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 Objections
Claims 1 and 10 are objected to because of the following informalities: there should be a comma preceding “for analyzing the IR video stream”. Appropriate correction is required.
Applicant is advised that should one of claim 9 or claim 10 be found allowable, the other will be objected to under 37 CFR 1.75 as being a substantial duplicate thereof. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m).
Claim Interpretation
The Federal Circuit’s en banc decision in Phillips v. AWH Corp., 415 F.3d 1303, 1316, 75 USPQ2d 1321, 1329 (Fed. Cir. 2005) expressly recognized that the USPTO employs the "broadest reasonable interpretation" standard:
The Patent and Trademark Office ("PTO") determines the scope of claims in patent applications not solely on the basis of the claim language, but upon giving claims their broadest reasonable construction "in light of the specification as it would be interpreted by one of ordinary skill in the art." In re Am. Acad. of Sci. Tech. Ctr., 367 F.3d 1359, 1364[, 70 USPQ2d 1827, 1830] (Fed. Cir. 2004). Indeed, the rules of the PTO require that application claims must "conform to the invention as set forth in the remainder of the specification and the terms and phrases used in the claims must find clear support or antecedent basis in the description so that the meaning of the terms in the claims may be ascertainable by reference to the description." 37 CFR 1.75(d)(1). MPEP 2111.
As of 13 November 2018, the claim construction standard used by the PTAB, which used to be the broadest reason interpretation (BRI) standard, is the same as used to construe claims in a civil action in federal district court, i.e., the Phillips standard.1 However, it is still current Office practice for examiners to apply the BRI standard.
A relevant Federal Circuit decision on claim construction to the present claims concerned the claim language “first means for storing at least one of a desired program start time, a desired program end time, a desired program service, and a desired program type”. See Superguide Corp. v. Direct TV Enterprises, Inc., 358 F.3d 870, 69 USPQ2d 1865 (Fed. Cir. 2004); see also claim 1 of U.S. Pat. No. 5,083,211. A key dispute concerned what “at least one of” modifies, whether the list of elements (categories) should be interpreted disjunctively or conjunctively. To resolve the dispute, the court interpreted the specific claim language before it according to ordinary English grammar and the intrinsic evidence including the specification and prosecution history.
The court determined that the plain and ordinary meaning of the disputed language supported the district court’s construction of “at least one of” as meaning “one or more”. The key issue however was over what “at least one of” modifies in the subsequently listed claim elements (categories) of the claim at issue. The court did not create a per se rule that claim language of the format “at least one of [one or more categories] ... and [category]” automatically connotes a conjunctive list absent a disclaimer in the specification. Rather, the court agreed that a lower court’s conjunctive interpretation was one possible and reasonable interpretation and determined that because the specification only described embodiments supporting the conjunctive interpretation, there was nothing in the specification to rebut the presumption that the patentee intended the plain and ordinary meaning to be conjunctive. In other words, had there been any description supporting the disjunctive interpretation, the presumption may have been rebutted, but the court never reached that point because there were only descriptions of conjunctive uses of the claim categories in the claim language at issue.
The import of Superguide to the claims here is that in a context-specific review of a patent claim in light of the specification thereof, the plain and ordinary meaning of “at least one of” may be conjunctive, but not in all cases as a per se rule. While claims 4 and 11 recite limitations conforming to the format at issue in Superguide, the claims here are given their broadest reasonable construction in light of the specification as would be interpreted by one of ordinary skill in the art.
Claim 4 recites, “wherein the detected object is at least one of a street sign and a lane marker.” Claim 11 recites, “wherein the detected object is at least one of a living object, a moving object, a street sign, and a lane marker.”
Paragraph 28 describes step 104, where the IR image frame is analyzed “to detect any objects of interest (living objects, moving objects, street signs, or lane markings)” (emphasis added). Paragraph 30 provides, “If the object of interest is classified in step 114 in one or more classes of objects such as moving objects, living objects, street signs, or lane markings, the processing circuit 30 may ...” (emphasis added). Based on the specification including those sections reproduced above, Applicant unambiguously contemplated embodiments where the categories listed in claims 4 and 11 would be interpreted as a disjunctive list. Even without these examples from the specification, a POSITA would understand from the nature of the claimed invention that from a technical standpoint, the difference between the conjunctive and disjunctive interpretation would have little to no bearing on the feasibility of actually implementing either option. Therefore, the BRI of claims 4 and 11 is that each claim recites a disjunctive list. In other words, a POSITA would find no difference in meaning had claims 4 and 11 used “or” instead of “and”.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1, 10 and 16 recite the phrase “long-range IR video camera”, which is vague, imprecise and subjective due to the relative term “long-range”, which could refer to an operating wavelength or range of wavelengths of the IR video camera, e.g., a long wavelength infrared spectrum (LWIR) video camera, imply that the visible light video camera has a lower maximum detection range as compared to the IR video camera, or merely that the infrared camera has a long range of detectable object-to-camera distances, whatever that range may be. Given the prevalence of long wavelength infrared (LWIR) in the relevant prior art of multi-wavelength driver assistance systems, it is conceivable that “long-range IR video camera” was actually meant to be “a long wavelength infrared (LWIR) video camera” or “a long-range thermal video camera” or the like. However, there is no explicit description of LWIR in the specification, and on its face, the term “range” could refer to “long” wavelengths that the camera is able to detect or long distances between target objects and the camera. Given these ambiguities, the scope of the claims is unclear. For purposes of applying prior art, the term “long-range” as used in “long-range IR video camera” is interpreted to mean the detection range of the IR video camera has a maximum detection range or distance that is at least as long as the visible light camera. Dependent claims 2-9, 11-15 and 17-20 are rejected for inheriting and not curing the deficiencies of their respective independent claims.
Claims 7 and 14 recite the phrase “the at least one object of interest is classified into specific classifications, and wherein the processing circuit does not modify the visible light video stream when the at least one object of interest is not classified into specific classifications” and claim 18 includes a similar phrase “the at least one object of interest is classified into specific classifications, and not performing the step of modifying the visible light image frame when the at least one object of interest is not classified into specific classifications.” These phrases are confusing because it is unclear if “into specific classifications” means one object is classified into one classification amongst a specific group of classifications, e.g., each object of interest is classified into one of a plurality of specific classifications, or the object is classified into multiple specific classifications. or something else. Additionally, it is unclear whether any of the specifications of the ”specific classifications” are the same between the two instances recited in claims. Despite being recited as “specific” classifications (classes), there is no distinction in the claims between the first and second instances of “specific classifications”. Consequentially, it is difficult to understand from the recited claim language when the “visible light video stream” is modified and when it is not. Furthermore, these claims are confusing because they appear to essentially require that something does not occur when another thing does not occur, which is difficult to define and understand. For purposes of applying prior art, claims 7, 14 and 18 are interpreted to mean that the visible light video stream continues until an object of a specific classification is detected.
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 5-20 are rejected under 35 U.S.C. 103 as being unpatentable over Deep Visible and Thermal Image Fusion for Enhanced Pedestrian Visibility to Shopovska et al. (hereinafter “Shopovska”) in view of Night Vision System in BMW to Ahire (hereinafter “Ahire”).
Regarding claim 1, Shopovska teaches an imaging and display system for a vehicle (Shopovska, pg. 2, “The goal is to present the resulting fused stream to a driver on a display, and not to an automatic decision system. Presenting such enhanced video feed to a human driver on an in-car display allows the driver to rely on it in situations such as partial or complete lack of light when approaching a tunnel or poorly lit roads at night and make more informed decisions.”, pg. 8, “in good visibility conditions the output should be similar to the RGB image since it contains more rich details and colors. At night typically the only available information for the pedestrians is present in the thermal image, and therefore this thermal information will need to be added to the background scene.”, pg. 13, “The edges of the pedestrian silhouettes are accentuated by contrasting colors. This kind of output can be displayed on an in-car assistance system, so that a human driver can rely on it in challenging conditions for early risk prevention.”), comprising:
a video display (Shopovska, pg. 2, “an in-car display”) of the vehicle (Shopovska, pg. 5, “The proposed method relies on front-looking cameras (referred to as exteroceptive sensors) and belongs to the type night-vision enhancement systems, where information is presented on a screen for a visual support.”);
a visible light video camera (Shopovska, pg. 13, “The RGB images were recorded with a GoPro Hero 6 camera”) configured to capture a visible light video stream (Shopovska, pg. 13, “The video sequences of both modalities were captured with a frame rate of 30 frames per second.”) of an exterior scene (Figure 5 shows exterior scenes as a vehicle and pedestrians move about. See Shopovska at page 14. See also id. at pg. 5, “The proposed method relies on front-looking cameras (referred to as exteroceptive sensors) and belongs to the type night-vision enhancement systems, where information is presented on a screen for visual support.”) in a visible region of the electromagnetic spectrum (Shopovska, pg. 13, “The video sequences of both modalities were captured with a frame rate of 30 frames per second. They were then synchronized and calibrated by applying a pre-computed, global geometric transformation to match the viewpoints as closely as possible.”);
a long-range IR video camera (Shopovska, pg. 13, “a FLIR ThermiCam BPL 390”) configured to capture an IR video stream (Shopovska, pg. 13, “The video sequences of both modalities were captured with a frame rate of 30 frames per second.”) of the exterior scene in an IR region of the electromagnetic spectrum (Shopovska, pg. 7, “As shown in Figure 2, the input to the network is composed of an RGB image and the corresponding, aligned LWIR image.”; pg. 2, “The goal is to present the resulting fused stream to a driver on a display, and not to an automatic decision system.”); and
a processing circuit (The training used a GTX 1070 GPU and the method was implemented as processor-executed software. See Shopovska at page 18, lines 4--10) for receiving the visible light video stream and the IR video stream (Shopovska, pg. 18, “trained fusion network fuses a single-precision pair of RGB and LWIR images of size 750 × 600 at about 1.3 frames per second on the Nvidia GeForce GTX 1070 GPU.”) for analyzing the IR video stream to detect an object with reduced visibility in the visible light video stream (Shopovska, pg. 5, “We compute saliency for both visible light and thermal inputs and obtain low-resolution maps that indicate regions of higher visual importance. The neural network can thus learn how to use the saliency maps to combine the inputs more efficiently and to increase the visibility of important objects.”), and
for modifying the visible light video stream by highlighting a region where the detected object should be located in the visible light video stream, the processing circuit configured to supply the modified visible light video stream to the video display for display thereon (Shopovska, pg. 13, “The post-processing step amplifies the intensity of the details added to the RGB image, and creates a higher contrast with the background. It is evident that the fusion network has learned to make pedestrians visible in nighttime images by applying false colors, while at daytime in images with good visibility the pedestrians are less affected by false colors and more similar to the visible light image. The edges of the pedestrian silhouettes are accentuated by contrasting colors. This kind of output can be displayed on an in-car assistance system, so that a human driver can rely on it in challenging conditions for early risk prevention. Figure 6 presents samples obtained in an original data collection session, from a moving platform. Here we show crops of the original frames for closer illustration of the important results. The inputs are presented in the first two columns, and the fusion results are presented in the third column.”), but does not teach that which is explicitly taught by Ahire.
Ahire teaches a video display disposed in an interior of the vehicle (Ahire, pg. 2, section 1, “on the redesigned 7-series, BMW added a pedestrian detection system which flashes a caution symbol on navigation / information screen and head up display when it detects a pedestrian. The objective of the pedestrian warning algorithms is to accurately detect pedestrian and provide the driver with informative warning. In eyes of the driver, the end product of the good system provides the timely warning and possibly, additional information such as the position of the pedestrian or an overlaid on the night vision display.”).
Shopovska discloses a CNN-based advanced driver assistance system (ADAS) that uses infrared video to supplement and modify visible light color video when low-visibility objects, e.g., pedestrians, are present in the system’s field of view. Thus, Shopovska shows that it was known in the art before the effective filing date of the claimed invention to use infrared light to detect low-visibility objects in a vehicle’s exterior environment and combine that information with a visible image stream to enhance visibility of objects, which is analogous to the claimed invention in that it is pertinent to the problem being solved by the claimed invention, improving an ADAS in low-light conditions. Ahire discloses an in-vehicle display and night vision system that overlays a yellow tint over detected pedestrians alongside a yellow caution symbol to draw the driver’s attention to the presence of the pedestrian in front of the vehicle. See Ahire at Fig. 1. Thus, Ahire shows that it was known in the art before the effective filing date of the claimed invention to output the ADAS sensor streams to an in-vehicle display and to select and display graphical symbols based on the classification of a detected object, which is analogous to the claimed invention in that it is pertinent to the problem being solved by the claimed invention, improving an ADAS in low-light conditions.
A person of ordinary skill in the art would have been motivated to combine the in-vehicle display, IR image display functionality, and pedestrian image processing disclosed by Ahire with the ADAS and modifiable RGB functionality disclosed by Shopovska, to thereby form a hybrid system that displays the RGB camera feed with fused output when objects are more visible in the visible light image (e.g., top-left image in Fig. 5 of Shopovska) and displays the IR camera feed with fused output when objects are more visible in the IR image, the fused output including an overlay highlighting a position corresponding to a detected object, e.g., a pedestrian, and a graphical symbol based on the classification of the object. 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 increasing the driver’s awareness of the environment around their vehicle.
Regarding claim 2, Shopovska in view of Ahire teaches the imaging and display system of claim 1, wherein the detected object is a living object (pedestrian. See Shopovska at Figure 1).
Regarding claim 3, Shopovska in view of Ahire teaches the imaging and display system of claim 1, wherein the detected object is a moving object (Shopovska, pg. 3, “our focus is directed towards maximizing the visibility of pedestrians in images of natural appearance for human drivers.”. Pedestrians are mobile objects.).
Regarding claim 5, Shopovska in view of Ahire teaches the imaging and display system of claim 1, wherein the visible light video camera is a color video camera (Shopovska, pg. 8, “the output of the fusion network should be a color image with natural appearance, similar to the RGB input. At the same time, the network should ensure that pedestrians are clearly visible, either by retaining the pedestrian regions similar to RGB in good visibility conditions, or by introducing false colors to create contrast at low visibility, based on the thermal image.”) and the visible light video stream is in color (Shopovska, pg. 13, “The RGB images were recorded with a GoPro Hero 6 camera (GoPro, Inc., San Mateo, CA, USA) ... The video sequences of both modalities were captured with a frame rate of 30 frames per second.”; Figure 3, “RGB input”).
Regarding claim 6, Shopovska in view of Ahire teaches the imaging and display system of claim 1, wherein the processing circuit classifies the detected object (Shopovska, pg. 8, “We adopted the Faster R-CNN model [39], a deep neural network that is trained to locate and recognize different classes of objects, including pedestrians, in regular RGB images.”).
Regarding claim 7, Shopovska in view of Ahire teaches the imaging and display system of claim 6, wherein the processing circuit modifies the visible light video stream to highlight the detected object (Shopovska, pgs. 6-7, “In the proposed method, the fusion network (generative) is trained to produce three-channel RGB images with high resemblance to the original RGB inputs to preserve natural appearance and intuitive results. At the same time, it is trained to modify the original RGB values in the pedestrian regions, so that the visibility of pedestrians is increased.”),when the at least one object of interest is classified into specific classifications (Shopovska, pg. 8, section 3.2, “We adopted the Faster R-CNN model [39], a deep neural network that is trained to locate and recognize different classes of objects, including pedestrians, in regular RGB images.”), and wherein the processing circuit does not modify the visible light video stream when the at least one object of interest is not classified into specific classifications (A pedestrian-related overlay is only displayed when pedestrians are detected. See Ahire at pg. 3).
The rationale for obviousness is the same as provided for claim 1.
Regarding claim 8, Shopovska in view of Ahire teaches the imaging and display system of claim 6, wherein the processing circuit modifies the visible light video stream to highlight the detected object by superimposing a graphic symbol at a location where the detected object is located in the corresponding IR video stream (Shopovska, pg. 13, “The post-processing step amplifies the intensity of the details added to the RGB image, and creates a higher contrast with the background. It is evident that the fusion network has learned to make pedestrians visible in nighttime images by applying false colors, while at daytime in images with good visibility the pedestrians are less affected by false colors and more similar to the visible light image. The edges of the pedestrian silhouettes are accentuated by contrasting colors. This kind of output can be displayed on an in-car assistance system, so that a human driver can rely on it in challenging conditions for early risk prevention. Figure 6 presents samples obtained in an original data collection session, from a moving platform. Here we show crops of the original frames for closer illustration of the important results. The inputs are presented in the first two columns, and the fusion results are presented in the third column.”), and the graphic symbol is selected based on the classification of the detected object (Shopovska - Based on the network trained to detect pedestrians).
Regarding claim 9, Shopovska in view of Ahire teaches the imaging and display system of claim 1, wherein the processing circuit modifies the visible light video stream to highlight the detected object by superimposing a graphic symbol at a location where the detected object is located in the corresponding IR video stream (Ahire, pg. 3, “When the car exceeds 25 mph, the system scans specifically for pedestrians by scanning the road up to 100 yards ahead of the vehicle. A pedestrian appears with the yellow tint.” The yellow tint coinciding with the image area occupied by the pedestrian is a computer-generated graphical symbol that represents a positive pedestrian detection at the respective pixels where the tint is displayed. The yellow caution symbol is also superimposed “at a location where the detected object is located” if the “location” is taken to be the area in front of the vehicle that is viewable by the registered color+IR camera pair.).
The rationale for obviousness is the same as provided for claim 1.
Claim 10 substantially corresponds to claims 1 and 9 viewed as a whole. Claim 11 substantially corresponds to claims 2-4 combined together. Claims 12-14 substantially correspond to claims 5-7. Claim 15 substantially corresponds to the final clause of claim 8.
The differences in scope arising from the particular arrangement of claim limitations in claims 10-15 as compared to the corresponding claim limitations in claims 1-7 does not require modification of the reasoning behind the rejection of claims 1-7 under 35 U.S.C. 103. As such, claims 10-15 are rejected for the same reasons provided for claims 1-7.
Claim 16 substantially corresponds to claim 1 by reciting a method comprising steps that substantially correspond to the functions of the imaging system of claim 1. Claim 16 primarily differs from claim 1 by reciting:
when no objects of interest are detected in the IR image frame (When no objects are detected in either frame, the default, per Shopovska, is to display the RGB image feed. See Shopovska at pg. 3, “we optimize the fusion network with respect to the similarity between the output and the RGB input.”), supplying the visible light image frame to a video display of the vehicle (Shopovska, pg. 8, “in good visibility conditions the output should be similar to the RGB image since it contains more rich details and colors. At night typically the only available information for the pedestrians is present in the thermal image, and therefore this thermal information will need to be added to the background scene.”);
when at least one object of interest is detected in the IR image frame, analyzing the visible light image frame to detect if the object is visible (Shopovska’s image is a fusion of RGB and thermal images. Both image feeds are searched via the generative network. See Shopovska at Figure 3);
when the at least one object of interest is visible in the visible light image frame, supplying the visible light image frame to the video display (If an object is visible in the RGB frame, it will be seen in the displayed image, i.e., “Fusion output”. See Shopovska at Figures 3 and 5.); and
when the at least one object of interest is not visible in the visible light image frame (When the hybrid system of the combination of Shopovska in view of Ahire favors the IR data over the RGB data, the object may be detected in the visible light image, but it would not be visible to the driver.), modifying the visible light image frame to highlight the detected object and supplying the modified visible light image frame to the video display (See Shopovska at Figure 5.).
The rationale for obviousness is the same as provided for claim 1.
Regarding claim 17, Shopovska in view of Ahire teaches the method of claim 16 and further comprising classifying the at least one object of interest detected in the IR image frame (Figure 3 of Shopovska illustrates the training, where an auxiliary detection network is used to detect pedestrians from the fusion of the RGB and Thermal camera feeds.).
Claims 18-20 substantially correspond to claims 7-9 by reciting a method comprising steps that substantially correspond to the functions of the imaging systems of claims 7-9. Thus, claims 18-20 are rejected for the same reasons as claims 7-9.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Shopovska in view of Ahire and in further view of Multiband Image Segmentation and Object Recognition for Understanding Road Scenes to Kang et al. (hereinafter “Kang”).
Regarding claim 4, Shopovska in view of Ahire teaches the imaging and display system of claim 1, but does not teach that which is explicitly taught by Kang.
Kang teaches wherein the detected object is a lane marker (Kang, pg. 1426, “Since we regard the near-infrared channel as another color channel of the CIELab color space, we proposed the 20-D filter bank. The 20-D set is expanded to responses of only the three Gaussians of the 17-D filter bank for the monochrome intensity of an infrared image. Fig. 4(c) shows the feature vectors of the 20-D set”, pg. 1431, “The assigned classes and colors were as follows: road, black; lane, yellow; sky, blue; tree, green; car, red; tree trunk and pole, brown; sidewalk, gray; building, magenta; and pedestrian, cyan”, pg. 1425, “texture features can be extracted from both the color image and the near-infrared image because they have the same field of view and optical axes.”).
Shopovska and Ahire are analogous to the claimed invention for the reasons provided above. Kang discloses an advanced driver assistance system that detects pedestrians and lane markers from a feature vector derived from aligned visible light and infrared images. Thus, Kang shows that it was known in the art before the effective filing date of the claimed invention to detect pedestrians and lane markers from visible and infrared image data, which is analogous to the claimed invention in that it is pertinent to the problem being solved by the claimed invention, improving an ADAS in low-light conditions.
A person of ordinary skill in the art would have been motivated to combine the feature vector disclosed by Kang with the ADAS of Shopovska in view of Ahire and either replace or supplement the LWIR camera with a NIR camera, to thereby detect infrared image features matching pedestrian or lane marker classes and display highlighted overlays of the detected objects on the in-vehicle display. 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 detecting low-visibility objects with better accuracy.
Conclusion
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.
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/RYAN P POTTS/Examiner, Art Unit 2672
/SUMATI LEFKOWITZ/Supervisory Patent Examiner, Art Unit 2672
1 See Changes to the Claim Construction Standard for Interpreting Claims in Trial Proceedings Before the Patent Trial and Appeal Board, 83 Fed. Reg. 51,340 (Oct. 11, 2018).
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