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 .
Information Disclosure Statement
The Information Disclosure Statement (IDS) received on January 22nd, 2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the IDS is being considered by the examiner.
Claim Objections
Claims 3, 5, 10, 12, 17, and 19 are objected to because of the following informalities:
Claims 3, 10, and 17 state “wherein the trained machine learning model comprises one of: a random forest regressor and a neural network.”. As written, the claim cannot contain both of these elements, and thus the use of “and” is considered a typographical error. For examination, this will be interpreted as “wherein the trained machine learning model comprises one of: a random forest regressor or a neural network.”
Claims 5, 12, and 19 state “wherein the trained machine learning model is trained by provided training date to a machine learning model,”. As written, this allows for the possibility of a different model being trained. From the specification it is obvious that the same model is being trained, and no other model has been declared within the scope of the claims. Thus for examination purposes this will be interpreted as “wherein the trained machine learning model is trained by provided training date to said machine learning model”
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-4, 6-11, and 13-20 are rejected under 35 USC § 101
Claims 1-4, 6-11, and 13-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Representative claim 1 recites the following limitations:
receive antenna measurements associated with the one or more incoming signals, the antenna measurements comprising phase measurements associated with the one or more incoming signals;
process the antenna measurements to generate a prediction of an angle of arrival associated with the one or more incoming signals;
Therefore, the claim as a whole is directed to “generating a prediction”, which is an abstract idea because it is a method of organizing human activity. “Generating a prediction” is considered to be is a method of organizing human activity because it is a process of using a set of measurements to predict an angle.
This judicial exception is not integrated into a practical application. In particular, claim 1 recites the following additional element(s): multiple antennas, at least one processing device, and a trained machine learning model, wherein the trained machine learning model is trained to generate the prediction of the angle or arrival even while compensating for phase errors affecting the antenna measurements. These elements individually or in combination do not integrate the abstract idea into a practical application because the additional elements to no more than generally link the use of a judicial exception to a particular technological environment or field of use. (see MPEP 2106.05(h)). Accordingly, these additional element(s) do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Claim 1 is directed to an abstract idea.
Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element(s) individually and in combination are merely being used to apply the abstract idea to a technological environment. Accordingly, claim 1 is ineligible.
Dependent claim(s) 2, 4, and 7 merely further limit the abstract idea and are thereby considered to be ineligible.
Dependent claim(s) 3 and 6 further recite the additional element(s) a random forest regressor, a neural network (claim 3), and a platform (claim 6). These additional element(s) do not integrate the abstract idea into a practical application because they generally link the use of a judicial exception to a particular technological environment or field of use, which does not integrate the abstract into a practical application nor does it render a claim as being significantly more than the abstract idea. Accordingly, claim(s) 3 and 6 is/are ineligible.
Dependent claim 5 directs the abstract idea towards a practical application, and therefore are directed towards eligible subject matter.
Claim(s) 8-11 and 13-14 is/are parallel in nature to claim(s) 1-4 and 6-7. Accordingly claim(s) 8-11 and 13-14 are rejected as being directed towards ineligible subject matter based upon the same analysis above.
Furthermore, dependent claim 12 directs the abstract idea towards a practical application, and therefore are directed towards eligible subject matter.
Claim(s) 15-18 and 20 is/are parallel in nature to claim(s) 1-4 and 7. Accordingly claim(s) 15-18 and 20 are rejected as being directed towards ineligible subject matter based upon the same analysis above.
Furthermore, dependent claim 19 directs the abstract idea towards a practical application, and therefore are directed towards eligible subject matter.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-3, 5-10, 12-17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Wozny et al. (US 20230097336 A1), herein after Wozny, in view of Pavel et al. (Pavel, Saidur R., et al. "Machine learning-based direction-of-arrival estimation exploiting distributed sparse arrays." 2021 55th Asilomar Conference on Signals, Systems, and Computers. IEEE, 2021), hereinafter Pavel.
Regarding claim 1, Wozny teaches an apparatus comprising [Note: what is not clearly disclosed is strike-through]:
multiple antennas each configured to receive one or more incoming signals (Wozny [0009] “detecting an incoming signal with an unknown direction of origin via an antenna array including a plurality of antennas carried by a platform;”); and
at least one processing device configured to:
receive antenna measurements associated with the one or more incoming signals, the antenna measurements comprising phase measurements associated with the one or more incoming signals (Wozny [0048] “This data captured by the antennas 14 may include at least one or more of the phase, amplitude, magnitude, frequency, pulse length, and repeatability of the signal.”); and
process the antenna measurements using a trained machine learning model to generate a prediction of an angle of arrival associated with the one or more incoming signals (Wozny [0039] “analyze the collected data using a neural network matrix trained on prior collected antenna data; and generate a direction finding solution representing the direction of origin for the incoming signal.”);
Wozny fails to teach the limitations below. Pavel teaches:
wherein the trained machine learning model is trained to generate the prediction of the angle of arrival even while compensating for phase errors affecting the antenna measurements(Pavel Pag. 242, col. 1, “When an array sensor has calibration error described by gain and phase errors
α
k
,
m
e
j
β
k
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m
for
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=
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and
m
=
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. Denote
g
k
=
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k
1
e
j
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, the actual array manifold Ak becomes”).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to incorporate the features as disclosed by Pavel into the invention of Wozny. Both Wozny and Pavel are considered analogous arts to the claimed invention as they both disclose methods for phase interference radars that utilize machine learning methods for object direction detection. It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the apparatus as disclosed by Wozny to consider phase errors affecting antenna measurements as taught by Pavel. One of ordinary skill in the art prior to the effective filing date of the claimed invention would have been motivated to modify the apparatus in order to perform an accurate calibration of an antenna array, even considering phase errors that are inherent to the collection of measured antenna data (See Wozny [0038-0039], Pavel Pg. 241 Col. 2, Pavel Pg. 244 Col. 1).
Regarding claim 2, Wozny in view of Pavel teaches the apparatus of claim 1. Wozny further teaches:
wherein the trained machine learning model is configured to implement one or more mappings between different antenna measurements and different angles of arrival (Wozny [0039] “By way of one non-limiting example, the data collected may contain expected signal characteristics from signals having vertical and horizontal polarizations emitted at intervals of every five degrees azimuth around the array 12. According to another aspect, signals can be emitted and the characteristics recorded at predetermined intervals in both azimuth and elevation.”).
Regarding claim 3, Wozny in view of Pavel teaches the apparatus of claim 1. Wozny further teaches:
The apparatus of Claim 1, wherein the trained machine learning model comprises one of: a random forest regressor and a neural network (Wozny [0008] “analyze the collected data using a neural network matrix trained on prior collected antenna data;”).
Regarding claim 5, Wozny in view of Pavel teaches the apparatus of claim 1. Wozny further teaches the limitations below [Note: what it not clearly disclosed is strike-through]:
wherein the trained machine learning model is trained
by providing training data to a machine learning model (Wozny [0043] “Once the antenna data is collected, deep learning techniques may be utilized to train the neural network 24.”),
and adjusting the machine learning model based on the comparison (Wozny [0043] “At this layer, each data point may be assigned a weight or bias which may then further propagate through the hidden layers 30 before providing a DF solution at output layer 28.”),
Wozny fails to teach the limitations below. Pavel teaches:
comparing outputs of the machine learning model to ground truths (Pavel Pg. 243 Col. 2 “For a given direction, 20 groups of snapshots are generated with random noises. We generate a total number of 3,040 data vectors in our training dataset. 90% of them are used for training and the other 10% are used for validation. These vectors are used as the training input of the neural network. For the label of the neural network, the true signal direction is used.”),
at least some of the training data including data modified using random phase errors (Pavel Pag. 243, Col. 2, “The antenna gains are independently generated from a uniform distribution between 0.9 and 1.1, whereas the phase errors are independently generated from a uniform distribution between −9◦ and 9 degree.”).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to incorporate the features as disclosed by Pavel into the invention of Wozny. Both Wozny and Pavel are considered analogous arts to the claimed invention as they both disclose methods for phase interference radars that utilize machine learning methods for object direction detection. It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the apparatus as disclosed by Wozny to compare training data to ground truths, and to introduce random phase errors into said training data, as taught by Pavel. One of ordinary skill in the art prior to the effective filing date of the claimed invention would have been motivated to modify the apparatus in order to train the machine model under similar circumstances to real-world calibration errors inherent to antenna arrays, especially in the case where the training data is simulated (See Wozny [0038-0039], Wozny [0040-0043] , Pavel Pg. 243-244).
Regarding claim 6, Wozny in view of Pavel teaches the apparatus of claim 1. Wozny further teaches:
The apparatus of Claim 1, wherein the antennas have arbitrary positions on a platform (Wozny [0023] “Although discussed predominantly herein in either linear arrangements or quadrant arrangements, antennas 14 may have any desired configuration, including as arranged in existing legacy configurations on platform 22 as dictated by the specific installation parameters and the type of platform 22 used.”).
Regarding claim 7, Wozny in view of Pavel teaches the apparatus of claim 1. Wozny further teaches:
The apparatus of Claim 1, wherein the at least one processing device is configured to repeatedly identify predictions of the angle of arrival associated with the one or more incoming signals in real-time (Wozny [0051] “The communication of the DF signal to the platform 22 and/or operator(s) thereof may further allow a decision to alter the operation of platform 22 to be made. According to one aspect, as discussed previously herein, determining the DF solution may allow platform 22 and/or the operator(s) thereof to perform automated actions such as steering towards the signal (as in a targeting situation), steering away from the signal (as in evasive maneuvers), jamming the signal, deploying defensive countermeasures, or any other appropriate responsive action.”, further, Wozny [0045] “Contrast this with the present matrix 24 where the use and operation thereof may reduce the required processing time by a factor of approximately 100 to result in a 43 microseconds approximate processing time.”. Examiner notes that the output of the machine learning model (a predicted angle) is used to perform real-time navigation tasks, thus requiring repeated real-time predictions from said model. ).
Regarding claim 8, Wozny teaches a method comprising [Note: what is not clearly disclosed is strike-through]:
receiving one or more incoming signals at multiple antennas (Wozny [0009] “detecting an incoming signal with an unknown direction of origin via an antenna array including a plurality of antennas carried by a platform;”);
providing antenna measurements associated with the one or more incoming signals to a trained machine learning model (Wozny [0008] “… detect an incoming signal; collect signal data from the incoming signal; analyze the collected data using a neural network matrix trained on prior collected antenna data; and generate a direction finding solution representing the direction of origin for the incoming signal.”),
the antenna measurements comprising phase measurements associated with the one or more incoming signals (Wozny [0048] “This data captured by the antennas 14 may include at least one or more of the phase, amplitude, magnitude, frequency, pulse length, and repeatability of the signal.”); and
processing the antenna measurements using the trained machine learning model to generate a prediction of an angle of arrival associated with the one or more incoming signals (Wozny [0039] “analyze the collected data using a neural network matrix trained on prior collected antenna data; and generate a direction finding solution representing the direction of origin for the incoming signal.”);
(Pavel Pag. 242, col. 1, “When an array sensor has calibration error described by gain and phase errors
α
k
,
m
e
j
β
k
,
m
for
k
=
1
,
…
K
and
m
=
1
,
…
,
M
. Denote
g
k
=
a
k
1
e
j
β
k
1
,
…
,
α
K
M
e
j
β
K
M
T
, the actual array manifold Ak becomes”).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to incorporate the features as disclosed by Pavel into the invention of Wozny. Both Wozny and Pavel are considered analogous arts to the claimed invention as they both disclose methods for phase interference radars that utilize machine learning methods for object direction detection. It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the method as disclosed by Wozny to consider phase errors affecting antenna measurements as taught by Pavel. One of ordinary skill in the art prior to the effective filing date of the claimed invention would have been motivated to modify the apparatus in order to perform an accurate calibration of an antenna array, even considering phase errors that are inherent to the collection of measured antenna data (See Wozny [0038-0039], Pavel Pg. 241 Col. 2, Pavel Pg. 244 Col. 1).
Regarding claim 9, Wozny in view of Pavel teaches the method of claim 8. Wozny further teaches:
wherein the trained machine learning model implements one or more mappings between different antenna measurements and different angles of arrival (Wozny [0039] “By way of one non-limiting example, the data collected may contain expected signal characteristics from signals having vertical and horizontal polarizations emitted at intervals of every five degrees azimuth around the array 12. According to another aspect, signals can be emitted and the characteristics recorded at predetermined intervals in both azimuth and elevation.”).
Regarding claim 10, Wozny in view of Pavel teaches the method of claim 8. Wozny further teaches:
wherein the trained machine learning model comprises one of: a random forest regressor and a neural network (Wozny [0008] “analyze the collected data using a neural network matrix trained on prior collected antenna data;”).
Regarding claim 12, Wozny in view of Pavel teaches the method of claim 8. Wozny further teaches [Note: what is not clearly disclosed is strike-through]:
wherein the trained machine learning model is trained by providing training data to a machine learning model (Wozny [0043] “Once the antenna data is collected, deep learning techniques may be utilized to train the neural network 24.”),
(Pavel Pg. 243 Col. 2 “For a given direction, 20 groups of snapshots are generated with random noises. We generate a total number of 3,040 data vectors in our training dataset. 90% of them are used for training and the other 10% are used for validation. These vectors are used as the training input of the neural network. For the label of the neural network, the true signal direction is used.”), and
adjusting the machine learning model based on the comparison (Wozny [0043] “At this layer, each data point may be assigned a weight or bias which may then further propagate through the hidden layers 30 before providing a DF solution at output layer 28.”),(Pavel Pag. 243, Col. 2, “The antenna gains are independently generated from a uniform distribution between 0.9 and 1.1, whereas the phase errors are independently generated from a uniform distribution between −9◦ and 9 degree.”).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to incorporate the features as disclosed by Dvorecki into the invention of Wozny in view of Pavel. The set of Wozny, Pavel, and Dvorecki are considered analogous arts to the claimed invention as they all disclose methods for using machine learning tools to calibrate phase characteristics of radars. It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the method as disclosed by Wozny in view of Pavel to perform phase measurements, where the phase measurements are different for each antenna, as taught by Dvorecki. One of ordinary skill in the art prior to the effective filing date of the claimed invention would have been motivated to modify the method in order to perform interferometry measurements, which are well-known in the art to require a comparison of at least two signals from two receivers (See Wozny [0001] and [0004] ), or to calibrate a Doppler radar to improve object velocity detection (See Dvorecki [0021-0024]).
Regarding claim 13, Wozny in view of Pavel teaches the method of claim 8. Wozny further teaches:
wherein the antennas have arbitrary positions on a platform (Wozny [0023] “Although discussed predominantly herein in either linear arrangements or quadrant arrangements, antennas 14 may have any desired configuration, including as arranged in existing legacy configurations on platform 22 as dictated by the specific installation parameters and the type of platform 22 used.”).
Regarding claim 14, Wozny in view of Pavel teaches the method of claim 8. Wozny further teaches:
repeatedly identifying predictions of the angle of arrival associated with the one or more incoming signals in real-time (Wozny [0051] “The communication of the DF signal to the platform 22 and/or operator(s) thereof may further allow a decision to alter the operation of platform 22 to be made. According to one aspect, as discussed previously herein, determining the DF solution may allow platform 22 and/or the operator(s) thereof to perform automated actions such as steering towards the signal (as in a targeting situation), steering away from the signal (as in evasive maneuvers), jamming the signal, deploying defensive countermeasures, or any other appropriate responsive action.”, further, Wozny [0045] “Contrast this with the present matrix 24 where the use and operation thereof may reduce the required processing time by a factor of approximately 100 to result in a 43 microseconds approximate processing time.”. Examiner notes that the output of the machine learning model (a predicted angle) is used to inform real-time navigation tasks, thus requiring repeated real-time predictions from said model. ).
Regarding claim 15, Wozny teaches the following device [Note: what is not clearly disclosed is strike-through]:
A non-transitory machine-readable medium containing instructions that when executed cause at least one processor to (Wozny [0008] “and at least one non-transitory computer readable storage medium having instructions encoded thereon that, when executed by the processor”):
obtain antenna measurements associated with one or more incoming signals received at multiple antennas (Wozny [0009] “detecting an incoming signal with an unknown direction of origin via an antenna array including a plurality of antennas carried by a platform;”), the antenna measurements comprising phase measurements associated with the one or more incoming signals (Wozny [0048] “This data captured by the antennas 14 may include at least one or more of the phase, amplitude, magnitude, frequency, pulse length, and repeatability of the signal.”);
provide the antenna measurements to a trained machine learning model; and
process the antenna measurements using the trained machine learning model to generate a prediction of an angle of arrival associated with the one or more incoming signals (Wozny [0039] “analyze the collected data using a neural network matrix trained on prior collected antenna data; and generate a direction finding solution representing the direction of origin for the incoming signal.”);
Wozny fails to teach the limitations below. Pavel teaches:
where in the trained machine learning model is trained to generate the prediction of the angle of arrival even while compensating for phase errors affecting the antenna measurements (Pavel Pag. 242, col. 1, “When an array sensor has calibration error described by gain and phase errors
α
k
,
m
e
j
β
k
,
m
for
k
=
1
,
…
K
and
m
=
1
,
…
,
M
. Denote
g
k
=
a
k
1
e
j
β
k
1
,
…
,
α
K
M
e
j
β
K
M
T
, the actual array manifold Ak becomes”).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to incorporate the features as disclosed by Pavel into the invention of Wozny. Both Wozny and Pavel are considered analogous arts to the claimed invention as they both disclose methods for phase interference radars that utilize machine learning methods for object direction detection. It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the apparatus as disclosed by Wozny to consider phase errors affecting antenna measurements as taught by Pavel. One of ordinary skill in the art prior to the effective filing date of the claimed invention would have been motivated to modify the apparatus in order to perform an accurate calibration of an antenna array, even considering phase errors that are inherent to the collection of measured antenna data (See Wozny [0038-0039], Pavel Pg. 241 Col. 2, Pavel Pg. 244 Col. 1).
Regarding claim 16, Wozny in view of Pavel teaches the non-transitory machine-readable medium of Claim 15. Wozny further teaches:
wherein the trained machine learning model is configured to implement one or more mappings between different antenna measurements and different angles of arrival (Wozny [0039] “By way of one non-limiting example, the data collected may contain expected signal characteristics from signals having vertical and horizontal polarizations emitted at intervals of every five degrees azimuth around the array 12. According to another aspect, signals can be emitted and the characteristics recorded at predetermined intervals in both azimuth and elevation.”).
Regarding claim 17 Wozny in view of Pavel teaches the non-transitory machine-readable medium of Claim 15. Wozny further teaches:
wherein the trained machine learning model comprises one of: a random forest regressor and a neural network (Wozny [0008] “analyze the collected data using a neural network matrix trained on prior collected antenna data;”).
Regarding claim 19, Wozny in view of Pavel teaches the non-transitory machine-readable medium of Claim 15. Wozny further teaches [Note: what is not clearly disclosed is strike-through]:
The non-transitory machine-readable medium of Claim 15, wherein the trained machine learning model is trained by providing training data to a machine learning model (Wozny [0043] “Once the antenna data is collected, deep learning techniques may be utilized to train the neural network 24.”),
adjusting the machine learning model based on the comparison (Wozny [0043] “At this layer, each data point may be assigned a weight or bias which may then further propagate through the hidden layers 30 before providing a DF solution at output layer 28.”), at least some of the training data including data modified using random phase errors (Pavel Pag. 243, Col. 2, “The antenna gains are independently generated from a uniform distribution between 0.9 and 1.1, whereas the phase errors are independently generated from a uniform distribution between −9◦ and 9 degree.”) .
Wozny fails to teach the limitations below. Pavel teaches:
comparing outputs of the machine learning model to ground truths, and (Pavel Pg. 243 Col. 2 “For a given direction, 20 groups of snapshots are generated with random noises. We generate a total number of 3,040 data vectors in our training dataset. 90% of them are used for training and the other 10% are used for validation. These vectors are used as the training input of the neural network. For the label of the neural network, the true signal direction is used.”),
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to incorporate the features as disclosed by Pavel into the invention of Wozny. Both Wozny and Pavel are considered analogous arts to the claimed invention as they both disclose methods for phase interference radars that utilize machine learning methods for object direction detection. It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the apparatus as disclosed by Wozny to compare training data to ground truths, and to introduce random phase errors into said training data, as taught by Pavel. One of ordinary skill in the art prior to the effective filing date of the claimed invention would have been motivated to modify the apparatus in order to train the machine model under similar circumstances to real-world calibration errors inherent to antenna arrays, especially in the case where the training data is simulated (See Wozny [0038-0039], Wozny [0040-0043] , Pavel Pg. 243-244).
Regarding claim 20, Wozny in view of Patel teaches the non-transitory machine readable medium of Claim 15. Wozny further teaches:
The non-transitory machine-readable medium of Claim 15, further containing instructions that when executed cause the at least one processor to repeatedly identify predictions of the angle of arrival associated with the one or more incoming signals in real-time (Wozny [0051] “The communication of the DF signal to the platform 22 and/or operator(s) thereof may further allow a decision to alter the operation of platform 22 to be made. According to one aspect, as discussed previously herein, determining the DF solution may allow platform 22 and/or the operator(s) thereof to perform automated actions such as steering towards the signal (as in a targeting situation), steering away from the signal (as in evasive maneuvers), jamming the signal, deploying defensive countermeasures, or any other appropriate responsive action.”, further, Wozny [0045] “Contrast this with the present matrix 24 where the use and operation thereof may reduce the required processing time by a factor of approximately 100 to result in a 43 microseconds approximate processing time.”. Examiner notes that the output of the machine learning model (a predicted angle) is used to perform real-time navigation tasks, thus requiring repeated real-time predictions from said model. ).
Claim(s) 4, 11, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Wozny et al. (US 20230097336 A1), herein after Wozny, in view of Pavel et al. (Pavel, Saidur R., et al. "Machine learning-based direction-of-arrival estimation exploiting distributed sparse arrays." 2021 55th Asilomar Conference on Signals, Systems, and Computers. IEEE, 2021), hereinafter Pavel, and further in view of Dvorecki et al. (US 20200132812 A1 ), hereinafter Dvorecki.
Regarding claim 4, Wozny in view of Pavel teaches the apparatus of claim 1. Wozny in view of Pavel fails to teach the limitations below. Dvorecki teaches:
The apparatus of Claim 1, wherein the phase measurements are based on antenna responses of the multiple antennas, each of the antennas having a different antenna response than one or more others of the antennas (Dvorecki [0020] “In operation, the radar system 104 may transmit a radar signal (e.g., an electromagnetic wave in the radio or microwave spectrum) and antennas 106 of the radar system 104 may each detect the radar signal reflected by a target object in response to the transmitted radar signal. Each one of the antennas will receive a different phase of the reflected signal.”).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to incorporate the features as disclosed by Dvorecki into the invention of Wozny in view of Pavel. The set of Wozny, Pavel, and Dvorecki are considered analogous arts to the claimed invention as they all disclose methods for using machine learning tools to calibrate phase characteristics of radars. It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the apparatus as disclosed by Wozny in view of Pavel to perform phase measurements, where the phase measurements are different for each antenna, as taught by Dvorecki. One of ordinary skill in the art prior to the effective filing date of the claimed invention would have been motivated to modify the apparatus in order to perform interferometry measurements, which are well-known in the art to require a comparison of at least two signals from two receivers (See Wozny [0001] and [0004] ), or to calibrate a Doppler radar to improve object velocity detection (See Dvorecki [0021-0024]).
Regarding claim 11, Wozny in view of Pavel teaches the method of claim 8. Wozny in view of Pavel fails to teach the limitation below. Dvorecki teaches:
wherein the phase measurements are based on antenna responses of the multiple antennas, each of the antennas having a different antenna response than one or more others of the antennas (Dvorecki [0020] “In operation, the radar system 104 may transmit a radar signal (e.g., an electromagnetic wave in the radio or microwave spectrum) and antennas 106 of the radar system 104 may each detect the radar signal reflected by a target object in response to the transmitted radar signal. Each one of the antennas will receive a different phase of the reflected signal.”).
Regarding claim 18, Wozny in view of Pavel teaches the non-transitory machine-readable medium of Claim 15. Wozny in view of Pavel fails to teach the limitation below. Dvorecki teaches:
wherein the phase measurements are based on antenna responses of the multiple antennas, each of the antennas having a different antenna response than one or more others of the antennas (Dvorecki [0020] “In operation, the radar system 104 may transmit a radar signal (e.g., an electromagnetic wave in the radio or microwave spectrum) and antennas 106 of the radar system 104 may each detect the radar signal reflected by a target object in response to the transmitted radar signal. Each one of the antennas will receive a different phase of the reflected signal.”).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to incorporate the features as disclosed by Dvorecki into the invention of Wozny in view of Pavel. The set of Wozny, Pavel, and Dvorecki are considered analogous arts to the claimed invention as they all disclose methods for using machine learning tools to calibrate phase characteristics of radars. It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the apparatus as disclosed by Wozny in view of Pavel to perform phase measurements, where the phase measurements are different for each antenna, as taught by Dvorecki. One of ordinary skill in the art prior to the effective filing date of the claimed invention would have been motivated to modify the apparatus in order to perform interferometry measurements, which are well-known in the art to require a comparison of at least two signals from two receivers (See Wozny [0001] and [0004] ), or to calibrate a Doppler radar to improve object velocity detection (See Dvorecki [0021-0024]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to THOMAS JAMES HALLORAN whose telephone number is (571)272-8643. The examiner can normally be reached Mon-Fri. 7:30am-5pm.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. DePoy et al. (US 12045699 B2) discloses a method and apparatus for using machine learning methods to localize an electromagnetic signal, a predicted direction of arrival is compared to a ground truth.
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, Resha H. Desai can be reached at (571) 270-7792. 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.
/T.J.H./Examiner, Art Unit 3648
/RESHA DESAI/Supervisory Patent Examiner, Art Unit 3648
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