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 .
Status of Claims
Claims 1-21 are presented for examination.
Claims 1-21 are rejected.
Claim Rejections - 35 USC § 102
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-21 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Oder et al. (US Pub. No.: 2018/0067495 A1: hereinafter “Oder”).
Consider claims 1, 3:
Oder teaches a system (See Oder, e.g., “…a computing system to implement event-driven region of interest management in an assisted or automated driving system of a vehicle. The computing system can identify a portion of an environmental model for a vehicle that corresponds to a region of interest for a driving functionality system. The computing system can detect at least one event associated with the region of interest in the environmental model, and provide data corresponding to the detected event to the driving functionality system. The driving functionality system can utilize the data corresponding to the detected event to control operation of the vehicle…”, of Abstract, ¶ [0007], ¶ [0019]-¶ [0046], ¶ [0051]-¶ [0068], ¶ [0070]-¶ [0085], ¶ [0087]-¶ [0092], and Figs. 1-5 elements 100-430, Fig. 5, 7-8 Steps 501-804, and Figs, 9-10 elements 901-913), comprising: a radar device mounted to or associated with a vehicle (See Oder, e.g., “…a Radio Detection and Ranging (Radar) device 112, a Light Detection and Ranging (Lidar) device 113, an ultra-sonic device 114, one or more microphones, infrared or night-vision cameras, time-of-flight cameras, cameras capable of detecting and transmitting differences in pixel intensity…the vehicle 200 can include multiple different sensors capable of detecting incoming signals, such as light signals, electromagnetic signals, and sound signals. Each of these different sensors can have a different field of view into an environment around the vehicle 200. These fields of view can allow the sensors to measure light and/or sound in different measurement coordinate fields…”, of Abstract, ¶ [0007], ¶ [0019]-¶ [0046], ¶ [0051]-¶ [0068], ¶ [0070]-¶ [0085], ¶ [0087]-¶ [0092], and Figs. 1-5 elements 100-430, Fig. 5, 7-8 Steps 501-804, and Figs, 9-10 elements 901-913), the radar device providing a field of view within an environment traversed by the vehicle (See Oder, e.g., “…the vehicle 200 can include multiple different sensors capable of detecting incoming signals, such as light signals, electromagnetic signals, and sound signals. Each of these different sensors can have a different field of view into an environment around the vehicle 200. These fields of view can allow the sensors to measure light and/or sound in different measurement coordinate fields…”, of Abstract, ¶ [0007], ¶ [0019]-¶ [0046], ¶ [0051]-¶ [0068], ¶ [0070]-¶ [0085], ¶ [0087]-¶ [0092], and Figs. 1-5 elements 100-430, Fig. 5, 7-8 Steps 501-804, and Figs, 9-10 elements 901-913); a localization system configured to provide location data regarding a location of the radar device and/or the vehicle (See Oder, e.g., “…The localization system 400 can selectively perform in-lane localization and map data correlation with the annotated environmental model 332 to identify the vehicle location 344, for example, based on a driving situation, processing resource utilization, or the like. The localization system 400 can output the vehicle location 344, which, in some embodiments, can be stored by the memory system 330 as another annotation to the annotated environmental model 332…”, of Abstract, ¶ [0007], ¶ [0019]-¶ [0049], ¶ [0051]-¶ [0068], ¶ [0070]-¶ [0085], ¶ [0087]-¶ [0092], and Figs. 1-5 elements 100-430, Fig. 5, 7-8 Steps 501-804, and Figs, 9-10 elements 901-913); at least one memory storing a predetermined map of the environment (See Oder, e.g., “…The localization system 400 can receive the global positioning system (GPS) information and map data 331, for example, from the memory system 330. The map data 331 can include topographical maps, terrain maps, street view maps, or the like, of an area corresponding to a location of the vehicle…”, of Abstract, ¶ [0007], ¶ [0019]-¶ [0049], ¶ [0051]-¶ [0068], ¶ [0070]-¶ [0085], ¶ [0087]-¶ [0092], and Figs. 1-5 elements 100-430, Fig. 5, 7-8 Steps 501-804, and Figs, 9-10 elements 901-913); and a processor operatively coupled to the radar device, the localization system, and the at least one memory, wherein the at least one memory further stores computer readable instructions (See Oder, e.g., “…the computing device 901 includes a computing unit 903 with a processing unit 905 and a system memory 907. The processing unit 905 may be any type of programmable electronic device for executing software instructions, but will conventionally be a microprocessor. The system memory 907 may include both a read-only memory (ROM) 909 and a random access memory (RAM) 911. As will be appreciated by those of ordinary skill in the art, both the read-only memory (ROM) 909 and the random access memory (RAM) 911 may store software instructions for execution by the processing unit 905…”, of Abstract, ¶ [0007], ¶ [0019]-¶ [0049], ¶ [0051]-¶ [0068], ¶ [0070]-¶ [0085], ¶ [0087]-¶ [0092], and Figs. 1-5 elements 100-430, Fig. 5, 7-8 Steps 501-804, and Figs, 9-10 elements 901-913) that, when executed by the process or, causes the processor to: generate a focal region within the field of view based on location data from the localization system and the predetermined map (See Oder, e.g., “…the region of interest detection unit 621 can monitor a portion of the environmental model 609 corresponding to the region of interest 603. The region of interest detection unit 621 can receive at least the portion of the environmental model 609 from a memory system or one or more components of the sensor fusion system…The region of interest management system 620 can include a dynamic region of interest adaptation unit 622 to modify a region of interest 603 based on vehicle situation information 607. The vehicle situation information 607 can describe a current situation of the vehicle…”, of Abstract, ¶ [0007], ¶ [0019]-¶ [0049], ¶ [0051]-¶ [0068], ¶ [0070]-¶ [0085], ¶ [0087]-¶ [0092], and Figs. 1-5 elements 100-430, Fig. 5, 7-8 Steps 501-804, and Figs, 9-10 elements 901-913); and determine a location of a target within in the focal region by processing data collected by the one or more radar devices representative of the focal region (See Oder, e.g., “…The sensor event detection and fusion unit 321 can analyze the raw measurement data 301 in the environmental model 315 at the locations associated with the sensor measurement events to detect one or more sensor detection events. In some embodiments, the sensor event detection and fusion unit 321 can identify a sensor detection event when the raw measurement data 301 associated with a single sensor…the sensor event detection and fusion unit 321 can analyze an image captured by a camera in the raw measurement data 301 to identify edges in the image, shapes in the image, or the like, which the sensor event detection and fusion unit 321 can utilize to identify a sensor detection event for the image…”, of Abstract, ¶ [0007], ¶ [0019]-¶ [0049], ¶ [0051]-¶ [0068], ¶ [0070]-¶ [0085], ¶ [0087]-¶ [0092], and Figs. 1-5 elements 100-430, Fig. 5, 7-8 Steps 501-804, and Figs, 9-10 elements 901-913).
Consider claim 2:
Oder teaches everything claimed as implemented above in the rejection of claim 1. In addition, Oder teaches wherein the processor executing the computer program instructions in the memory to control the system to: modify the focal region based on detection of one or more obstacles within the field of view (See Oder, e.g., “…the region of interest management system 620 can include an event-triggered focus unit 623 to identify a portion of a region of interest 603 to suggest for focused analysis and identify the area of focus in the detection information 608. In some embodiments, the subscription information 602 can include the event-driven focus indicator 606, which can allow devices, components, or applications elect whether to receive suggestions on where to focus analysis of data detected to be within the region of interest 603. By suggesting focus areas in the detection information 608, subscribing devices, components, or applications may be able to order processing of data associated with the detection information 608 and may reduce processing latency…”, of Abstract, ¶ [0007], ¶ [0019]-¶ [0049], ¶ [0051]-¶ [0068], ¶ [0070]-¶ [0085], ¶ [0087]-¶ [0092], and Figs. 1-5 elements 100-430, Fig. 5, 7-8 Steps 501-804, and Figs, 9-10 elements 901-913); and process data collected by the radar representative of the modified focal region to identify the target (See Oder, e.g., “…The sensor event detection and fusion unit 321 can analyze the raw measurement data 301 in the environmental model 315 at the locations associated with the sensor measurement events to detect one or more sensor detection events. In some embodiments, the sensor event detection and fusion unit 321 can identify a sensor detection event when the raw measurement data 301 associated with a single sensor…the sensor event detection and fusion unit 321 can analyze an image captured by a camera in the raw measurement data 301 to identify edges in the image, shapes in the image, or the like, which the sensor event detection and fusion unit 321 can utilize to identify a sensor detection event for the image…”, of Abstract, ¶ [0007], ¶ [0019]-¶ [0049], ¶ [0051]-¶ [0068], ¶ [0070]-¶ [0085], ¶ [0087]-¶ [0092], and Figs. 1-5 elements 100-430, Fig. 5, 7-8 Steps 501-804, and Figs, 9-10 elements 901-913).
Consider claim 4:
Oder teaches everything claimed as implemented above in the rejection of claim 3. In addition, Oder teaches wherein the computer readable instructions, when executed by the processor, causes the processor to determine the location of the target by: discretizing a search space based on the predetermined map representative of the focal region (See Oder, e.g., “…The region of interest management system 620 can include a dynamic region of interest adaptation unit 622 to modify a region of interest 603 based on vehicle situation information 607. The vehicle situation information 607 can describe a current situation of the vehicle…”, of Abstract, ¶ [0007], ¶ [0019]-¶ [0049], ¶ [0051]-¶ [0068], ¶ [0070]-¶ [0085], ¶ [0087]-¶ [0092], and Figs. 1-5 elements 100-430, Fig. 5, 7-8 Steps 501-804, and Figs, 9-10 elements 901-913); and prioritizing computations to areas of the search space where a probability of finding the target is greater than a predetermined threshold (See Oder, e.g., “…The region of interest management system 620 can include a dynamic region of interest adaptation unit 622 to modify a region of interest 603 based on vehicle situation information 607. The vehicle situation information 607 can describe a current situation of the vehicle…the vehicle situation information 607 can include vehicle movement information, such as global positioning system (GPS) data, inertial measurements, vehicle odometer data, video images, or the like. The vehicle situation information 607 also may include vehicle-centric information corresponding to a predicted trajectory of the vehicle, information corresponding to the vehicle location…”, of Abstract, ¶ [0007], ¶ [0019]-¶ [0049], ¶ [0051]-¶ [0068], ¶ [0070]-¶ [0085], ¶ [0087]-¶ [0092], and Figs. 1-5 elements 100-430, Fig. 5, 7-8 Steps 501-804, and Figs, 9-10 elements 901-913).
Consider claim 5:
Oder teaches everything claimed as implemented above in the rejection of claim 3. In addition, Oder teaches wherein the focal region corresponds to locations of one or more roads in the field of view of the radar device (See Oder, e.g., “…the dynamic region of interest adaptation unit 622 can determine which portions of the vehicle situation information 607 can prompt the dynamic adaption of the region of interest 603, and in a block 804, the dynamic region of interest adaptation unit 622 can modify the region of interest 603 based on the determined portion of the vehicle situation information 607…the vehicle situation information 607 may indicate that the vehicle is approaching a traffic intersection, which may prompt the dynamic adaption of the region of interest 603 to include possible cross-traffic, pedestrian crossings, bike lanes, or the like. When the vehicle approaching the traffic intersection has a path planned turn, the region of interest 603 may dynamically adapt to include portions of the roadway after the turn, along with possible cross-traffic, pedestrian crossings, bike lanes, or the like. The vehicle situation information 607 also may include locational information regarding another vehicle in an adjacent lane of traffic, which may prompt the dynamic adaption of the region of interest 603 to include the other vehicle for possible lane change…”, of Abstract, ¶ [0007], ¶ [0019]-¶ [0049], ¶ [0051]-¶ [0068], ¶ [0070]-¶ [0085], ¶ [0087]-¶ [0092], and Figs. 1-5 elements 100-430, Fig. 5, 7-8 Steps 501-804, and Figs, 9-10 elements 901-913).
Consider claim 6:
Oder teaches everything claimed as implemented above in the rejection of claim 3. In addition, Oder teaches wherein the computer readable instructions, when executed by the processor, causes the processor to determine the location of the target by computing a range-angle-doppler (RAD) tensor only for regions with a non-zero probability of finding the target (See Oder, e.g., “…The radar device 112 can emit radio signals into the environment surrounding the vehicle. Since the emitted radio signals may reflect off of objects in the environment, the radar device 112 can detect the reflected radio signals incoming from the environment. The radar device 112 can measure the incoming radio signals by, for example, measuring a signal strength of the radio signals, a reception angle, a frequency, or the like. The radar device 112 also can measure a time delay between an emission of a radio signal and a measurement of the incoming radio signals from the environment that corresponds to emitted radio signals reflected off of objects in the environment. The radar device 112 can output the measurements of the incoming radio signals as the raw measurement data 115…”, of Abstract, ¶ [0007], ¶ [0019]-¶ [0049], ¶ [0051]-¶ [0068], ¶ [0070]-¶ [0085], ¶ [0087]-¶ [0092], and Figs. 1-5 elements 100-430, Fig. 5, 7-8 Steps 501-804, and Figs, 9-10 elements 901-913).
Consider claim 7:
Oder teaches everything claimed as implemented above in the rejection of claim 3. In addition, Oder teaches wherein the computer readable instructions, when executed by the processor, causes the processor to determine the location of the target by computing a range-angle-doppler (RAD) tensor for regions where the predetermined map indicates that targets could be found (See Oder, e.g., “…The radar device 112 can emit radio signals into the environment surrounding the vehicle. Since the emitted radio signals may reflect off of objects in the environment, the radar device 112 can detect the reflected radio signals incoming from the environment. The radar device 112 can measure the incoming radio signals by, for example, measuring a signal strength of the radio signals, a reception angle, a frequency, or the like. The radar device 112 also can measure a time delay between an emission of a radio signal and a measurement of the incoming radio signals from the environment that corresponds to emitted radio signals reflected off of objects in the environment. The radar device 112 can output the measurements of the incoming radio signals as the raw measurement data 115…”, of Abstract, ¶ [0007], ¶ [0019]-¶ [0049], ¶ [0051]-¶ [0068], ¶ [0070]-¶ [0085], ¶ [0087]-¶ [0092], and Figs. 1-5 elements 100-430, Fig. 5, 7-8 Steps 501-804, and Figs, 9-10 elements 901-913).
Consider claim 8:
Oder teaches everything claimed as implemented above in the rejection of claim 3. In addition, Oder teaches wherein the computer readable instructions, when executed by the processor, causes the processor to determine the location of the target by collecting data from the one or more radar devices for regions where differences of time of arrival match areas of interest based on the predetermined map (See Oder, e.g., “…dynamic region of interest adaptation unit 622 to modify a region of interest 603 based on vehicle situation information 607. The vehicle situation information 607 can describe a current situation of the vehicle…the vehicle situation information 607 can include vehicle movement information, such as global positioning system (GPS) data, inertial measurements, vehicle odometer data, video images, or the like. The vehicle situation information 607 also may include vehicle-centric information corresponding to a predicted trajectory of the vehicle, information corresponding to the vehicle location…”, of Abstract, ¶ [0007], ¶ [0019]-¶ [0049], ¶ [0051]-¶ [0068], ¶ [0070]-¶ [0085], ¶ [0087]-¶ [0092], and Figs. 1-5 elements 100-430, Fig. 5, 7-8 Steps 501-804, and Figs, 9-10 elements 901-913).
Consider claim 9:
Oder teaches everything claimed as implemented above in the rejection of claim 3. In addition, Oder teaches wherein the computer readable instructions, when executed by the processor, causes the processor to determine the location of the target by processing parts of the field of view that having incoming targets (See Oder, e.g., “…dynamic region of interest adaptation unit 622 to modify a region of interest 603 based on vehicle situation information 607. The vehicle situation information 607 can describe a current situation of the vehicle…the vehicle situation information 607 can include vehicle movement information, such as global positioning system (GPS) data, inertial measurements, vehicle odometer data, video images, or the like. The vehicle situation information 607 also may include vehicle-centric information corresponding to a predicted trajectory of the vehicle, information corresponding to the vehicle location…”, of Abstract, ¶ [0007], ¶ [0019]-¶ [0049], ¶ [0051]-¶ [0068], ¶ [0070]-¶ [0085], ¶ [0087]-¶ [0092], and Figs. 1-5 elements 100-430, Fig. 5, 7-8 Steps 501-804, and Figs, 9-10 elements 901-913).
Consider claim 10:
Oder teaches everything claimed as implemented above in the rejection of claim 3. In addition, Oder teaches wherein the computer readable instructions, when executed by the processor, causes the processor to determine the location of the target by discretizing the field of view based on the predetermined map and classes therein (See Oder, e.g., “…The sensor event detection and fusion unit 321 can analyze the raw measurement data 301 in the environmental model 315 at the locations associated with the sensor measurement events to detect one or more sensor detection events. In some embodiments, the sensor event detection and fusion unit 321 can identify a sensor detection event when the raw measurement data 301 associated with a single sensor…the sensor event detection and fusion unit 321 can analyze an image captured by a camera in the raw measurement data 301 to identify edges in the image, shapes in the image, or the like, which the sensor event detection and fusion unit 321 can utilize to identify a sensor detection event for the image…”, of Abstract, ¶ [0007], ¶ [0019]-¶ [0049], ¶ [0051]-¶ [0068], ¶ [0070]-¶ [0085], ¶ [0087]-¶ [0092], and Figs. 1-5 elements 100-430, Fig. 5, 7-8 Steps 501-804, and Figs, 9-10 elements 901-913).
Consider claim 11:
Oder teaches everything claimed as implemented above in the rejection of claim 3. In addition, Oder teaches wherein the predetermined map provides one or more two-dimensional (2D) regions, one or more three-dimensional (3D) regions, or combinations thereof (See Oder, e.g., “…the vehicle situation information 607 can include vehicle movement information, such as global positioning system (GPS) data, inertial measurements, vehicle odometer data, video images, or the like. The vehicle situation information 607 also may include vehicle-centric information corresponding to a predicted trajectory of the vehicle, information corresponding to the vehicle location, for example, relative to map data, information corresponding to a route or planned path for the vehicle to traverse, or the like…”, of Abstract, ¶ [0007], ¶ [0019]-¶ [0049], ¶ [0051]-¶ [0068], ¶ [0070]-¶ [0085], ¶ [0087]-¶ [0092], and Figs. 1-5 elements 100-430, Fig. 5, 7-8 Steps 501-804, and Figs, 9-10 elements 901-913).
Consider claim 12:
Oder teaches everything claimed as implemented above in the rejection of claim 3. In addition, Oder teaches wherein the predetermined map defines a probability of finding the target (See Oder, e.g., “…identify the focus area in the region of interest based on data populated in the environmental model 609, the vehicle situation information 607, map data, a combination thereof, or the like. For example, when the environmental model 609 includes a sensor event having a shape indicative of traffic signage or signaling device, the event-triggered focus unit 623 can generate the focus area to at least partially encapsulate the shape corresponding to the sensor event. The event-triggered focus unit 623 also can analyze the vehicle situation information 607 or associated map data to identify locations in an environment around the vehicle that may include the traffic signage or signaling devices…”, of Abstract, ¶ [0007], ¶ [0019]-¶ [0049], ¶ [0051]-¶ [0068], ¶ [0070]-¶ [0085], ¶ [0087]-¶ [0092], and Figs. 1-5 elements 100-430, Fig. 5, 7-8 Steps 501-804, and Figs, 9-10 elements 901-913).
Consider claim 13:
Oder teaches everything claimed as implemented above in the rejection of claim 12. In addition, Oder teaches wherein the computer readable instructions, when executed by the processor, causes the processor to determine the location of the target by discretizing the field of view based on the probability (See Oder, e.g., “…The sensor event detection and fusion unit 321 can analyze the raw measurement data 301 in the environmental model 315 at the locations associated with the sensor measurement events to detect one or more sensor detection events. In some embodiments, the sensor event detection and fusion unit 321 can identify a sensor detection event when the raw measurement data 301 associated with a single sensor…the sensor event detection and fusion unit 321 can analyze an image captured by a camera in the raw measurement data 301 to identify edges in the image, shapes in the image, or the like, which the sensor event detection and fusion unit 321 can utilize to identify a sensor detection event for the image…”, of Abstract, ¶ [0007], ¶ [0019]-¶ [0049], ¶ [0051]-¶ [0068], ¶ [0070]-¶ [0085], ¶ [0087]-¶ [0092], and Figs. 1-5 elements 100-430, Fig. 5, 7-8 Steps 501-804, and Figs, 9-10 elements 901-913).
Consider claim 14:
Oder teaches everything claimed as implemented above in the rejection of claim 3. In addition, Oder teaches wherein the computer readable instructions, when executed by the processor, causes the processor to determine the location of the target by multi-resolution data collection, multi-resolution discretization of the focal region, or any combination thereof (See Oder, e.g., “…the tracking unit 323 can track the sensor detection event or the fused sensor detection event utilizing at least one state change prediction model, such as a kinetic model, a probabilistic model, or other state change prediction model. The tracking unit 323 can select the state change prediction model to utilize to track the sensor detection event or the fused sensor detection event based on the assigned pre-classification of the sensor detection event or the fused sensor detection event by the pre-classification unit 322. The state change prediction model may allow the tracking unit 323 to implement a state transition prediction, which can assume or predict future states of the sensor detection event or the fused sensor detection event…When the tracking unit 323, based on the tracking of the sensor detection event or the fused sensor detection event with the state change prediction model, determines the sensor detection event or the fused sensor detection event is an object, the object tracking unit 323 can annotate the environmental model 315 to indicate the presence of the object…”, of Abstract, ¶ [0007], ¶ [0019]-¶ [0049], ¶ [0051]-¶ [0068], ¶ [0070]-¶ [0085], ¶ [0087]-¶ [0092], and Figs. 1-5 elements 100-430, Fig. 5, 7-8 Steps 501-804, and Figs, 9-10 elements 901-913).
Consider claim 15:
Oder teaches everything claimed as implemented above in the rejection of claim 3. In addition, Oder teaches wherein the computer readable instructions, when executed by the processor, causes the processor to determine the location of the target by irregular discretization quanta that matches information of the predetermined map (See Oder, e.g., “…The sensor event detection and fusion unit 321 can analyze the raw measurement data 301 in the environmental model 315 at the locations associated with the sensor measurement events to detect one or more sensor detection events. In some embodiments, the sensor event detection and fusion unit 321 can identify a sensor detection event when the raw measurement data 301 associated with a single sensor…the sensor event detection and fusion unit 321 can analyze an image captured by a camera in the raw measurement data 301 to identify edges in the image, shapes in the image, or the like, which the sensor event detection and fusion unit 321 can utilize to identify a sensor detection event for the image…”, of Abstract, ¶ [0007], ¶ [0019]-¶ [0049], ¶ [0051]-¶ [0068], ¶ [0070]-¶ [0085], ¶ [0087]-¶ [0092], and Figs. 1-5 elements 100-430, Fig. 5, 7-8 Steps 501-804, and Figs, 9-10 elements 901-913).
Consider claim 16:
Oder teaches everything claimed as implemented above in the rejection of claim 3. In addition, Oder teaches wherein the predetermined map comprises information representative of a probability density function of the target being at the location (See Oder, e.g., “…The sensor event detection and fusion unit 321 can analyze the raw measurement data 301 in the environmental model 315 at the locations associated with the sensor measurement events to detect one or more sensor detection events. In some embodiments, the sensor event detection and fusion unit 321 can identify a sensor detection event when the raw measurement data 301 associated with a single sensor…the sensor event detection and fusion unit 321 can analyze an image captured by a camera in the raw measurement data 301 to identify edges in the image, shapes in the image, or the like, which the sensor event detection and fusion unit 321 can utilize to identify a sensor detection event for the image…”, of Abstract, ¶ [0007], ¶ [0019]-¶ [0049], ¶ [0051]-¶ [0068], ¶ [0070]-¶ [0085], ¶ [0087]-¶ [0092], and Figs. 1-5 elements 100-430, Fig. 5, 7-8 Steps 501-804, and Figs, 9-10 elements 901-913).
Consider claim 17:
Oder teaches everything claimed as implemented above in the rejection of claim 3. In addition, Oder teaches wherein the target is a moving target (See Oder, e.g., “…The object trajectory prediction unit 341 can utilize the annotated environmental model 332 along with the state change prediction model corresponding to the tracked object to predict movement of the tracked object relative to the vehicle in the future. Since a tracked object may have a multitude of options for moving in the future…the object trajectory prediction unit 341 can generate a range of expected trajectories along with probabilities associated with the expected trajectories in the range. The object trajectory prediction unit 341 can annotate the environmental model 315 with the projected object trajectory 343, for example, by storing the projected object trajectory 343 in the annotated environmental model 332 residing in the memory system 330…”, of Abstract, ¶ [0007], ¶ [0019]-¶ [0049], ¶ [0051]-¶ [0068], ¶ [0070]-¶ [0085], ¶ [0087]-¶ [0092], and Figs. 1-5 elements 100-430, Fig. 5, 7-8 Steps 501-804, and Figs, 9-10 elements 901-913).
Consider claim 18:
Oder teaches everything claimed as implemented above in the rejection of claim 3. In addition, Oder teaches wherein the radar device comprises a plurality of antennas (See Oder, e.g., “…The radar device 112 can emit radio signals into the environment surrounding the vehicle. Since the emitted radio signals may reflect off of objects in the environment, the radar device 112 can detect the reflected radio signals incoming from the environment. The radar device 112 can measure the incoming radio signals by, for example, measuring a signal strength of the radio signals, a reception angle, a frequency, or the like. The radar device 112 also can measure a time delay between an emission of a radio signal and a measurement of the incoming radio signals from the environment that corresponds to emitted radio signals reflected off of objects in the environment. The radar device 112 can output the measurements of the incoming radio signals as the raw measurement data 115…”, of Abstract, ¶ [0007], ¶ [0019]-¶ [0049], ¶ [0051]-¶ [0068], ¶ [0070]-¶ [0085], ¶ [0087]-¶ [0092], and Figs. 1-5 elements 100-430, Fig. 5, 7-8 Steps 501-804, and Figs, 9-10 elements 901-913).
Consider claim 19:
Oder teaches everything claimed as implemented above in the rejection of claim 18. In addition, Oder teaches wherein the radar device comprises a chirp generator operatively coupled to drive each antenna (See Oder, e.g., “…The radar device 112 can emit radio signals into the environment surrounding the vehicle. Since the emitted radio signals may reflect off of objects in the environment, the radar device 112 can detect the reflected radio signals incoming from the environment. The radar device 112 can measure the incoming radio signals by, for example, measuring a signal strength of the radio signals, a reception angle, a frequency, or the like. The radar device 112 also can measure a time delay between an emission of a radio signal and a measurement of the incoming radio signals from the environment that corresponds to emitted radio signals reflected off of objects in the environment. The radar device 112 can output the measurements of the incoming radio signals as the raw measurement data 115…”, of Abstract, ¶ [0007], ¶ [0019]-¶ [0049], ¶ [0051]-¶ [0068], ¶ [0070]-¶ [0085], ¶ [0087]-¶ [0092], and Figs. 1-5 elements 100-430, Fig. 5, 7-8 Steps 501-804, and Figs, 9-10 elements 901-913).
Consider claim 20:
Oder teaches everything claimed as implemented above in the rejection of claim 19. In addition, Oder teaches wherein each antenna is configured to emit a chirp pulse signal and subsequently detect a resulting pulse reflected from objects in the environment (See Oder, e.g., “…The radar device 112 can emit radio signals into the environment surrounding the vehicle. Since the emitted radio signals may reflect off of objects in the environment, the radar device 112 can detect the reflected radio signals incoming from the environment. The radar device 112 can measure the incoming radio signals by, for example, measuring a signal strength of the radio signals, a reception angle, a frequency, or the like. The radar device 112 also can measure a time delay between an emission of a radio signal and a measurement of the incoming radio signals from the environment that corresponds to emitted radio signals reflected off of objects in the environment. The radar device 112 can output the measurements of the incoming radio signals as the raw measurement data 115…”, of Abstract, ¶ [0007], ¶ [0019]-¶ [0049], ¶ [0051]-¶ [0068], ¶ [0070]-¶ [0085], ¶ [0087]-¶ [0092], and Figs. 1-5 elements 100-430, Fig. 5, 7-8 Steps 501-804, and Figs, 9-10 elements 901-913).
Consider claim 21:
Oder teaches everything claimed as implemented above in the rejection of claim 3. In addition, Oder teaches wherein the localization system comprises a global navigation satellite system (GNSS), an inertial measurement unit, an odometry system or any combination thereof (See Oder, e.g., “…The ego motion unit 313 can estimate motion of the sensor capturing the raw measurement data 301, for example, by utilizing tracking functionality to analyze vehicle motion information, such as global positioning system (GPS) data, inertial measurements, vehicle odometer data, video images, or the like. The tracking functionality can implement a Kalman filter, a Particle filter, optical flow-based estimator, or the like, to track motion of the vehicle and its corresponding sensors relative to the environment surrounding the vehicle…”, of Abstract, ¶ [0007], ¶ [0019]-¶ [0049], ¶ [0051]-¶ [0068], ¶ [0070]-¶ [0085], ¶ [0087]-¶ [0092], and Figs. 1-5 elements 100-430, Fig. 5, 7-8 Steps 501-804, and Figs, 9-10 elements 901-913).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Cattle et al. (US Pub. No.: 2020/0158861 A1) teaches “In some examples, a first plurality of independent waveforms can be generated and converted into a first plurality of independent transmitted radar signals transmitted towards a field of view using a transmitter array comprising a first plurality of transmitter antennas. Further, a second plurality of receive radar signals to the first plurality of independent transmitted radar signals can be received from the field of view using a receiver array comprising a second plurality of receiver antennas. The second plurality of receive radar signals can be combined to form a combined receive radar signal and a representation of one or more areas of interest in the field of view can be provided using the combined receive radar signal. One or more attributes of the one or more areas of interest can be rendered using the representation of the one or more areas of interest.”
Chaves et al. (US Pub. No.: 2020/0202706 A1) teaches “controlling a vehicle that may include receiving an intention message including a motion plan for a vehicle transmitting the motion plan (the “transmitting vehicle”), wherein the motion plan comprises a trajectory of the transmitting vehicle and one or more vehicle descriptors associated with the transmitting vehicle, parsing the intention message to identify the motion plan for the transmitting vehicle, and controlling the vehicle based at least in part on the motion plan. In some aspects, the one or more vehicle descriptors may include a sensor perceptible attribute.”
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/BABAR SARWAR/Primary Examiner, Art Unit 3667
