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 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.
In claim 1, it is unclear whether “detect plastic” in the “providing” clause refers back to the “one or more images of plastic” recited in the “obtaining” clause and whether the “one or more items of plastic” in the “obtaining” clause refers back to or corresponds to the “plastic” recited in the “providing” clause or the “one or more images of plastic” recited in the “obtaining and “providing” clauses, with
“detect plastic” (singular) in the “providing clause being inconsistent with “one or more images of plastic” (singular or plural) and “one or more items of plastic” (singular or plural) recited elsewhere in the claim.
In claim 5, it is unclear whether “to process plastic” refers back to or corresponds to the preceding recitation of “one or more items of plastic”.
In claim 6, “transducers are configured along a system” is grammatically confusing or non-idiomatic (“transducers are located within a system…” is suggested).
In claim 7, “second image of the one or more images” (plural images) is inconsistent with recitation of “one or more images” (singular or plural) in claim 1 (amending claim 7 to recite “The method of claim 1, wherein the “one or more images” comprises two or more images…” is suggested).
In claim 8, “the vessel is connected to a water source” is non-idiomatic (“the vessel is operated to obtain water from a first location of the water source…and to provide the water…to a second location of the water source” is suggested); and
it is unclear whether the recitation of “to move the one or more items of plastic within a vessel” further defines the recitation of “controlling the one or more acoustic transducers to move the one or more items” recited in claim 1, or instead corresponds to a separate movement of the one or more items of plastic.
In claim 13, it is unclear whether “detect plastic” in the “providing” clause refers back to the “one or more images of plastic” recited in the “obtaining” clause and whether the “one or more items of plastic” in the “obtaining” clause refers back to or corresponds to the “plastic” recited in the “providing” clause or the “one or more images of plastic” recited in the “obtaining and “providing” clauses, with
“detect plastic” (singular) in the “providing clause being inconsistent with “one or more images of plastic” (singular or plural) and “one or more items of plastic” (singular or plural) recited elsewhere in the claim.
In claim 17, it is unclear whether “to process plastic” refers back to or corresponds to the preceding recitation of “one or more items of plastic”.
In claim 18, “transducers are configured along a system” is grammatically confusing or non-idiomatic (“transducers are located within a system…” is suggested).
In claim 19, “second image of the one or more images” (plural images) is inconsistent with recitation of “one or more images” (singular or plural) in claim 13 (amending claim 19 to recite “The method of claim 13, wherein the “one or more images” comprises two or more images…” is suggested).
In claim 20, it is unclear whether “detect plastic” in the “providing” clause refers back to the “one or more images of plastic” recited in the “obtaining” clause and whether the “one or more items of plastic” in the “obtaining” clause refers back to or corresponds to the “plastic” recited in the “providing” clause or the “one or more images of plastic” recited in the “obtaining and “providing” clauses, with
“detect plastic” (singular) in the “providing clause being inconsistent with “one or more images of plastic” (singular or plural) and “one or more items of plastic” (singular or plural) recited elsewhere in the claim.
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.
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 1-12 are rejected under 35 U.S.C. 103 as being unpatentable over Blevins et al publication entitled “Field-Portable Microplastic Sensing in Aqueous Environments: A Perspective on Emerging Techniques", published in "Sensors" (Blevins) in view of McDonagh et al PGPUBS Document US 2021/0179443 (McDonagh). Referenced paragraph numbers of the Specification of the applied PGPUBS Document are identified by “[ ]” symbols.
For independent claim 1, Blevins discloses: A method comprising:
obtaining one or more images of plastic in water (Abstract regarding “microplastics (MPs)” and Sections 2.1, 2.2 and Table 1 regarding using imaging from any one or more of a plurality of types of measuring and imaging equipment to obtain and collect data characterizing images of MPs that are present);
providing the one or more images to a computer trained to detect plastic
(Section 4, 1st paragraph regarding “automated MP sensors and Section 3.2.1., last paragraph, re an acoustophoresis system associated with a “control computer” and processing steps automated using computer-controlled fluidic components as part of an automated system);
obtaining output from the computer indicating one or more items of plastic (inherent from Table 1 regarding “Data Products” characterizing particle polymer type and size for acoustophoresis and other types of measuring and imaging equipment taken with providing the one or more images to a computer trained to detect plastic Section 3.2.1., last paragraph, re an acoustophoresis system associated with a “control computer” and processing steps automated using computer-controlled fluidic components as part of an automated system);
; and
controlling one or more acoustic transducers to move the one or more items of plastic using the output from the computer (again, see Section 3.2.1, last three paragraphs concerning transfer and diverting of particles or MPs, separating MPs by size and “computer-controlled fluidic components”, and Figure 10 re acoustophoresis focusing of particles and directing the MPs from inlet ports to outlet ports of a microchannel, taken with Section 3.2.1, last paragraph re “computer-controlled fluidic components”) .
Claim 1, and claims dependent therefrom, differ from Blevins by requiring that the computer includes a machine learning model utilized in the providing images, obtaining output and controlling of acoustic transducer movement method steps.
McDonagh teaches an apparatus comprising a computer program product for receiving data of microplastic (MP) concentration from a plurality of remote devices [0019], and collecting or moving such MPs to and through collection and filtration or other separation devices [0019, 0022 and 0023]. Such apparatus and computer are configured to simulate and model the receiving of data and MP collecting and moving, as well as eventual transport and processing by microplastics processing factories using modelling and model training techniques for “collecting…retraining and applying” of data [0004, 0017-0019 and 0030-0032].
McDonagh teaches that such apparatus and computer control for varying local environmental conditions, microplastic concentrations, and locations and effectively result in
optimizing and making effective microplastic clean-up operations, accounting for changing water column and extent of microplastic pollution, changing target sizes of microplastics [0019, last 5 lines, 0028 and 0032, last 5 lines through 0033].
Thus, it would have been obvious to one of ordinary skill in the arts of evaluating microplastic contaminant pollution and associated cleanup of water contaminated with microplastics, to have modified the Blevins method, by incorporating the modelling and model-training computer components and method steps taught by McDonagh, in order to optimize and making more-effective microplastic clean-up operations, accounting for changing water column and extent of microplastic pollution, and changing target sizes of microplastics.
For claim 2, Blevins in view of McDonagh teaches wherein the output from the machine learning model comprises: a location of each of the one or more items of plastic (see McDonagh at [0018 regarding “retraining a local model of likely locations and concentrations of microplastics] and [0032 regarding data concerning MP location and depth]).
For claim 3, Blevins in view of McDonagh teaches wherein the output from the machine learning model comprises: a value indicating a quantity of the one or more items of plastic (see McDonagh at [0018 regarding “retraining a local model of likely locations and concentrations of microplastics] and [0032 regarding data concerning MP location and depth]).
For claim 4, Blevins in view of McDonagh further teaches data concerning MP concentrations and types transmitted as input data to control one or more plastic processing stages
(See McDonagh at [0029 re data provided to cleaning or processing apparatuses] and [0030 re providing of external computerized data 404 to factories producing and discharging microplastics]).
For claim 5, Blevins in view of McDonagh further teaches controlling the one or more acoustic transducers to move the one or more items of plastic to one or more processing stages configured to process plastic
(see McDonagh at [0029 re data provided to cleaning or processing apparatuses] and [0030 re providing of external computerized data 404 to factories producing and discharging microplastics, such factories thus being “processing stages to process plastic]).
For claim 6, Blevins further discloses wherein the one or more acoustic transducers are configured along a system connected to a water source
(see Figure 10 regarding transducer (PZT) along a microfluidic channel and paragraph below concerning the flow in such channel as being pumped from an Ocean seawater source).
For claim 7, Blevins further discloses where obtaining images optionally comprises controlling a 1st light of a 1st color to illuminate, controlling a camera to capture a 1st image of the one or more images while the 1st light is illuminated, controlling a 2nd light of a 2nd color to illuminate, and controlling the camera to capture a 2nd image of the one or more images while the 2nd light is illuminated
(all disclosed in Blevins at Section 3.1.2 regarding short-wave infrared multispectral imaging, using hyperspectral or multispectral images to count and classify MPs, with low-cost SWIR camera and color wheel filters interrogating different wave lengths).
For claim 8, Blevins in view of McDonagh further discloses controlling the one or more acoustic transducers to move the one or more items of plastic within a vessel, wherein the vessel is connected to a water source at a first location and a second location
(see Blevins at (Figure 10 regarding transducer (PZT) along a microfluidic channel and paragraph below concerning the flow in such channel as being pumped from an Ocean seawater source, and at Section 2.1, 2nd paragraph re the system being deployable on a boat or underwater vehicle, and see
McDonagh at [0025 regarding the apparatus operating in fresh or salt water] or [0026 re water flowing from an aquatic environment to a cleaning chamber], [0028 for the apparatus being placed below a dock which may be at a coast line] and [0029 for collected, cleaned MPs being removed and the separated and cleaned microplastics being returned to another vessel]).
, and
the vessel is configured to obtain the water that includes the one or more items of plastic from the first location and provide the water without the one or more items of plastic to the second location (suggested by McDonagh at [0029 for collected, cleaned MPs being removed and the separated and cleaned microplastics being returned to another vessel]).
For claim 9, Blevins in view of McDonagh further discloses wherein the first location and the second location are the same location (see McDonagh at [0018 re the apparatus being placed below a dock 302, thus 1st location of obtaining the water and 2nd location of returning cleaned water without microplastics necessarily being at approximately the same location near a dock]).
For claim 10, Blevins further discloses wherein the vessel is within the water source
(see Blevins at (Figure 10 regarding transducer (PZT) along a microfluidic channel and paragraph below concerning the flow in such channel as being pumped from an Ocean seawater source, and at Section 2.1, 2nd paragraph re the system being deployable on a boat or underwater vehicle).
For claim 11, Blevins further discloses where obtaining the one or more images of plastic in water optionally comprises: controlling a camera to capture the one or more images of plastic (Section 3.1.2 drawn to short-wave infrared multispectral imaging implemented with a low cost or multispectral camera).
For claim 12, Blevins further discloses:
detecting a first type of plastic using the one or more images of plastic in water, and
detecting a second type of plastic using the one or more images of plastic in water
(Blevins at Section 2.2 and Table 1 concerning quantifying of masses of MP particles, according to MP size distribution, polymer type and morphology, and utilization of various types of measurement devices which may include hyperspectral or multispectral imaging);
controlling the one or more acoustic transducers to move one or more items of the first type of plastic to a first location; and
controlling the one or more acoustic transducers to move one or more items of the second type of plastic to a second location (suggested by Blevins at Table 1 concerning acoustophoresis being demonstrated for cell manipulation and MP sorting).
Claims 13-20 are rejected under 35 U.S.C. 103 as being unpatentable over McDonagh et al PGPUBS Document US 2021/0179443 (McDonagh) in view of Blevins et al publication entitled “Field-Portable Microplastic Sensing in Aqueous Environments: A Perspective on Emerging Techniques", published in "Sensors" (Blevins). Referenced paragraph numbers of the Specification of the applied PGPUBS Document are identified by “[ ]” symbols.
For independent claim 13, McDonagh discloses a system for cleaning and collecting microplastics (MPs) so as to remove them from water sources (Abstract and [0002]) comprising:
one or more computers (500/502) and one or more storage devices (system memory 560) storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations ([0019, 0032 and 0034-0039 regarding central computer system 500/502 with system memory 560]), the operations comprising:
obtaining one or more items of data concerning plastic in water [0018-0019 regarding obtaining data concerning environmental conditions, locations of microplastics, and microplastic concentrations from a plurality of remote devices];
providing the data to a machine learning model trained to detect plastic [0018-0019 regarding the data being received by a local model so as to retrain and apply the retrained model];
obtaining output from the machine learning model indicating one or more items of plastic [0029-0031 regarding sending and applying the model information or data to cleaning apparatuses 416, 426 and 436]; and
controlling devices for movement of the items of MPs one or more acoustic transducers to move the one or more items of plastic using the output from the machine learning center [0028-0029 regarding controlling MP collecting apparatus(es) movement to determined locations for plastics processing]..
Claim 13 differs by requiring the one or more computers as being operable to obtain the items of data in the form of one or more images of plastic in water, and provide such one or more images to the machine learning model.
Blevins teaches a method comprising:
obtaining one or more images of plastic in water (Abstract regarding “microplastics (MPs)” and Sections 2.1, 2.2 and Table 1 regarding using imaging from any one or more of a plurality of types of measuring and imaging equipment to obtain and collect data characterizing images of MPs that are present); and
providing the one or more images to a computer trained to detect plastic
(Section 4, 1st paragraph regarding “automated MP sensors and Section 3.2.1., last paragraph, re an acoustophoresis system associated with a “control computer” and processing steps automated using computer-controlled fluidic components as part of an automated system); and also teaches the method being operable for
obtaining output from the computer indicating one or more items of plastic (inherent from Table 1 regarding “Data Products” characterizing particle polymer type and size for acoustophoresis and other types of measuring and imaging equipment taken with providing the one or more images to a computer trained to detect plastic Section 3.2.1., last paragraph, re an acoustophoresis system associated with a “control computer” and processing steps automated using computer-controlled fluidic components as part of an automated system);
; and
controlling one or more acoustic transducers to move the one or more items of plastic using the output from the computer (again, see Section 3.2.1, last three paragraphs concerning transfer and diverting of particles or MPs, separating MPs by size and “computer-controlled fluidic components”, and Figure 10 re acoustophoresis focusing of particles and directing the MPs from inlet ports to outlet ports of a microchannel, taken with Section 3.2.1, last paragraph re “computer-controlled fluidic components”) .
Blevins, in Table 1, teaches that various forms of obtaining the MP-characterizing data by utilizing imaging, results in rapid sampling and enables use of portable and relatively low cost equipment, and allow reduced sample preparation time for the characterizing.
Thus, it would have been obvious to one of ordinary skill in the art of characterizing and collecting and removing microparticles (MPs) from water bodies to have modified the software of the McDonagh computer system so as to incorporate instructions for the one or more computers to perform operations including operating imaging equipment as a portion of the remote devices utilized for obtaining the MP characterizing data, and providing the images to the machine learning model, as taught or suggested by Blevins. Such computer system functionality or operability, would have advantageously and obviously facilitated a more rapid sampling of microplastics-containing water, enabled use of portable and relatively low cost equipment, and allowed reduced sample preparation time for the characterizing.
Claim 13 also differs by requiring the devices for movement of the items of MPs being one or more acoustic transducers operative to move the one or more items of plastic.
Again, Blevins also teaches controlling one or more acoustic transducers to move one or more items of detected and characterized plastic using the output from the computer (again, see Section 3.2.1, last three paragraphs concerning transfer and diverting of particles or MPs, separating MPs by size and “computer-controlled fluidic components”, and Figure 10 description re acoustophoresis focusing of particles and directing the MPs from inlet ports to outlet ports of a microchannel, taken with Section 3.2.1, last paragraph re “computer-controlled fluidic components”) .
Blevins teaches that such utilization of acoustic transducers facilitates demonstrated MP sorting in Table 1, and teaches that such acoustophoresis achieves a high 96-98% separation efficiency of different types of MPs and of MPs from non-microplastic particles in the explanation of Figure 10 and discussion in Section 3.2.1 of “In a second experiment”.
Thus, it would have been further obvious to the skilled artisan to have modified the software of the McDonagh computer system so as to incorporate instructions for the one or more computers to perform operations including utilizing one or more acoustic transducers for the moving of the one or more items of plastic, as taught by Blevins, in order to facilitate MP sorting, and achieve a high 96-98% separation efficiency of different types of MPs and of MPs from non-microplastic particles.
For claim 14, McDonagh also discloses wherein the output from the machine learning model comprises: a location of each of the one or more items of plastic (see McDonagh at [0018 regarding “retraining a local model of likely locations and concentrations of microplastics] and [0032 regarding data concerning MP location and depth]).
For claim 15, McDonagh also discloses wherein the output from the machine learning model comprises: a value indicating a quantity of the one or more items of plastic (see McDonagh at [0018 regarding “retraining a local model of likely locations and concentrations of microplastics] and [0032 regarding data concerning MP location and depth]).
For claim 16, McDonagh further discloses data concerning MP concentrations and types transmitted as input data to control one or more plastic processing stages
(McDonagh at [0029 re data provided to cleaning or processing apparatuses] and [0030 re providing of external computerized data 404 to factories producing and discharging microplastics]).
For claim 17, McDonagh further discloses controlling the one or more acoustic transducers to move the one or more items of plastic to one or more processing stages configured to process plastic
(see McDonagh at [0029 re data provided to cleaning or processing apparatuses] and [0030 re providing of external computerized data 404 to factories producing and discharging microplastics]).
For claim 18, Blevins further teaches wherein the one or more acoustic transducers are configured along a system connected to a water source
(see Figure 10 regarding transducer (PZT) along a microfluidic channel and paragraph below concerning the flow in such channel as being pumped from an Ocean seawater source).
For claim 19, Blevins further teaches where obtaining images optionally comprises controlling a 1st light of a 1st color to illuminate, controlling a camera to capture a 1st image of the one or more images while the 1st light is illuminated, controlling a 2nd light of a 2nd color to illuminate, and controlling the camera to capture a 2nd image of the one or more images while the 2nd light is illuminated
(all disclosed in Blevins at Section 3.1.2 regarding short-wave infrared multispectral imaging, using hyperspectral or multispectral images to count and classify MPs, with low-cost SWIR camera and color wheel filters interrogating different wave lengths).
For claim 20, McDonagh discloses a non-transitory computer readable medium storing software comprising instructions executable by one or more computers, which upon execution (see computer system 500/502, described in [0019 and 0030-0035, particularly [0019], as including machine readable storage medium having program instructions embodied therein, cause the one or more computers to perform operations comprising:
obtaining one or more items of data concerning plastic in water [0018-0019 regarding obtaining data concerning environmental conditions, locations of microplastics, and microplastic concentrations from a plurality of remote devices];
providing the data to a machine learning model trained to detect plastic [0018-0019 regarding the data being received by a local model so as to retrain and apply the retrained model];
obtaining output from the machine learning model indicating one or more items of plastic [0029-0031 regarding sending and applying the model information or data to cleaning apparatuses 416, 426 and 436]; and
controlling devices for movement of the items of MPs one or more acoustic transducers to move the one or more items of plastic using the output from the machine learning center [0028-0029 regarding controlling MP collecting apparatus(es) movement to determined locations for plastics processing].
Claim 20 differs by requiring the one or more computers as being operable to obtain the items of data in the form of one or more images of plastic in water, and provide such one or more images to the machine learning model.
Blevins teaches a method comprising:
obtaining one or more images of plastic in water (Abstract regarding “microplastics (MPs)” and Sections 2.1, 2.2 and Table 1 regarding using imaging from any one or more of a plurality of types of measuring and imaging equipment to obtain and collect data characterizing images of MPs that are present); and
providing the one or more images to a computer trained to detect plastic
(Section 4, 1st paragraph regarding “automated MP sensors and Section 3.2.1., last paragraph, re an acoustophoresis system associated with a “control computer” and processing steps automated using computer-controlled fluidic components as part of an automated system); and also teaches the method being operable for
obtaining output from the computer indicating one or more items of plastic (inherent from Table 1 regarding “Data Products” characterizing particle polymer type and size for acoustophoresis and other types of measuring and imaging equipment taken with providing the one or more images to a computer trained to detect plastic Section 3.2.1., last paragraph, re an acoustophoresis system associated with a “control computer” and processing steps automated using computer-controlled fluidic components as part of an automated system);
; and
controlling one or more acoustic transducers to move the one or more items of plastic using the output from the computer (again, see Section 3.2.1, last three paragraphs concerning transfer and diverting of particles or MPs, separating MPs by size and “computer-controlled fluidic components”, and Figure 10 re acoustophoresis focusing of particles and directing the MPs from inlet ports to outlet ports of a microchannel, taken with Section 3.2.1, last paragraph re “computer-controlled fluidic components”) .
Blevins, in Table 1, teaches that various forms of obtaining the MP-characterizing data by utilizing imaging, results in rapid sampling and enables use of portable and relatively low cost equipment, and allow reduced sample preparation time for the characterizing.
Thus, it would have been obvious to one of ordinary skill in the art of characterizing and collecting and removing microparticles (MPs) from water bodies to have modified the software of the McDonagh computer readable medium so as to incorporate instructions for the one or more computers to perform operations including: obtaining plastic images with imaging equipment for obtaining the MP characterizing data, and providing the images to the machine learning model, all as taught or suggested by Blevins.
Such computer software functionality would have advantageously and obviously provided rapid sampling and enables use of portable and relatively low cost equipment, and allow reduced sample preparation time for the characterizing.
Claim 20 also differs by requiring the devices for movement of the items of MPs being one or more acoustic transducers operative to move the one or more items of plastic.
Blevins teaches controlling one or more acoustic transducers to move one or more items of detected and characterized plastic using the output from the computer (again, see Section 3.2.1, last three paragraphs concerning transfer and diverting of particles or MPs, separating MPs by size and “computer-controlled fluidic components”, and Figure 10 description re acoustophoresis focusing of particles and directing the MPs from inlet ports to outlet ports of a microchannel, taken with Section 3.2.1, last paragraph re “computer-controlled fluidic components”) .
Blevins teaches that such utilization of acoustic transducers facilitates demonstrated MP sorting in Table 1, and teaches that such acoustophoresis achieves a high 96-98% separation efficiency of different types of MPs and of MPs from non-microplastic particles in the explanation of Figure 10 and discussion in Section 3.2.1 of “In a second experiment”.
Thus, it would have been further obvious to the skilled artisan to have modified the software of the McDonagh computer readable medium to incorporate instructions for the one or more computers to perform operations including utilizing one or more acoustic transducers for the moving of the one or more items of plastic, as taught by Blevins, in order to facilitate MP sorting, and achieve a high 96-98% separation efficiency of different types of MPs and of MPs from non-microplastic particles.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Edgell PGPUBS Document US 2020/0002208 (Edgell) is of interest regarding particular separation techniques for removing microplastics from water bodies. Kattentidt et al Patent Publication WO 2005/083367 (Kattentidt) concerns details of detecting objects using acoustics. Nottke et al PGPUBS Document US 2014/0332406 concerns details of utilizing acoustic cavitation for moving and treating contaminating objects from water.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Primary Examiner Joseph Drodge at his direct government telephone number of 571-272-1140. The examiner can normally be reached on Monday-Friday from approximately 8:00 AM to 1:00PM and 2:30 PM to 5:30 PM.
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The formal facsimile phone number, for official, formal communications, for the examining group where this application is assigned is 571-273-8300.
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JWD
11/21/2025
/JOSEPH W DRODGE/ Primary Examiner, Art Unit 1773