DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This is non final.
Claims 1-15 are present for examination.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No.20177575.6, filed on 05/29/2020.
Information Disclosure Statement
The IDS filed on 11/29/2022 is reviewed. The US patent applications are used as a prior art. See the attached document for consideration.
Drawings
The drawings are objected to under 37 CFR 1.83(a). The drawings must show every feature of the invention specified in the claims. Therefore, the storage component must be shown or the feature(s) canceled from the claim(s). No new matter should be entered.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The disclosure is objected to because of the following informalities:
Page 27, line 6, servers 500, should be servers 800.
Page 27, line 15, processing component 800, should be processing component 100.
Page 27, line 12, storing component 800, should be storing component, or label storing component on the drawing.
Page 30, line 5, model generator 300, should be model analyzer 300.
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are:
Model analyzer in claim 1.
Weight analyzed in claim 1.
Noise decoder in claim 9.
An interface in claim 14.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Examiner note: For the rest of the claims those elements are considered as any system, method, or machine that is capable to perform the specified actions under their broadest interpretation.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 13 and 15 are rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Claim 13 is dependent on the method of claim 12 and the system of claim 1. Claim 15 is also dependent on any of the system claims ( 1-11) and any of a method claims (12 -13).
Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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.
Claim 15 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because only if at least one of the claimed elements of the system is a physical part of a device can the system as claimed constitute part of a device or combination of devices to be a machine within the meaning of 101. Since a computer program product consists merely instruction and the system is not the part of claim so the claim can be reasonably implemented as software routines, the claim is at best a system of software, failing to fall within a statutory category of invention.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
Claims 1 -5, 7, and 11-15 are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Nasle (US 20090113049 A1).
Regarding claim 1 Nasle anticipates a system comprising:
A, at least one processing component: (para 14, The system includes a data acquisition component, a power analytics server, and a client termina, and para 271, The embodiments described herein, can be practiced with other computer system configurations including hand-held devices, microprocessor systems, microprocessor-based or programmable consumer electronics, minicomputers, mainframe computers and the like)
B, at least one storage component;( Fig1, pare 74 databases 130 and 132, can be configured to store one or more virtual system models, virtual simulation models, and real-time data values, each customized to a particular).
C. a plurality of sensor nodes (Fig. 1 element 104, 106, 108)
wherein, the processing component is configured to receive sensor data from the at least one sensor node(para 14, The data acquisition component is communicatively connected to a sensor configured to acquire real-time data output from the electrical system)
D. at least one model analyzer, configured to generate at least one simulation model;( Fig 2, element 124 simulation engine inside element 116 analytical sever)
E. a weight analyzer, configured to automatically associate a statistical weight to at least one infrastructural feature. (para 246, As known input/output data is continually fed into the neural network in real-time, the various weighting factors in the neural network automatically self-adjusts (i.e., learns) to allow the power analytics prediction engine to make more accurate predictions/forecasts about the health, reliability, and performance of the monitored system)
Regarding claim 2, Nasle anticipates claim 1. Nasle also anticipates wherein the weight analyzer is configured with machine learning techniques, such as deep learning techniques, further configured to self-learn at least one infrastructural feature (Para 247, As more "teaching patterns" are fed into the network, the various weights of the internal neural network algorithm can iteratively self-adjust to minimize the resulting SSE percentage between the target and estimated output values).
Regarding claim 3, Nasle anticipates claim 1, Nasle also anticipates wherein the sensor node comprises a pressure sensor and/or accelerometer and/or inclinometer and/or thermal sensor and/or acoustic sensor and/or strain gauge sensor and/or water pressure sensor and/or linear variable displacement sensor and/or visual sensor and/or load sensors and/or any combination thereof (Para 49 -50, For a monitored system 102 that is an electrical power generation, transmission, or distribution system, the sensors can provide data such as voltage, frequency, current, power, power factor, and the like…In one embodiment, the sensors are configured to also measure additional data that can affect system operation. For example, for an electrical power distribution system, the sensor output can include environmental information, e.g., temperature, humidity, etc., which can impact electrical power demand and can also affect the operation and efficiency of the power distribution system itself).
Regarding Claim 4, Nasle anticipates claim 1, Nasle also anticipates wherein the model analyzer is further configured to automatically generate the at least one infrastructural feature using the simulation model(para 62, Simulation engine 208 operates on complex logical models 206 of facility 102. These models are continuously and automatically synchronized with the actual facility status based on the real-time data provided by hub 204. In other words, the models are updated based on current switch status, breaker status, e.g., open-closed, equipment on/off status, etc. Thus, the models are automatically updated based on such status, which allows simulation engine to produce predicted data based on the current facility status. This in turn, allows accurate and meaningful comparisons of the real-time data to the predicted data)
Regarding Claim 5, Nasle anticipates claim 1, Nasle also anticipates wherein the processing component is configured to generate a database using the sensor data (Para 69, The real-time data, as well as trending produced by analytics engine 118 can be stored in a real-time data acquisition database 132 and Para 74, The virtual system model database 126, as well as databases 130 and 132, can be configured to store one or more virtual system models, virtual simulation models, and real-time data values, each customized to a particular system being monitored by the analytics server 118)
Regarding Claim 7, Nasle anticipates claim 1, Nasle also anticipates wherein the processing component is further configured to store the database and/or classified database in the storage component ( para 74. In other embodiments, databases 126, 130, and 132 can be hosted on a separate database server (not shown) that is communicatively connected to the analytics server 116 in a manner that allows the virtual system modeling engine 124 and analytics engine 118 to access the databases as needed)
Regarding Claim 11, Nasle anticipates claim 1, Nasle also anticipates wherein the model analyzer is further configured to generate at least a portion of the simulation model based on a user input (Para 166. Accordingly, existing systems rely on exhaustive studies to be performed off-line by a power system engineer or a design professional/specialist. Often the specialist must manually modify a simulation model so that it is reflective of the proposed facility operating condition and then conduct a static simulation or a series of static simulations in order to come up with recommended safe working distances, energy calculations and PPE levels).
Regarding Claim 12, Nasle anticipates A method, comprising the steps of:
a. obtaining sensor data from at least one or a plurality of sensor node(s);( Para 113, Real time sensor data can be received in step 1012. This real time data can be used to monitor the status in step 1002 and it can also be compared with the predicted values in step 1014).
b. generating simulation model(s);( Fig 2, element 208 simulation engine)
c. automatically fusing at least portion of the sensor data with the simulation model (Para 62. Simulation engine 208 operates on complex logical models 206 of facility 102. These models are continuously and automatically synchronized with the actual facility status based on the real-time data provided by hub 204. In other words, the models are updated based on current switch status, breaker status, e.g., open-closed, equipment on/off status, etc. Thus, the models are automatically updated based on such status, which allows simulation engine to produce predicted data based on the current facility status).
d. automatically predicting at least one infrastructural feature, preferably associated with the sensor node (para 17, In another aspect, a method for utilizing a neural network algorithm utilized to make real-time predictions about the health, reliability, and performance of a monitored system is disclosed. Real-time data output is received from one or more sensors interfaced to the monitored system. Predicted data output is generated for the one or more sensors interfaced to the monitored system utilizing a virtual system model of the monitored system)
Regarding claim 13, Nasle anticipates all the limitations of claim 12 and Nasle also anticipates wherein the method comprises the step of carrying out the method on the system according to claim 1 (Para 13. Systems and methods for utilizing a neural network to make real-time predictions about the health, reliability, and performance of a monitored system are disclosed).
Regarding claim 14, Nasle anticipates
a. a device processing component, configured for an interactive model analysis (para 271, The embodiments described herein, can be practiced with other computer system configurations including hand-held devices, microprocessor systems, microprocessor-based or programmable consumer electronics, minicomputers, mainframe computers and the like)
b. an interface, configured to pull user input;(Fig 1, element 128, para 76, [0076] The client 128 may utilize a variety of network interfaces (e.g., web browser, CITRIX.TM., WINDOWS TERMINAL SERVICES.TM., telnet, or other equivalent thin-client terminal applications, etc.) to access, configure, and modify the sensors (e.g., configuration files, etc.)…)
c. a memory component, configured to store the user input( para 274, The computer readable medium is any data storage device that can store data, which can thereafter be read by a computer system. Examples of the computer readable medium include hard drives, network attached storage (NAS), read-only memory, random-access memory…)
Regarding claim 15, Nasle anticipates A computer program product comprising instructions, when the program is executed by any of the system claims causes the system to perform the method steps according to any of the method claims (para 273, he systems and methods described herein can be specially constructed for the required purposes, such as the carrier network discussed above, or it may be a general-purpose computer selectively activated or configured by a computer program stored in the computer. In particular, various general-purpose machines may be used with computer programs written in accordance with the teachings herein, or it may be more convenient to construct a more specialized apparatus to perform the required operations).
Examiner note; As of claim 15, the system is recited on claim 1 and the method is also recited on claim 12.
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.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Nasle (US 20090113049 A1) in the view of Cella 1 (US 20190129405 A1).
As of claim 6, Nasle anticipated all the limitation of claim 1 but Nasle does not explicitly teach automatically classify at least a portion of the database, using the machine learning techniques, such as pattern recognition.
While Cella 1 teaches wherein the processing component is further configured to automatically classify at least a portion of the database, using the machine learning techniques, such as pattern recognition (Para 311. The platform 100 may employ many pattern recognition techniques including those that may classify input data into classes based on key features with the objective of recognizing patterns or regularities in data. The platform 100 may also implement pattern recognition processes with machine learning operations and may be used in applications such as computer vision, speech and text processing, radar processing, handwriting recognition, CAD systems, and the like. The platform 100 may employ supervised classification and unsupervised classification).
Cella 1 is considered to be analogous to Nasle and the claim invention, because they teach data collection and analysis. Therefore it would have been obvious to one of the ordinary skill in the art, before the effective filing data to combine the teaching of Cella 1 automatically classifying a dataset using machine learning techniques on Nasle’s processing component.
The motivation would have been to improve data collection in industrial environments, as well as for improved methods and systems for using collected data to provide improved monitoring, control, and intelligent diagnosis of problems and intelligent optimization of operations in various heavy industrial environments (Cella 1, para 41).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Nasle (US 20090113049 A1) in the view of R. Gao Liang (CN 101719183 B).
As of claim 8, Nasle anticipates all the limitations of claim 1 but Nasle does not explicitly teach model analyzer is configured to generate the simulation model based on finite element method and/or multi-body simulation method and/or finite difference method and/or lumped parameter method and/or any combination thereof.
While Liang teaches model analyzer is configured to generate the simulation model based on finite element method and/or multi-body simulation method and/or finite difference method and/or lumped parameter method and/or any combination thereof (Abstract, Abstract, The invention discloses a test simulation system for rail structures of high-speed railways and a urban railway system. The system is based on a finite element theory to establish models of rail structures and various components, and highly simulates indoor test and field test conditions; and the system simulates conditions of key components of a rail and whole rail structures such as stress and deformation under various loading conditions by replacing and combining different types of components, and arranges and analyzes obtained simulation test data to generate vivid animation for demonstration).
Liang is considered to be analogous to Nasle and the claim invention because they teach data analysis using simulation. Therefore it would have been obvious to one of the ordinary skill in the art, before the effective filing data to combine the teaching of Liang teaching of finite element method to generate simulation model on Nasle’s model analyzer.
The motivation would have been improvement of system functions in the future by using the finite element analysis on simulation model and this can provide an economical, quick and vivid simulation test and demonstration platform for related scientific research. (Liang, “abstract” and “detailed ways”).
Claims 9 and 10 is rejected under 35 U.S.C. 103 as being unpatentable over Nasle (US 20090113049 A1) in the view of Cella 2 (US20200110401A1).
Regarding Claim 9, Nasle anticipates claim 1 but Nasle does not explicitly teach a noise decoder, the noise decoder comprises machine learning techniques, and is configured to automatically determine a noise pattern in the database.
While Cella 2 teach wherein the system further comprises a noise decoder, the noise decoder comprises machine learning techniques, and is configured to automatically determine a noise pattern in the database (Para 715, A library may be populated with each of the three noise types for various conditions (e.g., start up, shut down, normal operation, other periods of operation as described elsewhere herein). In other embodiments, the library may be populated with noise patterns representing the aggregate ambient, local, and/or vibration noise. Analysis (e.g., filtering, signal conditioning, spectral analysis, trend analysis) may be performed on the aggregate noise to obtain a characteristic noise pattern and identify changes in noise pattern as possible indicators of a changed condition…the library of noise patterns may be used by an expert system, such as one with machine learning capacity, to confirm a status of a machine, predict when maintenance is required (e.g., off-nominal measurement, artifacts in signal), predict a failure or an imminent failure, predict/diagnose a problem, and the like). As it cited above Cella 2 determines a noise pattern using analysis e.g., filtering, signal conditioning, spectral analysis, trend analysis) but is Cella 2 also teaches any of this embodiment may include machine learning system on para 44, “A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the switching is controlled by at least one of a model, a set of rules, or a machine learning system”.
Cella 2 is considered to be analogous to Nasle and the claim invention, because they teach data collection and analysis. Therefore it would have been obvious to one of the ordinary skill in the art, before the effective filing data to combine the teaching of Cella 2 of finding a noise pattern on using a machine learning system on Nasle’s system.
The motivation would have been to improve data collection in industrial environments, as well as for improved methods and systems for using collected data to provide improved monitoring, control, and intelligent diagnosis of problems and intelligent optimization of operations in various heavy industrial environments (Cella 2, para 77).
Regarding Claim 10, Nasle anticipates claim 1 including a model analyzer and simulation model but Nasle does not explicitly teach wherein the model analyzer is configured to generate at least one noise model, based on the database, and further configured to fuse the noise model to the simulation model.
While Cella 2 teaches the model analyzer is configured to generate at least one noise model, based on the database, and further configured to fuse the noise model to the simulation model (para 717, For example, a noise pattern for a thermic heating system in a pharmaceutical plant or cooking system may include local, ambient, and vibration noise. The ambient noise may be a result of, for example, various pumps to pump fuel into the system. Local noise may be a result of a local security camera chirping with every detection of motion. Vibration noise may result from the combustion machinery used to heat the thermal fluid. These noise sources may form a noise pattern which may be associated with a state of the thermic system. The noise pattern and associated state may be stored in a library. An expert system used to monitor the state of the thermic heating system may be seeded with noise patterns and associated states from the library. As current data are received into the expert system, it may predict a state based on having learned noise patterns and associated states, para 757, A sensor fusion includes a determination of second order data from sensor data, and further includes a determination of second order data from sensor data of multiple sensors, including involving multiplexing of streams of data, combinations of batches of data, and the like from the multiple sensors. Second order data includes a determination about a system or operating condition beyond that which is sensed directly. For example, temperature, pressure, mixing rate, and other data may be analyzed to determine which parameters are result-effective on a desired outcome (e.g., a reaction rate))
Cella 2 is considered to be analogous to Nasle and the claim invention, because they teach data collection and analysis. Therefore it would have been obvious to one of the ordinary skill in the art, before the effective filing data to combine the teaching of Cella 2 of finding a noise model and further fuse with the simulated data on Nasle’s system.
The motivation would have been to improve data collection in industrial environments, as well as for improved methods and systems for using collected data to provide improved monitoring, control, and intelligent diagnosis of problems and intelligent optimization of operations in various heavy industrial environments (Cella 2, para 77).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Bickford (US 20040002776 A1, Date Published 2004-01-01) is also similar to the claimed invention since this application also teaches provides a surveillance system and method that provides an operating mode partitioned decision model that can be accomplished by observation and analysis of a time sequence of process signal data and by a combination of a plurality of techniques.
Dejaco (WO 2019219756 A1, Date Published 2019-11-21) is also similar to the claimed invention since this application also teaches a particularly reliable detection of damage as well as effective, condition-based maintenance and repair.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ABRHAM A. TAMIRU whose telephone number is (571)272-6987. The examiner can normally be reached Monday - Friday 8:00am - 5:00pm.
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, Ryan Pitaro can be reached at 571 272 4071. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/A.A.T./Examiner, Art Unit 2188
/RYAN F PITARO/Supervisory Patent Examiner, Art Unit 2188