Response to Amendment
This communication is in response to the amendment filed on 11/16/2021. Claims 1-13 are pending.
Drawings
The objections to Fig. 3, Fig. 4, Fig. 5, Fig. 7, Fig. 8, and Fig. 11 are withdrawn based on the replacement drawings submitted on 2/11/2026.
Claim Objections
The objections to Claims 9 and 16 are withdrawn based on the amendments filed on 2/11/2026.
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 without significantly more.
Step 1: Is the Claim to a Process, Machine, Manufacture or Composition of Matter?
Claim 1 recites a method, Claim 9 recites a system, and Claim 16 recites a non-transitory storage medium. Thus, the claims are to a method, machine, and manufacture which are among the statutory categories of invention.
Step 2A: Prong One: Does the Claim Recite an Abstract Idea?
Independent claim 1 recites:
A method for analyzing droplets based on volume distribution, comprising:
preparing a system to be emulsified using a sample containing target molecules, and obtaining a total volume V of the sample containing the target molecules;
emulsifying the system into droplets, subjecting the droplets to conditions and operations for performing amplification reactions, causing the droplets containing the sample to undergo amplification reactions and obtaining a droplet system;
acquiring a droplet image of the droplet system using a fluorescence microscope, and obtaining a total number n of droplets comprised in the droplet system based on the droplet image;
obtaining a droplet volume distribution of the droplet system based on the droplet image [the examiner finds that the foregoing underlined element recites mathematical concepts, and a mental process because they can be performed by a human using pen and paper];
counting a number j of negative droplets or a number n-j of positive droplets among the n droplets [the examiner finds that the foregoing underlined element recites mathematical concepts, and a mental process because they can be performed by a human using pen and paper]; and
performing a quantitative analysis for the target molecules according to the total volume V of the sample containing the target molecules, the total number n of droplets, the droplet volume distribution, and the number j of negative droplets or the number n- j of positive droplets [the examiner finds that the foregoing underlined element recites mathematical concepts, and a mental process because they can be performed by a human using pen and paper].
Step 2A: Prong Two: Does the Claim Recite Additional Elements That Integrate The Abstract Idea Into a Practical Application?
The elements that are not underlined above are the additional elements (i.e., “preparing a system to be emulsified using a sample containing target molecules, and obtaining a total volume V of the sample containing the target molecules”; “emulsifying the system into droplets, subjecting the droplets to conditions and operations for performing amplification reactions, causing the droplets containing the sample to undergo amplification reactions and obtaining a droplet system”; and “acquiring a droplet image of the droplet system using a fluorescence microscope, and obtaining a total number n of droplets comprised in the droplet system based on the droplet image”).
The examiner submits that each of the following additional elements does no more than generally link the use of the abstract idea to a particular technological environment or field of use because they are merely an incidental or token addition to the claim that does not alter or affect how the process steps of the abstract idea are performed. The preparing, emulsifying, and acquiring steps are mere gathering of data for use in the abstract idea. It is noted that preparing, emulsifying, and acquiring an image of a sample are well-known and conventional operations using well known and conventional techniques, i.e., the fluorescence microscope (see, e.g., Chiu et al, U.S. Pub. No. 2017/0175174, Fig. 2B and paragraph [0033]; and Colston, JR. et al, U.S. Pub. No. 2022/0008928, Figs. 33-40).
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. For example, there is no indication that the combination of elements improves the functioning of a computer or improves any other technology.
Step 2B: Does the Claim Recite Additional Elements That Amount to Significantly More Than the Abstract Idea?
The examiner submits that the additional elements do not amount to significantly more than the abstract idea for the same reasons discussed above with respect to the conclusion that the additional elements do not integrate the abstract idea into a practical application.
Claims 9 and 16 recite the same abstract idea steps that are recited in Claim 1, in addition to generic computer hardware for performing the abstract idea, and are also not patent eligible.
Dependent Claims 2-8, 10-15, and 17-20 merely recite further details of the data gathering, mathematical concepts, and/or mental process, and are also not patent eligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 2, 9, 10, 16, and 17 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Chiu et al (U.S. Pub. No. 2017/0175174, hereinafter “Chiu”).
Regarding Claim 1, Chiu teaches a method for analyzing droplets based on volume distribution (Fig. 2B, Fig. 8C, Fig. 10, paragraphs [0070]-[0072]), comprising: preparing a system to be emulsified using a sample containing target molecules (Fig. 2B block 1110), and obtaining a total volume V of the sample containing the target molecules (paragraph [0129]); emulsifying the system into droplets, subjecting the droplets to conditions and operations for performing amplification reactions, causing the droplets containing the sample to undergo amplification reactions and obtaining a droplet system (Fig. 2B, blocks 1110 and 1120, paragraphs [0288]-[0289], amplification, Fig. 2C) using a fluorescence microscope (paragraph [0033]); acquiring a droplet image of the droplet system (Fig. 2B, block 1140), and obtaining a total number n of droplets comprised in the droplet system based on the droplet image (Fig. 2B, block 1140, detecting or recognizing droplets; paragraph [0218], counting a number of droplets); obtaining a droplet volume distribution of the droplet system based on the droplet image (Fig. 2B, Fig. 8, Fig. 10, paragraphs [0070]-[0072], Fig. 2B, block 1150); counting a number j of negative droplets or a number n-j of positive droplets among the n droplets (Fig. 2B, block 1160, paragraph [0075], presence or absence of amplification product in each droplet is determined); and performing a quantitative analysis for the target molecules according to the total volume V of the sample containing the target molecules, the total number n of droplets, the droplet volume distribution, and the number j of negative droplets or the number n- j of positive droplets (Fig. 2B, block 1170).
Regarding Claim 2, Chiu teaches everything that is claimed above with respect to Claim 1. Chiu further teaches wherein the droplet volume distribution of the droplet system comprises a droplet volume cumulative distribution function, a droplet volume probability density function, and/or an expectation and a variance of a droplet volume distribution (paragraphs [0069]-[0074], Fig. 8C, Fig. 10).
Regarding Claim 9, Chiu teaches a computer device, comprising: a storage device and a processor, the storage device storing at least one computer- readable instruction, the processor executing the at least one computer-readable instruction (paragraph [0207]) to implement following functions: obtaining a total volume V of a sample containing target molecules (paragraph [0129]); acquiring a droplet image of the droplet system (Fig. 2B, block 1140) using a fluorescence microscope (paragraph [0033]), and obtaining a total number n of droplets comprised in the droplet system based on the droplet image (Fig. 2B, block 1140, detecting or recognizing droplets; paragraph [0218], counting a number of droplets), wherein the droplet system is obtained by preparing a system to be emulsified using the sample containing target molecules; emulsifying the system into droplets; subjecting the droplets to conditions and operations for performing amplification reactions, causing the droplets containing the sample to undergo amplification reactions (Fig. 2B, blocks 1110 and 1120, paragraphs [0288]-[0289], amplification, Fig. 2C); obtaining a droplet volume distribution of the droplet system based on the droplet image (Fig. 2B, Fig. 8, Fig. 10, paragraphs [0070]-[0072], Fig. 2B, block 1150); counting a number j of negative droplets or a number n-j of positive droplets among the n droplets (Fig. 2B, block 1160, paragraph [0075], presence or absence of amplification product in each droplet is determined); and performing a quantitative analysis for the target molecules according to the total volume V of the sample containing the target molecules, the total number n of droplets, the droplet volume distribution, and the number j of negative droplets or the number n- j of positive droplets (Fig. 2B, block 1170).
Regarding Claim 10, Chiu teaches everything that is claimed above with respect to Claim 9. Chiu further teaches wherein the droplet volume distribution of the droplet system comprises a droplet volume cumulative distribution function, a droplet volume probability density function, and/or an expectation and a variance of a droplet volume distribution (paragraphs [0069]-[0074], Fig. 8C, Fig. 10).
Regarding Claim 16, Chiu teaches a non-transitory storage medium having at least one computer-readable instruction stored thereon, and the at least one computer-readable instruction being executed by a processor (paragraph [0207]), to implement-following functions: obtaining a total volume V of a sample containing target molecules (paragraph [0129]); acquiring a droplet image of the droplet system (Fig. 2B, block 1140) using a fluorescence microscope (paragraph [0033]), and obtaining a total number n of droplets comprised in the droplet system based on the droplet image (Fig. 2B, block 1140, detecting or recognizing droplets; paragraph [0218], counting a number of droplets), wherein the droplet system is obtained by preparing a system to be emulsified using the sample containing target molecules; emulsifying the system into droplets; subjecting the droplets to conditions and operations for performing amplification reactions, causing the droplets containing the sample to undergo amplification reactions (Fig. 2B, blocks 1110 and 1120, paragraphs [0288]-[0289], amplification, Fig. 2C); obtaining a droplet volume distribution of the droplet system based on the droplet image (Fig. 2B, Fig. 8, Fig. 10, paragraphs [0070]-[0072], Fig. 2B, block 1150); counting a number j of negative droplets or a number n-j of positive droplets among the n droplets (Fig. 2B, block 1160, paragraph [0075], presence or absence of amplification product in each droplet is determined); and performing a quantitative analysis for the target molecules according to the total volume V of the sample containing the target molecules, the total number n of droplets, the droplet volume distribution, and the number j of negative droplets or the number n- j of positive droplets (Fig. 2B, block 1170).
Regarding Claim 17, Chiu teaches everything that is claimed above with respect to Claim 16. Chiu further teaches wherein the droplet volume distribution of the droplet system comprises a droplet volume cumulative distribution function, a droplet volume probability density function, and/or an expectation and a variance of a droplet volume distribution (paragraphs [0069]-[0074], Fig. 8C, Fig. 10).
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) 3-5, 11-13, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chiu in view of Hand, Mannila, and Smyth, “Principle of Data Mining”, MIT Press 2001 (hereinafter “Hand”).
Regarding Claim 3, Chiu teaches everything that is claimed above with respect to Claim 2. Chiu further teaches wherein the obtaining of the droplet volume distribution of the droplet system based on the droplet image comprises: determining a smallest droplet and a largest droplet from the n droplets based on the droplet image (paragraph [0075], size determined by taking an image; Figs. 8C and 10, showing largest and smallest droplet diameters, paragraph [0070]); acquiring a volume of the smallest droplet and a volume of the largest droplet (Figs. 8C and 10 and paragraphs [0071]-[0074]); obtaining a droplet volume distribution interval by taking the volume of the smallest droplet as an upper boundary and taking the volume of the largest droplet as a lower boundary (paragraph [0072], upper and lower boundaries are defined); dividing the droplet volume distribution interval into a preset number of subintervals (paragraph [0072], ranges of volumes in the volume distribution); judging the subintervals into which each of the n droplets falls (Fig. 8C and 10 and paragraphs [0072]-[0074]); and obtaining a droplet volume frequency distribution of the droplet system by counting a number of droplets falling into each of the subintervals (Fig. 8C, droplet frequency).
Chiu does not specifically teach obtaining the expectation and the variance of the droplet volume distribution of the droplet system according to the droplet volume frequency distribution of the droplet system; and obtaining the droplet volume probability density function of the droplet system based on the expectation and the variance of the droplet volume distribution of the droplet system. However, Chiu does teach analyzing droplet volume distribution data in a droplet system (see paragraphs [0069]-[0074]). Further, Hand teaches obtaining the expectation (page 486) and the variance (page 486) of a distribution of a system according to a frequency distribution of the system; and obtaining the a probability density function of the system based on the expectation and the variance of distribution system (pages 485-486). It would have been obvious to one skilled in the art at the effective filing date of the invention to apply the probability density function calculations described in Hand to the droplet volume distribution data of Chiu, because such probability density calculations are common in data mining (see Hand, page 487, heading A.2).
Regarding Claim 4, Chiu in view of Hand teaches everything that is claimed above with respect to Claim 3. Chiu further teaches wherein the determining of the smallest droplet and the largest droplet from the n droplets based on the droplet image comprises: determining, based on the droplet-image, a number of pixels comprised in each of the n droplets according to boundary position; and sorting the n, droplets according to the number of pixels comprised in each droplet, taking a droplet with fewest pixels as the smallest droplet, and taking a droplet with most pixels as the largest droplet (paragraphs [0020], [0249], [0299], pixel sets or pixel groups are equated to claimed determined number of pixels; Figs. 2D and 2G; droplets are sorted in Figs. 8C and 10).
Regarding Claim 5, Chiu in view of Hand teaches everything that is claimed above with respect to Claim 3. Chiu further teaches wherein the determining of the smallest droplet and the largest droplet from the n droplets based on the droplet image comprises: determining spatial coordinates and boundary position of each droplet of the n droplets in a space coordinate system based on the droplet image, and determining, according to the spatial coordinates and boundary position of each droplet, a minimum point and a maximum point of each droplet in the space coordinate system, and calculating a boundary range of each droplet based on the minimum point and the maximum point corresponding to each droplet; and sorting the n droplets according to the boundary range of each droplet, taking a droplet with a smallest boundary range as the smallest droplet, and taking a droplet with a largest boundary range as the largest droplet (paragraphs [0110] and [0199], simple boundary method, pixel set locations are equated to claimed spatial coordinates; Fig. 2E; detected droplets are sorted according to volume in Figs. 8C and 10).
Regarding Claim 11, Chiu teaches everything that is claimed above with respect to Claim 10. Chiu further teaches wherein the obtaining of the droplet volume distribution of the droplet system based on the droplet image comprises: determining a smallest droplet and a largest droplet from the n droplets based on the droplet image (paragraph [0075], size determined by taking an image; Figs. 8C and 10, showing largest and smallest droplet diameters, paragraph [0070]); acquiring a volume of the smallest droplet and a volume of the largest droplet (Figs. 8C and 10 and paragraphs [0071]-[0074]); obtaining a droplet volume distribution interval by taking the volume of the smallest droplet as an upper boundary and taking the volume of the largest droplet as a lower boundary (paragraph [0072], upper and lower boundaries are defined); dividing the droplet volume distribution interval into a preset number of subintervals (paragraph [0072], ranges of volumes in the volume distribution); judging the subintervals into which each of the n droplets falls (Fig. 8C and 10 and paragraphs [0072]-[0074]); and obtaining a droplet volume frequency distribution of the droplet system by counting a number of droplets falling into each of the subintervals (Fig. 8C, droplet frequency).
Chiu does not specifically teach obtaining the expectation and the variance of the droplet volume distribution of the droplet system according to the droplet volume frequency distribution of the droplet system; and obtaining the droplet volume probability density function of the droplet system based on the expectation and the variance of the droplet volume distribution of the droplet system. However, Chiu does teach analyzing droplet volume distribution data in a droplet system (see paragraphs [0069]-[0074]). Further, Hand teaches obtaining the expectation (page 486) and the variance (page 486) of a distribution of a system according to a frequency distribution of the system; and obtaining the a probability density function of the system based on the expectation and the variance of distribution system (pages 485-486). It would have been obvious to one skilled in the art at the effective filing date of the invention to apply the probability density function calculations described in Hand to the droplet volume distribution data of Chiu, because such probability density calculations are common in data mining (see Hand, page 487, heading A.2).
Regarding Claim 12, Chiu in view of Hand teaches everything that is claimed above with respect to Claim 11. Chiu further teaches wherein the determining of the smallest droplet and the largest droplet from the n droplets based on the droplet image comprises: determining, based on the droplet-image, a number of pixels comprised in each of the n droplets according to boundary position; and sorting the n, droplets according to the number of pixels comprised in each droplet, taking a droplet with fewest pixels as the smallest droplet, and taking a droplet with most pixels as the largest droplet (paragraphs [0020], [0249], [0299], pixel sets or pixel groups are equated to claimed determined number of pixels; Figs. 2D and 2G; droplets are sorted in Figs. 8C and 10).
Regarding Claim 13, Chiu in view of Hand teaches everything that is claimed above with respect to Claim 11. Chiu further teaches wherein the determining of the smallest droplet and the largest droplet from the n droplets based on the droplet image comprises: determining spatial coordinates and boundary position of each droplet of the n droplets in a space coordinate system based on the droplet image, and determining, according to the spatial coordinates and boundary position of each droplet, a minimum point and a maximum point of each droplet in the space coordinate system, and calculating a boundary range of each droplet based on the minimum point and the maximum point corresponding to each droplet; and sorting the n droplets according to the boundary range of each droplet, taking a droplet with a smallest boundary range as the smallest droplet, and taking a droplet with a largest boundary range as the largest droplet (paragraphs [0110] and [0199], simple boundary method, pixel set locations are equated to claimed spatial coordinates; Fig. 2E; detected droplets are sorted according to volume in Figs. 8C and 10).
Regarding Claim 19, Chiu teaches everything that is claimed above with respect to Claim 18. Chiu further teaches wherein the obtaining of the droplet volume distribution of the droplet system based on the droplet image comprises: determining a smallest droplet and a largest droplet from the n droplets based on the droplet image (paragraph [0075], size determined by taking an image; Figs. 8C and 10, showing largest and smallest droplet diameters, paragraph [0070]); acquiring a volume of the smallest droplet and a volume of the largest droplet (Figs. 8C and 10 and paragraphs [0071]-[0074]); obtaining a droplet volume distribution interval by taking the volume of the smallest droplet as an upper boundary and taking the volume of the largest droplet as a lower boundary (paragraph [0072], upper and lower boundaries are defined); dividing the droplet volume distribution interval into a preset number of subintervals (paragraph [0072], ranges of volumes in the volume distribution); judging the subintervals into which each of the n droplets falls (Fig. 8C and 10 and paragraphs [0072]-[0074]); and obtaining a droplet volume frequency distribution of the droplet system by counting a number of droplets falling into each of the subintervals (Fig. 8C, droplet frequency).
Chiu does not specifically teach obtaining the expectation and the variance of the droplet volume distribution of the droplet system according to the droplet volume frequency distribution of the droplet system; and obtaining the droplet volume probability density function of the droplet system based on the expectation and the variance of the droplet volume distribution of the droplet system. However, Chiu does teach analyzing droplet volume distribution data in a droplet system (see paragraphs [0069]-[0074]). Further, Hand teaches obtaining the expectation (page 486) and the variance (page 486) of a distribution of a system according to a frequency distribution of the system; and obtaining the a probability density function of the system based on the expectation and the variance of distribution system (pages 485-486). It would have been obvious to one skilled in the art at the effective filing date of the invention to apply the probability density function calculations described in Hand to the droplet volume distribution data of Chiu, because such probability density calculations are common in data mining (see Hand, page 487, heading A.2).
Claim(s) 8 is rejected under 35 U.S.C. 103 as being unpatentable over Chiu in view of Hand and Ohta (U.S. Pat. No. 9395387).
Regarding Claim 8, Chiu in view of Hand teaches everything that is claimed above with respect to Claim 3. Chiu further teaches wherein the volume of the smallest droplet is obtained according to a total number of pixels in a region occupied by the smallest droplet in the droplet image (paragraphs [0020], [0249], [0299], pixel sets or pixel groups are equated to claimed determined number of pixels; Figs. 2D and 2G); wherein the volume of the largest droplet is obtained according to a total number of pixels in a region occupied by the largest droplet in the droplet image (paragraphs [0020], [0249], [0299], pixel sets or pixel groups are equated to claimed determined number of pixels; Figs. 2D and 2G).
Chiu does not specifically teach determining the volumes according to the predetermined conversion ratio, wherein the predetermined conversion ratio is determined by: capturing an image of a preset square using a microscope under a preset magnification of the microscope; calculating a total number of pixels comprised in a side length of the preset square in the captured image; and calculating the conversion ratio based on the total number of pixels comprised in the side length of the preset square and an actual side length of the preset square. However, Ohta teaches, in column 5, lines 29-40, determining the volumes according to the predetermined conversion ratio (known imaging magnification ratio), wherein the predetermined conversion ratio is determined by: capturing an image of a preset square using a microscope under a preset magnification of the microscope (a pixel is a square); calculating a total number of pixels comprised in a side length of the preset square in the captured image (1 pixel); and calculating the conversion ratio based on the total number of pixels comprised in the side length of the preset square and an actual side length of the preset square (magnification ratio gives actual side length for a single pixel, and is used to determine length/breadth based on numbers of pixels). It would have been obvious to one skilled in the art before the effective filing date of the invention to include the magnification ratio of Ohta in the system of Chiu, in order to perform optical microscope observations and determine the actual size of elements viewed under the microscope (see Ohta, column 5, lines 29-40).
Prior Art of Record
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure.
Staker (U.S. Pub. No. 2012/0224050) teaches determining the ratio of the size of pixel to an actual object size for microscope analysis in paragraph [0046].
Sasaki et al (U.S. Pub. No. 2006/0278530) teaches determining the actual length of the side of a pixel based on a magnification ratio of a fluorescent microscope in paragraph [0080].
Allowable Subject Matter
Although there are no prior art rejections for claims 6-7, 14-15, and 19-20, the Examiner cannot comment on their allowability until the rejections under 35 U.S.C 101 are satisfactorily addressed.
Response to Arguments
Applicant's arguments filed 2/11/2026 have been fully considered but they are not persuasive.
Regarding the 101 rejection, Applicant admits on page 18 that Claim 1 recites an abstract idea, but argues on page 19 that the recitation of a fluorescence microscope in the amended claims, because it requires a physical device, is not a mathematics of mental step. However, the Examiner does not deem this claim feature to integrate the abstract idea into a practical application, because a fluorescence microscope is generic, well known hardware that is being used for mere gathering of data for use in the abstract idea. Applicant goes on to argue on page 20 that because the present application eliminates the reliance on measurement of exact volume of each droplet, the amended claim 1 is integrated into a practical application. The Examiner disagrees, because such techniques are known in the art (see the prior art rejections above).
Regarding prior art rejections, Applicant argues on pages 20-22 that Chiu does not teach “obtaining a total number of n droplets comprised in the droplet system based on the droplet image”. The Examiner disagrees. Chiu teaches recognizing the droplets in the droplet system in block 1140 of Fig. 2B, which would necessarily include obtaining a number of the droplets (i.e., storing data corresponding to each recognized droplet gives the number of droplets). Further, Chiu explicitly teaches counting the droplets in paragraph [0218]. It is further noted that Chiu explicitly teaches the claimed quantitative analysis, including identifying negative and positive droplets among the recognized droplets (see the prior art rejections above).
Applicant further argues on page 22 that the present application does not measure the volume of each droplet to achieve analysis of the droplets; however, in at least dependent Claims 3 and 6 the volume of each of the droplets in the system is determined.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CYNTHIA L DAVIS whose telephone number is (571)272-1599. The examiner can normally be reached Monday-Friday, 7am to 3pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shelby A Turner can be reached at 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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/CYNTHIA L DAVIS/Examiner, Art Unit 2857
/SHELBY A TURNER/Supervisory Patent Examiner, Art Unit 2857