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 .
This Office Action is in response to Applicant Amendment and Arguments filed on 3/8/2026.
Claim(s) 1, 3-6, 8-13, 15-18, and 20-23 is pending for examination.
Claim(s) 2, 14, and 19 are canceled.
This Action is made FINAL.
Previous Claim Rejections - 35 USC § 112
Claim(s) 2, 7, 11, and 13-20 were previously rejected under 35 U.S.C. 112(b). In response to Applicant's amendment, the 35 U.S.C. 112(b) rejection(s) of claim(s) 2, 7, and 13-20 have been withdrawn.
Previous Claim Rejections - 35 USC § 101
Claim(s) 1 - 20 were previously rejected under 35 U.S.C. 101. In response to Applicant's amendment, the 35 U.S.C. 101 rejection(s) of claim(s) 1 - 20 have been withdrawn.
Response to Arguments
With regards to claim(s) 1-9 and 11-20 previously rejected under 35 U.S.C. 102 and claim(s) 10 previously rejected under 35 U.S.C. 103, have been fully considered, but are deemed moot in view of new grounds of rejection necessitated by Applicant's amendment.
Claim Objections
Claim 23 objected to because of the following informalities: The phrase “stopping in front of another road element selected of a pedestrian or a motorbike” does not make grammatical sense. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim(s) 11 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 11 recites the limitation "the detected object the detected road element”. There is insufficient antecedent basis for this limitation in the claim, and is grammatically incorrect. For the purpose of continued the examination the detected object will be interpreted as the detected road element.
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 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.
Claim(s) 1, 3-6, 8-9, 11-13, 15-18, and 20-21 is rejected under 35 U.S.C. 103 as being unpatentable over Narang et al. (US 20220234610 A1, hereinafter known as Narang) in view of Creusot (US 20200081448 A1).
Narang was cited in a previous office action
Regarding claim 1, Narang teaches A method for contextual attribute-based perception, the method comprises: obtaining, by a processing circuit, contextual attributes generated at a machine learning process in association with a detected road element;
{Para [0062-0063] “The contexts are preferably assigned to one or more particular regions in the map (e.g., hard-coded into the map, soft-coded into the map, etc.), such that a particular context relevant to the agent (e.g., context in which agent is located, context in which the agent is about to be located, context that agent has departed, etc.) can be determined (e.g., with one or more inputs received in S210 such as pose information of the autonomous agent) in S220.
The contexts are preferably assigned to locations and/or regions within the map. Each location and/or region in the map can be assigned any or all of: a single context; multiple contexts (e.g., indicating an intersection of multiple routes, wherein a single context is selected based on additional information such as any or all of the inputs received in S210, etc.); no context (e.g., indicating a location and/or region not on a fixed route option for the autonomous agent); and/or any combination of contexts. The particular context(s) assigned to the location and/or region are preferably determined based on the static environment at that location and/or within that region, such as any or all of: features of the roadway within that region (e.g., number of lanes, highway vs. residential road, one-way vs. two-way, dirt and/or gravel vs. asphalt, curvature, shoulder vs. no shoulder, etc.); landmarks and/or features within that region (e.g., parking lot, roundabout, etc.); a type of zone associated with that location and/or region (e.g., school zone, construction zone, hospital zone, residential zone, etc.); a type of dynamic objects encountered at the location and/or region (e.g., pedestrians, bicycles, vehicles, animals, etc.); traffic parameters associated with that location and/or region (e.g., speed limit, traffic sign types, height limits for semi trucks, etc.); and/or any other environmental information.”
Para [0094] “A context refers to a high level driving environment of the agent, which can inform and restrict the vehicle's decision at any given time and/or range of times. The context can include and/or define and/or be determined based on any or all of: a region type of the vehicle (e.g., residential, non-residential, highway, school, commercial, parking lot, etc.); a lane feature and/or other infrastructure feature of the road the vehicle is traversing (e.g., number of lanes, one-way road, two-way road, intersection, two-way stop and/or intersection, three-way stop and/or intersection, four-way stop and/or intersection, lanes in a roundabout, etc.); a proximity to one or more static objects and/or environmental features (e.g., particular building, body of water, railroad track, parking lot, shoulder, region in which the agent can pull over/pull off to the side of a road, etc.); a proximity a parameter associated with the location (e.g., speed limit, speed limit above a predetermined threshold, speed limit below a predetermined threshold, etc.); road markings (e.g., yellow lane, white lane, dotted lane line, solid lane line, etc.); and/or any other suitable information.”
Where the individual attributes/features can be considered contextual attributes and the “context” can be considered a group of contextual attributes
All this happens during a process that includes machine learning as shown in fig. 2 and thus can be considered happening at a machine learning process
Para [0076-0077] “The set of inputs received in S210 preferably includes sensor information collected at a sensor subsystem of the autonomous agent, such as any or all of: a sensor system onboard the autonomous agent, a sensor system remote from the autonomous agent, and/or a sensor system in communication with the autonomous agent and/or a computing system (e.g., onboard computing system, remote computing system, etc.) of the autonomous agent. Additionally or alternatively, the sensor information can be collected from any other suitable sensor(s) and/or combination of sensors, S210 can be performed in absence of collecting sensor inputs, and/or S210 can be performed in any other suitable way(s).
The sensor information preferably includes location information associated with the autonomous agent, such as any or all of: position, orientation (e.g., heading angle), pose, geographical location (e.g., using global positioning system [GPS] coordinates, using other coordinates, etc.), location within a map, and/or any other suitable location information. In preferred variations, for instance, S210 includes receiving pose information from a localization module of the sensor subsystem, wherein the localization module includes any or all of: GPS sensors, IMUs, LIDAR sensors, cameras, and/or any other sensors (e.g., as described above). Additionally or alternatively, any other sensor information can be received from any suitable sensors.”
}
identifying a selected group of contextual attributes in accordance with one or more criteria; and
{Para [0081] “The set of inputs received in S210 further preferably includes the map and/or any information determined from (e.g., determined based on, derived from, included in, etc.) the map, such as any or all of the information described above in S205. In some variations, this includes one or more contexts (and/or transition zones) selected based on (e.g., predetermined/assigned to) a region/location of the autonomous agent (e.g., as determined based on sensor information as described above). In additional or alternative variations, the map information includes any or all of: road infrastructure information and/or other static environment information, route information, and/or any other suitable information.”
Para [0086] “In a first set of variations (e.g., as shown in FIG. 12), S210 includes receiving a map specifying a set of assigned contexts for an agent; optionally a route (e.g., fixed route) of the agent; and sensor information from a set of sensors onboard the autonomous agent, wherein the sensor information includes at least a pose of the autonomous agent, wherein the pose and optionally the route are used to select a context for the agent based on the map. Additionally or alternatively, S210 can include receiving any other suitable inputs.”
The criteria is the location of the vehicle and the context is selected based of that.
}
making, by the processing circuit, a determination with respect the detected road element, based on the selected group of contextual attributes;
{Para [0055] “As shown in FIG. 2, the method 200 includes receiving a set of inputs S210 and determining a context associated with an autonomous agent based on the set of inputs S220. Additionally or alternatively, the method 200 can include any or all of: labeling a map S205; selecting a learning module (context-specific learning module) based on the context S230; defining an action space based on the learning module S240; selecting an action from the action space S250; planning a trajectory based on the action S260; and/or any other suitable processes.”
}
And autonomously driving the vehicle, based on the determination, under a control of an autonomous driving unit.
{Para [0055] “As shown in FIG. 2, the method 200 includes receiving a set of inputs S210 and determining a context associated with an autonomous agent based on the set of inputs S220. Additionally or alternatively, the method 200 can include any or all of: labeling a map S205; selecting a learning module (context-specific learning module) based on the context S230; defining an action space based on the learning module S240; selecting an action from the action space S250; planning a trajectory based on the action S260; and/or any other suitable processes.”
Para [0137] “S260 can optionally additionally or alternatively include any or all of: validating the trajectory, implementing a fallback mechanism, operating the vehicle according to a trajectory, determining control commands with which to operate the vehicle based on a trajectory, and/or any other suitable output.”
}
Narang does not teach, wherein the detected road element appears in a sensed information unit, wherein the selected group of contextual attributes comprises an occlusion indicator indicative of whether the detected road elements is partially occluded by another road element in the sensed information unit;
However, Creusot teaches wherein the detected road element appears in a sensed information unit, wherein the selected group of contextual attributes comprises an occlusion indicator indicative of whether the detected road elements is partially occluded by another road element in the sensed information unit;
{Para [0010-0011] “In object perception system can additionally receive the region of interest capturing the traffic light, which may include the occlusion polygon defined around a foreground object. The object perception system likewise comprises an occlusion reasoning module configured to track, store, and predict the position of a traffic light based upon positive traffic light detections. Thus, when the foreground object/occlusion polygon occupies the same spatial position in the region of interest as the traffic light, the occlusion reasoning module can determine that the traffic light is occluded. Full occlusion occurs when the foreground object completely covers the detected location of the traffic light, whereas partial occlusion occurs when the foreground object blocks only a portion of the detected traffic light. The occlusion reasoning module may also detect and exclude emitted bursts of light from traffic light configuration determinations. For example, an object detector may detect an extraneous burst of yellow light, but the occlusion reasoning module may confirm that the light is still green and exclude the detected burst of yellow light.
Accordingly, the occlusion reasoning module can determine the configuration of the traffic light by sampling range images at a particular sampling rate and excluding samples where the traffic light was occluded or otherwise obscured to generate a directive based upon only non-occluded/non-obscured samples. In some instances, the occlusion reasoning module may also determine the configuration of a detected traffic light from partially occluded range image samples. For example, if the position of a green light is occluded but a red light is detected and not occluded, the occlusion reasoning module may include the sample in a traffic light configuration determination regarding red light directives. Alternatively, if the red light is no longer illuminated, the occlusion reasoning module could make a configuration determination regarding an occluded bulb that is illuminated (e.g., reasoning that an occluded green light is illuminated when non-occluded red and yellow lights are not illuminated).”
}
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Narang to incorporate the teachings of Creusot to Occlusion context to make autonomous diving decisions because can prevent erroneous detection of a traffic situation Creusot para [0003-0004] and para [0006]
Regarding claim 3, Narang in view of Creusot teaches The method according to claim 1.
Creusot further teaches wherein the occlusion indicator is indicative of an amount of occlusion of the detected road elements by the other road element in the sensed information unit.
{Para [0037] “The occlusion reasoning module 306 confirms or rejects the detected configuration of the traffic light by the object detector 302 based upon whether the range image includes an occlusion that is fully or partially blocking the traffic light from sensor signals emitted by the sensor systems 102-104. If the configuration of the traffic light cannot be confirmed in a particular range image, such as when an emitted burst of light obscures object detection, the range image may be excluded by the occlusion reasoning module 306 from traffic light configuration determinations.”
}
Regarding claim 4, Narang in view of Creusot teaches The method according to claim 1. Narang further teaches comprising evaluating interdependencies between at least a portion of the contextual attributes, and making the determination based in the evaluation.
{Para [0070] “The map can optionally include (e.g., assign, prescribe, etc.) one or more transition zones which are arranged between different contexts, and can indicate, for instance, a change in context (e.g., along a fixed route, along a dynamically determined route, etc.), thereby enabling a switching of contexts to occur smoothly (e.g., by defining an action space). Assigning transition zones can function, for instance, to define an action space subsequently in the method which smoothly transitions the vehicle from one context to the next (e.g., preventing the availability of certain actions, prescribing that the agent maintain his or her lane, preventing a turn, etc.). The transition zones can be any or all of: overlapping with (e.g., partially overlapping with, fully overlapping with, etc.) one or more contexts; non-overlapping with one or more contexts; and/or any combination of overlapping and non-overlapping. Additionally or alternatively, the transition zones can be contexts themselves; the method can be performed in absence of labeled transition zones (e.g., by anticipating the subsequent context); and/or be otherwise performed.”
}
Regarding claim 5, Narang in view of Creusot teaches The method according to claim 1. Narang further teaches where the making of the determination comprising identifying the selected group of the contextual attributes in accordance with an indication that corresponds to the autonomous driving application.
{Abstract “A system for context-aware decision making of an autonomous agent includes a computing system having a context selector and a map. A method for context-aware decision making of an autonomous agent includes receiving a set of inputs, determining a context associated with an autonomous agent based on the set of inputs, and optionally any or all of: labeling a map; selecting a learning module (context-specific learning module) based on the context; defining an action space based on the learning module; selecting an action from the action space; planning a trajectory based on the action S260; and/or any other suitable processes.”
Para [0081] “The set of inputs received in S210 further preferably includes the map and/or any information determined from (e.g., determined based on, derived from, included in, etc.) the map, such as any or all of the information described above in S205. In some variations, this includes one or more contexts (and/or transition zones) selected based on (e.g., predetermined/assigned to) a region/location of the autonomous agent (e.g., as determined based on sensor information as described above). In additional or alternative variations, the map information includes any or all of: road infrastructure information and/or other static environment information, route information, and/or any other suitable information.”
Para [0086] “In a first set of variations (e.g., as shown in FIG. 12), S210 includes receiving a map specifying a set of assigned contexts for an agent; optionally a route (e.g., fixed route) of the agent; and sensor information from a set of sensors onboard the autonomous agent, wherein the sensor information includes at least a pose of the autonomous agent, wherein the pose and optionally the route are used to select a context for the agent based on the map. Additionally or alternatively, S210 can include receiving any other suitable inputs.”
}
Regarding claim 6, Narang in view of Creusot teaches The method according to claim 1. Narang further teaches comprising evaluating the detected road element with respect to the autonomous driving application, and making the determination in accordance with the evaluation.
{ Abstract “A system for context-aware decision making of an autonomous agent includes a computing system having a context selector and a map. A method for context-aware decision making of an autonomous agent includes receiving a set of inputs, determining a context associated with an autonomous agent based on the set of inputs, and optionally any or all of: labeling a map; selecting a learning module (context-specific learning module) based on the context; defining an action space based on the learning module; selecting an action from the action space; planning a trajectory based on the action S260; and/or any other suitable processes.”
Para [0121] “The method 200 can include defining an action space based on the learning module S240, which functions to define a set of actions (equivalently referred to herein as behaviors) available to the agent in light of the vehicle's context and/or environment. Additionally or alternatively, S240 can function to minimize a number of available actions to the agent as informed by the context, which functions to simplify the process (e.g., reduce the time, prevent selection of an incompatible action, etc.) required to select an action for the vehicle. The method 200 can optionally additionally or alternatively include selecting an action from the action space S250, which functions to determine a next behavior (e.g., switching and/or transitioning to a different behavior than current behavior, maintaining a current behavior, etc.) of the vehicle.”
}
Regarding claim 8, Narang in view of Creusot teaches The method according to claim 1. Narang further teaches wherein the contextual attributes comprises behavioral attributes.
{Para [0063] “The contexts are preferably assigned to locations and/or regions within the map. Each location and/or region in the map can be assigned any or all of: a single context; multiple contexts (e.g., indicating an intersection of multiple routes, wherein a single context is selected based on additional information such as any or all of the inputs received in S210, etc.); no context (e.g., indicating a location and/or region not on a fixed route option for the autonomous agent); and/or any combination of contexts. The particular context(s) assigned to the location and/or region are preferably determined based on the static environment at that location and/or within that region, such as any or all of: features of the roadway within that region (e.g., number of lanes, highway vs. residential road, one-way vs. two-way, dirt and/or gravel vs. asphalt, curvature, shoulder vs. no shoulder, etc.); landmarks and/or features within that region (e.g., parking lot, roundabout, etc.); a type of zone associated with that location and/or region (e.g., school zone, construction zone, hospital zone, residential zone, etc.); a type of dynamic objects encountered at the location and/or region (e.g., pedestrians, bicycles, vehicles, animals, etc.); traffic parameters associated with that location and/or region (e.g., speed limit, traffic sign types, height limits for semi trucks, etc.); and/or any other environmental information.”
Para [0094] “A context refers to a high level driving environment of the agent, which can inform and restrict the vehicle's decision at any given time and/or range of times. The context can include and/or define and/or be determined based on any or all of: a region type of the vehicle (e.g., residential, non-residential, highway, school, commercial, parking lot, etc.); a lane feature and/or other infrastructure feature of the road the vehicle is traversing (e.g., number of lanes, one-way road, two-way road, intersection, two-way stop and/or intersection, three-way stop and/or intersection, four-way stop and/or intersection, lanes in a roundabout, etc.); a proximity to one or more static objects and/or environmental features (e.g., particular building, body of water, railroad track, parking lot, shoulder, region in which the agent can pull over/pull off to the side of a road, etc.); a proximity a parameter associated with the location (e.g., speed limit, speed limit above a predetermined threshold, speed limit below a predetermined threshold, etc.); road markings (e.g., yellow lane, white lane, dotted lane line, solid lane line, etc.); and/or any other suitable information.”
}
Regarding claim 9 Narang in view of Creusot teaches The method according to claim 1. Narang further teaches wherein the contextual attributes comprise spatial attributes.
{Para [0094] “A context refers to a high level driving environment of the agent, which can inform and restrict the vehicle's decision at any given time and/or range of times. The context can include and/or define and/or be determined based on any or all of: a region type of the vehicle (e.g., residential, non-residential, highway, school, commercial, parking lot, etc.); a lane feature and/or other infrastructure feature of the road the vehicle is traversing (e.g., number of lanes, one-way road, two-way road, intersection, two-way stop and/or intersection, three-way stop and/or intersection, four-way stop and/or intersection, lanes in a roundabout, etc.); a proximity to one or more static objects and/or environmental features (e.g., particular building, body of water, railroad track, parking lot, shoulder, region in which the agent can pull over/pull off to the side of a road, etc.); a proximity a parameter associated with the location (e.g., speed limit, speed limit above a predetermined threshold, speed limit below a predetermined threshold, etc.); road markings (e.g., yellow lane, white lane, dotted lane line, solid lane line, etc.); and/or any other suitable information.”
}
Regarding claim 11, Narang in view of Creusot teaches The method according to claim 1. Narang further teaches comprising making the selected group of contextual attributes available in association with the determination for the detected object the detected road element for use, at a signature generation process, in generating a signature.
{Para [0044-0047] “The computing system further preferably includes a processing system, which functions to process the inputs received at the computing system. The processing system preferably includes a set of central processing units (CPUs) and a set of graphical processing units (GPUs), but can additionally or alternatively include any other components or combination of components (e.g., processors, microprocessors, system-on-a-chip (SoC) components, etc.).
The computing system can optionally further include any or all of: memory, storage, and/or any other suitable components.
In addition to the planning module, the computing system can include and/or interface with any or all of: a localization module, prediction module, perception module, and/or any other suitable modules for operation of the autonomous agent.
The computing system (e.g., onboard computing system) is preferably in communication with (e.g., in wireless communication with, in wired communication with, coupled to, physically coupled to, electrically coupled to, etc.) a vehicle control system, which functions to execute commands determined by the computing system.”
Applicant has not defined what constitutes availability. Additionally applicant not claimed that the signature generation process is performed merely that the data is available for the process. As context is present within the computing system it can be said to be available for use.
}
Regarding claim 12, Narang in view of Creusot teaches The method according to claim 1. Narang further teaches wherein the machine learning process is trained to map the contextual attributes to information generated during a detection of the detected road element.
{Para [0120] “The method 200 can include defining an action space based on the learning module S240, which functions to define a set of actions (equivalently referred to herein as behaviors) available to the agent in light of the vehicle's context and/or environment.”
}
Regarding claim 13, it recites A non-transitory computer readable medium having limitations similar to those of claim 1 and therefore is rejected on the same basis.
Additionally Narang teaches A non-transitory computer readable medium for contextual attribute-based perception, the non-transitory computer readable medium comprises
{Para [0044-0045] “The computing system further preferably includes a processing system, which functions to process the inputs received at the computing system. The processing system preferably includes a set of central processing units (CPUs) and a set of graphical processing units (GPUs), but can additionally or alternatively include any other components or combination of components (e.g., processors, microprocessors, system-on-a-chip (SoC) components, etc.).
The computing system can optionally further include any or all of: memory, storage, and/or any other suitable components.”
}
Regarding claim 15, it recites A non-transitory computer readable medium having limitations similar to those of claim 3 and therefore is rejected on the same basis.
Regarding claim 16, it recites A non-transitory computer readable medium having limitations similar to those of claim 4 and therefore is rejected on the same basis.
Regarding claim 17, it recites A non-transitory computer readable medium having limitations similar to those of claim 5 and therefore is rejected on the same basis.
Regarding claim 18, it recites A non-transitory computer readable medium having limitations similar to those of claim 6 and therefore is rejected on the same basis.
Regarding claim 20, it recites A non-transitory computer readable medium having limitations similar to those of claim 8 and therefore is rejected on the same basis.
Regarding claim 21, Narang in view of Creusot teaches The method according to claim 1.
Creusot further teaches further comprising validating, using the selected group of contextual attributes, a presence of one or more road elements within an environment of the vehicle.
{Para [0037] “The occlusion reasoning module 306 confirms or rejects the detected configuration of the traffic light by the object detector 302 based upon whether the range image includes an occlusion that is fully or partially blocking the traffic light from sensor signals emitted by the sensor systems 102-104. If the configuration of the traffic light cannot be confirmed in a particular range image, such as when an emitted burst of light obscures object detection, the range image may be excluded by the occlusion reasoning module 306 from traffic light configuration determinations.”
}
Claim(s) 10 is rejected under 35 U.S.C. 103 as being unpatentable over Narang et al. (US 20220234610 A1, hereinafter known as Narang) in view of Creusot (US 20200081448 A1) and Barrera (US 20240416949 A1).
Barrera was cited in a previous office action
Regarding Claim 10, Narang in view of Creusot teaches The method according to claim 1
Narang in view of Creusot does not teach, wherein the contextual attributes comprise in-vehicle information.
However, Barrera teaches wherein the contextual attributes comprise in-vehicle information.
{Para [0235-0237] “In an embodiment, advanced driver assistance systems (ADAS) may have sensors and algorithms that can detect the condition of the tires, including the tire pressure, tread depth, and type of tire, that are configured to provide warnings to the driver if the tires are not suitable for the current driving conditions, including icy roads. Avs use a combination of sensors, algorithms, and machine learning to detect and respond to different driving conditions, including the condition of the tires. Autonomous vehicles may have sensors that measure tire pressure, wear, and traction, and use this data to adjust the vehicle's behavior accordingly. For example, if a tire has less traction, the autonomous vehicle may detect this and adjust its driving style to compensate for the reduced traction.
In an embodiment, a sensor of the autonomous vehicle detects the tire parameters and provides an alert that the tire is not appropriate for the road surface condition. In an embodiment, the autonomous vehicle would adjust the driving behavior according to the road surface conditions.
In an embodiment, the road conditions may not be icy even when the weather is cold weather. In an embodiment, ice may be detected by vehicles who are driving on the icy roads by detecting that the friction is not as good when compared to normal road surface condition. In an embodiment, the vehicles that are already on the road, are reporting back that the road surface condition is slippery. Based on the input that the road conditions are icy, the routing system may provide alternate routes which have no icy roads or at best least icy roads.”
}
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Narang in view of Creusot to incorporate the teachings of Barrera to have the contextual attributes comprise in vehicle information specifically tire wear because it improves safety by reducing the likely hood of the vehicle slipping as discussed in para [0235-0237] of Barrera.
Claim(s) 22-23 is rejected under 35 U.S.C. 103 as being unpatentable over Narang et al. (US 20220234610 A1, hereinafter known as Narang) in view of Creusot (US 20200081448 A1) and Zhou et al. (US 20250026338 A1, hereinafter known as Zhou).
Regarding Claim 22, Narang in view of Creusot teaches The method according to claim 21
Narang in view of Creusot does not teach, wherein the driving operation is an emergency braking at a certain scenario.
However, Barrera teaches wherein the driving operation is an emergency braking at a certain scenario.
{Para [0064] “The sensor detects a pedestrian and other object in real time, and by analyzing the speed information and position information of the pedestrian and other object at different moments, it is determined whether there are a pedestrian and other object on the zebra crossing, whether the pedestrian and other object near the zebra crossing have a tendency to pass through the zebra crossing, and whether the pedestrian and other object will collide with the vehicle in the collision area, and then the vehicle is controlled to take necessary measures in advance, such as vehicle early warning and vehicle emergency braking.”
}
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Narang in view of Creusot to incorporate the teachings of Zhou to brake based on the vehicle situation because it improves safety by potentially avoiding a collision.
Regarding Claim 23, Narang in view of Creusot teaches The method according to claim 21
Narang in view of Creusot does not teach, wherein the driving operation is stopping in front of another road element selected of a pedestrian or a motorbike.
However, Barrera teaches wherein the driving operation is stopping in front of another road element selected of a pedestrian or a motorbike.
{Para [0064] “The sensor detects a pedestrian and other object in real time, and by analyzing the speed information and position information of the pedestrian and other object at different moments, it is determined whether there are a pedestrian and other object on the zebra crossing, whether the pedestrian and other object near the zebra crossing have a tendency to pass through the zebra crossing, and whether the pedestrian and other object will collide with the vehicle in the collision area, and then the vehicle is controlled to take necessary measures in advance, such as vehicle early warning and vehicle emergency braking.”
}
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Narang in view of Creusot to incorporate the teachings of Zhou to brake based on the vehicle situation because it improves safety by potentially avoiding a collision.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 ALEXANDER MATTA whose telephone number is (571)272-4296. The examiner can normally be reached Mon - Fri 10:00-6:00.
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/A.G.M./Examiner, Art Unit 3668
/ABDHESH K JHA/Primary Examiner, Art Unit 3668
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