DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim 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 non-statutory subject matter.
Step 1:
According to the first part of the analysis, in the instant case, claims 1-16 is directed to a method, claim 17-18 is directed to using a wind farm controller to perform the method, and claim 19 is directed to a wind farm controller. Thus, each of the claims falls within one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter).
Regarding claim 1:
According to the first part of the analysis, in the instant case, claims 1-19 is directed to a method of detecting leakage using a machine learning model, claim 20 is directed to method of training a machine learning model for detecting leakage. Thus, each of the claims falls within one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter).
Regarding claim 1:
A method of detecting leakage in a microfluidic device, the microfluidic device having a test plurality of partitions, the method comprising:
receiving test input data, the test input data includes, for each partition of the test plurality of partitions, the following properties obtained from pixels of one or more images of the microfluidic device: a location of the partition within the microfluidic device, a value of an intensity of pixels associated with the partition, and a status of an analyte being absent or present in the partition; and
determining a classification of whether a first partition of the test plurality of partitions is characterized by leakage using a machine learning model, wherein the machine learning model uses the test input data and is trained by:
receiving training input data, the training input data obtained from pixels of images of a plurality of training microfluidic devices, each training microfluidic device having a first plurality of partitions, the training input data including for each partition: the same properties as the test input data, and first labels indicating a known classification of whether a partition is characterized by leakage for each partition in the first plurality of partitions, and
optimizing, using the training input data, parameters of the machine learning model based on outputs of the machine learning model matching or not matching corresponding labels of the first labels when the machine learning model is executed using the training input data, wherein an output of the machine learning model specifies whether a partition is characterized by leakage.
Step 2A Prong 1:
“receiving test input data” is directed to mental step of data gathering.
“the test input data includes, for each partition of the test plurality of partitions, the following properties obtained from pixels of one or more images of the microfluidic device: a location of the partition within the microfluidic device, a value of an intensity of pixels associated with the partition, and a status of an analyte being absent or present in the partition” is directed to math because location describes the position of a partition within the microfluidic device involves coordinates, which are mathematical concepts, intensity value: pixel intensity is a numerical value representing the brightness of a pixel, which is directly related to the amount of light captured by the camera sensor, a quantitative measurement, analyte status: the "presence/absence" of an analyte is often determined by comparing the intensity values of pixels associated with a partition to a reference value, which involves a mathematical comparison.
“receiving training input data, the training input data obtained from pixels of images of a plurality of training microfluidic devices, each training microfluidic device having a first plurality of partitions, the training input data including for each partition: the same properties as the test input data, and first labels indicating a known classification of whether a partition is characterized by leakage for each partition in the first plurality of partitions” is directed to mental step of data gathering and identification of data.
Each limitation recites in the claim is a process that, under BRI covers performance of the limitation in the mind but for the recitation of a generic “sensor, body part, and measurement” which is a mere indication of the field of use. Nothing in the claim elements precludes the steps from practically being performed in the mind. Thus, the claim recites a mental process.
Further, the claim recites the step of "for each partition of the test plurality of partitions, the following properties obtained from pixels of one or more images of the microfluidic device: a location of the partition within the microfluidic device, a value of an intensity of pixels associated with the partition, and a status of an analyte being absent or present in the partition” which as drafted, under BRI recites a mathematical calculation. The grouping of "mathematical concepts” in the 2019 PED includes "mathematical calculations" as an exemplar of an abstract idea. 2019 PEG Section |, 84 Fed. Reg. at 52. Thus, the recited limitation falls into the "mathematical concept" grouping of abstract ideas. This limitation also falls into the “mental process” group of abstract ideas, because the recited mathematical calculation is simple enough that it can be practically performed in the human mind, e.g., scientists and engineers have been solving the Arrhenius equation in their minds since it was first proposed in 1889.
Note that even if most humans would use a physical aid (e.g., pen and paper, a slide rule, or a calculator) to help them complete the recited calculation, the use of such physical aid does not negate the mental nature of this limitation. See October Update at Section I(C)(i) and (iii).
Additional Elements:
Step 2A Prong 2:
“determining a classification of whether a first partition of the test plurality of partitions is characterized by leakage using a machine learning model, wherein the machine learning model uses the test input data and is trained” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“optimizing, using the training input data, parameters of the machine learning model based on outputs of the machine learning model matching or not matching corresponding labels of the first labels when the machine learning model is executed using the training input data, wherein an output of the machine learning model specifies whether a partition is characterized by leakage” is directed to insignificant activity and does not integrate the judicial exception into a practical application. See MPEP 2106.05(g).
The claim is merely selecting data, manipulating or analyzing the data using math and mental process, and displaying the results.
This is similar to electric power: MPEP 2106.05(h) vi. Limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to the electric power grid, because limiting application of the abstract idea to power-grid monitoring is simply an attempt to limit the use of the abstract idea to a particular technological environment, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016).
Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
The claim as a whole does not meet any of the following criteria to integrate the judicial exception into a practical application:
An additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Step 2B:
“determining a classification of whether a first partition of the test plurality of partitions is characterized by leakage using a machine learning model, wherein the machine learning model uses the test input data and is trained” does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“optimizing, using the training input data, parameters of the machine learning model based on outputs of the machine learning model matching or not matching corresponding labels of the first labels when the machine learning model is executed using the training input data, wherein an output of the machine learning model specifies whether a partition is characterized by leakage” is directed to insignificant activity and does not amount to significantly more than the judicial exception in the claim. See MPEP 2106.05(g) and 2106.05(d)(ii), third list, (iv).
The claim is therefore ineligible under 35 USC 101.
Claim 20 is directed to a method similar to claim 1.
The step of “receiving training input data, the training input data obtained from pixels of images of a plurality of training microfluidic devices” is directed to mental step of data gathering.
The step of “each training microfluidic device having a first plurality of partitions, the training input data including for each partition: a location of the partition within each training microfluidic device, a value of an intensity of pixels associated with the partition, a status of an analyte being absent or present in the partition, and first labels indicating a known classification of whether a partition is characterized by leakage for each partition in the first plurality of partitions” is directed to math because location describes the position of a partition within the microfluidic device involves coordinates, which are mathematical concepts, intensity value: pixel intensity is a numerical value representing the brightness of a pixel, which is directly related to the amount of light captured by the camera sensor, a quantitative measurement, analyte status: the "presence/absence" of an analyte is often determined by comparing the intensity values of pixels associated with a partition to a reference value, which involves a mathematical comparison.
Further, the claim recites the step of "each training microfluidic device having a first plurality of partitions, the training input data including for each partition: a location of the partition within each training microfluidic device, a value of an intensity of pixels associated with the partition, a status of an analyte being absent or present in the partition, and first labels indicating a known classification of whether a partition is characterized by leakage for each partition in the first plurality of partitions” which as drafted, under BRI recites a mathematical calculation. The grouping of "mathematical concepts” in the 2019 PED includes "mathematical calculations" as an exemplar of an abstract idea. 2019 PEG Section |, 84 Fed. Reg. at 52. Thus, the recited limitation falls into the "mathematical concept" grouping of abstract ideas. This limitation also falls into the “mental process” group of abstract ideas, because the recited mathematical calculation is simple enough that it can be practically performed in the human mind, e.g., scientists and engineers have been solving the Arrhenius equation in their minds since it was first proposed in 1889.
Note that even if most humans would use a physical aid (e.g., pen and paper, a slide rule, or a calculator) to help them complete the recited calculation, the use of such physical aid does not negate the mental nature of this limitation. See October Update at Section I(C)(i) and (iii).
Additional Elements:
The step of “optimizing, using the training input data, parameters of the machine learning model based on outputs of the machine learning model matching or not matching corresponding labels of the first labels when the machine learning model is executed using the training input data, wherein an output of the machine learning model specifies whether a partition is characterized by leakage.” is directed to insignificant activity and does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. See MPEP 2106.05(g) and 2106.05(d)(ii), third list, (iv).
Regarding claim 2, “wherein each partition of the test plurality of partitions is hexagonal” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claim 3, “wherein the machine learning model uses a statistical value of the statuses of partitions within a threshold distance away from the first partition” is directed to math.
Regarding claim 4, “wherein the statistical value is of the statuses of partitions along a common axis” is directed to math.
Regarding claim 5 “wherein the machine learning model uses a value representing intensity of pixels in multiple partitions of the test plurality of partitions” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claim 6, “wherein the properties of the test input data further include for each partition of the test plurality of partitions, a categorization of whether the partition is valid or invalid” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claim 7, “wherein the microfluidic device is a digital PCR plate” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claim 8, “wherein the test plurality of partitions comprises 20,000 partitions” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claim 9, “wherein the machine learning model is a decision tree learning model” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claim 10, “wherein the intensity of the pixels is a fluorescence intensity” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claim 11, “wherein: the properties include a value for each intensity of a plurality of intensities associated with the partition, and the plurality of intensities comprises intensities of different fluorescence channels” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claim 12, “wherein determining the classification comprises determining the first partition is characterized by leakage using a first intensity of the plurality of intensities, and upon determining the first partition is characterized by leakage in the first intensity of the plurality of intensities, determining the first partition is characterized by leakage in all other intensities in the plurality of intensities” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claim 13, “for each partition of the test plurality of partitions, determining the status of the analyte using the value of the intensity of the pixels associated with the partition” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claim 14, “determining the classification is that the first partition is characterized by leakage” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claim 15, “determining a classification of whether a copy number variation exists in a subject from a plurality of statuses from a subset of the test plurality of partitions not including the first partition” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claim 16, “wherein the classification is a first classification, the method further comprising: determining a plurality of second classifications for each partition of the test plurality of partitions other than the first partition” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claim 17, “determining an amount of partitions of the test plurality of partitions having the first classification or the second classification indicating leakage, comparing the amount to a threshold value, and outputting that the test plurality of partitions is not suitable for further analysis based on the comparison” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claim 18, “acquiring the one or more images of the microfluidic device” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claim 19, “performing an assay to detect the analyte in the test plurality of partitions using the microfluidic device” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Hence the claims 1-20 are treated as ineligible subject matter under 35 U.S.C. § 101.
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(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sarofim (US 2022/0062903 A1) in view of Chu et al. (US 2020/0074303 A1).
Regarding claims 1 and 20, Sarofim disclose a method of detecting leakage in a microfluidic device ([0002]: "as for example applicable in microfluidic devices for diagnostic assays"; [0125]: "Analyzing the analytical result and the device for leakage"), the microfluidic device having a test plurality of partitions (figure 1: cavities 3; [0057]: "The number of cavities of the device is preferably at least 96"), the method comprising:
receiving test input data ([0132]: "e.g. by visual inspection e.g. using a fluorescence microscope"), the test input data includes, for each partition of the test plurality of partitions ([0228]: "the device, and in particular the cavities, have been fluorescence imaged"), the following properties obtained from pixels of one or more images of the microfluidic device:
a location of the partition within the microfluidic device ([0229]: "visual inspection was used, especially looking at positive cavities and their neighboring (initially negative) cavities, or looking to wider areas, showing in case of large fields of cavities being more or less positive (showing fluorescence).". From "their neighboring cavities" it is implicit that the location of the partition is used),
a value of an intensity of pixels associated with the partition ([0229]: "visual inspection was used, especially looking at positive cavities and their neighboring (initially negative) cavities, or looking to wider areas, showing in case of large fields of cavities being more or less positive (showing fluorescence).". From "more or less positive" it is implicit that the intensity of the pixels is used), and
a status of an analyte being absent or present in the partition ([0229]: "For validating the effectiveness of the separation by the separation fluid, the rate of positive cavities was determined and calculated and compared to the value which is expected by a leakage free situation"); and
determining a classification of whether a first partition of the test plurality of partitions is characterized by leakage ([0229]: "For validating the effectiveness of the separation by the separation fluid").
Sarofim is silent teaching determining the classification of whether a first partition of the test plurality of partitions is characterized by leakage is performed using a machine learning model, wherein the machine learning model uses the test input data and is trained by:
receiving training input data, the training input data obtained from pixels of images of a plurality of training microfluidic devices, each training microfluidic device having a first plurality of partitions, the training input data including for each partition:
the same properties as the test input data, and
first labels indicating a known classification of whether a partition is characterized by leakage for each partition in the first plurality of partitions, and
optimizing, using the training input data, parameters of the machine learning model based on outputs of the machine learning model matching or not matching corresponding labels of the first labels when the machine learning model is executed using the training input data, wherein an output of the machine learning model specifies whether a partition is characterized by leakage.
The technical effect of these differences is that the determination of whether the first partition is leaking can be performed with reduced human effort.
The objective technical problem may therefore be regarded as to reduce the manual labor required in the method of Sarofim.
However, the differences of claim 1 simply amount to applying the inputs used to determine leakage in Sarofim into a machine learning model that is trained using generally know techniques. Since the use of machine learning to characterize images is known in the field of microfluidic devices for diagnostic assays for the purpose of reducing manual labor from e.g. Chu et al. ([0001] - [0006], Note that Chu et al. also discloses using machine learning to detect abnormalities such as leaking). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claim invention to incorporate a machine learning of Chu et al. with the method of Sarofim for the purposes of providing the machine learning model uses the test input data and is trained to implement the method of Sarofim.
Regarding claim 2, Sarofim disclose wherein each partition of the test plurality of partitions is hexagonal (para. [0056]).
Regarding claim 3, Sarofim disclose wherein the machine learning model uses a statistical value of the statuses of partitions within a threshold distance away from the first partition (para. [0229]).
Regarding claim 4, Sarofim disclose wherein the statistical value is of the statuses of partitions along a common axis (para. [0229]).
Regarding claim 5, Chu et al. disclose wherein the machine learning model uses a value representing intensity of pixels in multiple partitions of the test plurality of partitions (para. [0006], [0066]).
Regarding claim 6, Sarofim disclose wherein the properties of the test input data further include for each partition of the test plurality of partitions, a categorization of whether the partition is valid or invalid (para. [0029]: For validating the effectiveness of the separation by the separation fluid, the rate of positive cavities was determined and calculated and compared to the value which is expected by a leakage free situation).
Regarding claim 7, Sarofim disclose wherein the microfluidic device is a digital PCR plate (para. [0009]).
Regarding claim 8, wherein the test plurality of partitions comprises 20,000 partitions.
Regarding claim 9, Sarofim disclose wherein the machine learning model is a decision tree learning model are well known machine learning models.
Regarding claim 10, Sarofim disclose wherein the intensity of the pixels is a fluorescence intensity (para. [0229]).
Regarding claim 11, Sarofim disclose wherein: the properties include a value for each intensity of a plurality of intensities associated with the partition, and the plurality of intensities comprises intensities of different fluorescence channels (para. [0229]).
Regarding claim 12, Sarofim disclose wherein determining the classification comprises determining the first partition is characterized by leakage using a first intensity of the plurality of intensities, and upon determining the first partition is characterized by leakage in the first intensity of the plurality of intensities, determining the first partition is characterized by leakage in all other intensities in the plurality of intensities (para. [0152] or [0225]).
Regarding claim 13, Sarofim disclose further comprising: for each partition of the test plurality of partitions, determining the status of the analyte using the value of the intensity of the pixels associated with the partition (para. [0230]).
Regarding claim 14, Sarofim disclose further comprising determining the classification is that the first partition is characterized by leakage (para. [0230]).
Regarding claim 15, Sarofim disclose further comprising: determining a classification of whether a copy number variation exists in a subject from a plurality of statuses from a subset of the test plurality of partitions not including the first partition (para. [0232] - [0236]).
Regarding claim 16, Chu et al. disclose wherein the classification is a first classification, the method further comprising: determining a plurality of second classifications for each partition of the test plurality of partitions other than the first partition (para. [0232] - [0236]).
Regarding claim 17, Sarofim disclose further comprising: determining an amount of partitions of the test plurality of partitions having the first classification or the second classification indicating leakage, comparing the amount to a threshold value, and outputting that the test plurality of partitions is not suitable for further analysis based on the comparison (para. [0232] - [0236]).
Regarding claim 18, Sarofim disclose further comprising acquiring the one or more images of the microfluidic device (para. [0152]).
Regarding claim 19, Sarofim disclose further comprising performing an assay to detect the analyte in the test plurality of partitions using the microfluidic device (para. [0182], [0198]).
Other Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Steiman et al. (USP 2022/0036538 A1) disclose a computerized system of generating training data for training a Deep Neural Network (DNN training data) usable for examination of a semiconductor specimen, the system comprising a processor and memory circuitry (PMC) configured to: obtain a first training image representative of at least a portion of the semiconductor specimen, and first labels respectively associated with a group of pixels selected in each of one or more segments identified by a user from the first training image; extract a set of features characterizing the first training image, each feature having feature values corresponding to pixels in the first training image, the set of features including first features informative of contextual relations between the one or more segments in the first training image, and second features informative of pixel distribution in the first training image relative to a statistical measure of the group of pixels in each segment; train a machine learning (ML) model using the first labels, values of the group of pixels selected in each segment associated with the first labels, and the feature values of each feature of the set of features corresponding to the group of pixels in each segment, wherein the ML model is trained for image segmentation; process the first training image using the trained ML model to obtain a first segmentation map informative of predicted labels associated with respective pixels in the first training image, each predicted label indicative of a segment that a respective pixel belongs to; and determine to include a first training sample comprising the first training image and the first segmentation map into the DNN training data upon a criterion being met, and to repeat the extracting of the second features, the training and the processing upon the criterion not being met.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN H LE whose telephone number is (571)272-2275. The examiner can normally be reached on Monday-Friday from 7:00am – 3:30pm Eastern Time.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shelby A. Turner can be reached on (571) 272-6334. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JOHN H LE/Primary Examiner, Art Unit 2857