Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 7-8 and 18 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 7 reads,
“The method as claimed in claim 6,
wherein determining the direction of movement of the target implements a search for the signal the phase of which leads that of the other, out of the return pulse signal mixed with the transmitted pulse signal or its carrier, and the return pulse signal mixed with the transmitted pulse signal phase-shifted by an angle of 90° or its carrier phase-shifted by an angle of 90°.”
It is unclear what the limitation “the other, out of the return pulse signal” refers to. As such, the claim is rendered indefinite.
Claims 8 and 18 read,
“[…] wherein determining the additional distance to the target implements resetting of the current value of the additional distance to the target to the value zero when a variation in the time offset between a pulse of the return pulse signal and the corresponding pulse of the transmitted pulse signal was detected in step a).”.
The language suggests that step a) of claim 1 comprises a detection of a variation in the time offset between a pulse of the return pulse signal and the corresponding pulse of the transmitted pulse signal, however step a) only mentions performing a single time offset measurement. Thus, the claims are rendered indefinite.
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 1-6 and 9-17 are rejected under 35 U.S.C. 103 as being unpatentable over Alalusi et al. (US 20210033729 A1), hereinafter Alalusi, in view of Stadelmayer et al. (US 20230108140 A1), hereinafter Stadelmayer.
Regarding claim 1, Alalusi teaches a movement detection method implemented within a motor vehicle so as to detect movement of a user (para. 187, “The sensing assembly 102 can direct the transmitted signals 106 toward the first zone 1902 and monitor the received echoes 108 to determine if the operator 1906 enters into the first zone 1902. For example, intrusion of the operator 1906 into the first zone 1902 may be detected by identification of movement using the one or more of the coarse, fine, and/or ultrafine stage determination techniques described herein.”; para. 189, “FIG. 21 is a schematic diagram of one embodiment of a mobile system 2100 that includes the sensing assembly 102. The system 2100 includes a mobile apparatus 2102 with the sensing assembly 102 coupled thereto. In the illustrated embodiment, the mobile apparatus 2102 is a mobilized robotic system. Alternatively, the mobile apparatus 2102 may represent another type of mobile device, such as an automobile, an under-ground drilling vessel, or another type of vehicle.”), the method using a return pulse signal resulting from the reflection of a transmitted pulse signal from a target, the return pulse signal and the transmitted pulse signal each consisting of radiofrequency pulses, the method comprising:
a) determining an approximate distance to the target, implementing measurement of a time offset between a pulse of the return pulse signal and the corresponding pulse of the transmitted pulse signal (para. 56, “A time delay be-tween the start of the data stream and the matching portion of the coarse stage receive pattern may represent the time of flight of the transmitted signal. This measurement of the time of flight may be used to calculate a separation distance to the target.”),
b) determining an additional distance to the target, implementing monitoring of phase shift values between the return pulse signal and the transmitted pulse signal (para. 58, “The ultrafine stage determination can use the quadrature (Q) component or channel of the receive pattern and the data stream to measure an additional amount of overlap or mismatch between the waveforms of the receive pattern and the data stream.”; paras. 159-161, “The ultrafine stage determination can include the baseband processor 232 (shown in FIG. 2) projecting a characteristic of the I and Q components of the baseband echo signal 226 onto a vector. […] The carrier phase or the change in carrier phase can be used to calculate the distance or change in distance through the equation: distance = (ϕ × λ) / 360”),
c) combining the approximate distance to the target and the additional distance to the target, so as to obtain an estimated value of distance to the target (para. 163, “The coarse, fine, and/or ultrafine stage determinations described above may be used in a variety of combinations. For example, the coarse stage determination may be used to calculate the separation distance 110 [shown in FIG. 1], even if the approximate distance from the sensing device 102 [shown in FIG. 1] to the target object 104 [shown in FIG. 1] is not known. Alternatively, the coarse stage may be used with the fine and/or ultrafine stage determinations to obtain a more precise calculation of the separation distance 110.”), and
d) repeating steps a) to c), so as to obtain a series of estimated values of distance to the target, said series of values defining movement of the user (para. 193, “The object motion vectors 2200A-F can be generated by tracking changes in the separation distances 110 over time. In order to estimate motion characteristics (e.g., speed and/or heading) of the mobile apparatus 2102, these object motion vectors 2200 can be combined, such as by summing and/or averaging the object motion vectors 2200. For ex-ample, a motion vector 2202 of the mobile apparatus 2102 may be estimated by determining a vector that is an average of the object motion vectors 2200 and then determining an opposite vector as the motion vector 2202. The combining of several object motion vectors 2200 can tend to correct spurious object motion vectors that are due to other mobile objects in the environment, such as the object motion vectors 2200C, 2200F that are based on movement of other mobile objects in the vicinity.”; Fig. 19, sensing assembly 102 detects movement of operator 1906), but fails to teach
a predetermined gesture performed by a user, said gesture being intended to control the opening of an opening element of the motor vehicle.
However, Stadelmayer teaches
a predetermined gesture performed by a user, said gesture being intended to control the opening of an opening element of the motor vehicle (para. 46, “Other examples of motion classification would pertain to kick motion classification. Smart Trunk Opener [STO] is a concept of opening and closing a trunk or door of a vehicle without using keys, automatic and hands-free. A kick-to-open motion is performed by the user using the foot. STO provides a flexible approach to the user by recognizing and classifying the hand movement or leg kick of the user and transmits the intention of the authorized user to open or close the trunk or door.”).
Alalusi and Stadelmayer are considered to be analogous to the claimed invention because they are in the same field of range sensing-based movement detection. Therefore, it would have been obvious to one of ordinary skill in the art to have modified Alalusi with the teachings of Stadelmayer with the motivation of being able to more specifically distinguish movement types.
Regarding claim 2, Alalusi in view of Stadelmayer teaches the method as claimed in claim 1,
wherein step a) comprises identifying, on a temporally sampled amplitude signal, a sampling time window receiving a local maximum of said amplitude signal (Alalusi; para. 110, “As shown in FIG. 4, several peaks 406, 408 may be identified based on the multiple correlation values 400 that are grouped over several transmitted signals 106. The peaks 406, 408 may be associated with one or more target objects 104 [shown in FIG. 1] off which the transmitted signals 106 reflected. The time delays associated with one or more of the peaks 406, 408 [e.g., the time along the horizontal axis 402] can be used to calculate the separation distance(s) 110 of one or more of the target objects 104 associated with the peaks 406, 408, as described above.”).
Regarding claim 3, Alalusi in view of Stadelmayer teaches the method as claimed in claim 1, but Alalusi fails to teach
wherein, in step b), the phase shift values each consist of a low-frequency component of a phase shift between the return pulse signal and the transmitted pulse signal.
However, Stadelmayer teaches
wherein, in step b), the phase shift values each consist of a low-frequency component of a phase shift between the return pulse signal and the transmitted pulse signal (para. 169, “There are multiple advantages of using the time series 101-104 as described above. First, the signal was transformed from a 2-D and frame wise signal into a 1-D frame-continuous signal. Thus, a continuous 1-D convolution with individual kernel sizes can be applied in a convolutional layer without being restricted to the measurement frames 45. Second, the approach makes use of the fact that only a single target is within the field of view. In conventional processing, an FT resolves the entire range-Doppler domain in the RDI, however, when there is only a single target, most of the range Doppler bins will just be zero. With the presented data representation, only the target signal is captured. Third, due to integration the SNR of the phase, azimuth and elevation signal can be significantly improved. And forth, the target with the highest radar cross section, which is usually the palm of the hand for gesture sensing, leads to the lowest frequency part in the phase signal. Finger movements introduce phase offsets on top of this. Thus, by band pass filtering the macro motion of the hand and the micro motions of the fingers can be independently analyzed.”).
Alalusi and Stadelmayer are considered to be analogous to the claimed invention because they are in the same field of range sensing-based movement detection. Therefore, it would have been obvious to one of ordinary skill in the art to have modified Alalusi with the teachings of Stadelmayer with the motivation of being able to more accurately estimate distance.
Regarding claim 4, Alalusi in view of Stadelmayer teaches the method as claimed in claim 1,
wherein, in step b) the phase shift values each relate to a predetermined time belonging to a sampling time window identified in step a) (Alalusi; para. 158, “In one embodiment, the ultrafine stage determination described above can be used to determine relatively small movements that change the separation distance 110 [shown in FIG. 1]. For example, the ultrafine stage may be used to identify relatively small movements within a portion of the separation distance 110 that is associated with the subset of interest in the baseband echo signal 226.”; paras. 159-161, “The ultrafine stage determination can include the baseband processor 232 (shown in FIG. 2) projecting a characteristic of the I and Q components of the baseband echo signal 226 onto a vector. […] The carrier phase or the change in carrier phase can be used to calculate the distance or change in distance through the equation: distance = (ϕ × λ) / 360”).
Regarding claim 5, Alalusi in view of Stadelmayer teaches the method as claimed in claim 1, wherein, in step b), determining an additional distance to the target implements:
detection of at least one local maximum, on data relating to the monitoring of phase shift values between the return pulse signal and the transmitted pulse signal (Alalusi; paras. 84-86, “The correlation values calculated by the ultrafine stage determination can be used to calculate an additional time delay that can be added to the time delays from the coarse stage and/or the fine stage to determine a time of flight and/or separation distance to the tar-get. Alternatively or additionally, the correlation values of the waveforms in the I channel and Q channel can be examined to resolve phases of the echoes in order to calculate separation distance or motion of the target. […] Correlation values representative of degrees of match between the pulse sequence in the correlation window and the subsets or portions of the baseband echo signal 226 can be calculated and a time delay of interest [e.g., approximately the time of flight] can be deter-mined based on the time difference between the start of the baseband echo signal 226 and one or more maximum or relatively large correlation values. The maximum or relatively large correlation value may represent at least partial reflection of the transmitted signals 106 off the target object 104, and may be referred to as a correlation value of interest.”), and
when a new local maximum is detected, updating of a current value of the additional distance to the target, so as to vary it by an elementary target offset value (Alalusi; para. 164, “As another example, if the separation distance 110 [shown in FIG. 1] is known, the fine or ultrafine stage determinations can be activated without the need for first identifying the bit of interest using the coarse stage determination. For example, the system 100 [shown in FIG. 1] may be in a ‘tracking’ mode where updates from the initial known separation distance 110 are identified and/or recorded using the fine and/or ultrafine state determinations.”; para. 259, “In another aspect, the baseband processing system is configured to resolve phases of the first echo and the second echo based on the I component of the temporal misalignment and the Q component of the temporal misalignment. The time of flight is calculated based on the phases that are resolved. For example, the time of flight may be increased or decreased by a predetermined or designated amount based on an identified or measured difference in the phases that are resolved.”).
Regarding claim 6, Alalusi in view of Stadelmayer teaches the method as claimed in claim 5,
wherein said updating comprises determining a direction of movement of the target, so as to determine whether the updating consists in adding or subtracting the elementary target offset value (Alalusi; Fig. 22, object motion vectors represent a direction of movement; para. 192, “To compute the motion [e.g., speed] of the mobile apparatus 2102, the mobile apparatus 210 can track changes in separation distances 110 to the objects 2104 and generate object motion vectors associated with the objects 2104 based on the changes in the separation distances 110.”; para. 259, “In another aspect, the baseband processing system is configured to resolve phases of the first echo and the second echo based on the I component of the temporal misalignment and the Q component of the temporal misalignment. The time of flight is calculated based on the phases that are resolved. For example, the time of flight may be increased or decreased by a predetermined or designated amount based on an identified or measured difference in the phases that are resolved.”).
Regarding claim 9, Alalusi in view of Stadelmayer teaches the method as claimed in claim 1, wherein steps a) to c) are implemented by implementing the following steps, for each of a plurality of pulses of the transmitted pulse signal:
i) determining a time offset between said pulse of the transmitted pulse signal and the corresponding pulse of the return pulse signal, this time offset defining the value of the approximate distance to the target (Alalusi; para. 56, “A time delay be-tween the start of the data stream and the matching portion of the coarse stage receive pattern may represent the time of flight of the transmitted signal. This measurement of the time of flight may be used to calculate a separation distance to the target.”),
ii) comparing between the time offset determined in step i) and a reference time offset (Alalusi; para. 57, “The additional signals may include a fine stage transmit pattern that is the same or different pattern as the coarse stage transmit pattern. The fine stage determination can use the time of flight measured by the coarse stage determination (or as input by an operator) and compare a fine stage receive pattern that is delayed by the measured time of flight to a corresponding portion of the data stream.”),
iii) when the time offset determined in step i) is different from the reference time offset, updating the value of the reference time offset so as to set it to the value determined in step i), and setting the additional distance to the target to the value zero (the claimed invention does not require the time offset determined in step i) to be different from the reference time offset, see MPEP 2111.04 section II),
iv) calculating a value of a phase shift between said pulse of the transmitted pulse signal and the corresponding pulse of the return pulse signal, and using said phase shift value to supplement phase shift value monitoring data (Alalusi; para. 58, “The ultrafine stage determination can use the quadrature [Q] component or channel of the receive pattern and the data stream to measure an additional amount of overlap or mismatch between the waveforms of the receive pattern and the data stream.”; paras. 159-161, “The ultrafine stage determination can include the baseband processor 232 (shown in FIG. 2) projecting a characteristic of the I and Q components of the baseband echo signal 226 onto a vector. […] The carrier phase or the change in carrier phase can be used to calculate the distance or change in distance through the equation: distance = (ϕ × λ) / 360”),
v) searching for a new local maximum in said monitoring data, vi) when a new local maximum is detected, determining a direction of movement of the target, and updating the value of the additional distance to the target so as to increase it or reduce it depending on the direction of movement of the target (Alalusi; paras. 84-86, “The correlation values calculated by the ultrafine stage determination can be used to calculate an additional time delay that can be added to the time delays from the coarse stage and/or the fine stage to determine a time of flight and/or separation distance to the tar-get. Alternatively or additionally, the correlation values of the waveforms in the I channel and Q channel can be examined to resolve phases of the echoes in order to calculate separation distance or motion of the target. […] Correlation values representative of degrees of match between the pulse sequence in the correlation window and the subsets or portions of the baseband echo signal 226 can be calculated and a time delay of interest [e.g., approximately the time of flight] can be deter-mined based on the time difference between the start of the baseband echo signal 226 and one or more maximum or relatively large correlation values. The maximum or relatively large correlation value may represent at least partial reflection of the transmitted signals 106 off the target object 104, and may be referred to as a correlation value of interest.”),
vii) summing the approximate distance to the target and the additional distance to the target, so as to obtain the estimated value of the distance to the target (Alalusi; para. 60, “For example, the coarse stage determination may examine the I channel of the receive pattern and the data stream to determine correlation values of different subsets of the data stream and, from those correlation values, determine a subset of interest and a corresponding time-of-flight, as described herein. The ultrafine stage determination can use the Q channel of the receive pattern and the data stream to determine correlation values of different subsets of the data stream and, from those correlation values, determine a subset of interest and a time-of-flight. The times-of-flight from the I channel and Q channel can be combined [e.g., averaged] to calculate a time of flight and/or separation distance to the target. The correlation values calculated by the ultrafine stage determination can be used to calculate an additional time delay that can be added to the time delays from the coarse stage and/or the fine stage to determine a time of flight and/or separation distance to the tar-get. Alternatively or additionally, the correlation values of the waveforms in the I channel and Q channel can be examined to resolve phases of the echoes in order to calculate separation distance or motion of the target.”).
Regarding claim 10, Alalusi in view of Stadelmayer teaches the method as claimed in claim 1, but Alalusi fails to teach further comprising:
comparing said series of values with data defining a predetermined gesture, so as to detect when said predetermined gesture has been performed by the user, and
formulating a command to open the opening element of the motor vehicle when said predetermined gesture has been detected.
However, Stadelmayer teaches
comparing said series of values with data defining a predetermined gesture, so as to detect when said predetermined gesture has been performed by the user, and formulating a command to open the opening element of the motor vehicle when said predetermined gesture has been detected (para. 46, “Other examples of motion classification would pertain to kick motion classification. Smart Trunk Opener [STO] is a concept of opening and closing a trunk or door of a vehicle without using keys, automatic and hands-free. A kick-to-open motion is performed by the user using the foot. STO provides a flexible approach to the user by recognizing and classifying the hand movement or leg kick of the user and transmits the intention of the authorized user to open or close the trunk or door.”; para. 48, “In detail, motion classification can used to predict a motion class of a motion, e.g., a gesture. For example, there can be a predefined set of motion classes. Then, once such an object performs a motion, it can be judged whether this motion is part of one of the motion classes. For this, it can be judged whether certain features of the motion match respective feature ranges associated with the motion class.”).
Alalusi and Stadelmayer are considered to be analogous to the claimed invention because they are in the same field of range sensing-based movement detection. Therefore, it would have been obvious to one of ordinary skill in the art to have modified Alalusi with the teachings of Stadelmayer with the motivation of being able to more specifically distinguish movement types.
Regarding claim 11, Alalusi in view of Stadelmayer teaches
a non-transitory computer program product comprising instructions that, when the program is executed by a processor, cause said processor to implement the method as claimed in claim 1 (Alalusi; para. 66, “The control unit 112 shown in FIG. 1 may represent the hardware [e.g., processors] and/or logic of the hardware [e.g., one or more sets of instructions for directing operations of the hardware that is stored on a tangible and non-transitory computer readable storage medium, such as computer software stored on a computer memory].”).
Regarding claim 12, Alalusi in view of Stadelmayer teaches
a system-on-chip intended to be installed within a motor vehicle, comprising at least one processor and at least one memory, and configured to implement the steps of the method as claimed in claim 1 (Alalusi; para. 189, “FIG. 21 is a schematic diagram of one embodiment of a mobile system 2100 that includes the sensing assembly 102. The system 2100 includes a mobile apparatus 2102 with the sensing assembly 102 coupled thereto. In the illustrated embodiment, the mobile apparatus 2102 is a mobilized robotic system. Alternatively, the mobile apparatus 2102 may represent another type of mobile device, such as an automobile, an under-ground drilling vessel, or another type of vehicle.”; para. 268, “The processor 3104 can include a system-on-chip [SOC] or an application-specific-integrated-circuit [ASIC]. The processor 3104 is powered via an accumulator, such as a battery or others including any permutational combinations thereof, whether the accumulator is housed or is not housed via the structure 3102.”),
the system-on-chip being configured to receive, at input, data relating to the return pulse signal and data relating to the transmitted pulse signal, and the system-on-chip being configured to deliver, at output, said series of estimated values of the distance to the target, said series of values defining a movement performed by a user of the motor vehicle (Alalusi; para. 193, “The object motion vectors 2200A-F can be generated by tracking changes in the separation distances 110 over time. In order to estimate motion characteristics [e.g., speed and/or heading] of the mobile apparatus 2102, these object motion vectors 2200 can be combined, such as by summing and/or averaging the object motion vectors 2200. For ex-ample, a motion vector 2202 of the mobile apparatus 2102 may be estimated by determining a vector that is an average of the object motion vectors 2200 and then determining an opposite vector as the motion vector 2202. The combining of several object motion vectors 2200 can tend to correct spurious object motion vectors that are due to other mobile objects in the environment, such as the object motion vectors 2200C, 2200F that are based on movement of other mobile objects in the vicinity.”; Fig. 19, sensing assembly 102 detects movement of operator 1906), but fails to teach
a microcontroller, and
the microcontroller being configured to deliver, at output, said series of estimated values of the distance to the target, said series of values defining a gesture performed by a user of the motor vehicle.
However, Stadelmayer teaches
a microcontroller, and the microcontroller being configured to deliver, at output, said series of estimated values of the distance to the target, said series of values defining a gesture performed by a user of the motor vehicle (para. 28, “It is recognized that any circuit or other electrical device disclosed herein may include any number of microcontrollers […] and software which co-act with one another to perform operation(s) disclosed herein. In addition, any one or more of the electrical devices may be configured to execute a program code that is embodied in a non-transitory computer readable medium programmed to perform any number of the functions as disclosed.”; para. 46, “Other examples of motion classification would pertain to kick motion classification. Smart Trunk Opener [STO] is a concept of opening and closing a trunk or door of a vehicle without using keys, automatic and hands-free. A kick-to-open motion is performed by the user using the foot. STO provides a flexible approach to the user by recognizing and classifying the hand movement or leg kick of the user and transmits the intention of the authorized user to open or close the trunk or door.”; para. 48, “In detail, motion classification can used to predict a motion class of a motion, e.g., a gesture. For example, there can be a predefined set of motion classes. Then, once such an object performs a motion, it can be judged whether this motion is part of one of the motion classes. For this, it can be judged whether certain features of the motion match respective feature ranges associated with the motion class.”).
Alalusi and Stadelmayer are considered to be analogous to the claimed invention because they are in the same field of range sensing-based movement detection. Therefore, it would have been obvious to one of ordinary skill in the art to have modified Alalusi with the teachings of Stadelmayer with the motivation of being able to more specifically distinguish movement types.
Regarding claim 13, Alalusi in view of Stadelmayer teaches a gesture detection system, configured to be installed in a motor vehicle, and comprising:
a transceiver device, configured to transmit said transmitted pulse signal and to receive said return pulse signal (Alalusi; para. 270, “The output device 3112 can include a light source, a sound source, a radio wave source, a vibration source, a display, a speaker, a printer, a transmitter, a transceiver, a motor, an actuator, a valve, a pump, a solenoid valve, or others including any permutational combinations thereof.”), and
a signal processing module, connected to the transceiver device (Alalusi; Fig. 31, output device 3112 coupled to processor 3104), but fails to teach comprising
a microcontroller as claimed in claim 11.
However, Stadelmayer teaches comprising
a microcontroller as claimed in claim 11 (para. 28, “It is recognized that any circuit or other electrical device disclosed herein may include any number of microcontrollers […] and software which co-act with one another to perform operation(s) disclosed herein. In addition, any one or more of the electrical devices may be configured to execute a program code that is embodied in a non-transitory computer readable medium programmed to perform any number of the functions as disclosed.”).
Alalusi and Stadelmayer are considered to be analogous to the claimed invention because they are in the same field of range sensing-based movement detection. Therefore, it would have been obvious to one of ordinary skill in the art to have modified Alalusi with the teachings of Stadelmayer with the motivation that microcontrollers are compact and are easily reprogrammed and troubleshot.
Regarding claim 14, Alalusi in view of Stadelmayer teaches a system for controlling the opening of a motor vehicle opening element, comprising a gesture detection system as claimed in claim 13, but Alalusi fails to teach a computer configured to:
receive, at input, said series of values defining a gesture performed by a user of the motor vehicle,
compare said series of values with data defining a predetermined gesture, so as to detect when said predetermined gesture has been performed by the user, and
formulate a command to open the opening element of the motor vehicle, when said predetermined gesture has been detected, and
deliver, at output, said command for transmission to a system for controlling the opening of the opening element of the motor vehicle.
However, Stadelmayer teaches a computer configured to:
receive, at input, said series of values defining a gesture performed by a user of the motor vehicle, compare said series of values with data defining a predetermined gesture, so as to detect when said predetermined gesture has been performed by the user, and formulate a command to open the opening element of the motor vehicle, when said predetermined gesture has been detected, and deliver, at output, said command for transmission to a system for controlling the opening of the opening element of the motor vehicle (para. 7, “A computer-implemented method includes obtaining a time sequence of measurement frames of a radar measurement of a scene. The scene includes an object. The method also includes determining one or more one dimensional time series of respective observables of the radar measurement based on multiple subsequent measurement frames.”; para. 46, “Other examples of motion classification would pertain to kick motion classification. Smart Trunk Opener [STO] is a concept of opening and closing a trunk or door of a vehicle without using keys, automatic and hands-free. A kick-to-open motion is performed by the user using the foot. STO provides a flexible approach to the user by recognizing and classifying the hand movement or leg kick of the user and transmits the intention of the authorized user to open or close the trunk or door.”; para. 48, “In detail, motion classification can used to predict a motion class of a motion, e.g., a gesture. For example, there can be a predefined set of motion classes. Then, once such an object performs a motion, it can be judged whether this motion is part of one of the motion classes. For this, it can be judged whether certain features of the motion match respective feature ranges associated with the motion class.”).
Alalusi and Stadelmayer are considered to be analogous to the claimed invention because they are in the same field of range sensing-based movement detection. Therefore, it would have been obvious to one of ordinary skill in the art to have modified Alalusi with the teachings of Stadelmayer with the motivation of being able to more specifically distinguish movement types.
Regarding claim 15, Alalusi in view of Stadelmayer teaches
a motor vehicle equipped with a gesture detection system as claimed in claim 13 (Alalusi; para. 189, “FIG. 21 is a schematic diagram of one embodiment of a mobile system 2100 that includes the sensing assembly 102. The system 2100 includes a mobile apparatus 2102 with the sensing assembly 102 coupled thereto. In the illustrated embodiment, the mobile apparatus 2102 is a mobilized robotic system. Alternatively, the mobile apparatus 2102 may represent another type of mobile device, such as an automobile, an under-ground drilling vessel, or another type of vehicle.”).
Regarding claim 16, Alalusi in view of Stadelmayer teaches the method as claimed in claim 2, but Alalusi fails to teach
wherein, in step b), the phase shift values each consist of a low-frequency component of a phase shift between the return pulse signal and the transmitted pulse signal.
However, Stadelmayer teaches
wherein, in step b), the phase shift values each consist of a low-frequency component of a phase shift between the return pulse signal and the transmitted pulse signal (para. 169, “There are multiple advantages of using the time series 101-104 as described above. First, the signal was transformed from a 2-D and frame wise signal into a 1-D frame-continuous signal. Thus, a continuous 1-D convolution with individual kernel sizes can be applied in a convolutional layer without being restricted to the measurement frames 45. Second, the approach makes use of the fact that only a single target is within the field of view. In conventional processing, an FT resolves the entire range-Doppler domain in the RDI, however, when there is only a single target, most of the range Doppler bins will just be zero. With the presented data representation, only the target signal is captured. Third, due to integration the SNR of the phase, azimuth and elevation signal can be significantly improved. And forth, the target with the highest radar cross section, which is usually the palm of the hand for gesture sensing, leads to the lowest frequency part in the phase signal. Finger movements introduce phase offsets on top of this. Thus, by band pass filtering the macro motion of the hand and the micro motions of the fingers can be independently analyzed.”).
Alalusi and Stadelmayer are considered to be analogous to the claimed invention because they are in the same field of range sensing-based movement detection. Therefore, it would have been obvious to one of ordinary skill in the art to have modified Alalusi with the teachings of Stadelmayer with the motivation of being able to more accurately estimate distance.
Regarding claim 17, Alalusi in view of Stadelmayer teaches the method as claimed in claim 2,
wherein, in step b), the phase shift values each relate to a predetermined time belonging to a sampling time window identified in step a) (Alalusi; para. 158, “In one embodiment, the ultrafine stage determination described above can be used to determine relatively small movements that change the separation distance 110 [shown in FIG. 1]. For example, the ultrafine stage may be used to identify relatively small movements within a portion of the separation distance 110 that is associated with the subset of interest in the baseband echo signal 226.”; paras. 159-161, “The ultrafine stage determination can include the baseband processor 232 (shown in FIG. 2) projecting a characteristic of the I and Q components of the baseband echo signal 226 onto a vector. […] The carrier phase or the change in carrier phase can be used to calculate the distance or change in distance through the equation: distance = (ϕ × λ) / 360”).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ERIC K HODAC whose telephone number is (571) 270-0123. The examiner can normally be reached M-Th 8-6.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, VLADIMIR MAGLOIRE can be reached at (571) 270-5144. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ERIC K HODAC/Examiner, Art Unit 3648
/VLADIMIR MAGLOIRE/Supervisory Patent Examiner, Art Unit 3648
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