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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 04/16/2026 has been entered.
Claim(s) 1, 7, 10, and 16 have been amended. Claims(s) 4 and 12-13 have been cancelled. Claim(s) 1-3, 5-11, and 14-16 are pending examination.
Response to Arguments
Applicant presents the following argument(s) regarding the previous office action:
Applicant asserts that the 35 USC 101 rejection of independent claims 1, 10, and 16 is improper. Applicant argues that the claims represent a concrete and practical application and therefore are not an abstract idea. Accordingly the dependent claims are not abstract ideas.
Applicant asserts that the 35 USC 103 rejection of claims 1-16 is improper. Applicant asserts that the claims as amended are not taught by the prior art. Particularly the limitation of “generating the output data includes down selection based on the assigned priority values,” is not taught.
Applicant asserts that the 35 USC 103 rejection of claim 1-16 is improper. Applicant asserts that the claimed limitation of “a rate of variation is different between the x-axis and the y-axis;” is not taught by the prior art.
Applicant’s arguments, see Pages 5-6, "STATUATORY SUBJECT MATTER REJECTION", filed 04/16/2026, with respect to claims 1, 10, and 16 have been fully considered and are persuasive. The 35 USC 101 rejection of claims 1-16 has been withdrawn.
Regarding applicant’s argument A, the examiner agrees. Claim 1 as amened now recites, “generating the output data includes down selection based on the assigned priority values.” This step imposes a practical application into the abstract idea, in this case a modification of data. This step would integrate the abstract idea beyond simply a mental process. In light of this the 35 USC 101 rejection would be removed for claim 1. Claims 10 and 16 are similar in scope and would also no longer be rejected. All dependent claims are no longer rejected under 35 USC 101.
Applicant’s arguments with respect to claim(s) 1-16 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Regarding applicant’s argument B, the examiner finds it moot. After further search and consideration the examiner would rely on the newly cited art Yin (US PG Pub 2018/0373992). Looking at [0088]-[0089] of Yin it teaches, “the object filtering module 860 assigns a priority to each of the plurality of objects,” then further, “a fixed-length list of data structures representing the objects in the region of interest may be generated by the uniform representation module 865. If the number of objects within the region of interest exceeds the size of the fixed-length list, a predetermined number of objects may be selected for inclusion in this list based on their priorities.” (Emphasis added). The teachings of Yin clearly show a system that can down select based on a priority value. Yin does this to ensure that only data collected with a high priority value is processed by the computer. As Yin teaches in [0041] “object filtering may simplify the input to the controller of the autonomous system by filtering out objects that are expected to have a minimal impact on decisions made by the controller.” This shows that filtering allows the computer system to only process data that is deemed to be important to the operation of a vehicle. If the assigned priority of an object is significantly low, the computer determines this object will not impact its future operation, and can the disregard this detection. This provides for an improved processing with less computing power required.
Applicant's arguments filed 10/06/2025 have been fully considered but they are not persuasive.
Regarding applicant’s argument C, the examiner respectfully disagrees. Regarding the teachings of Sakamoto the applicant asserts that Sakamoto does not teach “a rate of variation is different between the x-axis and the y-axis.” Applicant asserts that Sakamoto fails because it does not determine the value, “in both axes simultaneously.” This is not supported by Sakamoto. Looking at Fig. 4, and [0097]-[0098] the computing system would calculate the approximation of the lateral and longitudinal lines at the same time. The flow chart of Fig. 4 shows the computer determining both measurements in subsequent steps before any further processing is carried out. Therefore the computer system would have both axes’ data at the same time. This data is used in conjunction for further steps such as step 460 of Fig 4, which uses both approximations in tandem to determine an average weight when compared to a threshold. Further even if this was not the case the teachings of Sakamoto still show that the system may vary the way it assigns weights in the x and y direction. The system of Sakamoto assigns the weights based on the distance to the detected point. As the distances between x and y detections vary so too can the determined weighting distribution.
In light of applicant’s arguments B and C, the examiner determines that the claims would remain rejected under 35 USC 103. For more detailed mapping and explanation see the section below titled, “Claim Rejections – 35 USC 103.”
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 1-3, 5-11, and 14-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bradley (US Pat 11,161,464) in view of Sakamoto (US PG Pub 2022/0005352) and Yin (US PG Pub 2018/0373992).
Regarding claim 1, Bradley teaches a method for processing perception sensor data in an ego vehicle, (Fig. 1 items 10, 106, and 110; and Col. 7, lines 38-48; teach a system that can process the perceived data around an ego vehicle) the method comprising:
receiving sensor data associated with locations of detections in a coordinate system having a y-axis and an x-axis; (Figs. 2 and 3; and Col. 7, lines 49-62; and Col. 15, lines 44-58; teach the system as able to receive sensor data around the vehicle. Col. 16, lines 6-11; teach that the sensor data is associated with the location of object surrounding the vehicle. Col. 16, lines 12-29; teach that the received data includes the location of a number of points in three dimensional spaces. This 3D space would inherently have at least two axes, i.e. an x and y axis. Therefore as interpreted by the examiner the prior art would teach a coordinate system having an x-axis and a y-axis, as required by the claim.)
assigning a priority value to each of detections based on its location in the coordinate system; (Col. 18, lines 65-67; and Col. 19, lines 1-61; teach assigning a priority classification value to objects detected around the vehicle based on their location. Col. 26, lines 37-55; additionally teaches the assigned priority value based on location) and
generating output data based on the detections and the assigned priority values, (Col. 18, lines 33-64; teach the system having outputs of data to send based on the detected object and its assigned priority. Col. 33, lines 32-40; teach the system outputting data based on the detected object and priority classification.) wherein:
,
the assigned priority values vary based on the location of the detections in x-axis and y-axis; (Col. 19, lines 11-61; teach the assigned priority values can be based on the location of the object and one or more pre-selected prioritization schemes. Col. 26, lines 37-55; additionally teaches the assigned priority value based on location. These locations vary and the values would indeed vary based on the detections locations in 3D space)
a rate of variation is different (Col. 19, lines 25-44; teaches that the system can have various priority classifications based on the objects position in space, this would include having different schemes for various features which indicate position/location/orientation of the detection)
Bradley does not teach generating output data includes down selection based on the assigned priority values and differing rates of variation between the x-axis and y-axis.
However, Sakamoto teaches “between the x-axis and y-axis” ([0097]-[0106] teach a weighting system based on the lateral and longitudinal distance an object is from the ego vehicle. The system weights the object’s location based on said distance. These weights vary according to the approximation error, that represents the error in either the lateral or longitudinal direction. As taught in [0138]-[0139] the lateral and longitudinal error is calculated the same way however, they are based on different minimum distances in different directions therefore the variance of the weighting in each direction is different)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Bradley and Sakamoto; and have a reasonable expectation of success. Both relate to the control of vehicle perception systems and are used to determine when and where to prioritize detections of objects. The use of varied rates for different axis would allow for the system to prioritize what is most important at that time. If the vehicle parked lateral detections, such as parked cars next to it, would not be as important as longitudinal detections, such as a wall it is backing into. As Sakamoto teaches in [0024] and [0030] vehicles’ reversing may have static elements that prevent other vehicle’s form contacting them, i.e. a guardrail. By ensuring that these static elements are properly analyzed by weighting the elements differently based on the axis of detection, false alerts for collision are avoided.
The combination of Bradley and Sakamoto does not teach generating output data includes down selection based on the assigned priority values.
However, Yin teaches “generating output data includes down selection based on the assigned priority values.” ([0041] and [0088]-[0089] teaches a computer system of a vehicle filtering out detections of various elements in an observed environment. This filtering is based on an assigned priority value to the detection based on the detected object’s relevance to the ego vehicle)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Bradley and Sakamoto with Yin; and have a reasonable expectation of success. All relate to vehicle sensor systems and assigning values to detected objects. As Yin teaches in [0041] “object filtering may simplify the input to the controller of the autonomous system by filtering out objects that are expected to have a minimal impact on decisions made by the controller.” This shows that filtering allows the computer system to only process data that is deemed to be important to the operation of a vehicle. If the assigned priority of an object is significantly low, the computer determines this object will not impact its future operation, and can the disregard this detection. This provides for an improved processing with less computing power required.
Claims 10 and 16 are substantially similar and would be rejected for the same rationale as above.
Regarding claim 2, Bradley teaches the method of claim 1 wherein the rate of variation is selected based on a set of criteria. (Col. 19, lines 11-61; teach the assigned priority values can be based on the location of the object and one or more pre-selected prioritization schemes. The schemes can be based on some form of criteria such as object speed, object distance, ego vehicle speed, etc.)
Claim 14 is substantially similar and would be rejected for the same rationale as above.
Regarding claim 3, Bradley teaches the method of claim 1 wherein rate of variation is configured to assign areas in the coordinate system with higher priority. (Col. 4, lines 9-21; teach the system having certain areas with a higher priority in the system. Col. 18, lines 65-67; and Col. 19, lines 1-61; teach assigning a priority classification value to objects detected around the vehicle based on their location.)
Claim 15 is substantially similar and would be rejected for the same rationale as above.
Regarding claim 5, Bradley teaches the method of claim 1 wherein generating output data includes selecting detections having an assigned priority value above a threshold. (Col. 19, lines 25-61; teach the system selecting data based on an assigned priority above a threshold, i.e. selecting a higher priority data for processing that the lower priority data)
Regarding claim 6, Bradley teaches the method of claim 5 wherein: the threshold is determined based on a number of available processing paths for detections, (Col. 22, lines 24-65; and Col 23, lines 1-14; teach the system collecting data in a certain angular slice, i.e. a channel, the system is configured to have a preset number of slices and can track data based on the number of slices) and generating output data includes selecting a detection to associate with each available processing path based on its assigned priority value. (Fig. 3, and Col. 23, lines 45-67, and Col. 24, lines 1-6; teach the system associating a tracked object based on each slice of sensed data)
Regarding claim 7, Bradley teaches the method of claim 1 further comprising forwarding the output data for downstream processing. (Col. 18, lines 33-64; teaches the system having a data stream controller that is configured to forward the data to other systems in the processor)
Regarding claim 8, Bradley teaches the method of claim 1 wherein the rate of variation is configured to assign a higher priority to detections coming from a selected location relative to the ego vehicle. (Col. 26, lines 37-55; teach the prioritization scheme may be based on an objects immediate impact on a vehicle this can be based on where the object is relative to the vehicle with certain areas being granted a higher priority than others)
Regarding claim 9, Bradley teaches the method of claim 1 wherein the sensor data is RADAR data. (Col. 16, lines 20-29; teaches the data as being radar data)
Regarding claim 11, Bradley teaches the perception sensor processing unit of claim 10 wherein:
the perception sensor processing unit is an automotive electronic control unit; (Fig. 1, item 106; and Col. 15, lines 44-64; teach the system being implemented on a vehicle electronic control unit) and
an origin of the coordinate system represents an ego vehicle. (Figs. 2 and 3, and Col. 16, lines 12-29; teach the locations in 3D space are relative to the ego vehicle, i.e. the ego vehicle serves as the origin point)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NICHOLAS STRYKER whose telephone number is (571)272-4659. The examiner can normally be reached Monday-Friday 7:30-5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Christian Chace can be reached at (571) 272-4190. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/N.S./Examiner, Art Unit 3665 /CHRISTIAN CHACE/Supervisory Patent Examiner, Art Unit 3665
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