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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
Claim Interpretation
According to the Federal Circuit’s decision in SuperGuide v. DirecTV, claim language of the type “at least one of … and …” creates a presumption that Applicant intended the plain and ordinary meaning of the claim language to be a conjunctive list, unless the Specification supports an interpretation of the claim language that rebuts the presumption.1
Claim 4 recites limitations that raise the presumption of a conjunctive list per SuperGuide:
[Claim 4] … the statistical property value comprises at least one of a minimum, a maximum, a median, an average, an absolute average, a mode, a range, and a variance of the distribution of the distances. (emphasis added).
The Specification at page 17 provides, “In an embodiment, the alignment method selection portion 211 may obtain statistical properties of the distance distribution by using the distances between the pluralities of points. The statistical properties of the distance distribution may include at least one of a minimum, a maximum, a median, an average, an absolute average, a mode, a range, and a variance of the distances between the first scan model and the second scan model. The alignment method selection portion 211 may determine whether a statistical property value of the distance distribution is less than or equal to a first threshold value, and when the statistical property value of the distance distribution is less than or equal to the first threshold value, determine that the relationship between the first scan model and the second scan model satisfies the first alignment criterion.” This embodiment provides a list of multiple statistical properties and refers to just one of the properties as being compared to a threshold.
Also, from a practical perspective, a single statistical property of the distribution of distances being a minimum, a maximum, a median, an average, an absolute average, a mode, a range, and a variance is confusing as to what it would represent and how a formula including all such properties would be constructed.
Based on the portions of the Specification and reasoning provided above, it is assumed that Applicant intended claim 4 to describe a disjunctive list, meaning a minimum of one of the listed elements is required.
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 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
[Claim 1] A data processing method performed by a data processing device, the data processing
method comprising:
(a) obtaining a first scan model and a second scan model;
(b) determining whether to initially align the first scan model and the second scan model; and
(c) in response to determining not to initially align the first scan model and the second scan model, precisely aligning the first scan model and the second scan model.
[Claim 9] A data processing device comprising a processor configured to execute one or more instructions to
(a’) obtain a first scan model and a second scan model,
(b’) determine whether to initially align the first scan model and the second scan model, and
(c’) in response to determining not to initially align the first scan model and the second scan model, precisely align the first scan model and the second scan model.
[claim 10] A computer-readable recording medium having recorded thereon a program for executing a data processing method, the data processing method comprising:
(a’’) obtaining a first scan model and a second scan model;
(b’’) determining whether to initially align the first scan model and the second scan model; and
(c’’) in response to determining not to initially align the first scan model and the second scan model, precisely aligning the first scan model and the second scan model.
Claim Interpretation
Under the broadest reasonable interpretation, the terms of the claims are presumed to have their plain meaning consistent with the specification as it would be interpreted by one of ordinary skill in the art. See MPEP 2111.
The bodies of claims 1, 9, and 10 are substantially similar and for the sake of brevity, the corresponding limitations, e.g., (a), (a’), and (a’’), are collectively referred to as “limitation (a)”, “limitation (b)” and “limitation (c)”. Regarding limitation (a), the claims do not put any limits on how the models are obtained or what they are beyond being “scan” models. The broadest reasonable interpretation of a “scan model” is a model obtained through scanning. A model obtained through scanning encompasses a person, e.g., a dentist, manually using a scanner and generic computer to obtain the models, the models representing scanned objects in nearly any form from three-dimensional point clouds obtained with intraoral scanners to two-dimensional images generated from an image sensor receiving light from an area being scanned by a camera. Regarding limitation (b), the independent claims do not put any limits on how the determination is made and whether the determination is a mental process of a person merely using a computer as a tool to input their determination or whether the determination is made without human input. Regarding limitation (c), the claims do not specify whether the “determining” in limitation (c) is related to the “determining” of limitation (b), meaning they could be the same determination or occur at different times.
Step 1: do the claims fall within any statutory category?
Claim 1 recites a series of steps and therefore, is a process. Claim 9 recites a device comprising a processor and therefore, is a machine. See id. Claim 10 recites a computer-readable recording medium. The disclosure gives magnetic media such as hard disks, floppy disks, and magnetic tapes, optical media such as a CD-ROM or DVD, magneto-optical media such as floptical disks, and hardware devices such as read-only memory ROM, random-access memory (RAM), and flash memory as non-limiting examples of a computer-readable recording medium. The broadest reasonable interpretation of claim 10 covers only statutory embodiments of a computer-readable recording medium in light of the disclosure and not a transitory signal. A nontransitory computer-readable storage medium falls within the “manufacture” category of invention. See MPEP 2106.03. (Step 1: YES).
Step 2A, Prong One: do the claims recite a judicial exception?
Fundamentally, the independent claims are about model registration/alignment, which is an application of spatial reasoning that could be performed as part of a series of judgments and evaluations that comprise a person’s mental process. The recitation of structure(s) in the preambles does not negate the mental nature of these limitations because the claims here merely use the “data processing device” of claim 1, the “processor” of claim 9, and the “program” recorded on the “computer-readable recording medium of claim 10, as tools to perform the otherwise mental process. In limitation (a), “obtaining” the scan models involves human input, whether it be a person seeing two objects or models on a display or using a computer as a tool to retrieve data files of scan models. In limitation (b), “determining” encompasses a human’s mental decision-making process and “initially align” encompasses a person either forming a mental image or using a computer as a tool to manually adjust two models into a rough alignment. In limitation (c) “precisely aligning” encompasses a person either forming a mental image or using a computer as a tool to manually adjust two models into a refined alignment. See MPEP 2106.04(a)(2), subsection III.C.
The independent claims, evaluated individually and as a whole are considered as reciting limitations which fall within the mental process grouping of abstract ideas. See MPEP 2106.04(a)(2), subsection III.
Claims 3-6 further describe the mental process abstract idea of claim 1, i.e., the “determining” and “in response two” limitations. Claims 3-6 also describe determining one or more distances between corresponding points, projecting one or more normal vectors, and obtaining proportions or statistical values based on said distance(s), which are mathematical relationships and calculations that fall under the mathematical concepts grouping of abstract ideas. See MPEP 2106.04(a)(2). It is important to note that a mathematical concept need not be expressed in mathematical symbols, because “[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula.” In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). As explained in the MPEP, when a claim recites multiple abstract ideas that fall in the same or different groupings, examiners should consider the limitations together as a single abstract idea, rather than as a plurality of separate abstract ideas to be analyzed individually. See MPEP 2106.04, subsection II.B. Accordingly, these limitations are considered together as a single abstract idea for further analysis.
Claim 7 further describes the mental process abstract idea of claim 1.
Claim 8 further describes the mental process abstract idea of claim 1 in relation to “a user interface screen for selecting automatic alignment”, which amounts to instructions to implement the abstract idea on a computer or merely using a computer as a tool (display data) to perform the abstract idea. See MPEP 2106.05(f).
The dependent claims, evaluated individually and as a whole are considered as reciting limitations which fall within the abstract idea. See MPEP 2106.04(a)(2), subsection III. (Step 2A, Prong One: YES).
Step 2A, Prong Two: Do the claims as a whole integrate the recited judicial exception into a practical application of the exception?
Claim 1 recites a first additional element (i) of “performed by a data processing device” in the preamble, which performs each step of the method. Claims 6-8 do not include additional elements as they merely further expound upon the mental process of aligning and estimating the proximity of the models through mathematical operations including normal vector projection. Claim 8 includes two additional elements (ii) “outputting a user interface screen for selecting automatic alignment” and (iii) “performed in response to the automatic alignment being selected on the user interface screen”.
Claim 9 recites an additional element (iv) of “a processor configured to execute one or more instructions” in the preamble, which performs each function of the device. Claim 10 recites an additional element (v) of “A computer-readable recording medium having recorded thereon a program” in the preamble, which records the program for executing each step of the method.
The additional elements (i)-(v) of claims 1 and 8-10 describe generic computer components (e.g., processor, display, GUI) and/or operations thereof (e.g., processing, displaying) recited at a high level of generality and do not amount to any of the relevant considerations for evaluating whether additional limitations integrate a judicial exception into a practical application provided in MPEP 2106.04(d), subsection I.
The additional elements (i)-(v) amount to merely including instructions to implement the abstract idea on a computer or merely using a computer as a tool to perform the abstract idea. See MPEP 2106.05(f). Limitation (a) amounts to mere data gathering (of two scan models) in conjunction with the abstract idea recited at a high-level of generality.
It should be noted that because the courts have made it clear that mere physicality or tangibility of an additional element or elements is not a relevant consideration in the eligibility analysis, the physical nature of the controller does not affect this analysis. See MPEP 2106.05(I).
Even in combination, these additional elements do not integrate the recited judicial exception into a practical application and the independent claims are directed to the judicial exception. (Step 2A, Prong Two: NO).
Step 2B: do the claims as a whole amount to significantly more than the recited exception?
As explained with respect to Step 2A Prong Two, the additional elements (i)-(v) amount to performing the abstract idea using a computer as a tool to perform the abstract idea (i.e., retrieve data and manipulate data at a user’s direction), which cannot provide an inventive concept. See MPEP 2106.05(f).
Also explained above, limitation (a) amounts to mere data gathering (obtaining two scan models) in conjunction with the abstract idea recited at a high-level of generality. Data gathering is a form of insignificant extra-solution activity. Under the 2019 PEG, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B. 2019 PEG Section III(B), 84 Fed. Reg. at 56. At Step 2B, the evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well-known. See MPEP 2106.05(g). Here, the recitation of obtaining a first and second “scan model” is recited at a high level of generality, and is also well-known. See e.g., U.S. Pat. Appl. Pub. No. 20230149135 at par. 64 (par. 59 of prov. appl.), “the initial oral model 501 can be acquired by a dentist or orthodontist using a dental scanner.” Limitation (a) therefore remains insignificant extra-solution activity.
Consequently, for the reasons discussed above, the additional elements individually or in combination with the judicial exception do not provide an inventive concept; so, the claims as a whole do not amount to significantly more than a generic instruction to “apply” the judicial exception. (Step 2B: NO). Claims 1-10 are not eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-3 and 7-10 are rejected under 35 U.S.C. 103 as being unpatentable over Evaluation of the ICP Algorithm in 3D Point Clous Registration to Li et al. (hereinafter “Li”) in view of U.S. Pat. Appl. Pub. No. 20190378344 to Long et al. (hereinafter “Long”).
Regarding claim 1, Li teaches a data processing method (The ICP algorithms, image processing, and experimental results disclosed by Li require a general purpose computer, which includes a general purpose processor and memory, or a network of computers.), the data processing method comprising (Li, pg. 68031, section I, “To solve this problem, the most common solution at present is to divide the registration process of the point cloud into two phases. First, the two datasets are roughly aligned by global registration (or coarse registration), and then local registration (or fine registration) is done by using ICP. This method solves the initial position problem of ICP by global registration and can effectively avoid the problem of local optimal solution. The study of global registration mainly focuses on two aspects: point feature descriptors and search strategies for correspondences. … two questions have been ignored over the years in the study of global registration and local registration, which is when must global registration be performed first and under what circumstances ICP can obtain the correct registration result without global registration? In general, there are three main factors that affect ICP as to whether there is a local optimal solution, which is the overlap ratio, the angle and the distance between the two datasets.”):
obtaining a first scan model (A first point cloud of “the two point cloud datasets”. See Li at pg. 68031, section I) and a second scan model (A second point cloud of “the two point cloud datasets”. See Li at pg. 68031, section I) comprises:
determining whether to initially align the first scan model and the second scan model (Li, pg. 68031, section I, “when must global registration be performed first and under what circumstances ICP can obtain the correct registration result without global registration?”; pg. 68031, section I, “the parameter range of the validity of the ICP algorithm is obtained, which provides a reference for whether or not to add global registration before performing ICP. In addition, the accuracy and efficiency of different factors are analyzed within the effective range of ICP”; pg. 68035, section III.D.1, “the validity of ICP is based on three factors: the overlap ratio, angle and distance, expressed in terms of sampling angle, rotation angle and distance, respectively”); and
in response to determining not to initially align the first scan model and the second scan model (Li, pg. 68031, section I, “without global registration”), precisely aligning the first scan model and the second scan model (Li, pg. 68031, section I, “local registration”), but does not teach that which is explicitly taught by Long.
Long teaches a data processing method performed by a data processing device (Long, par. 35, “The computing device 100 may use any stereoscopic imager, photometric scanner, laser scanner, infrared scanner, structured light sensor, or other three-dimensional scanning technology to scan the dental impressions.”; par. 25, “the computing device 100 illustratively include a processor 120, an input/output subsystem 122, a memory 124, a data storage device 126, and a communication subsystem 128, and/or other components and devices commonly found in a server computer or similar computing device.”);
determining whether (Long, par. 99, “automating alignment of the models”) to initially align the first scan model and the second scan model (Long, par. 95, “RANdom Sample Consensus (RANSAC) for global alignment”; par. 56, “The user may select (or the model manager 202 may automatically initiate) a merge model option 504. The merge model option 504 is shown to be represented as a button (“Merge STLs”) on the user interface”); and
in response (Long, par. 99, “automating alignment of the models”; Fine registration follows coarse registration. See Long at par. 95) precisely aligning the first scan model and the second scan model (Long, par. 95, “Iterative Closest Point (ICP) for local alignment”; par. 99, “by generating point clouds and automatically aligning those point clouds to generate a merged model, the aligned point clouds, and thus the models, are more accurately aligned than could otherwise be achieved if relying on user inputs, which require a user to manually select common points on the models to be merged”).
Li discloses the well-known problem of ICP algorithms falling into an incorrect local minimum and solution of using coarse (global) registration followed by fine (local) registration, and outstanding issues with this solution: most ICP algorithms assume the input datasets are initially aligned well enough for ICP and as a result may fall into incorrect local solutions, but there is a need to identify which parameters of the input datasets lead to good or poor registration results (i.e., accuracy). Li identifies three such parameters: overlap ratio, angle, and distance between the centers of the datasets. See Li at pg. 68046, section V. In investigating the initial alignment problem, Li presents the point-to-plane ICP algorithm that minimizes the orthogonal distance between points in one cloud and tangent planes in the other. See Li at. pg. 68044, section IV.C. Li uses the residual point-to-plane error to evaluate how well the algorithm converges under different conditions. Thus, Li shows that it was known in the art before the effective filing date of the claimed invention that the magnitude and distribution of point-to-plane errors directly reflect whether the alignment is good or poor, which is analogous to the claimed invention in that it is pertinent to the problem being solved by the claimed invention, efficiently aligning point clouds by knowing when to perform global registration and when not to.
Long discloses a computing device for reviewing the quality of automatically merging dental scans using a combination of global (rough) alignment and local (fine) alignment with ICP. See Long at pars. 51, 95. Long’s registration process is aimed at improving accuracy of the alignment. See Long at par. 99. Thus, Long shows that it was known in the art before the effective filing date of the claimed invention that the magnitude and distribution of point-to-plane errors directly reflect whether the alignment is good or poor, which is analogous to the claimed invention in that it is pertinent to the problem being solved by the claimed invention, efficiently aligning point clouds by knowing when to perform global registration and when not to.
A person of ordinary skill in the art would have been motivated to combine the point-to plane distance metric disclosed by Li with the computing device and point cloud alignment workflow disclosed by Long, to thereby program the computing device to compare the error distribution of the metric (which reflects alignment quality) to a threshold (e.g., distance or distribution of distances exceeding a threshold = bad alignment and need global registration) or other criterion as a quantifiable diagnostic measurement of initial alignment and the need to perform global registration, the threshold being used in a similar manner to the distance threshold disclosed by Li to quantify how distances between the input datasets correlate with particular alignment results. Based on the foregoing, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have made such modification according to known methods to yield the predictable results to have the benefit of converging on optimal solutions and reducing the need for manually selecting corresponding points before alignment and merging.
Regarding claim 2, Li in view of Long teaches the data processing method of claim 1, wherein the determining of whether to initially align the first scan model (A first point cloud of “the two point cloud datasets”. See Li at pg. 68031, section I) and the second scan model (A second point cloud of “the two point cloud datasets”. See Li at pg. 68031, section I) comprises:
determining (Long, par. 35, “computing device 100”) whether a relationship between the first scan model and the second scan model (Li, pg. 68031, section I, “the initial position problem of ICP”) satisfies a first alignment criterion (A threshold amount or range of point-to-plane distances between the point clouds; Li, pg. 68044, “In the point-to-plane ICP algorithm, the normal of a point, which is determined by the neighbor points, will be affected by noise points.”); and
in response to determining (Long, par. 35, “computing device 100”) that the relationship between the first scan model and the second scan model satisfies the first alignment criterion (Li, pg. 68046, section V, “Once the rotation angle and distance are known, coarse registration will become faster, if it is required.”), determining not to initially align the first scan model and the second scan model (Li, pg. 68031, section I, “without global registration”).
The rationale for obviousness is the same as provided for claim 1.
Regarding claim 3, Li in view of Long teaches wherein the determining of whether the relationship between the first scan model and the second scan model satisfies the first alignment criterion comprises:
determining whether a distance (The distance between the point of one point cloud and a tangent plane of another point cloud; Li, pg. 68044, “In the point-to-plane ICP algorithm, the normal of a point, which is determined by the neighbor points, will be affected by noise points.”) between a point on the first scan model from which a normal vector is projected (The distance is measured along the normal vector projection. See Li at pg. 68044), and a point at which the normal vector intersects the second scan model (In point-to-plane ICP, the distance that is minimized is a normal vector projected from one point cloud to intersect another, and the distance is minimized.), is less than or equal to a first threshold value (The aim of initial alignment is to bring the models closer together in overall alignment, which based on the combination of Li in view of Long in claim 1, would be checked by a threshold where distances exceeding the threshold are not suitable for initial alignment); and
in response to the distance being less than or equal to the first threshold value, determining that the first alignment criterion is satisfied (Li, pg. 68031, section I, “without global registration”).
The rationale for obviousness is the same as provided for claim 1.
Regarding claim 7, Li in view of Long teaches the data processing method of claim 1, further comprising:
in response to determining to initially align the first scan model and the second scan model, initially aligning the first scan model and the second scan model (Li, pg. 68031, section I, “when must global registration be performed first”); and
precisely aligning the initially aligned first scan model and second scan model (Li, pg. 68031, section I, “To solve this problem, the most common solution at present is to divide the registration process of the point cloud into two phases. First, the two datasets are roughly aligned by global registration (or coarse registration), and then local registration (or fine registration) is done by using ICP.).
Regarding claim 8, Li in view of Long teaches the data processing method of claim 1, but does not teach that which is explicitly further taught by Long.
Long teaches further comprising outputting a user interface screen (Long, par. 54, “FIG. 5 depicts a user interface for uploading models to be merged.”) for selecting automatic alignment (Long, pars. 56-57, “The user may select (or the model manager 202 may automatically initiate) a merge model option 504. The merge model option 504 is shown to be represented as a button (“Merge STLs”) on the user interface, though the merge model option 504 may be implemented in other ways via the user interface. Upon selection and/or initiation of the merge model option 504, the merge manager 208 may be configured to generate a rough merge 600 of the model.”),
Li in view of Long is analogous to the claimed invention for the same reasons provided above. Long further discloses a GUI element for initiating automatic coarse alignment followed by user-input to select correlation points, which is followed by local registration. See Long. At par. 57. Thus, Long shows that it was known in the art before the effective filing date of the claimed invention that the magnitude and distribution of point-to-plane errors directly reflect whether the alignment is good or poor, which is analogous to the claimed invention in that it is pertinent to the problem being solved by the claimed invention, efficiently aligning point clouds.
A person of ordinary skill in the art would have been motivated to combine the GUI element and its underlying programmatic functionality as further disclosed by Long with the computer system of Li in view of Long, to thereby provide the GUI element to a user, and in response to selecting the element, the system utilizes the point-to-plane distance metric to determine whether global registration is required and if it is not required, to bypass the manual point correspondence annotation step further disclosed by Long. Based on the foregoing, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have made such modification according to known methods to yield the predictable results to have the benefit of further reducing the need for manual input and saving computing resources.
Claim 9 substantially corresponds to claim 1 by reciting a data processing device comprising a processor (Long, par. 25, “processor 120”) configured to execute one or more instructions to perform operations corresponding to the steps of the method of claim 1.
The rationale for obviousness is the same as provided for claim 1.
Claim 10 substantially corresponds to claim 1 by reciting a computer-readable recording medium (Long, par. 35, “a memory 124”) having recorded thereon a program for executing the data processing method of claim 1.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
A Plane-based Approach for Indoor Point Clouds Registration to Favre et al. is pertinent for providing an example to conceptualize point-to-plane ICP, particularly Fig. 1, which illustrates the point-to-plane distance.
Iterative K-closest point algorithms for colored point cloud registration to Choi et al. is pertinent because Algorithm 1 is an ICP algorithm that computes surface normal vectors in line 4 and check if the initial registration error is large in line 2 before commencing with local registration.
U.S. Pat. Appl. Pub. No. 20230149135 is pertinent because it demonstrates that manually acquiring scan models is well-known.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RYAN P POTTS whose telephone number is (571)272-6351. The examiner can normally be reached M-F, 9am-5pm EST.
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, Sumati Lefkowitz can be reached at 571-272-3638. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RYAN P POTTS/Examiner, Art Unit 2672
/SUMATI LEFKOWITZ/Supervisory Patent Examiner, Art Unit 2672
1 See Superguide Corp. v. Direct TV Enterprises, Inc., 358 F.3d 870, 69 USPQ2d 1865 (Fed. Cir. 2004).