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 claims will be read under the broadest reasonable interpretation standard outlined in
MPEP § 2111.01.
Claim Objections
Claim 1 is objected to for the following informalities: “the image generated by a camera” and “the real world” are recited without antecedent basis.
Claim 8 is objected to for the following informalities: “another imaging device” is recited without antecedent basis.
Claim 11 is objected to for the following informalities: “A method of for providing” reads as a typographical error”.
Claim 17 is objected to for the following informalities: “the water surface extension data are represented” reads a typographical error for “the water surface extension data is represented”, in line with parallel claim 3.
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 1-20 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: With the exception of claim 12 (discussed below), claims 1-20 are directed to a method, system, and non-transitory computer-readable media (CRM) for “labelling a water surface within an image”, and a “method for providing a training dataset…” Machines (systems), processes (methods), and articles of manufacture (non-transitory CRM) are all 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."”; See MPEP 2106.03(I), “A manufacture is "a tangible article that is given a new form, quality, property, or combination through man-made or artificial means." Digitech, 758 F.3d at 1349, 111 USPQ2d at 1719-20 (citing Diamond v. Chakrabarty, 447 U.S. 303, 308, 206 USPQ 193, 197 (1980)). As the courts have explained, manufactures are articles that result from the process of manufacturing, i.e., they were produced "from raw or prepared materials by giving to these materials new forms, qualities, properties, or combinations, whether by hand-labor or by machinery." Samsung Electronics Co. v. Apple Inc., 137 S. Ct. 429, 120 USPQ2d 1749, 1752-3 (2016) (quoting Diamond v. Chakrabarty, 447 U. S. 303, 308, 206 USPQ 193, 196-97 (1980)); Nuijten, 500 F.3d at 1356-57, 84 USPQ2d at 1502.”; See MPEP 2016.03(II). (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 matching, labeling, and providing data. (Claim 1, “matching…labelling…”; Claim 10, “determining an object area…”; Claim 4, “extracting the area…projecting the area…”; Claim 11, “providing an amount of…images…”). The claims further implicate various mathematical calculations (Claim 1, “based on a spatial relationship…”; Claim 7, “projecting the area…based on the fixed spatial relationship…”).
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 “a controller configured to” therefore may be performed mentally, even if they may require the additional computer tool. Similarly, basic input and output features do not elevate these claims past a mental process.
Regarding artificial intelligence, to the extent it is implicated, claims 11’s “machine learning algorithm” 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 match and label image information does not elevate these claims beyond a mental process/mathematical calculation. (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
‘match/label’ that may be made mentally by a user, machine learning algorithm 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 receiving of images/information, output of labelled data), 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/transmit said imagery/information 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 a “camera”; Claim 5 recites a “sensor”; Claim 8 recites “another imaging device”; claim 9 recites a “lidar/radar sensor”; Claim 11 recites a “machine learning algorithm”; Claim 13 recites a “memory…a controller”; Claim 15 recites “a non-transitory computer-readable medium…”. The incorporation of conventional computer, imaging, 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 “receiving image data…receiving water surface extension data…”; Claim 10 recites “receiving object information”. These limitations constitute insignificant extra-solution activity under MPEP § 2106.05(g). Specifically, the limitations amount to no more than necessary data inputting/outputting, 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 images/information, 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 conventional machine learning algorithms at a non-limiting and high-level of recitation ([0004]; [0005]).
The claims in question recite little beyond those limitations recited at a broad level
of generality and falling under e.g. the mental processes/mathematical calculation Abstract Idea groupings and would monopolize the exceptions accordingly. The additional limitations of computer processing, image/information capture, and machine learning as recited are WURC, as evidenced by the body of prior art cited by the examiner and Applicant. (Step 2B: No).
Claim 12 is rejected under 35 U.S.C § 101 because it does not appear directed to any of the four statutory categories, and instead appears directed to non-statutory subject matter, specifically software per se. See MPEP § 2106.03(I), “Non-limiting examples of claims that are not directed to any of the statutory categories include…a computer program per se (often referred to as “software per se”)…”
In the context of the flowchart illustrated above, claim 12 fails at Step 1 of the Subject Matter Eligibility test (Step 1: No). Exemplary/non-limiting embodiments of a machine learning algorithm, even if generally understood to be coupled to physical hardware, do not serve to preclude an interpretation covering software per se, which does not fall within the definition of a process, machine, manufacture or composition of matter (In re Nujiten, 500 F.3d 1346, 1354, 84 USPQ2d 1495, 1500 (Fed. Cir. 2007)).
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-2, 5-7, 9-10, and 13-14 are rejected under 35 U.S.C. 102(a)(1) and 35 U.S.C. 102(a)(2) as being anticipated by Hong et. al (WO 2020237693 A1) (Hereinafter, “Hong”)
With respect to claim 1, Hong discloses:
A method for labelling a water surface within an image ([Abstract]), the method comprising:
receiving image data of the image generated by a camera, the image comprising at least one water surface ([0006], [0012], [0030] (collectively discussing step 10, the cited paragraphs hereinafter referred to in the office action as “S1”); [0032])
receiving water surface extension data, the water surface extension data representative of an area over which the water surface extends in the real world (S1; [0011], [0017], [0035], [0041] (discussing step 60, hereinafter referred to as “S6”))
matching the water surface extension data to the image data based on a spatial relationship between the camera and the area of the water surface ([0008], [0014], [0032], [0037] (discussing step 30, hereinafter referred to as “S3”); [0010], [0016], [0034], [0040] (discussing step 50, hereinafter referred to as “S5”); S6, “Step 60 above specifically includes that, in the information fusion node, based on the conversion equation between the LiDAR coordinate system and the camera coordinate system, the point cloud obtained by the LiDAR is converted into the camera coordinate. Then, through the conversion relationship between the camera coordinate system and the pixel coordinate system, the point cloud is projected onto the imaging plane, giving the image depth information. Finally, the prediction box output from Faster RCNN and the pixel coordinate information and depth information of the water surface boundary line output from the Deeplab model are combined to generate 3D coordinates. Based on the camera's external parameters calibrated, the corresponding world coordinates are converted, thereby determining the specific positions of obstacles and water surface boundaries in the world coordinate system”)
labelling the water surface in the image based on the matched water surface extension data (S3; S6; noting that “labeling the water surface in the image” does not necessarily require that the image itself be labeled, but rather “the water surface” (which incidentally was also depicted in “the image”)
With respect to claim 2, Hong discloses:
The method according to claim 1, wherein:
the spatial relationship between the camera and the area of the water surface is given by an absolute position and an absolute orientation of the camera and an absolute position of the area of the water surface (S5; S6)
With respect to claim 5, Hong discloses:
The method according to claim 1, wherein
the water surface extension data comprises sensor data gathered by a sensor (S1; S6)
the spatial relationship between the camera and the area of the water surface is given by a fixed spatial relationship between the camera and the sensor (S5, “Further, the step S5 specifically adopts a checkerboard calibration method to select several corner points on the checkerboard at different angles and different positions, and determine the coordinates of these corner points in the camera coordinate system, the coordinates in the world coordinate system and For the coordinates in the radar coordinate system, substitute the corresponding coordinates into the mathematical model of camera calibration and joint calibration, and solve them simultaneously to obtain three rotation parameters (rotation matrix) and three translation parameters (translation matrix) in the camera-radar coordinate conversion equation And a scale factor, as well as the rotation matrix and translation matrix in the camera-world coordinate conversion equation to determine the specific form of the coordinate conversion equation”)
With respect to claim 6, Hong discloses:
The method according to claim 5, wherein
the fixed spatial relationship between the camera and the sensor comprises a distance between the camera and the sensor, and/or a relative orientation of the camera and the sensor with respect to each other, and/or a height level of the camera relative to the sensor (S5; S6)
With respect to claim 7, Hong discloses:
The method according to claim 5, wherein matching the water surface extension data to the image data based on the spatial relationship between the camera and the area of the water surface comprises
extracting the area or a border of the area of the water surface from the sensor data (S1; S3; S6)
projecting the area or the border of the area to the image based on the fixed spatial relationship between the camera and the sensor (S5; S6)
With respect to claim 9, Hong discloses:
The method according to claim 5, wherein
the sensor comprises a lidar sensor and/or a radar sensor (S1; S5; S6)
With respect to claim 10, Hong discloses:
The method according to claim 1, further comprising, after matching the water surface extension data to the image data and before labelling the water surface in the image (S6, “Finally, the prediction box output from Faster RCNN and the pixel coordinate information and depth information of the water surface boundary line output from the Deeplab model are combined…”)
receiving object information regarding at least one object except from land, being in water and extending from the water surface (S1; S3; [0009], [0015], [0033], [0039] (hereinafter, “S4”); S6)
determining an object area within the image based on the object information, the object area covered by the object (S3; S4; S6)
labelling the water surface except from the object data (S3; S4; S6)
With respect to claim 13, Hong discloses:
A water surface detection system for detecting a water surface in an image, the water surface detection system ([Abstract]) comprising:
a camera configured to generate image data of the image, the image comprising at least one water surface ([0004]; S1; S3)
a memory comprising water surface extension data, the water surface extension data representative of an area over which the water surface extends in the real world (S1; [0007]; S6)
a controller configured to match the water surface extension data to the image data based on a spatial relationship between the camera and the area of the water surface, and label the water surface in the image based on the matched water surface extension data (S5; S6)
With respect to claim 14, Hong discloses:
The water surface detection system according to claim 13, further comprising:
a sensor configured to gather the water surface extension data (S1; S6)
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 3-4, 11-12 and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Hong in view of Suresh et. al (US 20200050893 A1) (Hereinafter, “Suresh”).
With respect to claim 3, Hong teaches the method of claim 2
Hong does not explicitly teach the further limitations of claim 3
However, Suresh, in the same field of endeavor of water surface imaging, teaches:
the water surface extension data is represented by a map comprising the area of the water surface ([0008]; [0103] “At 401, a map may display a vessel in proportionate size and show all other objects around it. The map may have zoom capability using two-finger expansion and/or contraction. The map may have a grid overlay function with options for changing grid size in the graphical user interface. The depth of the matter represented in each pixel on the map may be color coded. For example, depth greater than 80 feet may be light blue and gradually progress to darker shades of blue until those areas less than 40 feet in depth are black. Land may be represented as yellow, the vessel navigating may be light green, and other vessels may be red. As such, a two-dimensional grid map 402 is enabled”; [0105]; [0111])
the absolute position of the area of the water surface is extracted from the map ([0105]; [0107]-[0108]; [0109]; [0111])
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 Hong to include the limitations of map data extraction, as taught by Suresh. Doing so would have the advantage of improving overall navigation accuracy. The systems readily integrate, as the underlying software architecture of Hong remains unchanged.
With respect to claim 4, Hong/Suresh teaches:
The method according to claim 3, wherein matching the water surface extension data to the image based on the spatial relationship between the camera and the area of the water surface comprises:
extracting the area of the water surface from the map (Xiaboin, S2, S6; Suresh, [0103]; Suresh, [0105]; Suresh, [0111]; Suresh, Fig. 6)
projecting the area or a border of the area from the map to the image based on the absolute position and absolute orientation of the camera (Hong, S5, S6; Suresh, [0107]-[0108]; Suresh, [0111])
With respect to claim 11, Hong teaches the method of claim 1. As relevant to claim 11 (and a permissible reading under the open-ended transition phrase “comprising” of claim 1, which broadens “the method” beyond simply the recited steps), the examiner also notes that Hong teaches the generation of labelled images (outside the core mapping of claim 1). See Hong S3.
Hong does not explicitly teach the further limitations of claim 11
However, Suresh teaches:
A method of providing a training dataset for training, validating, and/or testing a machine learning algorithm in order to enable the machine learning algorithm to detect a water surface in an image ([0010]; [0014]; [0129]-[0132]; [0189]), the method comprising:
providing an amount of unlabelled images as features for the training, each of the images showing at least one water surface ([0010]; [0111]; [0129]-[0132]; [0189] “FIG. 26 is a flowchart of a deduplication embodiment. Steps are taken to prepare images for annotation. These images can then be used for training a neural network in the objection detection network. Videos from one or more ships are uploaded onto Google Drive. These videos are then parallel downloaded into CCR temporary memory. Using ffmpeg these frames are parsed into jpgs (1 every 15 seconds of video), which are saved in a separate folder in the CCR temporary memory”)
providing an amount of labelled images, wherein the labelled images respectively correspond to the unlabelled images, and wherein each of the labelled images has been labelled ([0111]; [0193] “FIG. 27 is a flowchart of an embodiment of image annotation. The deduplicated images are downloaded from Google Drive onto an Amazon EC2 instance. After every batch in the folder is downloaded, a Script is run to create an empty XML for each image that will later receive the annotation. Once a batch is successfully downloaded and prepared, the link to the batch is added to the redirect section of the target annotator in MongoDB or other similar platforms or database programs. As well, a script is run to find the first image in the batch, and the name of the first image is put at the end of the redirect link”)
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 Hong to include the limitations of training dataset labelling, as taught by Suresh. Doing so provides for an automatic labelling technique (Hong) that directly generates the input of Suresh, reducing the need for human labor. The systems readily integrate, as an output of Hong is an annotated dataset that could feasibly be employed in Suresh’s methods. See MPEP § 2143(I) – Rationale D, “Applying a known technique to a known device (method, or product) ready for improvement to yield predictable results”.
With respect to claim 12, Hong/Suresh teach:
A machine learning algorithm for detecting a water surface in an image, wherein the machine learning algorithm has been trained by the method according to claim 11 (Suresh, [Abstract]; Suresh, [0073]; Suresh, [0139]; Suresh, [0196]; in combination with Hong)
With respect to claims 15-20, they are functionally parallel to claims 1-6, which are taught by Hong and Suresh as discussed above. While Hong does not explicitly teach “A non-transitory computer-readable medium embodying programmed instructions which, when executed by a processor, direct the processor…”, Suresh teaches a similar method as Hong, stored in a computer program (Suresh, [0020], [0230]-[0232]). 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 Hong to include the limitations of computer program processing, as taught by Suresh. Doing so would allow for easily distributable and automatic execution of the methods of Hong with conventional digital hardware. The systems readily integrate, as the machine learning methods of Hong predictably function as code which may be stored.
Claim 8 is rejected under 35 § U.S.C. 103 as being unpatentable over Hong in view of Dongming et. al (CN 111310651 A) (Hereinafter, “Dongming”)
With respect to claim 8, Hong teaches the method of claim 5
Hong does not explicitly teach the further limitations of claim 8
However, Dongming, in the same field of endeavor of water imaging, teaches: the sensor comprises another imaging device, the other imaging device configured to detect water surfaces (Page 1, (1)-(5))
It would have been obvious to a person of ordinary skill in the art as of the effective filing date of the claimed invention to modify Hong to include the limitations of a water detecting imaging device, as taught by Dongming. Doing so would lower the cost of hardware, reduce the complexity of the system, and allow for dual-sensor visual/depth pixel fusion for enhanced segmentation. The systems readily integrate, as the central processing of Hong is preserved, while being enhanced with detail.
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 10:00 – 6:00 EST, Monday – Friday.
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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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