DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,066,832. Although the claims at issue are not identical, they are not patentably distinct from each other because as set forth below in the comparison view between the instant claims and allowed patent claims, the claims comprise the same scope overall, with the only meaningful differences (in bold italics) being certain elements of dependent claims 6 and 9 of the instant application are recited in the independent claims of the allowed Patent.
18/765,723 Instant Application
1. A processor-implemented method of performing an operation using a complex-valued attention network, the method comprising:
extracting a complex-valued attention weight from complex-valued input data;
normalizing a magnitude of the complex-valued attention weight while preserving a phase of the complex-valued attention weight;
determining complex-valued attention data by applying the normalized complex-valued attention weight to the complex-valued input data;
controlling any one or any combination of a velocity, an acceleration, and steering of a vehicle based on ego-motion information determined from the complex-valued attention data.
2. The method of claim 1, wherein the determining of the complex-valued attention data comprises applying a real value and an imaginary value of the extracted complex-valued attention weight to real data and imaginary data of the complex-valued input data respectively.
3. The method of claim 2, wherein the applying of the normalized complex-valued attention weight to the complex-valued input data comprises applying the normalized weight of the complex-valued attention weight to the real data and the imaginary data for each channel.
4. The method of claim 3, wherein the applying of the normalized weight comprises: applying a real value of the normalized weight of the complex-valued attention weight to a real input map of the complex-valued input data for each channel; and applying an imaginary value of the normalized weight of the complex-valued attention weight to an imaginary input map of the complex-valued input data for each channel.
5. The method of claim 3, wherein the normalizing of the magnitude of the complex-valued attention weight comprises: determining phase information of the complex-valued attention weight for each channel; and determining a bounded magnitude of the complex-valued attention weight within a threshold range for each channel.
6. The method of claim 5, further comprising: applying the determined phase information and the determined bounded magnitude to the complex-valued input data through the element-wise multiplication such that dimensions of the complex-valued attention data correspond to dimensions of the complex-valued input data.
7. The method of claim 5, wherein values of the normalized weight correspond to an inner region of a circle having a radius that is a threshold corresponding to the threshold range in a complex plane.
8. The method of claim 5, wherein the determining of the phase information comprises dividing the complex-valued attention weight by an absolute value of the complex-valued attention weight for each channel, and the determining of the bounded magnitude comprises applying an activation function to the absolute value of the complex-valued attention weight for each channel.
9. The method of claim 1, wherein the extracting of the complex-valued attention weight comprises: determining the complex-valued attention weight by extracting a value indicating a real component and a value indicating an imaginary component from the complex-valued input data for each channel using one or more convolution operations.
10. The method of claim 9, wherein the determining of the complex-valued attention weight comprises: performing pooling on a real representative value representing a real component and an imaginary representative value representing an imaginary component from the complex-valued input data for each channel; generating downscaled data by applying a convolution operation, of the one or more convolution operations, of reducing a number of channels to a result of the pooling including the real component and the imaginary component; and determining the complex-valued attention weight by applying a convolution operation, of the one or more convolution operations, of increasing a number of channels to the downscaled data.
11. The method of claim 1, further comprising: obtaining raw radar data by sensing a radar signal using a radar sensor; and generating the complex-valued input data by transforming the raw radar data.
12. The method of claim 11, wherein the obtaining of the raw radar data comprises obtaining an angle-velocity map for each range channel as the complex-valued input data.
13. The method of claim 11, further comprising: for the determining of the ego-motion information, determining the ego-motion information of the radar sensor from the complex-valued attention data based on an ego-motion estimation model.
14. The method of claim 13, wherein the determining of the ego-motion information comprises determining an acceleration with respect to at least one axis together with a velocity and an angular velocity of the radar sensor as the ego-motion information.
15. The method of claim 13, wherein the determining of the ego-motion information comprises determining the ego-motion information from residual data between the complex-valued input data and the complex-valued attention data based on the ego-motion estimation model.
16. The method of claim 13, wherein the radar sensor is mounted in the vehicle.
17. The method of claim 13, further comprising: determining at least one of a position and a heading direction of the vehicle, in which the radar sensor is mounted, based on the ego-motion information; and outputting an estimation result for at least one of the position and the heading direction.
18. The method of claim 1, further comprising: determining residual data by summing the complex-valued input data and the complex-valued attention data, and applying a complex-valued attention network-based operation to the residual data.
19. A non-transitory computer-readable storage medium storing instructions that, when executed by a processor, configure the processor to perform the method of claim 1.
20. A computing apparatus comprising: a memory configured to store a complex-valued attention network model; and a processor configured to extract a complex-valued attention weight from complex-valued input data using a first layer of the complex-valued attention network model, normalize a magnitude of the complex-valued attention weight while preserving a phase of the complex-valued attention weight, determine complex-valued attention data by applying the normalized complex-valued attention weight to real data and imaginary data of the complex-valued input data using a second layer of the complex-valued attention network model, and control any one or any combination of a velocity, an acceleration, and steering of a vehicle based on ego-motion information determined from the complex-valued attention data.
12,066,832 Granted Patent
1. A processor-implemented method of performing an operation using a complex-valued attention network, the method comprising:
extracting a complex-valued attention weight from complex-valued input data using one or more convolution operations;
normalizing a magnitude of the complex-valued attention weight while preserving a phase of the complex-valued attention weight;
determining complex-valued attention data by applying the normalized complex-valued attention weight to the complex-valued input data using an element-wise multiplication such that dimensions of the complex-valued attention data correspond to dimensions of the complex-valued input data;
determining ego-motion information of a vehicle from the complex-valued attention data based on an ego-motion estimation model; and
controlling any one or any combination of a velocity, an acceleration, and steering of the vehicle based on the ego-motion information.
2. The method of claim 1, wherein the determining of the complex-valued attention data comprises individually applying a real value and an imaginary value of the extracted complex-valued attention weight to real data and imaginary data of the complex-valued input data.
3. The method of claim 2, wherein the applying of the normalized complex-valued attention weight to the complex-valued input data comprises applying the normalized weight of the complex-valued attention weight to the real data and the imaginary data for each channel.
4. The method of claim 3, wherein the applying of the normalized weight comprises: applying a real value of the normalized weight of the complex-valued attention weight to a real input map of the complex-valued input data for each channel; and applying an imaginary value of the normalized weight of the complex-valued attention weight to an imaginary input map of the complex-valued input data for each channel.
5. The method of claim 3, wherein the normalizing of the magnitude of the complex-valued attention weight comprises: determining phase information of the complex-valued attention weight for each channel; and determining a bounded magnitude of the complex-valued attention weight within a threshold range for each channel.
6. The method of claim 5, further comprising: applying the determined phase information and the determined bounded magnitude to the complex-valued input data through the element-wise multiplication.
7. The method of claim 5, wherein values of the normalized weight correspond to an inner region of a circle having a radius that is a threshold corresponding to the threshold range in a complex plane.
8. The method of claim 5, wherein the determining of the phase information comprises dividing the complex-valued attention weight by an absolute value of the complex-valued attention weight for each channel, and the determining of the bounded magnitude comprises applying an activation function to the absolute value of the complex-valued attention weight for each channel.
9. The method of claim 1, wherein the extracting of the complex-valued attention weight comprises: determining the complex-valued attention weight by extracting a value indicating a real component and a value indicating an imaginary component from the complex-valued input data for each channel.
10. The method of claim 9, wherein the determining of the complex-valued attention weight comprises: performing pooling on a real representative value representing a real component and an imaginary representative value representing an imaginary component from the complex-valued input data for each channel; generating downscaled data by applying a convolution operation, of the one or more convolution operations, of reducing a number of channels to a result of the pooling including the real component and the imaginary component; and determining the complex-valued attention weight by applying a convolution operation, of the one or more convolution operations, of increasing a number of channels to the downscaled data.
11. The method of claim 1, further comprising: obtaining raw radar data based on a radar signal sensed by a radar sensor; and generating the complex-valued input data by transforming the raw radar data.
12. The method of claim 11, wherein the obtaining of the raw radar data comprises obtaining an angle-velocity map for each range channel as the complex-valued input data.
13. The method of claim 11, further comprising: for the determining of the ego-motion information, determining the ego-motion information of the radar sensor from the complex-valued attention data based on the ego-motion estimation model.
14. The method of claim 13, wherein the determining of the ego-motion information comprises determining an acceleration with respect to at least one axis together with a velocity and an angular velocity of the radar sensor as the ego-motion information.
15. The method of claim 13, wherein the determining of the ego-motion information comprises determining the ego-motion information from residual data between the complex-valued input data and the complex-valued attention data based on the ego-motion estimation model.
16. The method of claim 13, wherein the radar sensor is mounted in the vehicle.
17. The method of claim 13, further comprising: determining at least one of a position and a heading direction of the vehicle, in which the radar sensor is mounted, based on the ego-motion information; and outputting an estimation result for at least one of the position and the heading direction.
18. The method of claim 1, further comprising: determining residual data by summing the complex-valued input data and the complex- valued attention data, and applying a complex-valued attention network-based operation to the residual data.
19. A non-transitory computer-readable storage medium storing instructions that, when executed by a processor, configure the processor to perform the method of claim 1.
20. A computing apparatus comprising: a memory configured to store a complex-valued attention network model; and a processor configured to extract a complex-valued attention weight from complex-valued input data using one or more convolution operations of a first layer of the complex-valued attention network model, normalize a magnitude of the complex-valued attention weight while preserving a phase of the complex-valued attention weight, determine complex-valued attention data by applying the normalized complex-valued attention weight to real data and imaginary data of the complex-valued input data using a second layer of the complex-valued attention network model and an element-wise multiplication such that dimensions of the complex-valued attention data correspond to dimensions of the complex-valued input data, determine ego-motion information of a vehicle from the complex-valued attention data based on an ego-motion estimation model, and control any one or any combination of a velocity, an acceleration, and steering of the vehicle based on the ego-motion information.
Examiner’s Note
Although the instant claims are rejected based on Double Patenting in view of the parent case, it is noted that the claims are further rejected under Prior Art as they comprise the same scope as previous iterations of the claims that were rejected in preceding Office Actions, without including the limitations which were previously deemed to bring the claims into condition for allowance. Thus mapping of the instant claims based on previously mapped rejections from prior office actions for the parent case are set forth below.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-5, 7-9, and 11-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Fontijne et al. (US 2021/0255304).
Regarding claims 1 and 20, Fontijne discloses a computing apparatus and a method of performing an operation using a complex-valued attention network, the method comprising:
extracting a complex-valued attention weight from complex-valued input data (on-board computer of a host vehicle includes at least one processor configured to: receive, from a radar sensor of the host vehicle, a plurality of radar frames; execute a neural network on at least a subset of the plurality of radar frames; FONTIJNE at [0010]-[0012]);
normalizing a magnitude of the complex-valued attention weight while preserving a phase of the complex-valued attention weight (the compute frontend 508 may output a 3D complex-valued tensor representing range, azimuth, and Doppler. As yet another alternative, the compute frontend 508 may output a set of two-dimensional (2D) complex-valued tensors representing one or more of range and azimuth, range and Doppler, Doppler and azimuth, range and elevation, Doppler and elevation, or azimuth and elevation; FONTIJNE at [0058] and at least [0072]) ;
determining complex-valued attention data by applying the normalized complex-valued attention weight to the complex-valued input data (the compute frontend 508 may output a 3D complex-valued tensor representing range, azimuth, and Doppler. As yet another alternative, the compute frontend 508 may output a set of two-dimensional (2D) complex-valued tensors representing one or more of range and azimuth, range and Doppler, Doppler and azimuth, range and elevation, Doppler and elevation, or azimuth and elevation; FONTIJNE at [0058] and at least [0072]) ;
controlling any one or any combination of a velocity, an acceleration, and steering of a vehicle based on ego-motion information determined from the complex-valued attention data (The road world model can provide the location of other vehicles, pedestrians and static objects, their dimensions, velocity, and so on; FONTIJNE at [0084]).
Regarding claim 2, Fontijne discloses the determining of the complex-valued attention data comprises applying a real value and an imaginary value of the extracted complex-valued attention weight to real data and imaginary data of the complex-valued input data respectively (the network combines predictions from multiple feature maps with different resolutions to naturally handle objects of various sizes; FONTIJNE at [0069]) and (The feature maps 910 and 912 are in “ego coordinates,” meaning that the ego point (i.e., the location of the radar sensor) is always at a fixed location on the radar frame or latent representation. Thus, stationary objects move on the map, with the inverse of the ego motion; FONTIJNE at [0074).
Regarding claim 3, Fontijne discloses the applying of the normalized complex-valued attention weight to the complex-valued input data comprises applying the normalized weight of the complex-valued attention weight to the real data and the imaginary data for each channel (spatially variant convolutions (i.e., different convolution weights to different locations) can be used by the compute backend 512 as a deep learning technique to determine the results 514; FONTIJNE at [0063]-[0064]).
Regarding claim 4, Fontijne discloses the applying of the normalized weight comprises:
applying a real value of the normalized weight of the complex-valued attention weight to a real input map of the complex-valued input data for each channel (the compute frontend 508 may output a 3D complex-valued tensor representing range, azimuth, and Doppler. As yet another alternative, the compute frontend 508 may output a set of two-dimensional (2D) complex-valued tensors representing one or more of range and azimuth, range and Doppler, Doppler and azimuth, range and elevation, Doppler and elevation, or azimuth and elevation; FONTIJNE at [0058] and at least [0072]); and applying an imaginary value of the normalized weight of the complex-valued attention weight to an imaginary input map of the complex-valued input data for each channel (the compute frontend 508 may output a 3D complex-valued tensor representing range, azimuth, and Doppler. As yet another alternative, the compute frontend 508 may output a set of two-dimensional (2D) complex-valued tensors representing one or more of range and azimuth, range and Doppler, Doppler and azimuth, range and elevation, Doppler and elevation, or azimuth and elevation; FONTIJNE at [0058] and at least [0072]).
Regarding claim 5, Fontijne discloses the normalizing of the magnitude of the complex-valued attention weight comprises: determining phase information of the complex-valued attention weight for each channel; and determining a bounded magnitude of the complex-valued attention weight within a threshold range for each channel (the compute frontend 508 may output a 3D complex-valued tensor representing range, azimuth, and Doppler. As yet another alternative, the compute frontend 508 may output a set of two-dimensional (2D) complex-valued tensors representing one or more of range and azimuth, range and Doppler, Doppler and azimuth, range and elevation, Doppler and elevation, or azimuth and elevation; FONTIJNE at [0058] and at least [0072]).
Regarding claim 7, Fontijne discloses values of the normalized weight correspond to an inner region of a circle having a radius that is a threshold corresponding to the threshold range in a complex plane (the compute frontend 508 may output a 3D complex-valued tensor representing range, azimuth, and Doppler. As yet another alternative, the compute frontend 508 may output a set of two-dimensional (2D) complex-valued tensors representing one or more of range and azimuth, range and Doppler, Doppler and azimuth, range and elevation, Doppler and elevation, or azimuth and elevation; FONTIJNE at [0058] and at least [0072]) .
Regarding claim 8, Fontijne discloses the determining of the phase information comprises dividing the complex-valued attention weight by an absolute value of the complex-valued attention weight for each channel, and the determining of the bounded magnitude comprises applying an activation function to the absolute value of the complex-valued attention weight for each channel (spatially variant convolutions (i.e., different convolution weights to different locations) can be used by the compute backend 512 as a deep learning technique to determine the results 514; FONTIJNE at [0063]-[0064]).
Regarding claim 9, Fontijne discloses the extracting of the complex-valued attention weight comprises: determining the complex-valued attention weight by extracting a value indicating a real component and a value indicating an imaginary component from the complex-valued input data for each channel using one or more convolution operations (spatially variant convolutions (i.e., different convolution weights to different locations) can be used by the compute backend 512 as a deep learning technique to determine the results 514; FONTIJNE at [0063]-[0064]).
Regarding claim 11, Fontijne discloses: obtaining raw radar data by sensing a radar signal using a radar sensor; and generating the complex-valued input data by transforming the raw radar data (… a camera image 410 and a radar image 420 of the same scene. The camera image 410 may have been captured by the camera 212, and the radar image 420 may have been captured by the radar 214; FONTIJNE at [0053]).
Regarding claim 12, Fontijne discloses the obtaining of the raw radar data comprises obtaining an angle-velocity map for each range channel as the complex-valued input data(… a camera image 410 and a radar image 420 of the same scene. The camera image 410 may have been captured by the camera 212, and the radar image 420 may have been captured by the radar 214; FONTIJNE at [0051, 0053]) .
Regarding claim 13, Fontijne discloses for the determining of the ego-motion information, determining the ego-motion information of the radar sensor from the complex-valued attention data based on an ego-motion estimation model (The ego motion between frames 902 and 904 is the change in the position of the radar sensor. This can be obtained in various ways, such as GPS or other sensors, or the neural network can estimate the motion, including rotation (i.e., a change in orientation of the vehicle 100); FONTIJNE at [0075]).
Regarding claim 14, Fontijne discloses the determining of the ego-motion information comprises determining an acceleration with respect to at least one axis together with a velocity and an angular velocity of the radar sensor as the ego-motion information (The ego motion between frames 902 and 904 is the change in the position of the radar sensor. This can be obtained in various ways, such as GPS or other sensors, or the neural network can estimate the motion, including rotation (i.e., a change in orientation of the vehicle 100); FONTIJNE at [0075]) .
Regarding claim 15, Fontijne discloses the determining of the ego-motion information comprises determining the ego-motion information from residual data between the complex-valued input data and the complex-valued attention data based on the ego-motion estimation model (The feature maps 910 and 912 are in “ego coordinates,” meaning that the ego point (i.e., the location of the radar sensor) is always at a fixed location on the radar frame or latent representation. Thus, stationary objects move on the map, with the inverse of the ego motion; FONTIJNE at [0074]-[0075]).
Regarding claim 16, Fontijne discloses the radar sensor is mounted in the vehicle (see at least figure 2).
Regarding claim 17, Fontijne discloses determining at least one of a position and a heading direction of the vehicle, in which the radar sensor is mounted, based on the ego-motion information; and outputting an estimation result for at least one of the position and the heading direction (The feature maps 910 and 912 are in “ego coordinates,” meaning that the ego point (i.e., the location of the radar sensor) is always at a fixed location on the radar frame or latent representation. Thus, stationary objects move on the map, with the inverse of the ego motion; FONTIJNE at [0074]-[0075]).
Regarding claim 18, Fontijne discloses determining residual data by summing the complex-valued input data and the complex-valued attention data, and applying a complex-valued attention network-based operation to the residual data (The feature maps 910 and 912 are in “ego coordinates,” meaning that the ego point (i.e., the location of the radar sensor) is always at a fixed location on the radar frame or latent representation. Thus, stationary objects move on the map, with the inverse of the ego motion; FONTIJNE at [0074]-[0075]).
Regarding claim 19, Fontijne discloses a non-transitory computer-readable storage medium storing instructions that, when executed by a processor, configure the processor to perform the method of claim 1 (an on-board computer of a host vehicle includes at least one processor configured to: receive, from a radar sensor of the host vehicle, a plurality of radar frames; execute a neural network on at least a subset of the plurality of radar frames; FONTIJNE at [0010]-[0012]).
Potential Allowable Subject Matter
Claims 6 and 10 are rejected under double patenting as described above, and are also dependent upon rejected base claims. So, they would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims, and presuming there is a terminal disclaimer to overcome the double patenting rejections (or if the claims are amended in a way to overcome the double patenting rejections by diverging the subject matter from the US patent in question).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See attached 892 form.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JASON HOLLOWAY whose telephone number is (571)270-5786. The examiner can normally be reached M-F 9-5:30.
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/JASON HOLLOWAY/Primary Examiner, Art Unit 3658