circuit is clear that 35 U.S.C § 112 6th Paragraph was promulgated by Congress in order to limit such ‘purely functional language.’ As noted in Greenberg:
As this court has observed, the record is clear on why paragraph six was enacted. In Halliburton Oil Well Cementing Co. v. Walker, 329 U.S. 1, 67 S.Ct. 6, 91 L.Ed. 3, 71 USPQ 175 (1946), the Supreme Court held invalid a claim that was drafted in means-plus- function fashion. Congress enacted paragraph six, originally paragraph three, to overrule that holding. In place of the Halliburton rule, Congress adopted a compromise solution, one that had support in the pre-Halliburton case law: Congress permitted the use of purely functional language in claims, but it limited the breadth of such claim language by restricting its scope to the structure disclosed in the specification and equivalents thereof. [Emphasis added.]
Greenberg v. Ethicon Endo-Surgery, Inc., 91 F.3d 1580, 1582 (Fed. Cir. 1996) (select citations and quotations omitted) (“Greenberg”).
This reasoning was again set forth by the Federal Circuit in Aristocrat Techs. Australia PTY Ltd. v. Intl. Game Tech., 521 F.3d 1328, 1333 (Fed. Cir. 2008) (“Aristocrat”), “The point of the requirement that the patentee disclose particular structure in the specification and that the scope of the patent claims be limited to that structure and its equivalents is to avoid pure functional claiming. [Emphasis added.]”
Finally and to use colloquial terms, 35 U.S.C § 112 6th Paragraph is simply shorthand notation for structure. In essence, a claimed functional phrase that invokes 35 U.S.C § 112 6th Paragraph incorporates the “corresponding structure” from the specification into the claim as if it were expressly recited in the claim. If the claimed functional phrase is computer implemented as argued by Applicant, then the “corresponding structure” is an algorithm.27 Thus, a computer implemented functional phrase that invokes 35 U.S.C § 112 6th Paragraph incorporates the “corresponding structure” (i.e. algorithm) into the claim as if the algorithm were expressly recited in the claim.
As noted above, without the application of 35 U.S.C § 112 6th Paragraph, the Functional Phrases 2-3 would amount to pure functional claiming, as set forth by the Federal Circuit in Greenberg and Aristocrat.
In summary, without the application of 35 U.S.C § 112 6th Paragraph, Applicant is simply committing a cardinal sin of claim construction—reading the specification into the claim. See Teleflex, Inc. v. Ficosa N. Am. Corp., 299 F.3d 1313, 1324 (Fed. Cir. 2002) (noting that “the district court committed a ‘cardinal sin’ of claim construction by importing limitations from the written description into the claims”)(overruled on other grounds).
Thus, the Examiner concludes and maintains that the “program code” elements, as recited in claims 1-20, do invoke 35 U.S.C. § 112, sixth paragraph, and will be examined as such.
35 U.S.C. § 112 Rejections
35 U.S.C.§ 112(b) Rejections
With respect to the 35 U.S.C. 112(b) rejections of claims 16 and 26-29 (see May 2024 Final Office Action at 24-26), the Sept 2024 Applicant Response, including the Sept 2024 Claim Amendment and “Remarks,” has been fully considered and is persuasive. (See Sept 2024 Applicant Response at 22-23).
However, the Examiner finds that Functional Phrases 2-3 (i.e., program code) still invokes 35 U.S.C § 112 6th paragraph, and as set forth above, the ‘343 Patent fails to clearly link or associate the disclosed structures, materials, or acts to the claimed functions such that one of ordinary skill in the art would recognize what structures, materials, or acts perform the claimed function.
Thus, the Examiner finds that there are outstanding 35 U.S.C. § 112(b) rejection issues still present. (See § X.B, supra).
35 U.S.C.§ 112(d) Rejections
With respect to the 35 U.S.C. 112(d) rejection of claims 16 (see May 2024 Final Office Action at 19-20), the April 2024 Applicant Response, including the Sept 2024 Claim Amendment and “Remarks,” has been fully considered and is persuasive. (See Sept 2024 Applicant Response at23).
35 U.S.C. § 251 Rejections
Oath/Declaration Issue
With respect to the rejection of claims under 35 U.S.C. § 251, the Examiner finds that Applicant’s arguments are not persuasive. (Sept 2024 Applicant Response at 23). The Examiner finds that there is an outstanding 35 U.S.C. 251 rejection issue still present. (See §§ VII, XI.A, supra).
Original Patent Requirement
With respect to the rejection of claims under 35 U.S.C. § 251, the Sept 2024 Applicant Response, including the Sept 2024 Claim Amendment and “Remarks,” has been fully considered and is persuasive. (See April 2024 Applicant Response at 23).
However, in light of the Sept 2024 Claim Amendment, the Examiner finds that there is an outstanding 35 U.S.C. § 251, original patent requirement, rejection issue still present. (See § XI.B, supra).
35 U.S.C. § 103 Rejections
Claims 1-21, 24-26, 29, 30 and 33-3528
Applicant contends that Ladron is silent to distinguishing “acceptable and unacceptable types of identification cards,” and Rothberg is not combinable with Ladron because Rothberg (1) is not related to identification cards; and (2) utilizes an ultrasound device to capture digital images instead of a digital camera. (Sept 2024 Applicant Response at 30-32). In addition, Applicant contends that Das is silent about training a neural network on “acceptable and unacceptable types of identification cards.”
Combination of Ladron and Rothberg Issue
With respect to the contention that Rothberg is not combinable with Ladron, the Examiner respectfully disagrees. First, the Examiner finds that the instant rejection is an obviousness-type rejection and not an anticipation rejection. (See § XII.A, supra). Thus, neither Ladron nor Rothberg, alone, are cited for all of the structure and steps of claims. Moreover, while one of ordinary skill in the art would recognize that Rothberg discloses utilizing a different image capturing device to capture digital images than claims 1 and 18 of the instant ‘668 Reissue Application, the Examiner finds that Rothberg is cited to fulfill the differences between the prior art and the claims at issue. (Id.)
From this perspective, the Examiner finds that Ladron discloses a method for authenticating a document from, obtained images of the document. (Ladron at Abstract. p.1, ll.5-7; p.4, l.26 – p.2, l.12; p.3, l.26 – p.4, l.23; p.5, ll.16 -20; p.6, l.17 – p.7, l.27; p.8, ll.1-27; p.12, l.13 – p.13, l.21; p.14, l.19 – p.15, l.8; ; p.15, ll.25-28; p.16, ll.3-5, 16-20; p.18, l.27 – p.19, l.16; p.25, ll.11-34; see Figure 1, 4). In the method disclosed by Ladron, the Examiner finds that Ladron discloses a pre-validation processing including the capturing of images of ID documents, in real-time, and utilizing an image processing method based upon artificial intelligence indicating whether a complete view of the ID is depicted. (Id. at p.12, l.13 – p.13, l.21). In addition, the Examiner finds that Ladron specifically discloses a mobile computing device with a camera (i.e., mobile phone, camera, tablet, smart watch, etc.) capturing images of a document (i.e., ID card, driving license, credit, debit, business, medical insurance card, passport, tickets, etc.). (Id. at p.8, ll.1-27). However, since Ladron is silent to the actual image processing method being utilized to indicate whether a complete view of ID document is depicted, the Examiner finds that one of ordinary skill in the art would look to the teachings of image processing methods based upon one or more neural networks which are trained with set of images, including some images that have been rejected by the remote post-validation platform, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected to specifically indicate whether a complete view of an object is being depicted is unusable or usable to improve the method for authenticating a document of Ladron
In this light, the Examiner finds that Rothberg is concerned with image processing methods based upon one or more neural networks which are trained with set of images, including some images that have been rejected by the remote post-validation platform, using machine learning techniques in such a way that output of the networks represent the one or more parameters being detected to specifically indicate whether a complete view of an object is being depicted, and its ability to provide the improvement of ensuring correct and proper images for further and future processing, thereby dynamically determining whether a target image is acceptable (i.e., increasing the overall efficiency of the apparatus and method). (Rothberg at ¶¶ 0141-0142, 0145). Specifically, the Examiner finds that Rothberg discloses a portable handheld computing device 104, 1502, 1804 for capturing images in which a pre-validation process is performed on the captured images before further processing is performed on the captured images. (Rothberg at Abstract; ¶¶ 0005, 0009, 0011-0013, 0106, 0127, 0132, 0144-0147, 0149-0151, 0181-0193, 0227-0234, 0254, 0259-270; see Figures 1, 9, 14, 18A-18E). The Examiner finds that Rothberg discloses an automated image analysis which utilizes neural network model analysis to determine whether a particular image is in the correct view (i.e., unusable or usable) and, based upon the neural network model analysis, provide a user direction on how to correct the orientation to capture the correct view (i.e., usable) of the feature. (Id.)
In conclusion, since Ladron explicitly discloses a method for authenticating a document from, obtained images of the document utilizing image processing techniques and Rothberg teaches image processing methods based upon one or more neural networks which are trained with set of images, including some images that have been rejected by the remote post-validation platform, using machine learning techniques to specifically indicate whether a complete view of an object is being depicted in order to dynamically determine whether a target image is acceptable and ensure correct and proper images are provided for further and future processing, the Examiner finds that one of ordinary skill in that art would look to improve the method for authenticating a document of Ladron with utilizing the neural network model analysis to determine whether a particular image is in the correct view (i.e., unusable or usable) and, based upon the neural network model analysis, provide a user direction on how to correct the orientation to capture the correct view (i.e., usable) of the feature. (Rothberg at Abstract; ¶¶ 0005, 0009, 0011-0013, 0106, 0127, 0132, 0144-0147, 0149-0151, 0181-0193, 0227-0234, 0254, 0259-270; see Figures 1, 9, 14, 18A-18E).
Consequently, irrelevant of Rothberg not specifically utilizing a digital camera to capture images needing to be determined acceptable, the Examiner concludes that one of ordinary would look to the teachings of Rothberg and combine them with Ladron. Thus, the Office has properly established a prima facie case of obviousness.
Applicant contends that the “trained neural network” cannot being interpreted as a “product-by-process” limitation.
Das Issue
With respect to Das not specifically utilizing “acceptable and unacceptable types of identification cards” to train its neural network, the Examiner respectfully agrees. However, the Examiner finds that the teaching of utilizing acceptable and unacceptable types of categories to train neural network is applicable to the any training of a neural network, whether it is based on types of identification cards, or not. Specifically, the Examiner finds that Das teaches an automated image analysis which utilizes a neural network model analysis to determine whether a particular image is in a restricted or unrestricted image category. (Das at Abstract; c.2, ll.21-25; c.5, ll.22-29, 38-60; c.7, ll.12-56; c.8, l.13 – c.10, l.43; c.27, ll.6-27, 36-38, 45-46; c.29, l.1 – c.30, l.18; see Figures 2, 3). The Examiner finds that Das further teaches the neural network model including a convolutional neural network (“CNN”) which may include training images that belong to certain categories and images that do not belong to certain categories. (Id. at c.9, ll.15-25; emphasis on images belonging/not belong to certain categories being equivalent to images being acceptable/unacceptable types of images). The Examiner finds that one of ordinary skill in that art would look to improve the method for authenticating a document of Ladron and Rothberg with utilizing the neural network model trained additionally with images (i.e., identification card images) that belong to certain categories (i.e., acceptable types of ID cards) and images that do not belong to certain categories (i.e., unacceptable types of ID cards) since it provides a mechanism to utilize the “hard negative mining” technique to further distinguish between whether an image belongs to a certain category or not. (Das at c.9, ll.15-25).
Consequently, irrelevant of Das not specifically utilizing “acceptable and unacceptable types of identification cards” to train a neural network, the Examiner concludes that one of ordinary would look to the teachings of Das and combine them with Ladron and Rothberg. (See §§ XII.A.(1).b; XII.A.(2).d; and XII.A.(3).b, supra). Thus, the Office has properly established a prima facie case of obviousness.
Claims 13, 14, 23, 24, 28, 32 and 3329
Applicant contends that
Sliz fails to teach or suggest the recitations of claim 10 missing from the combination of Ladron, Rothberg, and Das. Accordingly, dependent claims 13 and 14 are allowable at least as being dependent on allowable claim 10.
(See Sept 2024 Applicant Response at 25).
The Examiner respectfully disagrees. The Examiner finds no specific arguments to the additional features, only to the previous combination failing to disclose or render obvious the claimed limitations. The Examiner finds this contention the same as previously set forth by Owner. (Sept 2024 Applicant Response at 23-24). Thus, the Examiner finds this argument addressed above. (See § XIII.F.(1), supra).
Conclusion
Applicant is respectfully reminded that any suggestions or examples of claim language provided by the Examiner are just that—suggestions or examples—and do not constitute a formal requirement mandated by the Examiner. To be especially clear, any suggestion or example provided in this Office Action (or in any future office action) does not constitute a formal requirement mandated by the Examiner.
Should Applicant decide to amend the claims, Applicant is also reminded that—like always—no new matter is allowed. The Examiner therefore leaves it up to Applicant to choose the precise claim language of the amendment in order to ensure that the amended language complies with 35 U.S.C. § 112 1st paragraph.
Independent of the requirements under 35 U.S.C. § 112 1st paragraph, Applicant is also respectfully reminded that when amending a particular claim, all claim terms must have clear support or antecedent basis in the specification. See 37 C.F.R. § 1.75(d)(1) and MPEP § 608.01(o). Should Applicant amend the claims such that the claim language no longer has clear support or antecedent basis in the specification, an objection to the specification may result. Therefore, in these situations where the amended claim language does not have clear support or antecedent basis in the specification and to prevent a subsequent ‘Objection to the Specification’ in the next office action, Applicant is encouraged to either (1) re-evaluate the amendment and change the claim language so the claims do have clear support or antecedent basis or, (2) amend the specification to ensure that the claim language does have clear support or antecedent basis. See again MPEP § 608.01(o) (¶3). Should Applicant choose to amend the specification, Applicant is reminded that—like always—no new matter in the specification is allowed. See 35 U.S.C. § 132(a). If Applicant has any questions on this matter, Applicant is encouraged to contact the Examiner via the telephone number listed below.
Applicant is reminded of the obligation to apprise the Office of any prior or concurrent proceedings in which the ‘343 Patent is or was involved, such as interferences or trials before the Patent Trial and Appeal Board, other reissues, reexaminations, or litigations and the results of such proceedings.
In accordance with MPEP § 1406, the Examiner has reviewed and considered the prior art cited or ‘of record’ in the original prosecution of the ‘343 Patent. Applicant is reminded that a listing of the information cited or ‘of record’ in the original prosecution of the ‘343 Patent need not be resubmitted in this reissue application unless Applicant desires the information to be printed on a patent issuing from this reissue application.
Applicant is further reminded of the continuing obligation under 37 C.F.R. §1.56 to timely apprise the Office of any information which is material to patentability of the claims under consideration in this reissue application.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to STEPHEN J RALIS whose telephone number is (571)272-6227. The examiner can normally be reached on Monday-Friday 8:30am-5:30pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Hetul Patel can be reached on 571-272-4184. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Stephen J. Ralis/Primary Examiner, Art Unit 3992 Conferees:
/LUKE S WASSUM/Primary Examiner, Art Unit 3992 /H.B.P/Hetul PatelSupervisory Patent Examiner, Art Unit 3992
SJR
05/07/2025
1 The Examiner notes that all of the Rejected Claims stood rejected under 251 and 103; claims 10-17 stood rejected under 112(a); claims 10-17 and 26-29 stood rejected under 112(b); claims 1-3, 5, 7-11, 16, 18-21, 25, 26, 29, 30 and 34 stood rejected under 102(a)(1-2); and claims 1-20 stood rejected under non-statutory double patenting.
2 Claims 1, 6, 8, 10, 11, 14, 16-18 and 20 amended in the instant April 2024 Claim Amendment.
3 Claims 3, 9 and 15 amended in the instant April 2024 Claim Amendment; and claim 20 amended in the Nov 2021 Claim Amendment.
4 Claims 24, 26-28 and 33 amended in instant April 2024 Claim Amendment.
5 Claim 35 added in the instant April 2024 Claim Amendment; and claims 21-23, 25, 29-32 and 34 added in the Nov 2021 Claim Amendment.
6 The Examiner notes that all of the Rejected Claims stood rejected under 251 and 103; claims 10-17 and 26-29 stood rejected under 112(b); and claim 16 stood rejected under 112(d).
7 Claims 1, 10, 16 and 18 amended in the instant Sept 2024 Claim Amendment.
8 Claims 6, 8, 11, 14 and 17 amended in the April 2024 Claim Amendment.
9 Claims 3, 9, 15 and 20 amended in the April 2024 Claim Amendment; and claim 20 amended in the Nov 2021 Claim Amendment.
10 Claims 21-35 canceled in the Sept 2024 Claim Amendment.
11 Even though the Examiner deems there is now two i.e., ”first and second means-plus function phrases” in the instant ‘668 Reissue Application, the Examiner finds that the instant Final Office action will continue with the numbering nomenclature designated in the Nov 2023 Non-Final Office Action for the “Program Code” functional phrase (i.e., “Functional Phrase 2” or “FP2”) and provide additional consecutive functional phrases (i.e., “Functional Phrase 3” or “FP3,” etc.) for consistency basis.
12 Since it is unclear what exact structure the Applicant intend to claim as the structure embodying the “program code” of Functional Phrase 2, the claims are subject to rejection under 35 U.S.C. § 112 second paragraph as failing to particularly point out and distinctly claim the subject matter which the inventors regard as the invention.
13 As set forth above, the ‘343 Patent does not sufficiently disclose the difference between “usable” and “acceptable.” Thus, the Examiner is construing “acceptable” as just the image has been “confirmed” as proper, and will be examined as such.
14 Even though the Examiner deems there is now two i.e., ”first and second means-plus function phrases” in the instant ‘668 Reissue Application, the Examiner finds that the instant Final Office action will continue with the numbering nomenclature designated in the Nov 2023 Non-Final Office Action for the “Program Code” functional phrase (i.e., “Functional Phrase 2” or “FP2”) and provide additional consecutive functional phrases (i.e., “Functional Phrase 3” or “FP3,” etc.) for consistency basis.
15 Although not necessary, the Examiners have reviewed the rest of claim 10 and the entirety of the claim does not contain sufficient structure for performing the functions as set forth within the Functional Phrase.
16 Since it is unclear what exact structure the Applicant intend to claim as the structure embodying the “program code” of Functional Phrase 3, the claims are subject to rejection under 35 U.S.C. § 112 second paragraph as failing to particularly point out and distinctly claim the subject matter which the inventors regard as the invention.
17 See footnote 11, supra.
18 Claims 1, 10 and 18.
19 Also see c.8, l.21 – c.10, l.15 for system and CRM disclosures.
20 Images belonging/not belong to certain categories being equivalent to images being acceptable/unacceptable types of images.
21 Id.
22 Images belonging/not belong to certain categories being equivalent to images being acceptable/unacceptable types of images.
23 Id.
24 Images belonging/not belong to certain categories being equivalent to images being acceptable/unacceptable types of images.
25 Id.
26 The Examiner finds that claim requirements of claims 13 and 14 are deemed inherent and/or obvious over Ladron. (See § XII.A, supra). However, claims 13 and 14 are additionally rejected in the instant rejection as, in the alternative, being obvious in view of Sliz.
27 “Aristocrat and related cases hold that, if a patentee has invoked computer-implemented means-plus-function claiming, the corresponding structure in the specification for the computer implemented function must be an algorithm unless a general purpose computer is sufficient for performing the function. [Emphasis added.]” Apple Inc. v. Motorola, Inc., 757 F.3d 1286, 1298 (Fed. Cir. 2014)(citing Aristocrat Techs. Austl. Pty Ltd. v. Int'l Game Tech., 521 F.3d 1328, 1333 (Fed. Cir. 2008)).
28 The Examiner finds that claims 21-35 have been canceled in the instant Sept 2024 Claim Amendment, thus, claims 1-20 are the only claims before the Office.
29 The Examiner finds that claims 21-35 have been canceled in the instant Sept 2024 Claim Amendment, thus, claims 1-20 are the only claims before the Office.
Prosecution Insights