DETAILED ACTION
Response to Amendment
The amendment filed September 30, 2026 has been entered.
Claim 1, 5, 10, 14-15 and 17 are amended.
Claim 20 is new.
Claims 1-20 are pending this application.
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.
Claims 1-19 are rejected under 35 U.S.C. 103 as being unpatentable over Metzler (US 2014/0350886 A1) in view of Zhang et al (US 2020/0401181 A1) and Liu (Sensors, 02017).
Regarding Claim 1, Metzler teaches a method, carried out by a system comprising [0022 for surveying device, and targeting device, with 0119 for the target object recorded in the camera image, recorded by a camera as the areal photosensitive detector of the surveying device]
a first device, hereinafter referred to as "device A", and a second device, hereinafter referred to as "device B", wherein device A and device B are moving, at least for a period of time, with respect to each other [0112 for target object moves so jerkily and rapidly that it disappears from the field of view of the detector or the cone of the laser beam];
and device A comprises a motion sensor [0121 for inertial measurement system typically comprises a combination of three accelerometers];
the method comprising: measuring, using the motion sensor of device A, a motion of device A, wherein data indicative of the motion of device A measured using the motion sensor of device A is hereinafter referred to as "measurement data A", and measurement data A is time-tagged using clock A [0123 for inertial measurement system—of accelerations and angular velocities or rotational speeds as second measurement data];
measuring, by device B, a motion of at least one point of device A with respect to device B [0120 for first measurement data with regard to the relative position of the target object with respect to the total station],
wherein data indicative of the motion of the at least one point of device A with respect to device B as measured by device B is hereinafter referred to as "measurement data B" [0123 for the first measurement data]
operating an estimation process, hereinafter referred to as "estimator" [0123 for evaluation means comprising an algorithm, in particular a Kalman filter],
and outputting, by the estimator, at least one of [0124 for driven in order to continuously maintain the targeting direction with respect to the target object or, as necessary, to track movements of the target object]:
and at least one estimated motion state of device A [0123 for supervisory unit calculates estimated values for the position and the movement speed of the target object].
Metzler fails to explicitly teach a first clock, hereinafter referred to as "clock A", runs on device A; a second clock, hereinafter referred to as "clock B", runs on device B.
Zhang has a method that includes receiving, from a controller, a data packet including a plurality of samples each corresponding to measurements from a motion sensor (abstract) and teaches a first clock, hereinafter referred to as "clock A", runs on device A [0021 for controller and headset may each include a clock with different capabilities (two devices with clocks];
a second clock, hereinafter referred to as "clock B", runs on device B [0021].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the position techniques, as disclosed by Metzler, further including the clock calculations as taught by Zhang for the purpose to determine the position or orientation of controller (Zhang, 0021).
Metzler fails to explicitly teach and measurement data B is time-tagged using clock B; wherein the estimator uses state variables comprising: at least one state variable representing an estimated offset between clock A and clock B or from which the estimated offset can be derived, and at least one position-related state variable; and the estimator computes values of its state variables based on: the time-tagged measurement data A, and the time-tagged measurement data B, the estimated offset between clock A and clock B to align the time-tagged measurement data A and the time-tagged measurement data B to determine at least one of: the estimated offset between clock A and clock B, and at least one estimated motion state of device A.
Lui has an algorithm can optimally estimate the time delay calibration parameters (abstract) and teaches and measurement data B is time-tagged using clock B [page 7, 3rd paragraph];
wherein the estimator uses state variables comprising: at least one state variable representing an estimated offset between clock A and clock B or from which the estimated offset can be derived [page 7, 5th paragraph and equation 22 for the time delay. W Lρ(t) is the modified Rodrigues parameter vector also page 8, 4th paragraph Time delay calibration parameters error model: the measurement noise vector],
and at least one position-related state variable [page 12, first two paragraphs];
and the estimator computes values of its state variables based on: the time-tagged measurement data A, and the time-tagged measurement data B [page 14, first paragraph for initial time delay alignment error is 9.58ms, the LiDAR-IMU alignment errors in the East, North and Up orientation]
the estimated offset between clock A and clock B [page 9, first paragraph for point on the respective curve has a corresponding timestamp, identifying the time at which the measurement arrived at the receiver]
to align the time-tagged measurement data A and the time-tagged measurement data B to determine at least one of: the estimated offset between clock A and clock B, and at least one estimated motion state of device A [page 9, first paragraph for registering the orientation curves, to use the timestamp values to estimate the relative delay between IMU and LiDAR data streams].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the position techniques, as disclosed by Metzler, further including the time delay calculations as taught by Lui for the purpose to perform the time delay calibration (Lui, page 14, first paragraph).
Regarding Claim 2, Metzler teaches device a comprises a target [0120 for reflector is mounted on a construction machine or a construction vehicle].
Regarding Claim 3, Metzler teaches device B is or comprises a robotic total station [0005 for Depending on the expansion stage of the total station, motorization of the targeting or sighting device and—in the case of the use of retroreflectors].
Regarding Claim 4, Metzler teaches the motion sensor of device A is, or comprises, at least one of: an inertial measurement unit and a distance measurement unit [0121 for three accelerometers, which determine accelerations in three orthogonal directions or axes].
Regarding Claim 5, Metzler teaches device B comprises a position sensor, which is at least one of: a stereo camera system, an optical instrument, a laser scanner, and a LIDAR [0022 for a photosensitive areal detector for continuously generating a respectively current offset signal directly dependent on a direction and an extent of a deviation].
Regarding Claim 6, Metzler teaches transmitting, from device B to device A, the time- tagged measurement data B [0123 for measurement data are communicated for example wirelessly, e.g. by means of radio waves, to the supervisory unit].
Regarding Claim 7, Metzler teaches transmitting, from device A to device B, the time-tagged measurement data A [0129 for measurement data of the inertial measurement system are communicated for example wirelessly, e.g. by means of radio waves, to the supervisory unit].
Regarding Claim 8, Metzler teaches the system further comprises a third device, hereinafter referred to as "device C” [0127 for dedicated separate external housing, but the supervisory unit can also be constructed from physically dispersed parts (third housing)];
the method further comprises: transmitting, from device A to device C, the time- tagged measurement data A [0054 and 0128 for wired data connections in this example between supervisory unit and inertial measurement system are indicated by solid lines, and wireless connections between supervisory unit and total station];
and transmitting, from device B to device C, the time- tagged measurement data B [0053-0054];
and operating the estimator is carried out on device C [0053 for housing of the surveying unit or can be situated in a separate dedicated housing and 0127].
Regarding Claim 9, Metzler teaches the estimator is or comprises at least one of: a Kalman filter, an unscented Kalman filter, a robust estimator, and a particle filter [0031 for Kalman filter].
Regarding Claim 10, Metzler fails to explicitly teach before the measuring operations, a preliminary time-synchronization of clock A and clock B, wherein outputting, by the estimator, the estimated offset between clock A and clock B, comprises outputting an estimated residual offset.
Zhang has a method that includes receiving, from a controller, a data packet including a plurality of samples each corresponding to measurements from a motion sensor (abstract) and teaches before the measuring operations, a preliminary time-synchronization of clock A and clock B [0039 for initialization state may include a BLE handshaking between the controller and headset. At state, the headset monitors the receive time of the data packets],
wherein outputting, by the estimator, the estimated offset between clock A and clock B, if applicable, comprises outputting an estimated residual offset [0034 for the controller clock operates at a slightly different rate than the headset clock, and noise is a variable that captures unmodeled latencies and may be used to model random perturbations].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the position techniques, as disclosed by Metzler, further including the clock calculations as taught by Zhang for the purpose to update parameters using every sample received from the controller (Zhang, 0039).
Regarding Claim 11, Metzler fails to explicitly teach the preliminary time-synchronization is carried out using a wireless communication link between device A and device B.
Zhang has a method that includes receiving, from a controller, a data packet including a plurality of samples each corresponding to measurements from a motion sensor (abstract) and teaches to explicitly teach the preliminary time-synchronization is carried out using a wireless communication link between device A and device B [0018, and 0039 for initialization state may include a BLE handshaking between the controller and headset. At state, the headset monitors the receive time of the data packets].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the position techniques, as disclosed by Metzler, further including the clock calculations as taught by Zhang for the purpose to update parameters using every sample received from the controller (Zhang, 0039).
Regarding Claim 12, Metzler fails to explicitly teach the state variables that the estimator uses further comprise a state variable representing an estimated drift of the offset between clock A and clock B.
Zhang has a method that includes receiving, from a controller, a data packet including a plurality of samples each corresponding to measurements from a motion sensor (abstract) and teaches to explicitly teach the state variables that the estimator uses further comprise a state variable representing an estimated drift of the offset between clock A and clock B [0034 for values of kskew and Ocontroller may be determined based on the least-square fit of equation].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the position techniques, as disclosed by Metzler, further including the clock calculations as taught by Zhang for the purpose to update parameters using every sample received from the controller (Zhang, 0039).
Regarding Claim 13, Metzler fails to explicitly teach the at least one state variable representing the estimated offset between clock A and clock B or from which the estimated offset can be derived, and the at least one position-related state variable, are accompanied in the estimator by models of their respective uncertainty.
Lui has an algorithm can optimally estimate the time delay calibration parameters (abstract) and teaches and measurement data B is time-tagged using clock B [page 7, 3rd paragraph];
wherein the estimator uses state variables comprising: at least one state variable representing an estimated offset between clock A and clock B or from which the estimated offset can be derived [page 7, 5th paragraph and equation 22 for the time delay. W Lρ(t) is the modified Rodrigues parameter vector also page 8, 4th paragraph Time delay calibration parameters error model: the measurement noise vector],
and at least one position-related state variable [page 8, 3rd paragraph for Time delay calibration parameters error model: and eq (28)];
and the estimator computes values of its state variables based on: the time-tagged measurement data A, and the time-tagged measurement data B [page 14, first paragraph for initial time delay alignment error is 9.58ms, the LiDAR-IMU alignment errors in the East, North and Up orientation].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the position techniques, as disclosed by Metzler, further including the time delay calculations as taught by Lui for the purpose to perform the time delay calibration (Lui, page 14, first paragraph).
Regarding Claim 14, Metzler fails to explicitly teach outputting, by the estimator, at least one of: the estimated offset between clock A and clock B; and the at least one estimated motion state of device A, is accompanied by outputting a corresponding, respective uncertainty measure.
Lui has an algorithm can optimally estimate the time delay calibration parameters (abstract) and teaches outputting, by the estimator, at least one of: the estimated offset between clock A and clock B [page 11, first paragraph for LiDAR and IMU measurement update, the enhanced state covariance matrix and equation (38)];
And the at least one estimated motion state of device A, is accompanied by outputting a corresponding, respective uncertainty measure [page 12, second paragraph for posteriori state vector, the state covariance matrix, and the Kalman gain and equation (45)].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the position techniques, as disclosed by Metzler, further including the time delay calculations as taught by Lui for the purpose to perform the time delay calibration (Lui, page 14, first paragraph).
Regarding Claim 15, Metzler teaches a method, carried out by a device or a set of devices [0022 for surveying device, and targeting device, with 0119 for the target object recorded in the camera image, recorded by a camera as the areal photosensitive detector of the surveying device],
device A and device B are moving, at least for a period of time, with respect to each other [0112 for target object moves so jerkily and rapidly that it disappears from the field of view of the detector or the cone of the laser beam];
and device A comprises a motion sensor [0121 for using an IMU];
the method comprising: obtaining data indicative of a motion of device A measured using the motion sensor of device A [0123 for total station continuously surveys the target object, e.g. embodied as a reflector, and in the process generates first measurement data],
obtaining data indicative of a motion of the at least one point of device A with respect to device B as measured by device B [0123 for using IMU with total station data],
operating an estimation process, hereinafter referred to as "estimator", wherein the estimator uses state variables comprising [0123 for evaluation means comprising an algorithm, in particular a Kalman filter]
and outputting, by the estimator, at least one of: the estimated offset between clock A and clock B [0124 for driven in order to continuously maintain the targeting direction with respect to the target object or, as necessary, to track movements of the target object];
and at least one estimated motion state of device A [0112, 0123].
Metzler fails to explicitly teach for estimating an offset between a first clock, hereinafter referred to as "clock A", and a second clock, hereinafter referred to as "clock B", wherein clock A runs on a first device, hereinafter referred to as "device A”; clock B runs on a second device, hereinafter referred to as "device B".
Zhang has a method that includes receiving, from a controller, a data packet including a plurality of samples each corresponding to measurements from a motion sensor (abstract) and teaches for estimating an offset between a first clock, hereinafter referred to as "clock A", and a second clock, hereinafter referred to as "clock B", wherein clock A runs on a first device, hereinafter referred to as "device A” [0021 for controller and headset may each include a clock with different capabilities (two devices with clocks];
clock B runs on a second device, hereinafter referred to as "device B" [0021].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the position techniques, as disclosed by Metzler, further including the clock calculations as taught by Zhang for the purpose to determine the position or orientation of controller (Zhang, 0021).
Metzler fails to explicitly teach wherein the data is hereinafter referred to as "measurement data A", and measurement data A is time-tagged using clock A; wherein the data is hereinafter referred to as "measurement data B", and measurement data B is time- tagged using clock B; and the estimator computes values of its state variables based on: the time-tagged measurement data A, and the time-tagged measurement data B, at least one state variable representing an estimated offset between clock A and clock B or from which the estimated offset can be derived, to align the time-tagged measurement data A and the time tagged measurement data B to determine at least one of: the estimated offset between clock A and clock B, and at least one estimated motion state of device A.
Lui has an algorithm can optimally estimate the time delay calibration parameters (abstract) and teaches wherein the data is hereinafter referred to as "measurement data A", and measurement data A is time-tagged using clock A [page 7, 3rd paragraph for timestamp tLk according the receiver clock,];
wherein the data is hereinafter referred to as "measurement data B", and measurement data B is time- tagged using clock B [page 12, second paragraph for the state covariance matrix, and the Kalman gain matrix with eq (45)],
and the estimator computes values of its state variables based on: the time-tagged measurement data A, and the time-tagged measurement data B [page 14, first paragraph for initial time delay alignment error is 9.58ms, the LiDAR-IMU alignment errors in the East, North and Up orientation]
at least one state variable representing an estimated offset between clock A and clock B or from which the estimated offset can be derived [page 9, first paragraph for point on the respective curve has a corresponding timestamp, identifying the time at which the measurement arrived at the receiver]
to align the time-tagged measurement data A and the time tagged measurement data B to determine at least one of: the estimated offset between clock A and clock B, and at least one estimated motion state of device A [page 9, first paragraph for registering the orientation curves, to use the timestamp values to estimate the relative delay between IMU and LiDAR data streams].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the position techniques, as disclosed by Metzler, further including the time delay calculations as taught by Lui for the purpose to perform the time delay calibration (Lui, page 14, first paragraph).
Regarding Claim 16, Metzler teaches the device or a set of devices carrying out the method is one of: device A [0121 in vehicle], device B [0129 total station], and a third device [0127 separate housing].
Regarding Claim 17, Metzler teaches a device or set of devices [0022 for surveying device, and targeting device, with 0119 for the target object recorded in the camera image, recorded by a camera as the areal photosensitive detector of the surveying device]
device A and device Bare configured for moving, at least for a period of time, with respect to each other [0112 for jerky movement];
and device A comprises a motion sensor [0121 for IMU];
the device, or the set of devices, being configured for: obtaining data indicative of a motion of device A measured using the motion sensor of device A [0123 for IMU and supervisory unit],
obtaining data indicative of a motion of the at least one point of device A with respect to device Bas measured by device B [0123 for using IMU with total station data],
operating an estimation process, hereinafter referred to as "estimator" [0123 for evaluation means comprising an algorithm, in particular a Kalman filter],
wherein the estimator uses state variables comprising [0123]:
and outputting, by the estimator, at least one of: the estimated offset between clock A and clock B [0124 for driven in order to continuously maintain the targeting direction with respect to the target object or, as necessary, to track movements of the target object];
and at least one estimated motion state of device A [0112].
Metzler fails to explicitly teach for estimating an offset between a first clock, hereinafter referred to as "clock A", and a second clock, hereinafter referred to as "clock B", wherein clock A is configured for running on a first device, hereinafter referred to as "device A"; clock B is configured for running on a second device, hereinafter referred to as "device B".
Zhang has a method that includes receiving, from a controller, a data packet including a plurality of samples each corresponding to measurements from a motion sensor (abstract) and teaches for estimating an offset between a first clock, hereinafter referred to as "clock A", and a second clock, hereinafter referred to as "clock B", wherein clock A runs on a first device, hereinafter referred to as "device A” [0021 for controller and headset may each include a clock with different capabilities (two devices with clocks];
clock B runs on a second device, hereinafter referred to as "device B" [0021].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the position techniques, as disclosed by Metzler, further including the clock calculations as taught by Zhang for the purpose to determine the position or orientation of controller (Zhang, 0021).
Metzler fails to explicitly teach wherein the data is hereinafter referred to as "measurement data A", and measurement data A is time-tagged using clock A; wherein the data is hereinafter referred to as "measurement data B", and measurement data B is time-tagged using clock B; and the estimator computes values of its state variables based on: the time-tagged measurement data A, and the time-tagged measurement data B; at least one state variable representing an estimated offset between clock A and clock B or from which the estimated offset can be derived, and at least one position-related state variable, to align the time-tagged measurement data A and the time-tagged measurement data B to determine at least one of: the estimated offset between clock A and clock B, and at least one estimated motion state of device A.
Lui has an algorithm can optimally estimate the time delay calibration parameters (abstract) and teaches wherein the data is hereinafter referred to as "measurement data A", and measurement data A is time-tagged using clock A [page 7, 3rd paragraph for timestamp tLk according the receiver clock,];
wherein the data is hereinafter referred to as "measurement data B", and measurement data B is time-tagged using clock B [page 12, second paragraph for the state covariance matrix, and the Kalman gain matrix with eq (45)],
and the estimator computes values of its state variables based on: the time-tagged measurement data A, and the time-tagged measurement data B [page 14, first paragraph for initial time delay alignment error is 9.58ms, the LiDAR-IMU alignment errors in the East, North and Up orientation]
at least one state variable representing an estimated offset between clock A and clock B or from which the estimated offset can be derived [page 9, first paragraph for point on the respective curve has a corresponding timestamp, identifying the time at which the measurement arrived at the receiver]
and at least one position-related state variable [page 8, 3rd paragraph for Time delay calibration parameters error model: and eq (28)]
to align the time-tagged measurement data A and the time-tagged measurement data B to determine at least one of: the estimated offset between clock A and clock B, and at least one estimated motion state of device A [page 9, first paragraph for registering the orientation curves, to use the timestamp values to estimate the relative delay between IMU and LiDAR data streams].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the position techniques, as disclosed by Metzler, further including the time delay calculations as taught by Lui for the purpose to perform the time delay calibration (Lui, page 14, first paragraph).
Regarding Claim 18, Metzler teaches a computer program or set of computer programs comprising computer-readable instructions configured, when executed on a computer or set of computers, to cause the computer or set of computers to carry out the method [0093].
Regarding Claim 19, Metzler teaches a computer program product or storage mediums comprising a computer program or set of computer programs [0093].
Claims 1-19 are rejected under 35 U.S.C. 103 as being unpatentable over Metzler (US 2014/0350886 A1) in view of Zhang et al (US 2020/0401181 A1) and Liu (Sensors, 02017) as applied to claim 1 above, and further in view of Huang et al (arXiv, 2020).
Regarding Claim 20, Metzler fails to explicitly teach feeding back the estimated offset between clock A and clock B from the estimator to a measurement processor; adjusting time tags of measurements in a buffer based on the fed back estimated offset; and resetting a value of the state variable representing the estimated offset between clock A and clock B within the estimator to zero.
Huang has bootstrapping the optimization-based monocular visual inertial odometry (page 1, abstract) and teaches feeding back the estimated offset between clock A and clock B from the estimator to a measurement processor [page 7, right column, first paragraph for After each execution of the three-step process or nonlinear optimization, the time offset is compensated by shifting the timestamps of subsequent visual measurements];
adjusting time tags of measurements in a buffer based on the fed back estimated offset [page 7, right column, first paragraph for the time offset is compensated by shifting the timestamps of subsequent visual measurements];
and resetting a value of the state variable representing the estimated offset between clock A and clock B within the estimator to zero [page 7, right column, first paragraph for system estimates a new time increment δtd between the compensated visual measurement and inertial measurement].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the position techniques, as disclosed by Metzler, further including the offset calculations as taught by Huang for the purpose to update the time offset and relaunch the system (Huang, page 7, right column, first paragraph).
Response to Arguments
Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a
general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references.
In applicant’s arguments page 8, fifth paragraph of applicant’s arguments, the applicant states that Metzler fails to teach the amendment claim limitations. The examiner thanks the applicant for the amendments. Liu teaches lining up the IMU and LIDAR data by their timestamps to find the time delay between them [Liu, page 9, first paragraph for registering the orientations curves to estimate delay between IMU and LiDAR].
In applicant’s arguments page 8, last paragraph of applicant’s arguments, the applicant states that Zhang fails to teach the amendment claim limitations. The examiner thanks the applicant for the amendments. Liu teaches lining up the IMU and LIDAR data by their timestamps to find the time delay between them [Liu, page 9, first paragraph for registering the orientations curves to estimate delay between IMU and LiDAR].
In applicant’s arguments page 9, second paragraph of applicant’s arguments, the applicant states that Liu fails to teach an inter device clock offset based on internal motion data and external relative motion data to align the time tagged measurements. The examiner respectfully disagrees, Liu uses the IMU’s own motion “integrated IMU gyroscope data” with the LiDAR’s outside view of motion “LiDAR scanner data” to find the time delay that lines up the two sensors [Liu, page 9, first paragraph for registering the orientations curves (lining up sensors) to estimate delay between IMU and LiDAR].
In applicant’s arguments page 9, third paragraph of applicant’s arguments, the applicant states that Liu fails cannot be combined with Metzler. The examiner respectfully disagrees: Metzler fuses IMU and total station data in a Kalman filter [Metzler, 0123] and Liu warns that “ a fundamental requirement in a multiple sensor system is the time delay calibration” [Liu, page 1, Introduction, 2nd paragraph] a person of ordinary skill in the art would add Liu’s delay estimation to keep Metzler’s mixed rate data [Metzler, 0036 “second rate is higher than the first rate”], lined up in time.
The examiner acknowledges that this is a broader interpretation than Applicant’s.
However, examiners are not only allowed to apply broad interpretations, but are required to do so, as it reduces the possibility that the claims, once issued, will be interpreted more broadly than is justified. MPEP §2111. Patentability is determined by the “broadest reasonable interpretation
consistent with the specification” (MPEP §2111), not the narrowest reasonable interpretation. And Applicant does not have an explicit lexicographical statement in line with MPEP §2111.01
subsection IV requiring a specific interpretation of the relevant phrases which forces the examiner to interpret them only one way.
The express, implicit, and inherent disclosures of a prior art reference may be relied upon in the rejection of claims under 35 U.S.C. 102 or 103. "The inherent teaching of a prior art reference, a question of fact, arises both in the context of anticipation and obviousness." In re Napier, 55 F.3d 610, 613, 34 USPQ2d 1782, 1784 (Fed. Cir. 1995).
For applicant’s benefit, portions of the cited reference(s) have been cited to aid in the review of the rejection(s). While every attempt has been made to be thorough and consistent within the rejection it is noted that the PRIOR ART MUST BE CONSIDERED IN ITS ENTIRETY, including disclosures that teach away from the claims. See MPEP 2141.02 VI.
“The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain.” In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)). A reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art, including non-preferred embodiments. Merck & Co. v.Biocraft Laboratories, 874 F.2d 804, 10 USPQ2d 1843 (Fed. Cir.), cert. denied, 493 U.S. 975 (1989). See also Upsher-Smith Labs. v. Pamlab, LLC, 412 F.3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir. 2005) See MPEP 2123.
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.
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/SAMARINA MAKHDOOM/
Examiner, Art Unit 3648
