DETAILED ACTION
This Office Action is in response to Applicant’s Amendments and Remarks filed on 01/29/2026.
Claims 1-20 are pending for examination.
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 .
Response to Amendment
With regards to the claim objection made to claim 17 in the previous office action, the amendments made to the claims have clarified the claim language. Therefore, the claim objection to claim 17 has been withdrawn.
With regards to U.S.C. 101 rejection made to claims 1-20 in the previous office action, the amendments made to independent claims 1, 9 and 17 add sufficient details to the mental processes discussed in the previous office action to bring the claim language outside the scope of a mental process. Therefore, the U.S.C. 101 rejection to claims 1-20 have been withdrawn.
With regards to the 102 rejection to claims 1-4, 7-12, 15-17, and 19-20 in the previous office action the amendments made to the independent claims recite limitations that are not taught by of Maurer et al. (US 12361567 B1; hereafter Maurer). Therefore, the 102 rejection to these claims has been withdrawn. However, upon further consideration, a new ground of rejection is made in view of Maurer and further evidenced by Li et al. (US 20240241261 A1; hereafter Li).
Claim Rejections - 35 USC § 103
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 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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being obvious in view of Maurer as evidenced by Li.
Maurer and Li were cited in the previous office action.
Regarding claim 1, Maurer discloses a perception system, comprising:
a plurality of sensors including a first sensor and a second sensor ([col. 7, lines 30-33]; “The sensing system 110 can include one or more lidars 112, which can be a laser-based unit capable of determining distances to the objects and velocities of the objects in the driving environment 101.”); and
at least one processor configured to execute instructions stored in at least one memory to perform operations comprising ([col. 27, lines 22-23]; “Example computer device 900 can include a processing device 902 (also referred to as a processor or CPU)”):
initializing a grid with default values for a set of points in an environment of a vehicle including the perception system ([Fig. 3 & co. 17 lines 36-37]; “The diagram 300 further shows ego flow 320, which represents the AV motion 312 (assuming all objects are static).”);
extracting, by a first learnable function, features corresponding to a first point cloud ([col. 22, lines 16-17]; “At operation 620, processing logic extracts, from the input data, a plurality of sets of BEV features.”
[col. 23, lines 51-52]; “At operation 710, processing logic obtains a set of BEV features associated with an initial timestep.”), the first point cloud corresponding to a first subset of the set of points based upon sensor data received from the first sensor ([col. 15, lines 33-37]; “The radar data feature transformation component 214 can utilize radar data feature transformation to transform the set of radar data features into a set of radar points. For example, the set of radar points can be a radar point cloud.”), extracting the features further comprising inputting i) the first subset of the set of points ([col. 15, lines 40-44]; “The set of pixel points generated by the camera data feature projection component 214 and the set of radar points generated by the radar data feature transformation component 224 can be provided to a BEV feature processing component 230.”)
updating the grid using the temporally aligned features corresponding to the first point cloud ([col. 24, lines 1-6]; “At operation 740A, processing logic generates a set of aggregated BEV features. The set of aggregated BEV features can be generated by combining the set of BEV features associated with the subsequent timestep with the set of warped BEV features generated from the set of BEV features associated with the initial timestep.”);
based upon sensor data received from the second sensor, identifying a second subset of the set of points corresponding to a second point cloud ([col. 22, lines 4-5]; “At operation 610, processing logic obtains input data associated with a plurality of timesteps.”);
extracting, by a first learnable function, features corresponding to a second point cloud, by inputting i) the second subset of the set of points ([col. 15, lines 61-66]; “The BEV feature processing component 230 can perform multi-frame temporal aggregation to generate a set of aggregated BEV features from a plurality of sets of BEV features. More specifically, the plurality of sets of BEV features can include a set of BEV features corresponding to the current timestep (e.g., t)”
Note: The BEV feature processing unit as disclosed by Maurer collects the data form a plurality of set of data meaning that the processing of extracting features from a first and second point cloud at a given timestep is all done by the BEV feature processing unit.)
updating the grid using the temporally aligned features corresponding to the second point cloud to display in a single reference frame with the temporally aligned features corresponding to the first point cloud ([col. 24, lines 1-6]; “At operation 740A, processing logic generates a set of aggregated BEV features. The set of aggregated BEV features can be generated by combining the set of BEV features associated with the subsequent timestep with the set of warped BEV features generated from the set of BEV features associated with the initial timestep.”).
Although Maurer discloses extracting and manipulating sensor data Maurer does not disclose extracting features further consisting a offset timestamp.
However Li, within the same field of endeavor does teach ii) a first timestamp offset between the first point cloud and a single reference frame as inputs into the first learnable function; ([0053]; “The one or more features may relate to timing of the output. For example, the respective outputs may include one or more frames with timestamped information. The one or more features may indicate drift of the Lidar sensor, alignment of the Lidar sensor and the camera, or other features.”)
Performing, by a second learnable function, temporal alignment of the extracted features corresponding to the first point cloud by applying the second learnable function to the features corresponding to the first point cloud; ([0054]; “In response to detecting the one or more features, the sensor synchronization module 360 may adjust the first control signal or the second control signal. For example, the frequency of a control signal may be increased or reduced to reduce a misalignment between the Lidar sensor and the camera.”)
ii) a second timestamp offset between the second point cloud and a single reference frame as inputs into the first learnable function; ([0053]; “The one or more features may relate to timing of the output. For example, the respective outputs may include one or more frames with timestamped information. The one or more features may indicate drift of the Lidar sensor, alignment of the Lidar sensor and the camera, or other features.”)
Performing, by a second learnable function, temporal alignment of the extracted features corresponding to the second point cloud by applying the second learnable function to the features corresponding to the second point cloud; ([0054]; “In response to detecting the one or more features, the sensor synchronization module 360 may adjust the first control signal or the second control signal. For example, the frequency of a control signal may be increased or reduced to reduce a misalignment between the Lidar sensor and the camera.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Maurer with Li. This modification would have been obvious because both Maurer and Li cover subject matter within the same field of endeavor (object detection in autonomous vehicles) and it would have been beneficial to incorporate the time offset correction taught by Li to ensure that the data collected from a plurality of sensors have the same timestamps.
Regarding claim 2, Maurer in combination with Li discloses all of the limitations of claim 1. Additionally, Maurer discloses the grid displays the temporally aligned features corresponding to the second point cloud and the first point cloud in a single reference frame as bird’s-eye-view features. ([col. 15, lines 61-63]; “The BEV feature processing component 230 can perform multi-frame temporal aggregation to generate a set of aggregated BEV features from a plurality of sets of BEV features.”).
Regarding claim 3, Maurer in combination with Li discloses all of the limitations of claim 1. Additionally, Maurer discloses the first sensor or the second sensor is a light detection and ranging (LiDAR) sensor. ([col. 7, lines 30-33]; “The sensing system 110 can include one or more lidars 112, which can be a laser-based unit capable of determining distances to the objects and velocities of the objects in the driving environment 101.”)
Regarding claim 4, Maurer in combination with Li discloses all of the limitations of claim 3. Additionally, Maurer discloses the LiDAR sensor is a frequency modulated continuous wave-based LiDAR sensor. ([col. 7, lines 43-46]; “Each of the lidar(s) 112 and radar(s) 114 can include a coherent sensor, such as a frequency-modulated continuous-wave (FMCW) lidar or radar sensor.”)
Regarding claim 5, Maurer in combination with Li discloses all the limitations of claim 1. Additionally Li teaches the sensor data includes a respective sensor identification (ID) of the first sensor or the second sensor. ([0069]; “Each output may include additional information such as an ID of the corresponding sensor (e.g., a camera ID or a Lidar ID), the make or model of the sensor, or other information that identifies which sensor generated the output, or a type of the sensor.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Maurer with Li. This modification would have been obvious because both Maurer and Li cover subject matter within the same field of endeavor (object detection in autonomous vehicles) and it would have been beneficial to utilize the identification methods of Li in order to confirm the type of data being collected. This would make data manipulation more accurate as methods for data manipulation would vary depending on the type of data being collected.
Regarding claim 6, Maurer in combination with Li discloses all the limitations of claim 5. Additionally Li teaches the respective sensor ID is associated with a sensor type or a position of a sensor on the vehicle. ([0069]; “Each output may include additional information such as an ID of the corresponding sensor (e.g., a camera ID or a Lidar ID), the make or model of the sensor, or other information that identifies which sensor generated the output, or a type of the sensor.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Maurer with Li. This modification would have been obvious because both Maurer and Li cover subject matter within the same field of endeavor (object detection in autonomous vehicles) and it would have been beneficial to utilize the identification methods of Li in order to confirm the type of data being collected. This would make data manipulation more accurate as methods for data manipulation would vary depending on the type of data being collected.
Regarding claim 7, Maurer in combination with Li discloses all of the limitations of claim 1. Additionally, Maurer discloses the operations further comprising storing data corresponding to the single reference frame including the temporally aligned features corresponding to the first point cloud and the second point cloud for access or query by a downstream task. ([Fig. 6 & col. 21, lines 35-43]; “FIG. 6 is a flow diagram illustrating an example method of implementing multi-frame temporal aggregation and dense motion estimation for autonomous vehicles (AVs), in accordance with some implementations of the present disclosure. A processing device, having one or more processing units (CPUs) and memory devices communicatively coupled to the CPU(s), can perform method 600 and/or each of their individual functions, routines, subroutines, or operations.”)
Regarding claim 8, Maurer in combination with Li discloses all of the limitations of claim 7. Additionally, Maurer discloses the downstream task includes at least one of an object detection task, a lane geometry detection task, or a vehicle localization task. ([co. 23, lines 3-12]; “At operation 640, processing logic can cause a driving path to be modified in view of at least one output. More specifically, the at least one output can include at least one of: the set of aggregated BEV features or the at least one object flow. For example, the at least one output can be processed by the data processing system of the AV (e.g., the data processing system 120), and the result of the processing by the data processing system can be provided to the AVCS of the AV (e.g., AVCS 140) to control the driving path of the AV.”)
Claim 9 recites a method to operate the system of claim 1. Therefore, is rejected under the same reasoning.
Claim 10 recites a method to operate the system of claim 2. Therefore, is rejected under the same reasoning.
Claim 11 recites a method to operate the system of claim 3. Therefore, is rejected under the same reasoning.
Claim 12 recites a method to operate the system of claim 4. Therefore, is rejected under the same reasoning.
Claim 13 recites a method to operate the system of claim 5. Therefore, is rejected under the same reasoning.
Claim 14 recites a method to operate the system of claim 6. Therefore, is rejected under the same reasoning.
Claim 15 recites a method to operate the system of claim 8. Therefore, is rejected under the same reasoning.
Claim 16 recites a method to operate the system of claim 2. Therefore, is rejected under the same reasoning.
Claim 17 recites a vehicle comprising the same limitations as claim 1. Maurer recites an autonomous vehicle that utilizes the system disclosed in claim 1 ([Fig. 1 & col. 5, lines 57-59]; “FIG. 1 is a diagram illustrating components of an example autonomous vehicle (AV) 100, in accordance with some implementations of the present disclosure.”). Therefore, is rejected under the same reasoning.
Regarding claim 18, Maurer in combination with Li discloses all the limitations of claim 17. Additionally Maurer discloses the grid displays the temporally aligned features corresponding to the second point cloud and the first point cloud in a single reference frame as bird’s-eye-view features ([col. 15, lines 61-63]; “The BEV feature processing component 230 can perform multi-frame temporal aggregation to generate a set of aggregated BEV features from a plurality of sets of BEV features.”).
Although Maurer utilizes various sensors such as LiDAR, RADAR, cameras, etc. ([Fig. 1]; The sensing system 110 comprises a plurality of sensor types.). Maurer does not explicitly disclose sensor identification that the system can use to verify the type of sensor that correlates with the data being collected.
However, Li within the same field of endeavor teaches the sensor data includes a respective sensor identification (ID) of the first sensor or the second sensor ([0069]; “Each output may include additional information such as an ID of the corresponding sensor (e.g., a camera ID or a Lidar ID), the make or model of the sensor, or other information that identifies which sensor generated the output, or a type of the sensor.”); and
the respective sensor ID is associated with a sensor type or a position of a sensor on the autonomous vehicle. ([0069]; “Each output may include additional information such as an ID of the corresponding sensor (e.g., a camera ID or a Lidar ID), the make or model of the sensor, or other information that identifies which sensor generated the output, or a type of the sensor.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Maurer with Li. This modification would have been obvious because both Maurer and Li cover subject matter within the same field of endeavor (object detection in autonomous vehicles) and it would have been beneficial to utilize the identification methods of Li in order to confirm the type of data being collected. This would make data manipulation more accurate as methods for data manipulation would vary depending on the type of data being collected.
Regarding claim 19, Maurer discloses all of the limitations of claim 17. Additionally, Maurer discloses the first sensor or the second sensor is a light detection and ranging (LiDAR) sensor, and wherein the LiDAR sensor is a frequency modulated continuous wave-based LiDAR sensor. ([col. 7, lines 43-46]; “Each of the lidar(s) 112 and radar(s) 114 can include a coherent sensor, such as a frequency-modulated continuous-wave (FMCW) lidar or radar sensor.”)
Regarding claim 20, Maurer discloses all of the limitations of claim 17. Additionally, Maurer discloses the operations further comprising storing data corresponding to the single reference frame including the temporally aligned features corresponding to the first point cloud and the second point cloud for access or query by a downstream task ([Fig. 6 & col. 21, lines 35-43]; “FIG. 6 is a flow diagram illustrating an example method of implementing multi-frame temporal aggregation and dense motion estimation for autonomous vehicles (AVs), in accordance with some implementations of the present disclosure. A processing device, having one or more processing units (CPUs) and memory devices communicatively coupled to the CPU(s), can perform method 600 and/or each of their individual functions, routines, subroutines, or operations.”), and wherein the downstream task includes at least one of an object detection task, a lane geometry detection task, or a vehicle localization task. ([co. 23, lines 3-12]; “At operation 640, processing logic can cause a driving path to be modified in view of at least one output. More specifically, the at least one output can include at least one of: the set of aggregated BEV features or the at least one object flow. For example, the at least one output can be processed by the data processing system of the AV (e.g., the data processing system 120), and the result of the processing by the data processing system can be provided to the AVCS of the AV (e.g., AVCS 140) to control the driving path of the AV.”)
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRANDON SUNG EUN LEE whose telephone number is (571)272-5684. The examiner can normally be reached Monday - Friday 9:00 am - 5:00 pm.
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/B.S.L./Examiner, Art Unit 3668
/JAMES J LEE/Supervisory Patent Examiner, Art Unit 3668
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