DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f):
(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”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f). The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f). The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f), 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. Such claim limitations are: “a capturing unit”; “a sampling unit”; “a generation unit”; “an input unit”; “a determination unit”.
Because the claim limitations are being interpreted under 35 U.S.C. 112(f), they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have these limitations interpreted under 35 U.S.C. 112(f) applicant may: (1) amend the claim limitations to avoid them being interpreted under 35 U.S.C. 112(f) (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitations recite sufficient structure to perform the claimed function so as to avoid them being interpreted under 35 U.S.C. 112(f).
Claim Objections
Claim 16 is objected to for the following informalities: a nesting issue is present. Following the recitation of “wherein:”, the multi-branch video liveness model is elaborated upon. Then a semicolon is used, and the determination unit is introduced. However, grammatically, the determination unit still exists under the “wherein:” colon. It is thus unclear whether the determination unit is part of the previously recited input unit, or exists as its own separate component. For the purposes of compact prosecution, the examiner will assume the presumably intended structure.
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 1-30 are rejected under 35 U.S.C. 101 because they are directed to ineligible patent subject matter. The claims are directed to the Abstract Idea grouping of mental processes under MPEP § 2106.04(a)(2)(III) and mathematical calculations under MPEP § 2106.04(a)(2)(I). These are judicial exceptions under Step 2A, Prong One of the framework established by the cases of Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 216, 110 USPQ2d 1976, 1980 (2014) and Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 (2012). See MPEP § 2106.04(II).
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Step 1: The claims in question are directed to a method, and system (machine) for “determining liveness.” Machines and processes are statutory categories. See MPEP 2106.03(I), “A machine is a "concrete thing, consisting of parts, or of certain devices and combination of devices." Digitech, 758 F.3d at 1348-49, 111 USPQ2d at 1719 (quoting Burr v. Duryee, 68 U.S. 531, 570, 17 L. Ed. 650, 657 (1863)). This category "includes every mechanical device or combination of mechanical powers and devices to perform some function and produce a certain effect or result." Nuijten, 500 F.3d at 1355, 84 USPQ2d at 1501 (quoting Corning v. Burden, 56 U.S. 252, 267, 14 L. Ed. 683, 690 (1854))”; See MPEP 2106.03(I), “NTP, Inc. v. Research in Motion, Ltd., 418 F.3d 1282, 1316, 75 USPQ2d 1763, 1791 (Fed. Cir. 2005) ("[A] process is a series of acts.") (quoting Minton v. Natl. Ass’n. of Securities Dealers, 336 F.3d 1373, 1378, 67 USPQ2d 1614, 1681 (Fed. Cir. 2003)). As defined in 35 U.S.C. 100(b), the term "process" is synonymous with "method."”. (Step 1: Yes).
Step 2A, Prong One: As explained in MPEP 2106.04(II), a claim “recites” a judicial
exception when the judicial exception is “set forth” or “described” in the claim. Here, each
claim recites or depends upon the mental processes of sampling, providing, generating (scores), checking, comparing, and detecting. (Claim 1, “sampling…providing…generating…a video liveness score…determining…the liveness”; Claim 2, “compliance checks…sanity checks”; Claim 6, “resized in a pre-defined size”; Claim 11, “determined based on a comparison…”; Claim 10, “provided as an input…”; Claim 12, “trained to detect”; Claim 15, “detecting a movement…”) or the mathematical calculations of optical flow (Claim 1, “generating…a set of optic flow images)
The claims are recited at a high level of generality and lack any specifics precluding such
an analysis from being interpreted under the mental processes grouping of “practically performed
in the mind” (see also MPEP § 2106.04(a)(2) identifying how e.g. a use of pen and paper, a ruler,
or a computer as a tool (to assist in visually/mentally analyzing/observing acquired
images/video) fails to preclude such an interpretation under the mental processes judicial
exception). Activities such as “by the (sampling, generation, input, determination, etc.) unit” therefore may be performed mentally, even if they may require an additional computer tool. Similarly, basic video capture components do not elevate these claims past a mental process.
Regarding artificial intelligence, to the extent it is implicated, the claims’ “multi-branch video liveness model” is comparable to Claim 2 of Example 47 of the July 2024 PEG regarding subject matter eligibility (https://www.uspto.gov/sites/default/files/documents/2024-AISMEUpdateExamples47-49.pdf). As stated therein, an artificial intelligence’s analyses, detections, and reinforcement learnings may be practically performed in the human mind. To the extent mathematical calculations are required to operate and train the artificial intelligence in image analysis, the separate judicial exception is also implicated.
As such, the usage of a computer to input, resize, alter, or score images does not elevate these claims beyond a mental process. (Step 2A, Prong One: Yes).
Step 2A, Prong Two: If Prong One of Step 2A is met, the examiner must consider (1)
whether there are any ‘additional elements’ recited in the claim beyond the judicial exception,
and (2) evaluate those additional elements individually and in combination to determine whether
the claim as a whole integrates the exception into a practical application. See MPEP §
2106.04(d).
Limitations the courts have found indicative of integration include: an improvement in
the functioning of a computer, or an improvement to other technology or technical field, as
discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a); applying or using a judicial exception to
effect a particular treatment or prophylaxis for a disease or medical condition, as discussed in
MPEP § 2106.04(d)(2); implementing a judicial exception with, or using a judicial exception in
conjunction with, a particular machine or manufacture that is integral to the claim, as discussed
in MPEP § 2106.05(b); effecting a transformation or reduction of a particular article to a
different state or thing, as discussed in MPEP § 2106.05(c); and applying or using the judicial
exception in some other meaningful way beyond generally linking the use of the judicial
exception to a particular technological environment, such that the claim as a whole is more than
a drafting effort designed to monopolize the exception, as discussed in MPEP § 2106.05(e).
Limitations that the courts have found non-indicative of integration include: merely
reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including
instructions to implement an abstract idea on a computer, or merely using a computer as a tool to
perform an abstract idea, as discussed in MPEP § 2106.05(f); adding insignificant extra-solution
activity to the judicial exception, as discussed in MPEP § 2106.05(g); and generally linking the
use of a judicial exception to a particular technological environment or field of use, as discussed
in MPEP § 2106.05(h).
As an additional note, ‘additional elements’ are generally limitations excluded from
interpretation under the Abstract Idea groupings, and may comprise portions of limitations
otherwise identified as falling under those Abstract Idea groupings of the 2019 PEG (e.g. any
‘determination’ that may be made mentally by a user, neural network and/or generic computer hardware is considered under the ‘apply it’ considerations of 2106.05(f)). Any ‘providing’/outputting broadly, and ‘collection/input’ of data (i.e. capturing of frames, output of a score), also fail(s) to integrate at least in view of MPEP 2106.05(g) (extra-solution data gathering/output) and/or 2106.05(h) as ‘generally linking’ the exception to a field of use involving machine learning and/or imagery so acquired (e.g. the use of a computer to acquire said video broadly). The same determination holds for dependent claims that serve to limit the collection/output of data/images (by means of what is collected based on recited conditions) and/or introduce limitations generally linking to a field of use.
None of the instant claims appear to explicitly/clearly capture/recite any disclosed
improvement in technology (see MPEP 2106.05(a), with note that ‘functioning of a computer’
concerns functions integral to the way a computer operates and not ‘functions’ that a generic
computer can be programmed/adapted to perform (see also 2106.05(f))) and any ‘additional
elements’, even when considered in combination, fail to integrate at Prong Two of Step 2A
accordingly. Integration in view of subsection (a) requires an identification of the manner in
which the improvement is achieved, to be explicitly and specifically recited in the claims, as
‘additional elements’ precluded from interpretation under any of the Abstract Idea groupings
(since the improvement cannot be to the exception itself). With reference to MPEP 2106.05(a):
It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981))
As applicable here, additional limitations not directed to a judicial exception fail to
integrate at Prong Two of Step 2A. Claim 1 recites various “unit[s]”. The incorporation of conventional video capture, computer and machine-learning systems does little more than generally link the judicial exceptions of mental processes to a field-of-use and technological environment. See MPEP §§ 2106.05(h); 2106.05(f).
Claim 1 recites “capturing, by a capturing unit”. This limitation constitutes insignificant extra-solution activity under MPEP § 2106.05(g). Specifically, the limitation amounts to no more than necessary data inputting under rationale 3 of MPEP § 2106.05(g).
Even when viewed in combination, any additional elements present do not integrate the
recited judicial exception into a practical application (Step 2A, Prong Two: No), and the claims
are directed to the judicial exception. (Revised Step 2A: Yes → Step 2B).
Step 2B: If Prong Two of Step 2A is not met, the examiner must consider whether the
claim as a whole amounts to ‘significantly more’ than the recited exception, i.e., whether any
‘additional element’, or combination of additional elements, adds an inventive concept to the
claim. The considerations of Step 2A Prong 2 and Step 2B overlap, but differ in that 2B also
requires considering whether the claims feature any “specific limitation(s) other than what is
well-understood, routine, conventional activity in the field” (WURC) (MPEP § 2106.05(d)).
Such a limitation if specifically recited however, must still be excluded from interpretation under
any of the Abstract Idea groupings. Step 2B further requires a re-evaluation of any additional
elements drawn to extra-solution activity in Step 2A (e.g. gathering frames, rendering output) – however no limitations appear directed to any novel collection or output generation per se. Limitations not indicative of an inventive concept/‘significantly more’ include those that are not specifically recited (instead recited at a high level of generality), those that are established as WURC (a plurality of cited references serve to evidence the WURC nature of ‘analysis’ based at least in part on corroborating/additional ground data), and/or those that are not ‘additional elements’ by nature of their analysis at Prong One of Step 2A (i.e. directed to the exception – see above re. deciding that a second acquisition may be advantageous/desired). The July 2024 PEG describes that an improvement/inventive concept (for ‘significantly more’ determination(s)) cannot be to the judicial exception itself. As additionally recited by the specification of the claimed invention, machine learning is understood to encompass a plurality of WURC machine-learning models. From page 20, paragraph 2 of the claimed invention’s specification, “The present solution encompasses using a convolutional neural network to directly estimate the optical flow field from the input video frames”
The claims in question recite little beyond those limitations recited at a high level
of generality and falling under e.g. the mental processes Abstract Idea grouping, and would
monopolize the exception accordingly. The additional limitations of computer processing, machine-learning, and video capture as recited are WURC, as evidenced by the body of prior art cited by the examiner in this office action. (Step 2B: No).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3-10, 12-16, 18-25, and 27-30 are rejected under 35 U.S.C. 103 as being unpatentable over Hua et. al (US 20190026544 A1) (Hereinafter, “Hua”) in view of Simonyan et. al Two-Stream Convolutional Networks for Action Recognition in Videos (Hereinafter, “Simonyan”)
With respect to claim 1, Hua teaches:
A method of video processing for determining liveness of a subject ([Abstract]), the method comprising:
capturing, by a capturing unit, a plurality of multi-media frames (Fig. 1; [0031])
sampling, by a sampling unit, a plurality of consecutive frames from the plurality of multi-media frames ([0078] “The frames may be extracted prior to receipt thereof, as in the method 500A, or the video sequence may be received as such, and the frames may be extracted from the video sequence upon receipt of the video sequence”)
generating, by a generation unit, a set of optic flow images based on the plurality of consecutive frames (Fig. 3; [0013])
providing, by an input unit to a Fig. 4, “Time-Stamped Frame Sequences”; Fig. 5A; [0078])
generating, by the Fig. 4; Fig. 5A; [0090]; [0102]-[0103])
determining, by a determination unit, the liveness of the subject based on the video liveness score ([0092] “This data can then be used to develop training rules for input into a classifier, so that the classifier can accurately distinguish between live and spoofed faces”)
Hua does not explicitly teach:
and the set of optic flow images
(As the sub-set of frames and the set of optic flow images are treated and processed as one and the same)
a multi-branch video liveness model
However, Simonyan, in the same field of endeavor of human action recognition, teaches:
A method of video processing for determining liveness (not explicitly directed to anti-spoofing, but with understanding that human motion is generally speaking a type of “liveness”) of a subject ([Abstract]), the method comprising:
capturing, by a capturing unit, a plurality of multi-media frames related to a video ([1] “The temporal part, in the form of motion across the frames, conveys the movement of the observer (the camera) and the objects”)
sampling, by a sampling unit, a plurality of consecutive frames from the plurality of multi-media frames ([5] “At test time, given a video, we sample a fixed number of frames (25 in our experiments)”)
generating, by a generation unit, a set of optic flow images based on the plurality of consecutive frames (Fig. 1)
providing, by an input unit to a multi-branch video liveness model, a sub-set of the plurality of consecutive frames (Fig. 1, “input video” stack; [5], the original 25 sampled frames as fed into the spatial stream) and the set of optic flow images (Fig. 1, “multi-frame optical flow” images generated from the 25 frames but otherwise taking on a new identity under the temporal stream branch; [1] “The spatial stream performs action recognition from still video frames”; [2] “Spatial stream ConvNet operates on individual video frames, effectively performing action recognition from still images”; [5])
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generating, by the multi-branch video liveness model, a video liveness score based on a processing of the sub-set of the plurality of consecutive frames and the set of optic flow images (Fig. 1, “softmax”)
determining, by a determination unit, the liveness of the subject based on the video liveness score (Fig. 1, “class score fusion”)
It would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify Hua to include the limitations of multi-branch optical flow image analysis as taught by Simonyan. A person of ordinary skill in the art would be motivated to consult Simonyan, as the core of its method uses optical flow to solve the problem of motion recognition. Modifying Hua as proposed would provide a more robust system that considers multiple streams of liveness information in making a final scoring determination. The systems readily integrate, as Hua is already configured to perform optical flow analysis on a video stream. An additional score fusion could readily be implemented by a person of ordinary skill in the art to predictable success, at the end of Hua’s pipeline.
With respect to claim 3, Hua/Simonyan teaches:
The method as claimed in claim 1, wherein the plurality of consecutive frames comprises seventeen consecutive multi-media frames from the plurality of multi-media frames (Hua, [0062] “The time-stamped frame sequence must include at least two images, but may alternatively comprise any number of images greater than two”)
With respect to claim 4, Hua/Simonyan teaches:
The method as claimed in claim 1, wherein the set of optic flow images comprises sixteen optic flow images (Hua, [0062]; Simonyan [5] “We sample a fixed number of frames (25 in our experiments)”; noting that “comprises” leaves the claim open to additional frames beyond the sixteenth)
With respect to claim 5, Hua/Simonyan teaches:
The method as claimed in claim 4, wherein each optic flow image from said sixteen optic flow images is generated by creating a pair of two consecutive frames from the plurality of consecutive frames, and by running the created pair on an optic flow model (Hua, [0063] “The purpose of the motion feature extraction module 412 is to extract motion features for each pair of consecutive frames from the video stream”; Simonyan, [6] “First, we can conclude that stacking multiple (L > 1) displacement fields in the input is highly beneficial, as it provides the network with long-term motion information, which is more discriminative than the flow between a pair of frames (L = 1 setting)”; Simonyan, Table 1).
With respect to claim 6, Hua/Simonyan teaches:
The method as claimed in claim 1, wherein prior to providing, by the input unit to the multi-branch video liveness model, the sub-set of the plurality of consecutive frames and the set of optic flow images are resized in a pre-defined size (Hua, [0027] “Once a face is detected, various image transformation methods may be applied, e.g. image rotation, resizing, grayscale conversion, noise filtering or pose correction, etc”; Simonyan [5])
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With respect to claim 7, Hua/Simonyan teaches:
The method as claimed in claim 1, wherein the sub-set of the plurality of consecutive frames comprises last “n” number of frames from the plurality of consecutive frames (Hua, Fig. 4; Hua, [0062]-[0063]; Hua, [0075] “and if the incremented i value is determined to be less than the total number of frames minus one in step 452, then the method returns to step 416 and is repeated for the next pair of frames. Thus, if the time-stamped frame sequence being analyzed contains N frames, there will be N−1 pair-decisions with respect to the spoofness or liveness of the sequence”; Hua, [0078]; Simonyan, [5])
With respect to claim 8, Hua/Simonyan teaches:
The method as claimed in claim 7, wherein the last “n” number of frames are last sixteen consecutive frames in an event the plurality of consecutive frames comprises seventeen consecutive multi-media frames from the plurality of multi-media frames (Hua, [0062] “The time-stamped frame sequence must include at least two images, but may alternatively comprise any number of images greater than two”, thus teaching “last sixteen”; Simonyan [5], teaching seventeen consecutive multi-media frames from the plurality of multi-media frames (as the word comprises was chosen, a sample of 25 satisfies the claim). However, the examiner also notes that this is a contingent limitation under MPEP § 2111.04, and is accordingly without weight for the purposes of distinguishing over prior art)
With respect to claim 9, Hua/Simonyan teaches:
The method as claimed in claim 1, wherein the set of optic flow images are related to the sub-set of the plurality of consecutive frames (Simonyan, Fig. 1)
With respect to claim 10, Hua/Simonyan teaches:
The method as claimed in claim 1, wherein the sub-set of the plurality of consecutive frames is provided as an input to a first branch of the multi-branch video liveness model, and the set of optic flow images is provided as an input to a second branch of the multi-branch video liveness model (Simonyan, Fig. 1)
With respect to claim 12, Hua/Simonyan teaches:
The method as claimed in claim 1, wherein the multi-branch video liveness model is trained to detect a movement of a target subject in a target video in an event of one of presence of a set of non-live attacks in said target video and an absence of the set of non-live attacks in said target video (Hua, [0004] “Face liveness detection is designed to detect live signs (e.g., signs that a facial image is an image of a living person's actual face, rather than an image of an image of a face) to guard against attempted misrepresentation of identity using spoofed facial images”; Hua, [0097] “After training, the target classifier can be used to differentiate spoof and live”)
With respect to claim 13, Hua/Simonyan teaches:
The method as claimed in claim 12, wherein the multi-branch video liveness model is further trained to generate a target video liveness score based on the movement of the target subject in the target video (Hua, [0102]-[0103] “These unique similarities, features and differences may then be converted into rules that may be used to classify new data sets as corresponding to a live face or a spoofed face. For example, if a new data set shares a unique similarity or feature with the positive data set, then it may be classified as a live face, while if the new data set shares a unique similarity or feature with a negative data set, then it may be classified as a spoofed face”)
With respect to claim 14, Hua/Simonyan teaches:
The method as claimed in claim 12, wherein the set of non-live attacks comprises at least one of one or more display attacks, one or more print attacks, and one or more mask-based attacks (Hua, [0007]; Hua, Fig. 2)
With respect to claim 15, Hua/Simonyan teaches:
The method as claimed in claim 1, wherein the processing of the sub-set of the plurality of consecutive frames and the set of optic flow images comprises detecting a movement of the subject in the video (Hua, Fig. 4; Simonyan, Fig. 1)
With respect to claim 16, it is functionally parallel to claim 1. The high-level hardware (configured to perform the method of claim 1) is taught by Hua/Simonyan (Hua, Fig. 1). Accordingly, the claim is rejected in line with the analysis above.
With respect to claims 18-25 and 27-30, they are rejected in line with the analysis of claims 16, 3-10, and 12-15.
Claims 2 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Hua/Simonyan in view of Saban (US 20220398781 A1) (Hereinafter, “Saban”)
With respect to claim 2, Hua/Simonyan teaches the method of claim 1.
Hua/Simonyan does not explicitly teach the further limitations of claim 2.
However, Saban, in the same field of endeavor of image capture, teaches:
wherein for capturing the image the method comprises performing at least one of a set of compliance checks and a set of sanity checks ([0063]-[0065])
It would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify Hua/Simonyan to include the limitations of sanity/compliance checking, as taught by Saban. Doing so would have the advantage of providing images more suitable for analysis. The systems readily integrate, as Saban represents a pre-process step that does not compromise the underlying function of Hua/Simonyan.
With respect to claim 17, it is rejected in line with the analysis of claims 16 and 2 above.
Claims 11 and 26 are rejected under 35 U.S.C. 103 as being unpatentable over Hua/Simonyan in view of Atoum Face Anti-Spoofing Using Patch and Depth-Based CNNs (Hereinafter, “Atoum”)
With respect to claim 11, Hua/Simonyan teaches the method of claim 1.
Hua/Simonyan does not explicitly teach the further limitations of claim 11.
However, Atoum, in the same field of endeavor of anti-spoofing, teaches:
wherein the liveness of the subject is determined based on a comparison of the video liveness score with a pre-defined threshold score ([3] “A face image or video clip is classified as spoof if its spoof-score is above a pre-defined threshold”)
It would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify Hua/Simonyan to include the limitations of threshold checking, as taught by Atoum. Doing so would provide a clear metric to make a final determination. The systems readily integrate, as Hua/Simonyan is otherwise already configured to classify and score motion.
With respect to claim 26, it is rejected in line with the analysis of claims 16 and 11 above.
Additional References
Additionally cited references (see attached PTO-892) otherwise not relied upon above have been made of record in view of the manner in which they evidence the general state of the art.
Inquiry
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NOAH WILLIAM BOYAR whose telephone number is (571)272-8392. The examiner can normally be reached 8:30 – 5:00 EST, Monday – Friday.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chan Park can be reached at 571-272-7409. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NOAH W BOYAR/Examiner, Art Unit 2669
/CHAN S PARK/Supervisory Patent Examiner, Art Unit 2669
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