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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 08/19/2026 has been entered.
Status of claims
Claims 1, 3,4,6-7,10-11,16,18-19 are amended.
Claims 9 and 17 are cancelled.
Claims 1-8, 10-16,18-20 are pending.
Response to arguments
With respect to Applicant’s remarks filed on 08/19/2026; Applicant's “Amendments and Remarks” have been fully considered. Applicant’s remarks will be addressed in sequential order as they were presented.
Applicant remarks:
Liang and Hartung do not teach “Dynamically selecting one or more vehicle..”.
Gallo, Ji and Park do not cure the deficiencies of Liang and Hartung.
Office Response:
Please see the new mapping of the independent and dependent claims.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-7, 16 and 19 are rejected under 35 U.S.C. 103 as being unpatented over US20190258878A1 to Koivisto et al. (herein after “Koivisto”) in view of US 20170347066 A1 to Song et al. (herein after “Song”).
Regarding claim 1, Koivisto teaches A method of autonomous emergency braking (AEB) performed by a vehicle, the method comprising: (See Koivisto para[0255] In an automatic emergency braking (AEB) system, false positive detections would cause the vehicle to automatically perform emergency braking, which is obviously undesirable. Therefore, only the most confident detections should be considered as triggers for AEB. The DLA may run a neural network for regressing the confidence value)
applying vehicle brakes to avoid the potential collision. (See Koivisto para [0297]AEB systems detect an impending forward collision with another vehicle or other object, and may automatically apply the brakes if the driver does not take corrective action within a specified time or distance parameter. AEB systems may use front-facing camera(s) and/or RADAR sensor(s) 1560)
However, Koivisto does not teach performing, based on one or more factors, at least one of dynamically selecting one or more vehicle sensors from a plurality of vehicle sensors, wherein the one or more factors include at least one factor that characterizes an environment in which the vehicle is operating, monitoring a forward path of travel of the vehicle for potential collisions, based on data comprising an output from the one or more dynamically selected and/or scaled vehicle sensors, detecting, based on the data comprising the output from the one or more dynamically selected vehicle sensors, a potential collision of the vehicle with an object in the forward path of travel of the vehicle. Nevertheless, Song same field of endeavor teaches performing, based on one or more factors, at least one of dynamically selecting one or more vehicle sensors from a plurality of vehicle sensors, wherein the one or more factors include at least one factor that characterizes an environment in which the vehicle is operating; (See Song para [0030] More specifically, this monitor region can be said to be in an environment where blind spot dynamically changes. In response to such a change in the blind spot, the monitor apparatus 1 dynamically selects the sensors of the moving objects 41, 42 used for monitoring, and executes processing for monitoring the specific target in the monitor region by using the data received from the moving object having the selected sensor and/or the fixed cameras 21, 22. )
monitoring a forward path of travel of the vehicle for potential collisions, based on data comprising an output from the one or more dynamically selected and/or scaled vehicle sensors; (See Song monitoring a pedestrian , para[0025] monitoring the target from among the moving object, para[0030] the monitor apparatus 1 notifies the information about the monitored pedestrian to, for example, the moving objects 41, 42, so that an accident of collision of the moving objects 41, 42 with the pedestrian can be prevented.)
detecting, based on the data comprising the output from the one or more dynamically selected vehicle sensors, a potential collision of the vehicle with an object in the forward path of travel of the vehicle (See Song para[0052] It is also possible to detect a pedestrian by using deep learning that can simultaneously perform the calculation of features and the identification.);
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Koivisto’s AEB with Song’s dynamically selecting sensors based on one or more factors and monitoring forward path of travel in order to allow to monitor the moving target sufficiently in an environment where the blind spot dynamically changes (See Song para[0004]).
Regarding claim 2, Koivisto and Song remain applied as claim 1. Koivisto teaches , wherein the one or more factors comprise at least one of:
a vehicle condition or location; (See Koivisto para[0210] For example, the vehicle 1500 may be capable of conditional automation (Level 3), high automation (Level 4), and/or full automation (Level 5), depending on the embodiment., para[0228] The CAN bus may be read to find steering wheel angle, ground speed, engine revolutions per minute (RPMs), button positions, and/or other vehicle status indicators. )
a vehicle sensor condition, capability, latency, or time-of-flight;
a weather condition; (See Koivisto para[0082] The aggregated detections and any associated aggregated detection data may be used for various purposes. Non-limiting examples include video surveillance, video or image editing, video or image search or retrieval, object tracking, weather forecasting (e.g., using RADAR data)
a road or traffic condition; (See Koivisto para [0311]The server(s) 1578 may receive, over the network(s) 1590 and from the vehicles, image data representative of images showing unexpected or changed road conditions, such as recently commenced road-work)
a required sensing accuracy;
a driver identity, behavior, or condition; a change of any of the above (See Koivisto para [0302] Conventional ADAS systems may be prone to false positive results which may be annoying and distracting to a driver, but typically are not catastrophic, because the ADAS systems alert the driver and allow the driver to decide whether a safety condition truly exists and act accordingly).
Regarding claim 3, Koivisto and Song remain applied as claim 1. However, Koivisto does not expressly disclose or otherwise teach wherein monitoring the forward path of travel of the vehicle for potential collisions comprises using one or more processors to process outputs from the one or more dynamically selected vehicle sensors. Nevertheless, Song same field of endeavor teaches wherein monitoring the forward path of travel of the vehicle for potential collisions comprises using one or more processors to process outputs from the one or more dynamically selected vehicle sensors (See Song para[0030] In response to such a change in the blind spot, the monitor apparatus 1 dynamically selects the sensors of the moving objects 41, 42 used for monitoring, and executes processing for monitoring the specific target in the monitor region by using the data received from the moving object having the selected sensor and/or the fixed cameras 21, 22.).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Koivisto’s AEB with Song’s dynamically selecting sensors based on one or more factors and monitoring forward path of travel in order to allow to monitor the moving target sufficiently in an environment where the blind spot dynamically changes (See Song para[0004]).
Regarding claim 4, Koivisto and Song remain applied as claim 1. Koivisto teaches wherein monitoring the forward path of travel of the vehicle for potential collisions comprises using a neural network to process outputs from the one or more dynamically selected and/or scaled vehicle sensors. (See Koivisto para [0096] The object detector 306 may comprise a deep neural network (DNN). In some examples, the DNN includes the output layer(s) 330, intermediate layers 332, and an input layer(s) 334, para[0314] The deep-learning infrastructure may run its own neural network to identify the objects and compare them with the objects identified by the vehicle 1500).
Regarding claim 5, Koivisto and Song remain applied as claim 1. Koivisto teaches wherein the neural network comprises at least one of: a fully connected layer (See Koivisto para[0085] the fully connected layers and/or average pooling layers used for image classification at the top of the network may be replaced by a convolutional layer with outputs for each spatial cell) ; a convolutional layer (See Koivisto para[0085] In the architecture, the fully connected layers and/or average pooling layers used for image classification at the top of the network may be replaced by a convolutional layer with outputs for each spatial cell ) ; a deconvolutional layer (See Koivisto para[0079] , neural networks (e.g., auto-encoders, convolutional, recurrent, perceptrons, Long/Short Term Memory (LSTM), Hopfield, Boltzmann, deep belief, deconvolutional, generative adversarial, liquid state machine, etc.), and/or other types of machine learning models.) ; or a recurrent layer (See Koivisto para[0079] neural networks (e.g., auto-encoders, convolutional, recurrent, perceptrons, Long/Short Term Memory (LSTM), Hopfield, Boltzmann, deep belief, deconvolutional, generative adversarial, liquid state machine, etc.), and/or other types of machine learning models.).
Regarding claim 6, Koivisto and Song remain applied as claim 1. . Koivisto teaches wherein the neural network comprises a single layer neural network for each of the one or more dynamically selected vehicle sensors. (See Koivisto para[0104] single dimensional or multi-dimensional convolutional recurrent units.).
Regarding claim 7, Koivisto and Song remain applied as claim 1. Koivisto teaches wherein the neural network comprises a multiple layer neural network for each of the one or more dynamically selected vehicle sensors. (See claim 8 the neural network is a multi-layer perceptron neural network.).
Regarding claim 16, Koivisto and Song remain applied as claim 1. Koivisto teaches An autonomous emergency braking (AEB) system of a vehicle, (See Koivisto para[0255] In an automatic emergency braking (AEB) system, false positive detections would cause the vehicle to automatically perform emergency braking, which is obviously undesirable. Therefore, only the most confident detections should be considered as triggers for AEB. The DLA may run a neural network for regressing the confidence value)
comprising: a plurality of vehicle sensors; (See para[0054] For example, in some embodiments, at least some of the sensors 1480 used to generate one or more portions of the sensor data may be distributed amongst multiple vehicles and/or objects in the environment and/or at least one of the sensors 1480 may be included in the vehicle 1500.)
one or more memories; one or more transceivers; (See para [0049] The one or more transceivers 225 can enable communications between the vehicle 200 and the remote operation computing system 300 and/or other devices or systems) and
one or more processors (See para [0202] The client device(s) 1420 may include one or more processors, and one or more computer-readable media) communicatively coupled to the one or more memories and the one or more transceivers (See Koivisto para[0326]The communication interface 1610 may include one or more receivers, transmitters, and/or transceivers that enable the computing device 1600 to communicate with other computing devices via an electronic communication network),
apply vehicle brakes to avoid the potential collision. (See Koivisto para [0297] AEB systems detect an impending forward collision with another vehicle or other object, and may automatically apply the brakes if the driver does not take corrective action within a specified time or distance parameter. AEB systems may use front-facing camera(s) and/or RADAR sensor(s) 1560)
However, Koivisto does not teach perform, based on one or more factors, dynamically selecting one or more vehicle sensors from the plurality of vehicle sensors, wherein the one or more factors include at least one factor that characterizes an environment in which the vehicle is operating, monitor a forward path of travel of the vehicle for potential collisions, based on data comprising an output from the one or more dynamically selected vehicle sensors; detect, based on the data comprising the output from the one or more dynamically selected vehicle sensors, a potential collision of the vehicle with an object in the forward path of travel of the vehicle. Nevertheless, Song same field of endeavor teaches the one or more processors, either alone or in combination,(See Koivisto 0202]The client device(s) 1420 may include one or more processors, and one or more computer-readable media. ) configured to:
perform, based on one or more factors, dynamically selecting one or more vehicle sensors from the plurality of vehicle sensors, wherein the one or more factors include at least one factor that characterizes an environment in which the vehicle is operating; (See Song para [0030] More specifically, this monitor region can be said to be in an environment where blind spot dynamically changes. In response to such a change in the blind spot, the monitor apparatus 1 dynamically selects the sensors of the moving objects 41, 42 used for monitoring, and executes processing for monitoring the specific target in the monitor region by using the data received from the moving object having the selected sensor and/or the fixed cameras 21, 22. )
monitor a forward path of travel of the vehicle for potential collisions, based on data comprising an output from the one or more dynamically selected vehicle sensors; (See Song monitoring a pedestrian , para[0025] monitoring the target from among the moving object, para[0030] the monitor apparatus 1 notifies the information about the monitored pedestrian to, for example, the moving objects 41, 42, so that an accident of collision of the moving objects 41, 42 with the pedestrian can be prevented.)
detect, based on the data comprising the output from the one or more dynamically selected vehicle sensors, a potential collision of the vehicle with an object in the forward path of travel of the vehicle; (See para[0052] It is also possible to detect a pedestrian by using deep learning that can simultaneously perform the calculation of features and the identification.)
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Koivisto’s AEB with Song’s dynamically selecting sensors based on one or more factors and monitoring forward path of travel in order to allow to monitor the moving target sufficiently in an environment where the blind spot dynamically changes (See Song para[0004]).
Regarding claim 19, Koivisto and Song remain applied as claim 16. Koivisto teaches wherein the one or more processors configured to detect the potential collision of the vehicle with an object in the forward path of travel of the vehicle comprises the one or more processors, either alone or in combination, configured to detect the potential collision (See Koivisto para [0096] The object detector 306 may comprise a deep neural network (DNN). In some examples, the DNN includes the output layer(s) 330, intermediate layers 332, and an input layer(s) 334, para[0314] The deep-learning infrastructure may run its own neural network to identify the objects and compare them with the objects identified by the vehicle 1500)
using a neural network to process outputs from the one or more dynamically selected vehicle sensors (See Song para[0030] In response to such a change in the blind spot, the monitor apparatus 1 dynamically selects the sensors of the moving objects 41, 42 used for monitoring, and executes processing for monitoring the specific target in the monitor region by using the data received from the moving object having the selected sensor and/or the fixed cameras 21, 22.)
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Koivisto’s AEB with Song’s dynamically selecting sensors based on one or more factors and monitoring forward path of travel in order to allow to monitor the moving target sufficiently in an environment where the blind spot dynamically changes (See Song para[0004]).
Claims 10-11 and 18 are rejected under 35 U.S.C. 103 as being unpatented over Koivisto in view of Song and US20210370937 A1 to Park (herein after “Park”).
Regarding claim 10, Koivisto and Song remain applied as claim 1. Koivisto teaches autonomously applying the vehicle brakes according to the braking profile (See Koivisto para[0224] An alternative stereo camera(s) 1568 may include a compact stereo vision sensor(s) that may include two camera lenses (one each on the left and right) and an image processing chip that may measure the distance from the vehicle to the target object and use the generated information (e.g., metadata) to activate the autonomous emergency braking and lane departure warning functions. Other types of stereo camera(s) 1568 may be used in addition to, or alternatively from, those described herein.).
However, Koivisto does not teach wherein autonomously applying vehicle brakes to avoid the potential collision comprises: determining a time to collision (TTC), calculating a braking profile that specifies when and at what strength to apply the vehicle brakes to avoid the potential collision, autonomously applying the vehicle brakes according to the braking profile. Nevertheless, Park same field of endeavor wherein autonomously applying vehicle brakes to avoid the potential collision comprises: determining a time to collision (TTC) (See Park para[0060] More specifically, the processor 30 may calculate a required deceleration based on the predicted collision time calculated by the detector 20 and determine the braking strategy of the vehicle); calculating a braking profile that specifies when (See Park para[0013] when the vehicle included in the platoon brakes at a preset second speed as the braking strategy) and at what strength to apply (See Park para[0101] For example, an equation for estimating the slope of the road using the accelerator pedal depression amount (APS opening: Accelerator Position Sensor Opening), the brake depression amount,) the vehicle brakes to avoid the potential collision (See Park para[0022] In the step of determining a braking strategy of a vehicle, a speed profile over time during braking of the vehicle may be determined as the braking strategy based on a target deceleration.).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Koivisto’s AEB with Park’s TTC and time and amount of brake applied force in order to allow to detect an obstacle positioned in a front according to a driving direction of a vehicle included in a platoon (See Abstract).
Regarding claim 11, Koivisto and Song remain applied as claim 1. However, Koivisto does not teach wherein the braking profile also specifies which of the plurality of vehicle sensors should be considered and at what time during the dynamic selection steps. Nevertheless, Song same filed of endeavor teaches wherein the braking profile also specifies which of the plurality of vehicle sensors should be considered and at what time during the dynamic selection steps (See Song para [0030] More specifically, this monitor region can be said to be in an environment where blind spot dynamically changes. In response to such a change in the blind spot, the monitor apparatus 1 dynamically selects the sensors of the moving objects 41, 42 used for monitoring, and executes processing for monitoring the specific target in the monitor region by using the data received from the moving object having the selected sensor and/or the fixed cameras 21, 22).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Koivisto’s AEB with Song’s dynamically selecting sensors based on one or more factors and monitoring forward path of travel in order to allow to monitor the moving target sufficiently in an environment where the blind spot dynamically changes (See Song para[0004]).
Regarding claim 18, Koivisto and Song remain applied as claim 16. Koivisto teaches wherein the one or more processors configured to autonomously apply vehicle brakes to avoid the potential collision (See Koivisto para [0297]AEB systems detect an impending forward collision with another vehicle or other object, and may automatically apply the brakes if the driver does not take corrective action within a specified time or distance parameter. AEB systems may use front-facing camera(s) and/or RADAR sensor(s) 1560)
comprises the one or more processors
autonomously apply the vehicle brakes according to the braking profile (See Koivisto para[0224] An alternative stereo camera(s) 1568 may include a compact stereo vision sensor(s) that may include two camera lenses (one each on the left and right) and an image processing chip that may measure the distance from the vehicle to the target object and use the generated information (e.g., metadata) to activate the autonomous emergency braking and lane departure warning functions. Other types of stereo camera(s) 1568 may be used in addition to, or alternatively from, those described herein.)
However, Koivisto does not teach wherein autonomously applying vehicle brakes to avoid the potential collision comprises: determining a time to collision (TTC), calculating a braking profile that specifies when and at what strength to apply the vehicle brakes to avoid the potential collision, autonomously applying the vehicle brakes according to the braking profile. Nevertheless, Park same field of endeavor wherein autonomously applying vehicle brakes to avoid the potential collision comprises: determining a time to collision (TTC) (See Park para[0060] More specifically, the processor 30 may calculate a required deceleration based on the predicted collision time calculated by the detector 20 and determine the braking strategy of the vehicle); calculating a braking profile that specifies when (See Park para[0013] when the vehicle included in the platoon brakes at a preset second speed as the braking strategy) and at what strength to apply (See Park para[0101] For example, an equation for estimating the slope of the road using the accelerator pedal depression amount (APS opening: Accelerator Position Sensor Opening), the brake depression amount,) the vehicle brakes to avoid the potential collision (See Park para[0022] In the step of determining a braking strategy of a vehicle, a speed profile over time during braking of the vehicle may be determined as the braking strategy based on a target deceleration.).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Koivisto’s AEB with Park’s TTC and time and amount of brake applied force in order to allow to detect an obstacle positioned in a front according to a driving direction of a vehicle included in a platoon (See Abstract).
Claims 12-15 and 20 are rejected under 35 U.S.C. 103 as being unpatented over Koivisto in view of Song and US 20220153262 A1 to Gallo et al. (herein after “Gallo”).
Regarding claim 12, Koivisto and Song remain applied as claim 1. However, Koivisto does not teach further comprising: determining that the detection of the potential collision was an AEB false positive, saving fingerprint information associated with the AEB false positive, wherein the fingerprint information associated with the AEB false positive comprises at least some of the data upon which the detection of the potential collision was based. Nevertheless, Gallo same field of endeavor teaches determining that the detection of the potential collision was an AEB false positive (See Gallo para[0057] A vehicle may need to determine a distance of these objects and whether these objects will be in close enough proximity where a potential collision may occur); and
saving fingerprint information associated with the AEB false positive (See Gallo para[0419] In at least one embodiment, touch sensors 2725 can include touch screen sensors, pressure sensors, or fingerprint sensors) , wherein the fingerprint information associated with the AEB false positive comprises at least some of the data upon which the detection of the potential collision was based
(see para[0057] A vehicle may need to determine a distance of these objects and whether these objects will be in close enough proximity where a potential collision may occur.)
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Koivisto’s AEB with Gallo’s determination of time of collision in order to allow to determine a distance of these objects and whether these objects will be in close enough proximity where a potential collision may occur (See Gallo para[0057]).
Regarding claim 13, Koivisto and Song remain applied as claim 1. However, Koivisto does not teach wherein determining that the detection of the potential collision was an AEB false positive comprises at least one of:
receiving, from a driver or occupant of the vehicle, an indication that the detection of the potential collision was an AEB false positive. Nevertheless, Gallo same field of endeavor teaches wherein determining that the detection of the potential collision was an AEB false positive comprises at least one of:
receiving, from a driver or occupant of the vehicle, an indication that the detection of the potential collision was an AEB false positive (See Gallo para[0190] In an embodiment in which an automatic emergency braking (“AEB”) system is used, false positive detections would cause vehicle to automatically perform emergency braking, which is obviously undesirable. In at least one embodiment, highly confident detections may be considered as triggers for AEB. In at least one embodiment, a confidence measure enables a system to make further decisions regarding which detections should be considered as true positive detections rather than false positive detections ).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Koivisto’s AEB with Gallo’s determination of time of collision in order to allow to determine a distance of these objects and whether these objects will be in close enough proximity where a potential collision may occur (See Gallo para[0057]).
Regarding claim 14, Koivisto and Song remain applied as claim 1. Koivisto teaches further comprising providing the fingerprint information to a network entity. (See Koivisto para[0037] The detections may be aggregated to particular objects by clustering the detections, and a confidence value may be assigned to each aggregated detection., clustering is similar as fingerprinting in data processing, para[0069] the detected object clusterer 108 may apply a clustering algorithm to detected objects provided by the object detector 106 to form a first cluster of detected objects that corresponds to the detected objects regions 250A, a second cluster of detected objects that corresponds to the detected objects regions 250B, a third cluster of detected objects that corresponds to the detected objects regions 250C, and a fourth cluster of detected objects that corresponds to the detected objects regions 250D.).
Regarding claim 15, Koivisto and Song remain applied as claim 1. However, Koivisto does not teach further comprising receiving fingerprint information from a network entity, and using the fingerprint information to detect and avoid AEB false positives. Nevertheless, Gallo same field of endeavor teaches further comprising receiving fingerprint information from a network entity, and using the fingerprint information to detect and avoid AEB false positives. (See Gallo para[0419] In at least one embodiment, data storage device 2724 can connect via a storage interface (e.g., SATA) or via a peripheral bus, such as a Peripheral Component Interconnect bus (e.g., PCI, PCI Express). In at least one embodiment, touch sensors 2725 can include touch screen sensors, pressure sensors, or fingerprint sensors).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Koivisto’s AEB with Gallo’s determination of time of collision in order to allow to determine a distance of these objects and whether these objects will be in close enough proximity where a potential collision may occur (See Gallo para[0057]).
Regarding claim 15, Koivisto and Song remain applied as claim 16. However, Koivisto does not teach wherein the one or more processors, either alone or in combination, are further configured to: determine that the detection of the potential collision was an AEB false positive, save fingerprint information associated with the AEB false positive, wherein the fingerprint information associated with the AEB false positive comprises at least some of the data upon which the detection of the potential collision was based. Nevertheless, Gallo same field of endeavor teaches wherein the one or more processors, either alone or in combination, are further configured to:
determine that the detection of the potential collision was an AEB false positive(See Gallo para[0057] A vehicle may need to determine a distance of these objects and whether these objects will be in close enough proximity where a potential collision may occur);; and
save fingerprint information associated with the AEB false positive, wherein the fingerprint information associated with the AEB false positive comprises at least some of the data upon which the detection of the potential collision was based(See Gallo para[0419] In at least one embodiment, touch sensors 2725 can include touch screen sensors, pressure sensors, or fingerprint sensors) , (see para[0057] . A vehicle may need to determine a distance of these objects and whether these objects will be in close enough proximity where a potential collision may occur.)
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Koivisto’s AEB with Gallo’s determination of time of collision in order to allow to determine a distance of these objects and whether these objects will be in close enough proximity where a potential collision may occur (See Gallo para[0057]).
Conclusion
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/NAZIA AFRIN/Examiner, Art Unit 3666
/SCOTT A BROWNE/Supervisory Patent Examiner, Art Unit 3666
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