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 .
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.
Response to Amendment
Claims 10-21 and 25-33 are currently pending.
Claims 30-33 have been newly added, and independent claim(s) 10 and dependent claims 13-19 and 25 have been amended by applicant’s amendments received 17 April 2026. No new matter has been introduced.
Claims 22-24 have been canceled, and therefore the prior rejections is/are moot.
Response to Arguments
Applicant’s arguments, see Remarks, pg. 7-10, filed 17 April 2026, with respect to the rejection of claims 10-29 under 35 USC § 101 have been fully considered and are persuasive. The rejections under 35 USC § 101 of claims 10-29 has been withdrawn based on the filed amendments to the claims. The examiner thanks the applicant for the explanation of the amendments, and the support within the specification as to not introduce new matter.
Applicant’s arguments with respect to claim(s) 10-12 and 26-27 have been considered but are moot because the new ground of rejection does not rely on the specific combination of references applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. An update to the rejection of the claims (10-12, 26-27, and newly added 30-33) under 35 USC § 103 in response to the amendments may be found in the appropriate section below.
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) 10-12, 26-27 and 30-33 is/are rejected under 35 U.S.C. 103 as being unpatentable over Suomi et al. (hereinafter Suomi, “Wind Gust Measurement Techniques—From Traditional Anemometry to New Possibilities”, MDPI Sensors 2018, 18, 1300) in view of Tsadka et al. (hereinafter Tsadka, US 20110106324 A1) and further in view of Tchoryk et al. (hereinafter Tchoryk, US 20130314694 A1).
Regarding claim 10, Suomi teaches a method for determining wind speed components of wind using a LiDAR sensor at a site, the LiDAR sensor being oriented vertically to perform measurements in at least one horizontal measurement plane, each horizontal measurement plane comprising at least two measurement points (pg. 13, Fig. 4, where LIDAR is aimed upwards and measurement planes include at least two points), the method comprising:
a) acquiring measurement signals from the LiDAR sensor for each measurement point of the at least one horizontal measurement plane (pgs. 12-13, where LIDAR collect data for multiple data points which include distance, coordinates, and wind velocity vectors)
b) determining an average wind direction and an average wind speed in the at least one horizontal measurement plane by reconstructing wind from the measurement signals (pg. 14, where a moving-average of horizontal wind speed is calculated based on LIDAR data);
d) determining a time shift between each measurement point of the at least one horizontal measurement plane and the constructed projection line by using the determined average wind speed (pg. 16, where a time shift between two points, utilizing wind speed, can be determined from the kinematic equation x=Ut).
e) for each measurement point of the at least one horizontal measurement plane, determining a corrected measurement signal, the corrected measurement signal corresponding to a measurement signal occurring at a time before acquiring the measurement signals of each measurement point equal to the time shift (pg. 14, where a correction to each point may be implemented to correct for bias in data);
f) determining the wind speed components in the at least one horizontal measurement plane by use of the corrected measurement signals (pg. 14, where a correction to each data point may be implemented to correct for bias in data to determine updated velocity data).
Suomi is silent on setting a projection line perpendicular to the wind’s velocity vector, and on determining a design of an installed turbine based on at least one of dimensions, class, and structure.
Tsadka teaches a system for monitoring wind characteristics utilizing LIDAR, where a perpendicular is set to the direction of the wind vector in a plane ([0061], [0070]).
Tchoryk teaches an atmospheric measurement system and method, where decisions for g) installing the wind turbine at the site with a design of the wind turbine including at least one of dimensions, class and structure of the wind turbine based on the wind speed components determined in step f) ([0225], [0645]; where information including wind velocity, temperature, density, or combinations thereof, can be used in conjunction with recording equipment such as a LIDAR system to determine statistics of associated measurement, over periods of time ranging from seconds to years, and where these statistics can be used to guide decision making processes for the size, type, and placement of wind turbines relative to the terrain and to each other, and to provide for estimating the expected energy output of the completed wind farm).
Therefore, to one of ordinary skill in the art before the effective filing date of the claimed invention, it would have been obvious prima facie to modify Suomi to incorporate the teachings of Tsadka to set a reference or projection plane perpendicular to the direction of average wind velocity and to incorporate the teachings of Tchoryk to specifically determine a turbine for installation based on the dimensions or class which best fit a location’s wind profile with a reasonable expectation of success. As Tsadka notes, this plane is generally set because it is the preferred orientation of a wind turbine, which allows for maximizing the amount of electricity which may be generated ([0061]). Using this perpendicular as the offset for data correction would have a predictable result of determining a mathematical difference between an optimal orientation of a turbine, and the current average wind velocity in a plane. Tsadka additionally teaches that wind information collected can inform potential future locations of wind turbine installations ([0014]), and Suomi notes that “For wind energy, information on wind extremes is needed in the planning phase to ensure the strength of the turbine structure” (pg. 2, lines 2-9), and therefore to one of ordinary skill in the art it would be obvious to specifically choose to install a turbine which will best perform, without being damaged, in a specific location.
Regarding claim 11, Suomi as modified above teaches a method as claimed in claim 10, wherein
the projection line is constructed as a straight line perpendicular to the wind direction passing through a barycenter of the measurement points of the at least one measurement plane or passing through a measurement point of the at least one measurement plane (pg. 13, Fig. 4, where Doppler lidar measures multiple points within a plane (in this example, 5 points) and average wind direction u-vector is focused on a measurement point within a plane.)
Regarding claim 12, Suomi as modified above teaches a method as claimed in claim 11, wherein
the projection line is constructed by a line perpendicular to the wind direction passing through the measurement point of the at least one measurement plane which was most recently measured (pg. 13 - pg. 14, where lidar scanning techniques include frequently updating scans of area separated by known durations, where data analysis is routinely updated by newest data set).
Regarding claim 26, Suomi as modified above teaches a method as claimed in claim 10, wherein
the average wind direction and the average wind speed (v-) are determined for a fixed duration time window or a sliding time window, with the fixed duration time window ranging between 1 min and 1 h (Pg. 15, Fig. 6, where sample windows were taken of LIDAR Doppler data in 10-minute intervals).
Regarding claim 27, Suomi as modified above teaches a method as claimed in claim 26, wherein
the time window ranges between 5 min and 30 min (Pg. 15, Fig. 6, where sample windows were taken of LIDAR Doppler data in 10-minute intervals).
Regarding claim 30, Suomi as modified above teaches a method as claimed in claim 11, but does not specifically teach determining an operating speed based on the production operating range of the turbine.
Tsadka teaches a system for monitoring wind characteristics utilizing LIDAR near current, or future, wind turbine locations where an operating speed of the wind turbine is within an operating range for producing energy from the wind turbine ([0068] - [0071]; where wind speed components are used to inform and control turbine alignment, or on/off of rotation, to fall within operating ranges without damaging the turbine).
Therefore, to one of ordinary skill in the art before the effective filing date of the claimed invention, it would have been obvious prima facie to further modify Suomi to incorporate the teachings of Tsadka determine an operating speed based on the operating range for the specific turbine with a reasonable expectation of success. Tsadka teaches that wind information collected can inform potential future locations of wind turbine installations ([0014]), and Suomi notes that “For wind energy, information on wind extremes is needed in the planning phase to ensure the strength of the turbine structure” (pg. 2, lines 2-9), and to one of ordinary skill in the art it would be obvious to specifically choose to operate a turbine within an appropriate operating range so that it is functioning, without being damaged, in a specific location.
Regarding claims 31 and 32, Suomi as modified above teaches a method as claimed in claim 11, but does not explicitly teach determining an operating orientation or control based on wind velocity at a multitude of sites.
Tsadka teaches a system for monitoring wind characteristics utilizing LIDAR near current, or future, wind turbine locations wherein at least one of orientation and control of the wind turbine is based on the wind speed components determined in step f) ([0068] - [0071]; where wind speed components are used to inform and control turbine alignment, blade orientation or on/off of rotation),
wherein the method is practiced at a plurality of sites ([0014], [0016],[0084] - [0088]; Fig. 4, where data from wind speed components may be used to calculate maximum output and optimal locations of a plurality of turbines (366) within a potential wind farm volume (352) from multiple measurement volumes (380)).
Therefore, to one of ordinary skill in the art before the effective filing date of the claimed invention, it would have been obvious prima facie to further modify Suomi to incorporate the teachings of Tsadka determine operating orientations and/or controls for multiple turbines based on the wind velocity information with a reasonable expectation of success. Tsadka teaches that wind information collected can inform potential future locations of wind turbine installations ([0014]), and that wind installations are not generally at singular site. To one of ordinary skill in the art it would be obvious to specifically choose to install and operate/orient a plurality of turbines which will best perform at specific locations, without being damaged, based on the wind velocity information collected at those sites.
Regarding claim 33, Suomi teaches a method for determining wind speed components of wind using a LiDAR sensor at a site, the LiDAR sensor being oriented vertically to perform measurements in at least one horizontal measurement plane, each horizontal measurement plane comprising at least two measurement points (pg. 13, Fig. 4, where LIDAR is aimed upwards and measurement planes include at least two points), the method comprising:
a) acquiring measurement signals from the LiDAR sensor for each measurement point of the at least one horizontal measurement plane (pgs. 12-13, where LIDAR collect data for multiple data points which include distance, coordinates, and wind velocity vectors)
b) determining an average wind direction and an average wind speed in the at least one horizontal measurement plane by reconstructing wind from the measurement signals (pg. 14, where a moving-average of horizontal wind speed is calculated based on LIDAR data);
d) determining a time shift between each measurement point of the at least one horizontal measurement plane and the constructed projection line by using the determined average wind speed (pg. 16, where a time shift between two points, utilizing wind speed, can be determined from the kinematic equation x=Ut).
e) for each measurement point of the at least one horizontal measurement plane, determining a corrected measurement signal, the corrected measurement signal corresponding to a measurement signal occurring at a time before acquiring the measurement signals of each measurement point equal to the time shift (pg. 14, where a correction to each point may be implemented to correct for bias in data);
f) determining the wind speed components in the at least one horizontal measurement plane by use of the corrected measurement signals (pg. 14, where a correction to each data point may be implemented to correct for bias in data to determine updated velocity data).
Suomi is silent on setting a projection line perpendicular to the wind’s velocity vector, and on determining a design of a plurality of installed turbines based on at least one of dimensions, class, and structure, for a multitude of sites.
Tsadka teaches a system for monitoring wind characteristics utilizing LIDAR, where a perpendicular is set to the direction of the wind vector in a plane ([0061], [0070]).
Tchoryk teaches an atmospheric measurement system and method, where decisions for g) installing the wind turbine at the site with a design of the wind turbine including at least one of dimensions, class and structure of the wind turbine based on the wind speed components determined in step f) ([0225], [0645]; where information including wind velocity, temperature, density, or combinations thereof, can be used in conjunction with recording equipment such as a LIDAR system to determine statistics of associated measurement, over periods of time ranging from seconds to years, and where these statistics can be used to guide decision making processes for the size, type, and placement of wind turbines relative to the terrain and to each other, and to provide for estimating the expected energy output of the completed wind farm).
Therefore, to one of ordinary skill in the art before the effective filing date of the claimed invention, it would have been obvious prima facie to modify Suomi to incorporate the teachings of Tsadka to set a reference or projection plane perpendicular to the direction of average wind velocity, and to use the system at a plurality of sites, and to incorporate the teachings of Tchoryk to specifically determine a turbine for installation based on the dimensions or class which best fit a location’s wind profile with a reasonable expectation of success. As Tsadka notes, this plane is generally set because it is the preferred orientation of a wind turbine, which allows for maximizing the amount of electricity which may be generated ([0061]). Using this perpendicular as the offset for data correction would have a predictable result of determining a mathematical difference between an optimal orientation of a turbine, and the current average wind velocity in a plane. Tsadka additionally teaches that wind information collected can inform potential future locations of wind turbine installations ([0014]), and Suomi notes that “For wind energy, information on wind extremes is needed in the planning phase to ensure the strength of the turbine structure” (pg. 2, lines 2-9). To one of ordinary skill in the art it would be obvious to specifically choose to install a plurality of turbines which will best perform at specific locations, without being damaged, based on the wind velocity information collected at those sites.
Claim(s) 13-15, 25 and 28-29 is/are rejected under 35 U.S.C. 103 as being unpatentable over Suomi et al. (hereinafter Suomi, “Wind Gust Measurement Techniques—From Traditional Anemometry to New Possibilities”, MDPI Sensors 2018, 18, 1300), in view of Tsadka et al. (hereinafter Tsadka, US 20110106324 A1) and Tchoryk et al. (hereinafter Tchoryk, US 20130314694 A1), and further in view of Bayon et al. (hereinafter Bayon, US 20150145253 A1).
Regarding claim 13, Suomi as modified above teaches the method as claimed in claim 10, but Suomi as modified above is silent on the use of a wind uniformity hypothesis in the data analysis.
Bayon teaches a method for wind turbine monitoring, where an estimator of the wind speed at the rotor uses reconstructing the wind, which is based on a hypothesis of wind uniformity in the at least one measurement plane ([0080] - [0082], where unitary coherence hypothesis is used to assign uniform wind vectors on a measurement plane)
Therefore, to one of ordinary skill in the art before the effective filing date of the claimed invention, it would have been obvious prima facie to further modify Suomi to incorporate the teachings of Bayon to utilize a wind uniformity hypothesis within the method of wind velocity measurement with a reasonable expectation of success. Use of wind uniformity models as noted by Bayon allow for simplification in mathematical analysis of velocity datasets by setting specific values equal to known values (such as an average or zero in a given direction) ([0078] – [0083]) and these approximations would lead to a predictable result in reducing mathematical efforts required to analyze the velocity data collected.
Claim 14 is similarly rejected to claim 13.
Claim 15 is similarly rejected to claim 13.
Regarding claim 25, Suomi as modified above teaches the method as claimed in claim 10, but Suomi as modified above is silent on the wind speed component equation.
Bayon teaches a method for wind turbine monitoring, where an estimator of the wind speed at the rotor is constructed by use of vector and matrix representation, where
w
x
(
t
)
w
y
(
t
)
w
z
(
t
)
=
L
1
N
+
m
1
(
t
-
δ
t
1
)
m
2
(
t
-
δ
t
2
)
m
N
(
t
-
δ
t
N
)
with
w
x
,
w
y
,
w
z
being wind speed components,
m
1
,
m
2
,
…
,
m
N
being measurement signals of measurement points I from 1 to N, δt being a time shift of the measurement points 1 to N, and
L
1
N
+
being a geometric reconstruction matrix of the wind speed components ([0104] - [0118]; where the reconstruction of wind velocity vectors at time t is related to measurement values before delay and a reconstruction matrix).
Therefore, to one of ordinary skill in the art before the effective filing date of the claimed invention, it would have been obvious prima facie to further modify Suomi to incorporate the teachings of Bayon to utilize transformation matrix which utilizes the time shift between two points in two reference frames within the method of wind velocity measurement with a reasonable expectation of success. Use of matrices to reconstruct, or apply correction values to vectors (such as a wind velocity vector) is a well-known mathematical process of matrix and vector math.
Regarding claim 28, Suomi as modified above teaches the method as claimed in claim 10, but Suomi is silent on the use of a frozen turbulence hypothesis in the data analysis.
Bayon teaches a method for wind turbine monitoring, where an estimator of the wind speed at the rotor is constructed by use of a frozen turbulence hypothesis with a vertical component of the speed being considered zero ([0077] - [0082], where frozen turbulence hypothesis is utilized).
Therefore, to one of ordinary skill in the art before the effective filing date of the claimed invention, it would have been obvious prima facie to further modify Suomi to incorporate the teachings of Bayon to utilize a frozen turbulence hypothesis within the method of wind velocity measurement with a reasonable expectation of success. Use of frozen turbulence models and unitary coherence hypothesis as noted by Bayon, allow for simplification in mathematical analysis of velocity datasets by setting specific values equal to known values (such as an average or zero in a given direction) ([0078] – [0083]) and these approximations would lead to a predictable result in reducing mathematical efforts required to analyze the velocity data collected.
Claim 29 is similarly rejected to claim 28.
Claim(s) 16-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Suomi et al. (hereinafter Suomi, “Wind Gust Measurement Techniques—From Traditional Anemometry to New Possibilities”, MDPI Sensors 2018, 18, 1300) in view of Tsadka et al. (hereinafter Tsadka, US 20110106324 A1) and Tchoryk et al. (hereinafter Tchoryk, US 20130314694 A1), and further in view of “Rotation of velocity vectors in Cartesian Coordinates” on Mathematics Stack Exchange (hereinafter “MSE”, Mathematics Stack Exchange via Wayback Machine, snapshot 20150921).
Regarding claim 16, Suomi as modified above teaches a method as claimed in claim 10, but Suomi as modified above is silent on the mathematical nature of the time shift associated between the original wind vector and the projection reference frame.
MSE teaches a time shift δt of each measurement point i may be determined with the formula:
δ
t
i
=
x
i
cos
Ψ
-
y
i
sin
Ψ
v
-
with
x
i
and
y
i
being coordinates of the measurement point i in a frame associated with the at least one measurement plane,
v
-
being the determined average wind speed, and Ψ being an angle formed between a y axis of the at least one measurement plane and the projection line (Where a time between two points with a known velocity, in two dimensions in Cartesian space which has been rotated by an angle Psi will maintain the form of the vector version of a kinematic equation
t
=
r
→
v
-
).
Therefore, to one of ordinary skill in the art before the effective filing date of the claimed invention, it would have been obvious prima facie to further modify Suomi to incorporate the teachings of MSE to utilize vector representations of the kinematic equations, with reference frame rotation between a velocity in Cartesian coordinates and a reference frame (in this instance perpendicular to the wind velocity) matrix which with a reasonable expectation of success. Use of two- and three-dimensional vector notation, kinematic equation, and changing of reference frames is a well-known mathematical process of matrix and vector math which is utilized in physics via the kinematic equations, and use here would have a predicable result of representing wind velocity and changes in reference frames in more than a single dimension.
Claim 17 is similarly rejected to claim 16.
Claim 18 is similarly rejected to claim 16.
Claim(s) 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Suomi et al. (hereinafter Suomi, “Wind Gust Measurement Techniques—From Traditional Anemometry to New Possibilities”, MDPI Sensors 2018, 18, 1300), in view of Tsadka et al. (hereinafter Tsadka, US 20110106324 A1), Tchoryk et al. (hereinafter Tchoryk, US 20130314694 A1), and Bayon et al. (hereinafter Bayon, US 20150145253 A1), and further in view of “Rotation of velocity vectors in Cartesian Coordinates” on Mathematics Stack Exchange (hereinafter “MSE”, Mathematics Stack Exchange via Wayback Machine, snapshot 20150921).
Regarding claim 19, Suomi as modified above teaches a method as claimed in claim 13, but Suomi as modified above is silent on the mathematical nature of the time shift associated between the original wind vector and the projection reference frame.
MSE teaches a time shift δt of each measurement point i may be determined with the formula:
δ
t
i
=
x
i
cos
Ψ
-
y
i
sin
Ψ
v
-
with
x
i
and
y
i
being coordinates of the measurement point i in a frame associated with the at least one measurement plane,
v
-
being the determined average wind speed, and Ψ being an angle formed between a y axis of the at least one measurement plane and the projection line (Where a time between two points with a known velocity, in two dimensions in Cartesian space which has been rotated by an angle Psi will maintain the form of the vector version of a kinematic equation
t
=
r
→
v
-
).
Therefore, to one of ordinary skill in the art before the effective filing date of the claimed invention, it would have been obvious prima facie to further modify Suomi to incorporate the teachings of MSE to utilize vector representations of the kinematic equations, with reference frame rotation between a velocity in Cartesian coordinates and a reference frame (in this instance perpendicular to the wind velocity) matrix with a reasonable expectation of success. Use of two- and three-dimensional vector notation, kinematic equation, and changing of reference frames is a well-known mathematical process of matrix and vector math which is utilized in physics via the kinematic equations, and use here would have a predicable result of representing wind velocity and changes in reference frames in more than a single dimension.
Claim(s) 20-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Suomi et al. (hereinafter Suomi, “Wind Gust Measurement Techniques—From Traditional Anemometry to New Possibilities”, MDPI Sensors 2018, 18, 1300) in view of Tsadka et al. (hereinafter Tsadka, US 20110106324 A1) and Tchoryk et al. (hereinafter Tchoryk, US 20130314694 A1), and further in view of Krumm (US 20060047472 A1).
Regarding claim 20, Suomi as modified above teaches a method as claimed in claim 10, but Suomi as modified above is silent on the use of interpolation in the dataset analysis.
Krumm teaches a method for measuring position and movement of objects, where an interpolation of prior and subsequent measurement signals of a measurement point being considered ([0052] - [0053]; where linear interpolation between a prior and later data set may be utilized to approximate a missing data point).
Therefore, to one of ordinary skill in the art before the effective filing date of the claimed invention, it would have been obvious prima facie to further modify Suomi to incorporate the teachings of Krumm to utilize interpolation within the data analysis of wind velocities with a reasonable expectation of success. As Krumm notes ([0006] - [0009]) interpolation in camera and LIDAR distance information reduces calculation time and difficulty in situations where data points may be missed, and therefore reduces resources necessary for the calculations present.
Claim 21 is similarly rejected to claim 20.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
O'Brien et al. (US 20150247953 A1) teaches a method for predicting arrival of wind events at aeromechanical structures which utilizes lidar to assist in maximizing turbine output by finding range-resolved wind profiles to improve spatial resolution of the measured wind field.
Hays et al. (US 20120050750 A1) teaches an atmospheric measurement system which operates with multiple LIDAR systems to detect at locations of multiple wind turbines, and where turbulence (such as that introduced by a perpendicular wind velocity component) is constantly measured and compensated for to avoid damage during operation.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 Kara Richter whose telephone number is (571)272-2763. The examiner can normally be reached Monday - Thursday, 8A-5P EST, Fridays are variable.
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, Helal Algahaim can be reached at (571) 270-5227. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/K.M.R./Examiner, Art Unit 3645
/HELAL A ALGAHAIM/SPE , Art Unit 3645
Prosecution Insights