DETAILED ACTION
Response to Amendment
Applicant’s amendment filed August 17, 2026, has been entered in full. Claims 2-21 are pending.
Election/Restrictions
Applicant's election with traverse of the species of Figure 4 in the reply filed on August 17, 2026, is acknowledged. The traversal is on the ground(s) that (a) claims directed to the non-elected species are not distinct from claims directed to the elected species (Remarks filed August 17, 2026, hereinafter Remarks: Page 1) and (b) there is no serious burden (Remarks: Pages 1-2). This is not found persuasive.
Regarding traverse (a), Applicant argues that the species are not distinct because “Claims 8 and 9 depend from claim 2 and further define a particular implementation of the same pixel-metadata consistency analysis recited in the elected claims.” (Remarks: Page 1). Applicant is apparently arguing that it is improper to restrict a species (i.e., a particular implementation) when a generic claim is present (i.e., the pixel-metadata analysis in claim 2). Examiner respectfully disagrees. “Where an application includes claims directed to different embodiments or species that could fall within the scope of a generic claim, restriction between the species may be proper if the species are independent or distinct.” MPEP 806.04. Examiner’s restriction requirement provided a detailed explanation of why the species are independent or distinct (Page 2) and Applicant has not disputed any of the specific factual determinations in that explanation, such as that the claims to the species recite mutually exclusive characteristics and that they are not obvious variants of each other based on the current record. For at least these reasons, Applicant’s traverse (a) is respectfully non-persuasive.
Regarding traverse (b), Applicant asserts that that “Any prior-art search directed to machine-learning-based analysis of pixel vectors, metadata vectors, predicted metadata, consistency information, and authenticity classification would reasonably encompass the subject matter of both groups.” Examiner respectfully disagrees. For example, as noted in the restriction requirement (Page 3), the non-elected species of Figure 5 (reflected in claims 8 and 9) requires search queries directed to Gaussian mixture models (Fig. 5, element 506). Such search queries are unlikely to result in finding prior art pertinent to the species of Figure 4 because that species does not use any Gaussian mixture models (Fig. 4). Furthermore, the Gaussian mixture model of Figure 5 is used in a fundamentally different way than the machine learning in the species of Figure 4. The Gaussian mixture model of Figure 5 accepts a concatenation of an embedding and a metadata vector as an input and outputs a vector of posterior probabilities, while the machine learning of Figure 4 accepts a pixel vector as input and outputs a predicted metadata vector. Contrary to Applicant’s assertions, there is no reason to believe that searches directed to machine learning models that predict a metadata vector from pixel data will identify prior art describing Gaussian mixture models that accept concatenations of embeddings and metadata vectors and output posterior probabilities, or vice versa. For at least these reasons, Applicant’s traverse (b) is respectfully non-persuasive.
The requirement is still deemed proper and is therefore made FINAL.
Claims 8 and 9 are withdrawn from further consideration pursuant to 37 CFR 1.142(b), as being drawn to a nonelected species, there being no allowable generic or linking claim. Applicant timely traversed the restriction (election) requirement in the reply filed on August 17, 2026.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. 17/374,439, filed on July 13, 2021.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on December 3, 2024, is being considered by the examiner.
Claim Objections
Claim(s) 7 and 16 is/are objected to because of the following informalities:
In claim 7, line 1, “the” should be inserted after “wherein”
In claim 16, line 1, “the” should be inserted after “wherein”
Appropriate correction is required.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 2-21 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-8 of U.S. Patent No. 12,165,424. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of the patent anticipate those of the instant application – see the summary table below.
Claims 2 and 11 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and 16 of U.S. Patent No. 12,518,141. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of the patent anticipate those of the instant application – see the summary table below. Note that at least the user identifier of the claims in the patent falls within the scope of an image metadata vector.
Summary of Double Patenting Rejections
Claim of This Application
Anticipating Claim of U.S. Patent No. 12,165,424
Anticipating Claim of U.S. Patent No. 12,518,141
2
1
16
3
2
4
1
5
1
6
1
7
1
8*
9*
10
3
11
4
1
12
2
13
4
14
4
15
4
16
4
17
5
18
6
19
7
20
8
21
7
*These claims have been withdrawn from consideration
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 19-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because their scope(s) include(s) embodiments of transitory forms of signal transmission.
MPEP 2106.03, Subsection II, includes the following instructions:
“A claim whose BRI covers both statutory and non-statutory embodiments embraces subject matter that is not eligible for patent protection and therefore is directed to non-statutory subject matter. Such claims fail the first step (Step 1: NO) and should be rejected under 35 U.S.C. 101, for at least this reason. In such a case, it is a best practice for the examiner to point out the BRI and recommend an amendment, if possible, that would narrow the claim to those embodiments that fall within a statutory category.
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18
19
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For example, the BRI of machine readable media can encompass non-statutory transitory forms of signal transmission, such as a propagating electrical or electromagnetic signal per se. See In re Nuijten, 500 F.3d 1346, 84 USPQ2d 1495 (Fed. Cir. 2007). When the BRI encompasses transitory forms of signal transmission, a rejection under 35 U.S.C. 101 as failing to claim statutory subject matter would be appropriate. Thus, a claim to a computer readable medium that can be a compact disc or a carrier wave covers a non-statutory embodiment and therefore should be rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. See, e.g., Mentor Graphics v. EVE-USA, Inc., 851 F.3d at 1294-95, 112 USPQ2d at 1134 (claims to a "machine-readable medium" were non-statutory, because their scope encompassed both statutory random-access memory and non-statutory carrier waves).”
Claim 19 recites a “computer program product … comprising a computer-readable medium having program instructions embodied therewith …”. The scope of such machine-readable media is broad enough to “encompass non-statutory transitory forms of signal transmission, such as a propagating electrical or electromagnetic signal per se.” MPEP 2106.03, Subsection II. For at least this reason, the claims are directed to non-statutory subject matter under 35 U.S.C. 101.
Examiner suggests amending claim 19 to recite “a non-transitory computer-readable medium” in order to overcome this rejection.
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.
Claim(s) 2-7, 10-16, and 19-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over ‘Fan’ (“Image Forensics on Exchangeable Image File Format Header,” 2014; cited and copy provided in “parent” U.S. Patent Application No. 17/374,439) in view of ‘Gupta’ (US 2022/0414854 A1).
Regarding claim 2, Fan teaches a system (see Note Regarding Implementation below), comprising:
a processor that executes computer-executable instructions stored in a computer-readable memory, which causes the system to (see Note Regarding Implementation below):
receive, from a client device, an electronic image (Note: the following mapping focuses on embodiments applying non-linear regression as described in Chapter 4, but aspects of the white-balance-based forgery detection described in Chapter 5 also read on the claim; e.g., Figure 4.2 (b), testing image is received; see Note Regarding Implementation below);
obtain a pixel vector and an image metadata vector that correspond to the electronic image (e.g., Fig. 4.2 (b), image metadata in form of EXIF header features are extracted along with pixel data used to extract image noise features; also see, e.g., Figs. 1.4-1.5 and 4.1, Section 1.3); and
determine a binary authenticity classification (e.g., Fig. 4.2, detection outcome; also see Sec. 4.2.4) based on an analysis, performed by at least one machine learning model, of the pixel vector and the image metadata vector (e.g., Fig. 4.2 (b), extraction of image noise and EXIF features, generation and computation of selected features, and computation of numerical difference for each EXIF feature; Sec. 4.2.3, non-linear regression model is applied to predict EXIF parameters including aperture, shutter speed and ISO given pixel data; Sec 4.2.4, differences between the predicted EXIF parameters and those stored in the original EXIF header are determined and used to determine whether the image is authentic or forged; The non-linear regression model is within the scope of a “machine learning model” or an “artificial intelligence algorithm” – see, e.g., [0067] of the published application).
Note Regarding Implementation. Fan’s disclosure focuses on algorithms for image forgery detection (e.g., Fig. 4.2). Fan does not specifically describe details of what hardware is used to implement its algorithms. In particular, Fan does not explicitly teach that its algorithms are implemented in a system comprising a processor that executes computer-executable instructions stored in a computer-readable memory, which causes the processor to perform the algorithm, or that the testing images are received from a client device.
However, Gupta does teach details of a hardware implementation of an algorithm for image forgery detection, which includes a system (Fig. 1, system 100) comprising a processor that executes computer-executable instructions stored in a computer-readable memory, which causes the processor to perform the algorithm ([0028]-[0030], server device includes processor that executes instructions stored in memory to perform forgery detection algorithm), and where testing images are received from a client device ([0027], Fig. 1, image may be received 150 from client/user device 105).
The computer implementation of Gupta advantageously allows an image forgery detection algorithm to be performed quickly and to be used by multiple clients/users.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to implement the image forgery detection algorithm of Fan with the computer implementation of Gupta in order to improve the algorithm with the reasonable expectation that this would result in an algorithm that could be performed quickly and used by multiple clients/users. This technique for improving the algorithm of Fan was within the ordinary ability of one of ordinary skill in the art based on the teachings of Fan and Gupta.
Therefore, it would have been obvious to one of ordinary skill in the art to combine the teachings of Fan and Gupta to obtain the invention as specified in claim 2.
Regarding claim 3, Fan in view of Gupta teaches the system of claim 2, and Gupta further teaches that the computer-executable instructions are further executable to cause the processor to:
in response to the binary authenticity classification being indicative of the electronic image being forged, transmit an unsuccessful validation message to the client device ([0027], Fig. 1, classification 155 as manipulated/forged is transmitted to client device 105; This is within the scope of an unsuccessful validation message at least because it indicates that the image has not been successfully validated as non-manipulated).
Regarding claim 4, Fan in view of Gupta teaches the system of claim 2, and Fan further teaches that the analysis of the pixel vector and the image metadata vector comprises generation of a consistency vector (e.g., Sec. 4.2.4, eqn. 8, set of differences between features
x
j
T
w
j
predicted from image data and features
y
of the metadata vector) with the at least one machine learning model, wherein the consistency vector is indicative of a level of consistency between:
a predicted metadata vector determined from the pixel vector; and
the image metadata vector (e.g., Sec. 4.2.4, the difference in eqn. 8 is indicative of consistency between metadata vector predicted from image pixels and the image metadata vector in the image file, lower differences indicating higher consistence and vice versa; e.g., Sec. 4.2.2, the vector includes three entries for aperture, shutter speed, and ISO speed rating, respectively).
Regarding claim 5, Fan in view of Gupta teaches the system of claim 2, and Fan further teaches that the at least one machine learning model comprises a first machine learning model configured to determine a predicted metadata vector from the pixel vector (e.g., Sec. 4.2.3, non-linear regression model, which is within the scope of a “machine learning model” or an “artificial intelligence model”, is used to generate predicted metadata from image pixel data; e.g., Sec. 4.2.2, the metadata can be seen as a vector with three entries for aperture, shutter speed, and ISO speed rating, respectively) and a second machine learning model configured to determine a level of consistency between the predicted metadata vector and the image metadata vector (Sec. 4.2.4, image is classified as authentic or forged based on predicted metadata vector – i.e.,
x
j
T
w
j
for each metadata field
j
– and image metadata vector – i.e.,
y
j
; Sec. 4.2.4, equation 4.10 and text below, classification
U
is based on threshold
ρ
whose value is learned/optimized from using a dataset so, for at least this reason, the classification is within the scope of a “machine learning model” or an “artificial intelligence algorithm”).
Regarding claim 6, Fan in view of Gupta teaches the system of claim 2, and Fan further teaches that the analysis of the pixel vector and the image metadata vector comprises execution of a trained machine learning model on the pixel vector, wherein the trained machine learning model is configured to generate a predicted metadata vector based on the pixel vector (e.g., Sec. 4.2.3, non-linear regression model, which is within the scope of a “machine learning model” or an “artificial intelligence model”, is used to generate predicted metadata from image pixel data; e.g., Sec. 4.2.2, the metadata can be seen as a vector with three entries for aperture, shutter speed, and ISO speed rating, respectively).
Regarding claim 7, Fan in view of Gupta teaches the system of claim 6, and Fan further teaches that the trained machine learning model is a first trained machine learning model, and wherein the analysis of the pixel vector and the image metadata vector further comprises execution of a second trained machine learning model based on the predicted metadata vector, wherein the second trained machine learning model is configured to classify the electronic image as authentic or forged based on the predicted metadata vector (Sec. 4.2.4, image is classified as authentic or forged based on error between predicted metadata vector – i.e.,
x
j
T
w
j
for each metadata field
j
– and image metadata vector – i.e.,
y
j
; Sec. 4.2.4, equation 4.10 and text below, classification
U
is based on threshold
ρ
whose value is learned/optimized from using a dataset so, for at least this reason, the classification is within the scope of a second trained machine learning model or an artificial intelligence algorithm).
Regarding claim 10, Fan in view of Gupta teaches the system of claim 2, and Fan further teaches that the image metadata vector is an Exif vector associated with the electronic image (e.g., Sec. 4.2.2; also see Secs. 1.3, 3.2.3 and 3.3.2).
Regarding claim 11, Examiner notes that the claim recites a method that is substantially the same as the method performed by the system of claim 2. Fan in view of Gupta teaches the system of claim 2 (see above). Accordingly, claim 11 is also rejected under 35 U.S.C. 103 as being unpatentable over Fan in view of Gupta for substantially the same reasons as claim 2.
Regarding claim 12, Examiner notes that the claim recites a method that is substantially the same as the method performed by the system of claim 3. Fan in view of Gupta teaches the system of claim 3 (see above). Accordingly, claim 12 is also rejected under 35 U.S.C. 103 as being unpatentable over Fan in view of Gupta for substantially the same reasons as claim 3.
Regarding claim 13, Examiner notes that the claim recites a method that is substantially the same as the method performed by the system of claim 4. Fan in view of Gupta teaches the system of claim 4 (see above). Accordingly, claim 13 is also rejected under 35 U.S.C. 103 as being unpatentable over Fan in view of Gupta for substantially the same reasons as claim 4.
Regarding claim 14, Examiner notes that the claim recites a method that is substantially the same as the method performed by the system of claim 5. Fan in view of Gupta teaches the system of claim 5 (see above). Accordingly, claim 14 is also rejected under 35 U.S.C. 103 as being unpatentable over Fan in view of Gupta for substantially the same reasons as claim 5.
Regarding claim 15, Examiner notes that the claim recites a method that is substantially the same as the method performed by the system of claim 6. Fan in view of Gupta teaches the system of claim 6 (see above). Accordingly, claim 15 is also rejected under 35 U.S.C. 103 as being unpatentable over Fan in view of Gupta for substantially the same reasons as claim 6.
Regarding claim 16, Examiner notes that the claim recites a method that is substantially the same as the method performed by the system of claim 7. Fan in view of Gupta teaches the system of claim 7 (see above). Accordingly, claim 16 is also rejected under 35 U.S.C. 103 as being unpatentable over Fan in view of Gupta for substantially the same reasons as claim 7.
Regarding claim 19, Examiner notes that the claim recites a computer program product that is substantially the same as the memory of the system of claim 2, except for specifying that the image is an image of a proof-of-identity document.
Fan in view of Gupta teaches the system of claim 2 (see above).
Fan does not explicitly teach that the image is an image of a proof-of-identity document.
However, Gupta does teach applying an image forgery detection algorithm to an image of a proof-of-identity document ([0020]). Gupta teaches that performing forgery detection on an image of a proof-of-identity document is advantageous in various contexts, such as facilitating financial transactions ([0020]).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to modify the system of Fan in view of Gupta as applied above with the proof-of-identity image processing of Gupta in order to improve the system with the reasonable expectation that this would result in a system that provided forgery detection in contexts where it was advantageous, such as facilitating financial transactions. This technique for improving the system of Fan in view of Gupta was within the ordinary ability of one of ordinary skill in the art based on the teachings of Gupta.
Accordingly, claim 19 is also rejected under 35 U.S.C. 103 as being unpatentable over Fan in view of Gupta for the reasons provided above and the reasons provided with respect to claim 2.
Regarding claim 20, Examiner notes that the claim recites features that are substantially the same as features recited in claim 3. Fan in view of Gupta teaches the system of claim 3 (see above). Accordingly, claim 20 is also rejected under 35 U.S.C. 103 as being unpatentable over Fan in view of Gupta for substantially the same reasons as claim 3.
Regarding claim 21, Fan in view of Gupta teaches the computer program product of claim 19, and Fan further teaches that the processor analyzes the pixel vector and the metadata vector by:
inputting, by the processor, the pixel vector to a first machine learning model, which outputs a predicted metadata vector (Sec. 4.2.3, non-linear regression model, which is within the scope of a “machine learning model” or an “artificial intelligence model”, is used to generate predicted metadata from image pixel data); and
inputting, by the processor, both the predicted metadata vector and the metadata vector to a second machine learning model, which outputs and authenticity label corresponding to the proof-of-identity document (Sec. 4.2.4, image is classified as authentic or forged based on predicted metadata vector – i.e.,
x
j
T
w
j
for each Exif field
j
– and image metadata vector – i.e.,
y
j
; Sec. 4.2.4, equation 4.10, classification
U
is based on threshold
ρ
whose value is learned/optimized from using a dataset so, for at least this reason, the classification model applied by Fan is within the scope of a “machine learning model” or an “artificial intelligence algorithm”).
Claim(s) 17-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Fan in view of Gupta as applied above, and further in view of ‘JEITA’ (“Exchangeable image file format for digital still cameras: Exif Version 2.2,” 2002; cited and copy provided in “parent” U.S. Patent Application No. 17/374,439).
Regarding claim 17, Fan in view of Gupta teaches the method of claim 11.
Fan further teaches that the metadata vector is an Exif vector (e.g., Sec. 4.2.2; also see Secs. 1.3, 3.2.3 and 3.3.2).
Fan teaches some features of the Exif vector (e.g., Secs. 1.3, 3.2.3, 3.3.2 and 4.2.2) but does not explicitly teach that the Exif vector includes a camera model feature which identifies a type of camera that captured the electronic image.
Gupta also does not explicitly teach this element.
However, JEITA does teach all of the features included in Exif metadata, which includes a camera model feature which identifies a type of camera that captured the electronic image (e.g., Pg. 22, Make indicates the manufacturer of the camera – i.e., “DSC” – that captured the image and Model indicates the model of the camera).
In view of JEITA, it is clear that the Exif metadata vector used by Fan includes a camera model feature which identifies a type of camera that captured the electronic image.
Regarding claim 18, Fan in view of Gupta teaches the method of claim 11.
Fan further teaches that the metadata vector is an Exif vector (e.g., Sec. 4.2.2; also see Secs. 1.3, 3.2.3 and 3.3.2).
Fan teaches some features of the Exif vector (e.g., Secs. 1.3, 3.2.3, 3.3.2 and 4.2.2) but does not explicitly teach that the Exif vector includes a software feature which identifies a type of software in which the electronic image was opened.
Gupta also does not explicitly teach this element.
However, JEITA does teach all of the features included in Exif metadata, which includes a software feature which identifies a type of software in which the electronic image was opened (e.g., Pg. 23, Software indicates software used to generate the image; also see, e.g., pg. 141, Sec. 7.4, and pg. 142, Explication Table 3, Software feature/tag is changed to identify a type of software used to open and edit the image).
In view of JEITA, it is clear that the Exif metadata vector used by Fan includes a software feature which identifies a type of software in which the electronic image was opened.
Conclusion
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/GEOFFREY E SUMMERS/Examiner, Art Unit 2669
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