DETAILED ACTION
Status of Claims
The present application, filed on or after 3/16/2013, is being examined under the first inventor to file provisions of the AIA .
This action is in reply to the Application and claims filed 02/01/2024.
Claims 1-20 have been examined and are pending.
(AIA ) Examiner Note
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 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.
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 at the time any inventions covered therein were effectively filed 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 at the time a later invention was effectively filed 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
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 the claimed invention is directed to an abstract idea (i.e. a judicial exception) without significantly more.
Per step 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance, the claims are directed towards a process, machine, or manufacture.
Per step 2A Prong One, the claims recite specific limitations which fall within at least one of the groupings of abstract ideas enumerated in the 2019 PEG, as follows:
Per Independent claims 1, 7, 13:
processing the set of images to determine a first set of attributes of the first product;
determining whether the authenticated physical record comprises a description of the first set of attributes; and
when a determination is made that the authenticated physical record comprises the description of the first set of attributes,… virtually tether the first product to the authenticated physical record.
As noted supra, these limitations fall within at least one of the groupings of abstract ideas enumerated in the 2019 PEG. Specifically, these limitations fall within a combination of the groups Mathematical Concepts (e.g. mathematical relationships; mathematical formulas or equations; mathematical calculations) Mental Processes (concepts performed in the human mind including an observation, evaluation, judgment, opinion) Certain Methods Of Organizing Human Activity (e.g. fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)..
That is, the steps as drafted, are a business decision to “virtually tether” (e.g. recording an association within a relational database or ledger system of a product to a record) a first product to an already authenticated physical record – which is a fundamental economic practice or commercial interaction; i.e. recording an association of an object/item to a record is fundamental to ledgers, transaction management, and business practices thus falling into Certain Methods of Organizing Human Activity.
Additionally, the steps of “processing” and “determining” may be performed mentally or manually when recited at this high level of generality. These are generic evaluations and judgements. Such evaluations and judgments are considered mental processes per Cyberfource and 2019 PEG. Furthermore, these steps are not technical in nature and applicant has not invented any particular technical step of “processing” and “determining”.
Furthermore, the steps of “processing” and “determining” when recited at this high level of generality are also akin to generic calculation steps, e.g. see specification at [0021] where it is noted that a generic AI/ML model “may” be used to perform the necessary calculations for “processing” a set of images. However, the original disclosure does not support that applicant has invented any particular AI/ML models. Therefore, these steps are also seen as a generic references to generic calculations performed by off-the-shelf calculators/models (i.e. the generic AI/ML model) – Mathematical calculations and formulas are abstract ideas per Flook, Benson, and the 2019 PEG.
Therefore, the claims are directed to the abstract idea of using either mental processes or mathematical calculations to make a business decision to “virtually tether” (e.g. recording an association within a relational database or ledger system of a product to a record) a first product to an already authenticated physical record – which is a fundamental economic practice or commercial interaction; which is a combination of mathematical concepts, mental processes, and organizing human activity.
Furthermore, the mere nominal recitation of a generic server does not take the claim limitation out of the enumerated grouping. Thus, the claims recite an abstract idea.
Per step 2A Prong 2, the Examiner finds that the judicial exception is not integrated into a practical application. Although there are additional elements, other than those noted supra, recited in the claims, none of these additional element(s) or a combination of elements as recited in the claims apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception. As drafted, the claims as a whole merely describe how to generally “apply” the aforementioned concepts or, link them to a field of use (i.e. in this case virtual association/tethering of a product to a record such as in a database) or, serve as insignificant extra-solution activity (e.g. data-gathering, collection, transmittal, and/or generic storage). The claimed computer components are recited at a high level of generality and are merely invoked as tools to implement the idea but are not technical in nature. Simply implementing the abstract idea on or with generic computer components is not a practical application of the abstract idea.
These additional limitations are as follows: “A method for implementing a tool that virtually tethers a product to an authenticated physical record of the product, the method comprising: receiving a set of images of a first product, wherein the set of images comprises at least one complete view of the first product, wherein the first product occupies a majority of a visible region of the at least one complete view, and wherein at least one view from among the at least one complete view further captures an authenticatable identification of the first product …”
However, these elements do not present a technical solution to a technical problem; The claim does not recite an improvement to computer technology or computer functionality. The receipt of data is generic data gathering; i.e. Applicant’s invention is not a technique nor technical solution for “receiving” data such as “a set of images” nor does the description of the received data alter this finding. The preamble’s recitation of “A method for implementing a tool that virtually tethers a product to an authenticated physical record of the product” is a field-of-use limitation. It merely limits the abstract idea to a particular technological environment without integrating it into a practical application. Per Bilski and 2019 PEG Section II(B)(1), merely limiting the use of the abstract idea to a particular field is insufficient. The additional elements do not recite a specific manner of performing any of the steps core to the already identified abstract idea. Instead, these features merely serve to generally “apply” the aforementioned concepts or, link them to a field of use or, are insignificant extra-solution activity (e.g. data-gathering, etc….) to the already identified abstract idea and do not integrate the abstract idea into a practical application thereof.
Per Step 2B, the Examiner does not find that the claims provide an inventive concept, i.e., the claims do not recite additional element(s) or a combination of elements that amount to significantly more than the judicial exception recited in the claim. As discussed with respect to Step 2A Prong Two, the additional elements in the independent claims were considered as merely serving to generally “apply” the aforementioned concepts via generically described computer components (e.g. a processor; and memory storing instructions that, when executed by the processor, cause the processor to perform operations including, etc…) and “link” them to a field of use (i.e. virtual “tethering”), or as insignificant extra-solution activity. For the same reason these elements are not sufficient to provide an inventive concept; i.e. the same analysis applies here in 2B. Mere instructions to apply an exception using a generic computer component and conventional data gathering cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. So, upon revaluating here in step 2B, these elements are determined to amount to no more than mere instructions to apply the exception using generic computer components (i.e. a server) and/or gather and transmit data which is well-understood, routine, conventional activity in the field; i.e. note the Symantec, TLI, and OIP Techs Court decisions cited in MPEP 2106.05(d)(ll) indicate that mere receipt or transmission of data over a network is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here).
Accordingly, alone and in combination, these elements do not integrate the abstract idea into a practical application, as found supra, nor provide an inventive concept, and thus the claims are not patent eligible.
As for the dependent claims, the dependent claims do recite a combination of additional elements. However, these claims as a whole, considered either independently or in combination with the parent claims, do not integrate the identified abstract idea into a practical application thereof nor do they provide an inventive concept.
For example, dependent claim 2 recites the following: “…wherein the first set of attributes comprises at least one from among: a first product type, a first product color, a first product model, a first product manufacturer, and a first product year of manufacture.” However, a description of received data does not illuminate any particular technique or process by which the data was identified and mere receiving data, regardless of its description is insignificant extra-solution activity. There is nothing which is significantly more than the already identified abstract idea recited here.
Therefore, the Examiner does not find that these additional claim limitations integrate the abstract idea into a practical application nor provide an inventive concept. Instead, these limitations, as a whole and in combination with the already recited claim elements of the parent claims, are not significantly more than the already identified abstract idea. A similar finding is found for the remaining dependent claims.
For these reasons, the claims are not found to include additional elements that are sufficient to amount to significantly more than the judicial exception and therefore the claims are not found to be patent eligible.
Please see the 2019 Revised Patent Subject Matter Eligibility Guidance published in the Federal Register (84 FR 50) on January 7, 2019 (found at http://www.uspto.gov/patent/laws-and-regulations/examination-policy/examination-guidance-and-training-materials).
Claim Rejections - 35 USC § 102 (AIA )
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, 4-6, 8-14, 16-19 are rejected under 35 U.S.C. 102(a)(1) and/or (a)(2) as being anticipated or clearly anticipated by Clinton Edward Ehrlich-Quinn (U.S. 2019/0236594 A1; hereinafter, "Quinn"
Claims 1, 7, 13:
Pertaining to claims 1, 11, 18 exemplified in the limitations of method claim 1, Quinn as shown teaches the following:
A method for implementing a tool that virtually tethers a product to an authenticated physical record of the product, the method comprising:
receiving a set of images of a first product (Quinn, see at least Figs. 23-27 and [0196]-[0205], e.g.: “…An image of the banknote is captured before the event (STEP 2305), then the event is executed on the mobile device (STEP 2310), either automatically or with manual user assistance, and then a second image of the banknote is captured (STEP 2315 )… numerous images [a set of images] may be captured [received] for each event, including extracting some or all frames from a video stream…”; the exemplary product of Quinn is a banknote but as noted per at least [0216] the product may be any product, e.g.: “Throughout this disclosure , banknotes may be substituted for numerous obvious analogs, including but not limited to… lottery tickets, …baseball cards, etc…”), wherein the set of images comprises at least one complete view of the first product, wherein the first product occupies a majority of a visible region of the at least one complete view, and wherein at least one view from among the at least one complete view further captures an authenticatable identification of the first product (Quinn, see citations noted supra e.g. Figs. 23-27 and [0196]-[0205], also in view of at least [0103]-[0104]: “…the region cropping module 415 includes functionality to identify the presence and individual boundaries of one or more regions of a banknote, including but not limited to regions containing denomination identifiers (e.g., the corners of a bill , the portrait region of a bill ), regions containing serialization information (e.g. serial numbers, series numbers, printing dates, names and signatures of government officials), regions containing anti - counterfeiting features (e.g., security threads , seals , micro - printed areas, watermarks, holograms ) or regions selected for anti - spoofing verification (e.g., bill areas chosen for illumination, folding, or creasing), such that each region of interest within the portion of an image depicting a banknote can be algorithmically extracted from the other regions therein…”; Quinn’s Figs. 25-27 demonstrate a camera being used to capture an image of a product in which the product, a bill, occupies a majority of a visible region of a camera display; Furthermore, applicant’s “complete view” is a colloquialism as used in his specification, e.g. see Spec at [0127], and does not mean “complete” in the sense of total such that all angles and perspectives of a product are included in the images but instead the meaning of “complete”, as used in applicant’s specification, is that sufficient information is captured, including identifiers of the product, so as to make a determination regarding authenticity such that “authenticatable identification” of the product may be made – e.g. via “barcodes, quick-response codes (QR codes), numbers, letters, and other characters and symbols (such as ASCII characters, for example)”. Therefore, Applicant’s limitation, in view of his own specification, reads on Quinn’s teachings.);
processing the set of images to determine a first set of attributes of the first product (Quinn, see citations noted supra, e.g. Figs. 23-27 and [0196]-[0205] and [0213]-[0216], teaching the system captures sets of images of the product, such as a banknote or any other product, and processes the images to determine various features [attributes] of the product, e.g.: “…The absence of a persistence violation is added as a condition of processing the captured images to verify the features they depict…” where such features are disclosed as including at least the following: “…security features that react to rear illumination (e.g., water marks). It may also be used on notes without such features, to assess the material the bill is printed on (by measuring translucency profile, both color and degree) or to examine content printed on opposite side that becomes visible during backlighting… The user holds the bill in front of the light source, while capturing it via the camera on the mobile device, which detects the watermark that is made visible by the rear illumination. That is the reference image for the event...”);
determining whether the authenticated physical record comprises a description of the first set of attributes (Quinn, see citations noted supra, e.g. per [0106]: “…the likeness evaluation module 425 includes functionality to calculate the strength of the resemblance between one or more received images and / or image sections and one or more real or composite exemplars of various authentic and counterfeit banknotes and / or banknote regions , such that a determination may be made algorithmically regarding whether the threshold for a match has been satisfied. In one or more embodiments of the invention, the algorithmic comparisons performed by the module may include the degree of similarity, as measured by one or more variables or trained functions, between sensor data and exemplars associated with authentication events or inter - frame object persistence…”); and
when a determination is made that the authenticated physical record comprises the description of the first set of attributes, utilizing the tool to virtually tether the first product to the authenticated physical record (Quinn, see citations noted supra, including again at least Fig. 7, [0081], and [0096]-[0097], teaching: “…The Proof of cash module 120 processes the sensor data from User A' s device(s) to verify that User A possesses an authentic serialized banknote. This may include verification [virtually tether] of not only the fact that the note is genuine, but also that its serial number has not been altered… The outcomes of remote - verification attempts may be recorded as inputs in a digital ledger stored within the native ledger repositories 130 or remotely. In one embodiment, to fork a currency, the cryptographic ledger module 100 makes an entry in the ledger upon the first verification of each unique serialized banknote, and corresponding digital money is issued to the holder of that note…”; applicant does not explicitly define what he means by “virtually tether” in his Specification and therefore this term is open to interpreting per its plain meaning which reads on Quinn’s teachings.)
Claim 2:
Quinn teaches the limitations upon which this claim depends. Furthermore, as shown, Quinn teaches the following:
The method of claim 1, wherein the first set of attributes comprises at least one from among: a first product type, a first product color, a first product model, a first product manufacturer, and a first product year of manufacture (Quinn, see at least [0104]-[0106], e.g.: attributes determined from the set of images include: “…denomination [model] identifiers (e.g., …the portrait region of a bill), regions containing serialization information (e.g. serial numbers, series numbers, printing dates [year of manufacture], names and signatures of government officials), regions containing anti-counterfeiting features (e.g., security threads, seals, micro-printed areas, watermarks, holograms ) or regions selected for anti-spoofing verification (e.g., bill areas chosen for illumination, folding, or creasing), such that each region of interest within the portion of an image depicting a banknote can be algorithmically extracted from the other regions therein…”).
Claims 4, 12, 19:
Quinn teaches the limitations upon which these claims depend. Furthermore, as shown, Quinn teaches the following:
. wherein the processing comprises: determining a predetermined number of product features that fall within a similarity threshold of at least one feature of the first product by comparing the first set of images to a reference data repository of the product features (Quinn, see citations noted supra, e.g. [0104]-[0106]: “…the algorithmic comparisons performed by the module may include the degree of similarity, as measured by one or more variables or trained functions, between sensor data and exemplars associated with authentication events or inter-frame object persistence…”).
Claims 5, 13:
Quinn teaches the limitations upon which these claims depend. Furthermore, as shown, Quinn teaches the following:
…wherein the processing comprises: determining a first product color and a first product model (Quinn, see citations noted supra, e.g. [0104]-[0106], e.g.: “…image size , dimensions , color , brightness , sharpness , contrast , geometry…” and “…denomination [model] identifiers…”; the denomination of a banknote, e.g. $5 vs $100, is type of model of the banknote.).
Claims 6, 14:
Quinn teaches the limitations upon which these claims depend. Furthermore, as shown, Quinn teaches the following:
…wherein the determining the first product color and the first product model comprises: utilizing, to perform the processing, an artificial intelligence and machine learning (AI/ML) model that evaluates the set of images (Quinn, see at least [0145], e.g.: “…machine learning and artificial intelligence models may gather training data from proof of cash executions by users of the platform. In this way, the proof of cash module can be configured to train the various anti - spoofing and anti - counterfeiting models using real world data , in order to prepare for final verification to occur upon execution of the hard fork…”).
Claims 8, 16:
Quinn teaches the limitations upon which these claims depend. Furthermore, as shown, Quinn teaches the following:
…further comprising: when at least one incomplete view of the first product is received, rejecting the at least one incomplete view (Quinn, see citations noted supra in view of at least [0212]-[0214] teaching persistence tracking; e.g. if an image of the set of images is incomplete by not adhering to persistence tracking, then the images are rejected as not authentic. For example, “…the persistence module determines whether frames captured by user' s device show the persistence of a single bill (e.g., by tracking points on bill surface)…”; if the fames do not show persistence, then the bill is not deemed authentic.)
Claims 9, 17:
Quinn teaches the limitations upon which these claims depend. Furthermore, as shown, Quinn teaches the following:
…further comprising: generating the at least one complete view by cropping a second image, wherein the first product occupies less than a majority of the visible region of the second image (Quinn, see at least citations noted supra, and also at least Fig. 4, and [0101], e.g. cropping module which crops an image, e.g. a second image, to capture only a region of interest of the banknote product; therefore the region of interest occupied less than a majority of the visible region of the second image.)
Claim 10:
Quinn teaches the limitations upon which this claim depends. Furthermore, as shown, Quinn teaches the following:
The method of claim 1, wherein each image included in the set of images is captured by at least one camera (Quinn, see citations noted supra, e.g. Figs. 11, 24-29, and [0195]-[0214], e.g.: “…images are being captured from the camera on the user 's device…”).
Claim Rejections - 35 USC § 103 (AIA )
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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 non-obviousness.
Claims 3, 7, 15, 20 are rejected under 35 U.S.C. 103 as obvious over Quinn in view of Javeri (US 2021/0122292 A1; hereinafter, "Javeri").
Claim 3:
Although Quinn teaches the limitations upon which these claims depend, and teaches as shown supra, e.g. per [0216], his product may be any product, he may not explicitly teach his product of interest for which his user captures an image is a vehicle with authenticatable identification information such as a license plate as recited below. However, regarding this feature, Quinn in view of Javeri teaches the following:
The method of claim 1, wherein the first product comprises: a vehicle and wherein the authenticatable identification comprises a license plate (Javeri, see at least [0008]-[0020] and Figs. 1A-1J, e.g.: “…For example, the vehicle device may receive an alert that includes first vehicle [first product] data associated with a first vehicle,… The first vehicle data may include data identifying a year of the first vehicle, a make of the first vehicle, a model of the first vehicle, a color of the first vehicle, or a license plate number of the first vehicle.…devices (e.g., dash cameras, parking assist cameras, backup assist cameras, and/or the like) that capture images or video, and/or the like associated with vehicles 110)… For example, vehicle device 105 may activate a dash camera of vehicle 110 to capture the images or video of the other vehicles… vehicle platform 115 may train the machine learning model, with historical images or video of vehicles, to generate a trained machine learning model that identifies vehicle data (e.g., years, makes, models, colors, license plate numbers, and/or the like of vehicles) based on the historical images or video of the vehicles. For example, the machine learning model may identify vehicle data based on object detection, image classification, optical character recognition, and/or the like… vehicle platform 115 may perform dimensionality reduction to reduce the historical images or video to a minimum feature set, thereby reducing resources (e.g., processing resources, memory resources, and/or the like) to train the machine learning model, and may apply a classification technique to the minimum feature set...”)
Therefore, the Examiner understands that the limitation in question is merely applying a known technique of Javeri (directed towards capturing image data of a product, such as a vehicle, which includes authenticatable identification information such as a license plate and using techniques to train machine learning models to identify features [attributes] of such images) which is applicable to a known base device/method of Quinn (already directed towards a system/method which captures images of products, any product, for the purpose of verifying authenticity of such a product against reference information or reference image data) to yield predictable results. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the technique of Javeri to the device/method of Quinn in order to perform the limitation in question because Javeri’s image processing techniques are pertinent to the objectives of Quinn and because according to MPEP 2143(I) (C) and/or (D), the use of known technique to improve a known device, methods, or products in the same way (or which is ready for improvement) is obvious.
Claims 7, 15, 20:
Although Quinn teaches the limitations upon which these claims depend, he may not explicitly teach the nuance as recited below. However, regarding this feature, Quinn in view of Javeri teaches the following:
…wherein the AI/ML model comprises: a first AI/ML model that is trained to determine the first product color and a second AI/ML model that is trained to determine the first product model (Javeri, see at least [0008]-[0020], e.g.: “Javeri, see at least [0008]-[0020] and Figs. 1A-1J, e.g.: “…For example, the vehicle device may receive an alert that includes first vehicle [first product] data associated with a first vehicle,… The first vehicle data may include data identifying a year of the first vehicle, a make of the first vehicle, a model of the first vehicle, a color of the first vehicle, or a license plate number of the first vehicle… devices (e.g., dash cameras, parking assist cameras, backup assist cameras, and/or the like) that capture images or video, and/or the like associated with vehicles 110)… vehicle platform 115 may train the machine learning model, with historical images or video of vehicles, to generate a trained machine learning model that identifies vehicle data (e.g., years, makes, models, colors, license plate numbers, and/or the like of vehicles) based on the historical images or video of the vehicles. For example, the machine learning model may identify vehicle data based on object detection, image classification, optical character recognition, and/or the like…”; obvious there may be more than one AI/ML model and each may be trained to identify a piece of vehicle identification data noted by Javeri, e.g. a first AI/ML model trained to determine color, and a second trained to determine vehicle model because per at least MPEP 2144.04 (V)(C) – Making separable is obvious).
Therefore, the Examiner understands that the limitation in question is merely applying a known technique of Javeri (directed towards capturing image data of a product, such as a vehicle, which includes authenticatable identification information such as a license plate and using techniques to train machine learning models to identify features [attributes] of such images) which is applicable to a known base device/method of Quinn (already directed towards a system/method which captures images of products, any product, for the purpose of verifying authenticity of such a product against reference information or reference image data) to yield predictable results. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the technique of Javeri to the device/method of Quinn in order to perform the limitation in question because Javeri’s image processing techniques are pertinent to the objectives of Quinn and because according to MPEP 2143(I) (C) and/or (D), the use of known technique to improve a known device, methods, or products in the same way (or which is ready for improvement) is obvious.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL J SITTNER whose telephone number is (571)270-3984. The examiner can normally be reached M-F; ~9:30-6:30. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Waseem Ashraf can be reached on (571) 270-3948. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Michael J Sittner/
Primary Examiner, Art Unit 3621