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 the Claims
Claims 1-20 of US application 18/661,541 filed 5/10/24 were examined. Examiner filed a non-final rejection on 11/19/25.
Applicant filed remarks and amendments on 2/18/26. Claims 17-20 were amended. Claims 1-20 are presently pending and presented for examination.
Response to Arguments
Regarding the claim rejections under 35 USC 101: applicant’s amendments have resolved the 101 rejections by preventing the claims from being construable as transitory signals. The previously given 101 rejections are therefore withdrawn.
Regarding the claim rejections under 35 USC 102: Applicant's arguments filed 2/18/26 (hereinafter referred to as the “Remarks”) have been fully considered but they are not persuasive.
Regarding claims 1, 9, and 17, applicant argues, “The Office Action relies on Matthews' [US 20250336291 A1] object detection and trajectory analysis to satisfy the ‘abnormal situation’ limitation (e.g., Matthews [0084]-[0086]). However, Matthews does not disclose determining that an abnormal situation exists on the area of the road ahead. Rather, Matthews is directed to visualizing and communicating normal right-of-way and yielding scenarios, such as pedestrians, oncoming traffic, or roadway obstructions encountered during ordinary driving. See, e.g., Matthews [0039]-[0041], [0108]. These are routine traffic conditions governed by traffic rules, not abnormal situations. Matthews never characterizes these situations as abnormal, unusual, or anomalous roadway conditions, nor does it perform any abnormality determination. Instead, Matthews assumes such situations are expected and focuses on communicating a yielding intent to a driver via augmented reality displays. Accordingly, Matthews does not disclose the claimed determination that an abnormal situation exists.” (See at least Page 7 in the Remarks).
Applicant’s argument is not persuasive. Applicant has failed to define “abnormal” in the claims. Therefore, anything can be “abnormal”. Examiner has chosen, on pages 5-6 of the non-final rejection filed 11/19/25, to interpret the “abnormal” situation to be a situation where the obstacle exists and is detected, as in the above citations of Matthews which applicant acknowledges. Therefore, the “normal” situation is the situation in which these obstacles are not detected. If applicant wishes to overcome this interpretation, then applicant can amend the claims to define the word “abnormal” differently than this.
Applicant further argues that, “The Office Action maps Matthews' yielding decision ([0086]) and braking behavior ([0094]) to the claimed ‘maneuver.’ This mapping is incorrect. In Matthews, the vehicle determines whether it should yield ([0086]), displays a closed digital gateway, and may reduce speed or stop until the path is clear ([0094]). Yielding or stopping in response to right-of-way traffic is not a maneuver ‘to avoid an abnormal situation,’ as required by the claims. Rather, yielding and stopping constitute routine compliance actions taken during normal traffic operation. Matthews does not disclose determining a maneuver designed to avoid an abnormal roadway condition, nor does it disclose maneuver selection driven by abnormality detection.”
Once again, applicant’s argument is not persuasive. Applicant has failed to define “abnormal” in the claims. Therefore, anything can be “abnormal”. Examiner has chosen, on pages 5-6 of the non-final rejection filed 11/19/25, to interpret the “abnormal” situation to be a situation where the obstacle exists and is detected, as in the above citations of Matthews which applicant acknowledges. Therefore, the “normal” situation is the situation in which these obstacles are not detected. Therefore, the “maneuvers” of Matthews cited in these portions of the non-final rejection are, as a matter of fact, maneuvers responsive to an “abnormal” situation. If applicant wishes to overcome this interpretation, then applicant can amend the claims to define the word “abnormal” differently than this.
Applicant further argues that, “Although Matthews references computer vision and machine learning ([0108]), those techniques are used for object detection and classification, not for determining a maneuver to avoid an abnormal situation. Matthews does not disclose executing an AI model to generate a maneuver decision as claimed. Instead, its control logic is rule-based yielding behavior following object classification. Thus, Matthews fails to disclose the AI-based maneuver determination required in independent claims 1, 9, and 17.” (See at least Pages 7-8 in the Remarks).
However, this argument is not persuasive, because as discussed on at least pages 5-6 of the non-final rejection filed 11/19/25, Matthews does teach wherein the processor is configured to determine a maneuver for the vehicle to perform to avoid the abnormal situation (See at least Fig. 10A in Matthews: Matthews discloses that At 1006, the AR module determines whether the host vehicle should yield for one or more of the objects [See at least Matthews, 0086]) based on an execution of an artificial intelligence (AI) model on the sensor data (Matthews discloses that The disclosed ADAS uses computer vision, machine learning and cloud computing to identify and communicate scenarios where a moving autonomous host vehicle should yield to pedestrian(s), obstructed roadway, and/or oncoming (right-of-way) traffic [See at least Matthews, 0108]).
In other words, [Matthews, 0108] is explicit that the computer vision and machine learning, which are both types of AI, are used for communicating scenarios where the vehicle should yield to pedestrians or other obstacles, which means that these AIs do ultimately generate maneuver decisions. If applicant would like to claim a more specific AI distinct from these, then applicant must amend the claims.
Regarding claims 3, 11, and 19, applicant argues that, “Matthews does not disclose determining or optimizing a travel path. Paragraph [0065] describes determining collision likelihood, issuing warnings, and optionally braking or steering to prevent collision. Matthews does not deal with generating, evaluating, or selecting an optimal travel path, nor does it involve AI-based path optimization. Matthews' actions are reactive safety countermeasures, not path planning.” (See at least Page 8 in the Remarks).
However, this is not persuasive because, as discussed on at least Pages 7-8 of the non-final rejection, Matthews does teach wherein the processor is configured to determine an optimal travel path along the road (See at least Fig. 1 in Mathews: Matthews discloses that The vehicle control module 103 determines locations of the objects relative to the host vehicle 100 and trajectories of the objects and the host vehicle 100 [See at least Matthews, 0065]) for avoiding the abnormal situation (See at least Fig. 1 in Matthews: Matthews discloses If it is determined that the host vehicle 100 is likely to collide with one of the objects, one or more warning signals may be generated to indicate to the driver and/or the object of concern of the potential collision [See at least Matthews, 0065]. Matthews further discloses that The vehicle control module 103 may also or alternatively perform one or more other countermeasures (e.g., apply brakes to decelerate the host vehicle, change a steering angle of the host vehicle, etc.) to prevent a collision [See at least Matthews, 0065]) based on execution of the AI model on the sensor data (Matthews discloses that Computer vision, machine learning and V2X communication are used to classify types and trajectory of obstacles located within range of the intended pathway of the host vehicle [See at least Matthews, 0108]), and control the vehicle to autonomously move based on the optimal travel path (Matthews further discloses that The vehicle control module 103 may also or alternatively perform one or more other countermeasures (e.g., apply brakes to decelerate the host vehicle, change a steering angle of the host vehicle, etc.) to prevent a collision [See at least Matthews, 0065]).
Regarding claims 4, 12, and 20, applicant argues that, “Claims 4, 12, and 20 require determining that an abnormal situation exists based on current speeds of one or more other vehicles. The Office Action asserts that because Matthews considers oncoming traffic, it necessarily uses vehicle speed. This reasoning is flawed. Matthews does not determine abnormality based on speed. It merely considers speed and trajectory information to decide whether to yield, which is a normal driving decision. See Matthews [0085]. Matthews never treats vehicle motion or speed as evidence of an abnormal roadway condition. Oncoming traffic moving at speed is an expected condition, not an abnormal one. Thus, Matthews fails to disclose the claimed limitation.” (See at least Page 8 in the Remarks).
Examiner glad that applicant concedes that Matthews “considers speed and trajectory information to decide whether to yield”. That alone covers the entirety of this limitation.
Notably, “expected” and “abnormal” are not opposites. So even though Matthews never says “expected”, even if it did, that would still not necessarily mean that a condition is “normal”. But this is a moot point because “expected” never appears in Matthews. Applicant is arguing against the Matthews reference by using imaginary parts of the Matthews reference that do not exist. And applicant still has not defined “abnormal”. So examiner can interpret “abnormal” using examiner’s broadest reasonable interpretation.
Accordingly, as stated on at least pages 8-9 of the non-final rejection, Matthews does disclose wherein the sensor data comprises current speeds of one or more other vehicles on the area of the road ahead (See at least Fig. 10A in Matthews: Matthews discloses that The AR module may determine types and trajectories of objects located within a predetermined range of the intended forward-facing pathway of the host vehicle [See at least Matthews, 0085]. Matthews discloses that The AR module may use cloud computing, V2V communication, V2X communication, and/or other communicating techniques to share object and vehicle speeds, headings, and other intentions of surrounding objects [See at least Matthews, 0085]. Matthews further discloses that The AR module may receive this information from one or more mobile network devices (e.g., mobile phones, wearable devices, etc.) external to the host vehicle [See at least Matthews, 0085]), and the processor is configured to determine the abnormal situation exists (See at least Fig. 10A in Matthews: Matthews discloses that At 1006, the AR module determines whether the host vehicle should yield for one or more of the objects [See at least Matthews, 0086]) based on the current speeds of the one or more other vehicles on the area of the road ahead (Matthews discloses that the ADAS uses computer vision, machine learning and cloud computing to identify, communicate, and evaluate scenarios where a moving host vehicle should yield to pedestrian(s), an obstructed roadway, and/or oncoming (right-of-way) traffic [See at least Matthew, 0040]. Obviously, if traffic is “oncoming” then it is moving; stationary vehicles are not oncoming traffic. So the ego vehicle does, as a matter of fact, use the fact that the other vehicles are moving, i.e., that they have non-zero speed, to yield to the other vehicles).
Regarding claims 5 and 13, applicant argues that, “The Office Action characterizes Matthews' computer vision and machine learning as a second AI model ([0108]). However, Matthews does not disclose multiple AI models, nor does Matthews disclose a first AI model for maneuver determination and a second AI model for abnormality detection. Moreover, Matthews fails to disclose any layered or sequential AI execution. Matthews describes a single perception pipeline for object detection and classification. Treating computer vision and machine learning collectively as a ‘second AI model’ is wholly unsupported by Matthews, and impermissibly broadens Matthews beyond its disclosure. Therefore, Matthews does not anticipate claims 5 or 13.”
However, this argument is not persuasive because machine learning is one type of AI model and computer vision is another type of AI model. So that is two AI models. Matthews uses both of them in identifying and responding to abnormal situations. It seems that applicant did not know that computer vision is a type of AI. But it is.
Therefore, Matthews does disclose wherein the sensor data comprises image data of the area of the road ahead (See at least Fig. 10A in Matthews: Matthews discloses that At 1002, the AR module scans for and detects objects based on the path of the host vehicle [See at least Matthews, 0084]), and the processor is configured to determine the abnormal situation exists based on execution of a second AI model on the image data of the area of the road ahead (Matthews discloses that The disclosed ADAS uses computer vision, machine learning and cloud computing to identify and communicate scenarios where a moving autonomous host vehicle should yield to pedestrian(s), obstructed roadway, and/or oncoming (right-of-way) traffic [See at least Matthews, 0108]. The computer vision and machine learning can both be regarded as “AI”: the computer vision allows the vehicle to “see” like a human and the machine-learning allows the vehicle to make decisions like a human. The computer vision may broadly be regarded as “a second AI model” executed on the image data).
Regarding claims 6 and 14, applicant argues that “Claims 6 and 14 require overtaking control of at least one of a steering wheel, engine, or braking system based on an advanced driver-assistance system (ADAS). Matthews describes vehicle control during autonomous or semi-autonomous operation ([0094]), but it does not disclose an ADAS overtaking control authority from a human driver in response to an abnormal situation. Further, because Matthews does not disclose abnormality detection or abnormal-situation-driven maneuver execution, it necessarily fails to disclose ADAS-based control overtaking as claimed.” (See at least Page 9 in the Remarks).
These arguments are not persuasive. The point about the “human driver” is moot because applicant never mentions a “human driver” in the claims anyway.
The point about the definition of “abnormal” is moot because applicant never defines “abnormal” in the claims. Therefore, examiner’s reading of “abnormal” as a situation in which an obstacle is detected in the path of the vehicle is perfectly reasonable.
Accordingly, Matthews does disclose wherein the processor is configured to overtake control of at least one of a steering wheel, an engine, and a braking system of the vehicle based on an advanced driver-assistance system (ADAS) and control the at least one of the steering wheel, the engine, and the braking system of the vehicle to execute the maneuver (See at least Fig. 10A in Matthews: Matthews discloses that At 1015, a vehicle control module (e.g., the vehicle control module 103 of FIGS. 1-2) may reduce speed of the vehicle to a stop [See at least Matthews, 0094]. Matthews discloses The host vehicle may remain stopped until objects are clear of the path of the host vehicle [See at least Matthews, 0094]).
For at least the above stated reasons, none of the claims are allowable.
Regarding the claim rejections under 35 USC 103: Applicant's arguments filed 2/18/26 (hereinafter referred to as the “Remarks”) have been fully considered but they are not persuasive.
Regarding claims 2, 10, and 18, applicant argues that, “Nothing in Matthews suggests a deficiency in its perception pipeline that would motivate importing Moustafa's remote image aggregation architecture. Matthews already achieves its stated goal-visualizing yielding intent-using local sensing and V2X metadata. Combining Moustafa with Matthews would provide many disadvantages. For example, the combination would introduce latency incompatible with Matthews' real-time AR visualization, fundamentally change Matthews' operational assumptions, and repurpose Matthews away from its stated goal of driver trust visualization. The Examiner's rationale ("doing so improves safety") is overly generic and conclusory, and insufficient under KSR.” (See at least Page 11 in the Remarks).
In other words, applicant is arguing that Moustafa and Matthews are not in the same field of endeavor and that combining them provides no advantage. Applicant is incorrect. As discussed on at least Pages 11-12 of the non-final rejection:
Matthews discloses The apparatus of claim 9, wherein the processor is configured to receive data captured by at least one of a different vehicle that is on the road of the vehicle and a sensor that is on the road of the vehicle (Matthews discloses that Vehicle perceives the road ahead with sensing devices (e.g., camera, radar and/or lidar) and V2X communication [See at least Matthews, 0108]. Matthews further discloses that Computer vision, machine learning and V2X communication are used to classify types and trajectory of obstacles located within range of the intended pathway of the host vehicle [See at least Matthews, 0108]), and input the image data to the AI model during the execution of the AI model (Matthews further discloses that Computer vision, machine learning and V2X communication are used to classify types and trajectory of obstacles located within range of the intended pathway of the host vehicle [See at least Matthews, 0108]. Matthews further discloses that ).
However, Matthews does not explicitly teach the apparatus wherein the data received is image data and where the sensor is a camera and wherein either of these are collected by a device located at a location ahead of the vehicle.
However, Moustafa does teach an apparatus wherein the data received is image data and where the sensor is a camera and wherein either of these are collected by a device located at a location ahead of the vehicle (Moustafa teaches that an autonomous driving system of a vehicle may access data collected by other remote sensors devices (e.g., other autonomous vehicles, drones, road side units, weather monitors, etc.) to determine, preemptively likely conditions on upcoming stretches of road [See at least Moustafa, 0251]. Moustafa further teaches the data sent by the autonomous vehicles comprises Image Data and Sensor Data and may also have some associated metadata [See at least Moustafa, 0315]. Moustafa further teaches that Both of the data sources can be used in conjunction or in isolation to extract and generate metadata/tags related to location [See at least Moustafa, 0315]. Moustafa further teaches that The cumulative location specific metadata can be information like geographic coordinates for example: “45° 31′ 22.4256” N and 1220 59′ 23.3880″ W″ [See at least Moustafa, 0315]). Both Moustafa and Matthews teach methods for determining the presence of upcoming road conditions based on V2X data. However, only Moustafa explicitly teaches where the data received by the ego vehicle may be image data, and where the device (another vehicle, an RSU, etc.) which sends the data collected the data that it sent at a location ahead of the ego vehicle.
It would have been obvious to anyone of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the upcoming road condition determination method of Matthews to also make the data received by the ego vehicle include image data, wherein the device (another vehicle, an RSU, etc.) which sends the data collected the data that it sent at a location ahead of the ego vehicle, as in Moustafa. Doing so improves safety by providing additional useful information to the ego vehicle to detect and respond to environmental conditions.
Regarding claims 7 and 15, applicant argues that, “Moustafa discloses training machine learning models using historical data collected across multiple vehicles and infrastructure nodes to predict future pullover or valet handoff likelihoods. See Moustafa [0251]. This training is centralized or cloud-based, retrospective, and unrelated to training a model based on a maneuver autonomously executed by the ego vehicle.” (See at least Page 12 in the Remarks).
Applicant further argues that, “Modifying Matthews to include post-maneuver training would require persistent learning infrastructure, fundamentally alter Matthews' closed-loop control assumptions, and contradict Matthews' role as a real-time AR visualization and yielding system. There is no teaching, suggestion, or motivation in Matthews to incorporate Moustafa's offline, infrastructure-driven training paradigm.” (See at least Page 12 in the Remarks).
In other words, applicant is arguing that Moustafa and Matthews are not in the same field of endeavor and that combining them provides no advantage. Applicant is incorrect. As discussed on at least Pages 13-14 of the non-final rejection:
Matthews discloses The method of claim 1, comprising collecting additional sensor data of the abnormal situation via one or more sensors of the vehicle as the vehicle travels through the area of the road ahead (See at least Fig. 10A in Matthews: Matthews discloses that At 1002, the AR module scans for and detects objects based on the path of the host vehicle [See at least Matthews, 0084]. Matthews further discloses that V2V may be used for object detection [See at least Matthews, 0085]. Also see at least Fig. 5 in Matthews: Matthews illustrates that the road area for which the vehicle is detecting data is indeed the road on which the vehicle is traveling [See at least Matthews, 0079]. The rest of the pictorial figures also illustrate this).
However, Matthews does not explicitly teach training the AI model based on the maneuver autonomously performed by the vehicle and the additional sensor data.
However, Moustafa does teach an method further comprising training the AI model based on the maneuver autonomously performed by the vehicle and the additional sensor data (Moustafa teaches that a variety of sensors may provide data to cloud-based systems to aggregate and process this collection of data to provide information to multiple autonomous vehicles concerning sections of roadway and conditions affecting these routes [See at least Moustafa, 0251]. Moustafa further teaches that As noted above, in some cases, cloud-based systems and other systems may receive inputs associated with previous pullover and remote valet handover events and may detect characteristics common to these events [See at least Moustafa, 0251]. Moustafa further teaches that machine learning models may be built and trained from this information and such machine learning models may be deployed on and executed by roadside units, cloud-based support systems, remote valet computing systems, or the in-vehicle systems of the autonomous vehicles themselves to provide logic for predictively determining potential remote valet handoffs [See at least Moustafa, 0251]. Moustafa further teaches that through sensor data accessed by a given autonomous vehicle, the vehicle may determine in advance the areas along each road, where frequent pull-overs have occurred and/or remote valet handoffs are common [See at least Moustafa, 0251]. Moustafa further teaches that the autonomous vehicle may determine (e.g., from a corresponding machine learning model) that conditions reported for an upcoming section of road suggest a likelihood of a pull-over and/or remote valet handover [See at least Moustafa, 0251]). Both Moustafa and Matthews teach methods for receiving additional sensor data from multiple sources and using AI to autonomously perform maneuvers in response. However, only Moustafa explicitly teaches where the AI model is trained to execute the maneuver based on the additional sensor data.
It would have been obvious to anyone of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the AI model of Matthews to also be trained to execute the maneuver based on the additional sensor data, as in Moustafa. Doing so improves the accuracy and effectiveness of the model by allowing it to tailor its approach to actual real-world scenarios.
Regarding claims 8 and 16, applicant argues, “Moustafa's learning models are trained to detect patterns across many vehicles for predicting likely future pullover locations, not for refining maneuver selection based on execution feedback. See Moustafa [0251]. This is a fundamentally different learning objective than claimed… The Examiner's rationale relies on Applicant's claim language to reconstruct a training loop across two unrelated systems. Neither reference suggests training an AI model using execution feedback from an abnormal-situation avoidance maneuver. Such reconstruction is classic hindsight and improper under § 103.” (See at least Page 12 in the Remarks).
In other words, applicant is arguing that Moustafa and Matthews are not in the same field of endeavor and that combining them provides no advantage. Applicant is incorrect. As stated on at least Pages 15-16 of the non-final rejection:
Matthews discloses The apparatus of claim 9, wherein the processor is configured to collect additional data of at least one of the abnormal situation and the maneuver (See at least Fig. 10A in Matthews: Matthews discloses that At 1002, the AR module scans for and detects objects based on the path of the host vehicle [See at least Matthews, 0084]. Matthews further discloses that V2V may be used for object detection [See at least Matthews, 0085]. Also see at least Fig. 5 in Matthews: Matthews illustrates that the road area for which the vehicle is detecting data is indeed the road on which the vehicle is traveling [See at least Matthews, 0079]. The rest of the pictorial figures also illustrate this).
However, Matthews does not explicitly teach wherein the processor is further configured to train the AI model based on execution of the AI model on the additional data of at least one of the abnormal situation and the maneuver.
However, Moustafa does teach an ADAS wherein the processor is further configured to train the AI model based on execution of the AI model on the additional data of at least one of the abnormal situation and the maneuver (Moustafa teaches that a variety of sensors may provide data to cloud-based systems to aggregate and process this collection of data to provide information to multiple autonomous vehicles concerning sections of roadway and conditions affecting these routes [See at least Moustafa, 0251]. Moustafa further teaches that As noted above, in some cases, cloud-based systems and other systems may receive inputs associated with previous pullover and remote valet handover events and may detect characteristics common to these events [See at least Moustafa, 0251]. Moustafa further teaches that machine learning models may be built and trained from this information and such machine learning models may be deployed on and executed by roadside units, cloud-based support systems, remote valet computing systems, or the in-vehicle systems of the autonomous vehicles themselves to provide logic for predictively determining potential remote valet handoffs [See at least Moustafa, 0251]. Moustafa further teaches that through sensor data accessed by a given autonomous vehicle, the vehicle may determine in advance the areas along each road, where frequent pull-overs have occurred and/or remote valet handoffs are common [See at least Moustafa, 0251]. Moustafa further teaches that the autonomous vehicle may determine (e.g., from a corresponding machine learning model) that conditions reported for an upcoming section of road suggest a likelihood of a pull-over and/or remote valet handover [See at least Moustafa, 0251]). Both Moustafa and Matthews teach methods for receiving additional sensor data pertaining to a maneuver from multiple sources and using AI to autonomously perform maneuvers in response. However, only Moustafa explicitly teaches where the AI model is trained to execute the maneuver based on the additional sensor data.
It would have been obvious to anyone of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the AI model of Matthews to also be trained to execute the maneuver based on the additional sensor data, as in Moustafa. Doing so improves the accuracy and effectiveness of the model by allowing it to tailor its approach to actual real-world scenarios.
Regarding the reasonable expectation of success, applicant further argues that, “Even if Matthews and Moustafa were combined, a skilled artisan would not reasonably expect success because Matthews is latency-sensitive and real-time; Moustafa's systems are predictive, aggregated, and delayed; and the references operate at incompatible temporal and architectural layers.” (See at least Page 13 in the Remarks).
Applicant seems to be suggesting that the prior art does not contemplate combining historical and real-time data in machine learning. This argument does not make any sense because machine learning is inherently based on historical data, and it has been well-established that at least [Matthews, 0108] utilizes machine-learning. Likewise, at least [Moustafa, 0251] also utilizes machine-learning. So both references use historical data, which means they are temporally compatible. Furthermore, architecturally, it is not a stretch to increase the amount of historical data available to the machine learning model of Matthews to include that of Moustafa; simply providing additional data is not tantamount to architectural incompatibility. If applicant truly believes that machine-learning does not utilize historical data (which is patently false), then examiner suggests that applicant study the prior art of record more to remedy applicant’s misunderstanding.
For at least the above stated reasons, none of the claims are allowable.
Examiner’s suggestion to help applicant overcome the prior art of record: none of the prior art references investigated this far appear to teach, “Context-based authorization to determine validity if a process can be executed allows ECUs to maintain secure operation and prevent unauthorized access from elements such as the vehicle’s CAN Bus [wherein the context includes one or more of] proximity contexts, nearby objects, distance to approaching objects, speed, and trajectory relative to other moving objects, and operational contexts such as an indication of whether the vehicle is moving or parked, the vehicle’s current speed, the transmission state,… cruise control, parking assist, driving assist, location-based contexts” as disclosed in paragraph [00145] of applicant’s specification. So amending the independent claims to include the following limitation would overcome the prior art of record:
“wherein the vehicle employs context-based authorization to prevent unauthorized access from a CAN bus of the vehicle based on one or more of: a nearby object, a parked state of the vehicle, a moving state of the vehicle, a current speed of the vehicle, a transmission state of the vehicle, a driving operation of the vehicle, or a location of the vehicle.”
The above amendment would overcome the prior art of record, although further search and consideration would be required before a determination of allowability could be made.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 3-6, 9, 11-14, 17, and 19-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Matthews et al. (US 20250336291 A1), hereinafter referred to as Matthews. Claims with similar limitations are grouped together for applicant’s reading convenience, with the narrowest claim in each group being mapped to the prior art. However, it will be appreciated that all claims in each group are rejected.
Regarding claims 1, 9, and 17, Matthews discloses An apparatus (See at least Fig. 1 in Matthews: Matthews discloses a host vehicle 100 including an ADAS 101 [See at least Matthews, 0043]) comprising:
a memory (See at least Fig. 1 in Matthews: Matthews discloses that The host vehicle 100 may further include the memory 180 [See at least Matthews, 0057]); and
a processor coupled to the memory, the processor configured to (See at least Fig. 1 in Matthews: Matthews discloses that The applications 186 may include applications executed by the modules 103, 104, 107, 108 [See at least Matthews, 0057]):
receive sensor data of an area of a road ahead of a vehicle traveling on the road (See at least Fig. 10A in Matthews: Matthews discloses that At 1002, the AR module scans for and detects objects based on the path of the host vehicle [See at least Matthews, 0084]. Matthews further discloses that V2V may be used for object detection [See at least Matthews, 0085]. Also see at least Fig. 5 in Matthews: Matthews illustrates that the road area for which the vehicle is detecting data is indeed the road on which the vehicle is traveling [See at least Matthews, 0079]. The rest of the pictorial figures also illustrate this),
determine that an abnormal situation exists on the area of the road ahead based on the sensor data (See at least Fig. 10A in Matthews: Matthews discloses that At 1004, the AR module may identify and classify the detected objects [See at least Matthews, 0085]. Matthews further discloses that This may include detecting objects having trajectories that cross the path of the host vehicle [See at least Matthews, 0084]),
determine a maneuver for the vehicle to perform to avoid the abnormal situation (See at least Fig. 10A in Matthews: Matthews discloses that At 1006, the AR module determines whether the host vehicle should yield for one or more of the objects [See at least Matthews, 0086]) based on an execution of an artificial intelligence (AI) model on the sensor data (Matthews discloses that The disclosed ADAS uses computer vision, machine learning and cloud computing to identify and communicate scenarios where a moving autonomous host vehicle should yield to pedestrian(s), obstructed roadway, and/or oncoming (right-of-way) traffic [See at least Matthews, 0108]), and
control the vehicle to autonomously perform the maneuver while the vehicle is travelling on the area of the road (See at least Fig. 10A in Matthews: Matthews discloses that At 1015, a vehicle control module (e.g., the vehicle control module 103 of FIGS. 1-2) may reduce speed of the vehicle to a stop [See at least Matthews, 0094]. Matthews discloses The host vehicle may remain stopped until objects are clear of the path of the host vehicle [See at least Matthews, 0094]).
Examiner’s suggestion to help applicant overcome the prior art of record: none of the prior art references investigated this far appear to teach, “Context-based authorization to determine validity if a process can be executed allows ECUs to maintain secure operation and prevent unauthorized access from elements such as the vehicle’s CAN Bus [wherein the context includes one or more of] proximity contexts, nearby objects, distance to approaching objects, speed, and trajectory relative to other moving objects, and operational contexts such as an indication of whether the vehicle is moving or parked, the vehicle’s current speed, the transmission state,… cruise control, parking assist, driving assist, location-based contexts” as disclosed in paragraph [00145] of applicant’s specification. So amending the independent claims to include the following limitation would overcome the prior art of record:
“wherein the vehicle employs context-based authorization to prevent unauthorized access from a CAN bus of the vehicle based on one or more of: a nearby object, a parked state of the vehicle, a moving state of the vehicle, a current speed of the vehicle, a transmission state of the vehicle, a driving operation of the vehicle, or a location of the vehicle.”
The above amendment would overcome the prior art of record, although further search and consideration would be required before a determination of allowability could be made.
Regarding claims 3, 11, and 19, Matthews discloses The apparatus of claim 9, wherein the processor is configured to determine an optimal travel path along the road (See at least Fig. 1 in Mathews: Matthews discloses that The vehicle control module 103 determines locations of the objects relative to the host vehicle 100 and trajectories of the objects and the host vehicle 100 [See at least Matthews, 0065]) for avoiding the abnormal situation (See at least Fig. 1 in Matthews: Matthews discloses If it is determined that the host vehicle 100 is likely to collide with one of the objects, one or more warning signals may be generated to indicate to the driver and/or the object of concern of the potential collision [See at least Matthews, 0065]. Matthews further discloses that The vehicle control module 103 may also or alternatively perform one or more other countermeasures (e.g., apply brakes to decelerate the host vehicle, change a steering angle of the host vehicle, etc.) to prevent a collision [See at least Matthews, 0065]) based on execution of the AI model on the sensor data (Matthews discloses that Computer vision, machine learning and V2X communication are used to classify types and trajectory of obstacles located within range of the intended pathway of the host vehicle [See at least Matthews, 0108]), and control the vehicle to autonomously move based on the optimal travel path (Matthews further discloses that The vehicle control module 103 may also or alternatively perform one or more other countermeasures (e.g., apply brakes to decelerate the host vehicle, change a steering angle of the host vehicle, etc.) to prevent a collision [See at least Matthews, 0065]).
Regarding claims 4, 12, and 20, Matthews discloses The apparatus of claim 9, wherein the sensor data comprises current speeds of one or more other vehicles on the area of the road ahead (See at least Fig. 10A in Matthews: Matthews discloses that The AR module may determine types and trajectories of objects located within a predetermined range of the intended forward-facing pathway of the host vehicle [See at least Matthews, 0085]. Matthews discloses that The AR module may use cloud computing, V2V communication, V2X communication, and/or other communicating techniques to share object and vehicle speeds, headings, and other intentions of surrounding objects [See at least Matthews, 0085]. Matthews further discloses that The AR module may receive this information from one or more mobile network devices (e.g., mobile phones, wearable devices, etc.) external to the host vehicle [See at least Matthews, 0085]), and the processor is configured to determine the abnormal situation exists (See at least Fig. 10A in Matthews: Matthews discloses that At 1006, the AR module determines whether the host vehicle should yield for one or more of the objects [See at least Matthews, 0086]) based on the current speeds of the one or more other vehicles on the area of the road ahead (Matthews discloses that the ADAS uses computer vision, machine learning and cloud computing to identify, communicate, and evaluate scenarios where a moving host vehicle should yield to pedestrian(s), an obstructed roadway, and/or oncoming (right-of-way) traffic [See at least Matthew, 0040]. Obviously, if traffic is “oncoming” then it is moving; stationary vehicles are not oncoming traffic. So the ego vehicle does, as a matter of fact, use the fact that the other vehicles are moving, i.e., that they have non-zero speed, to yield to the other vehicles).
Regarding claims 5 and 13, The apparatus of claim 9, wherein the sensor data comprises image data of the area of the road ahead (See at least Fig. 10A in Matthews: Matthews discloses that At 1002, the AR module scans for and detects objects based on the path of the host vehicle [See at least Matthews, 0084]), and the processor is configured to determine the abnormal situation exists based on execution of a second AI model on the image data of the area of the road ahead (Matthews discloses that The disclosed ADAS uses computer vision, machine learning and cloud computing to identify and communicate scenarios where a moving autonomous host vehicle should yield to pedestrian(s), obstructed roadway, and/or oncoming (right-of-way) traffic [See at least Matthews, 0108]. The computer vision and machine learning can both be regarded as “AI”: the computer vision allows the vehicle to “see” like a human and the machine-learning allows the vehicle to make decisions like a human. The computer vision may broadly be regarded as “a second AI model” executed on the image data).
Regarding claims 6 and 14, The apparatus of claim 9, wherein the processor is configured to overtake control of at least one of a steering wheel, an engine, and a braking system of the vehicle based on an advanced driver-assistance system (ADAS) and control the at least one of the steering wheel, the engine, and the braking system of the vehicle to execute the maneuver (See at least Fig. 10A in Matthews: Matthews discloses that At 1015, a vehicle control module (e.g., the vehicle control module 103 of FIGS. 1-2) may reduce speed of the vehicle to a stop [See at least Matthews, 0094]. Matthews discloses The host vehicle may remain stopped until objects are clear of the path of the host vehicle [See at least Matthews, 0094]).
Examiner’s suggestion to help applicant overcome the prior art of record: none of the prior art references investigated this far appear to teach, “Context-based authorization to determine validity if a process can be executed allows ECUs to maintain secure operation and prevent unauthorized access from elements such as the vehicle’s CAN Bus [wherein the context includes one or more of] proximity contexts, nearby objects, distance to approaching objects, speed, and trajectory relative to other moving objects, and operational contexts such as an indication of whether the vehicle is moving or parked, the vehicle’s current speed, the transmission state,… cruise control, parking assist, driving assist, location-based contexts” as disclosed in paragraph [00145] of applicant’s specification. So amending the independent claims to include the following limitation would overcome the prior art of record:
“wherein the vehicle employs context-based authorization to prevent unauthorized access from a CAN bus of the vehicle based on one or more of: a nearby object, a parked state of the vehicle, a moving state of the vehicle, a current speed of the vehicle, a transmission state of the vehicle, a driving operation of the vehicle, or a location of the vehicle.”
The above amendment would overcome the prior art of record, although further search and consideration would be required before a determination of allowability could be made.
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 2, 7-8, 10, 15-16 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Matthews et al. (US 20250336291 A1) in view of Moustafa et al. (US 20220126864 A1), hereinafter referred to as Moustafa.
Regarding claims 2, 10, and 18, Matthews discloses The apparatus of claim 9, wherein the processor is configured to receive data captured by at least one of a different vehicle that is on the road of the vehicle and a sensor that is on the road of the vehicle (Matthews discloses that Vehicle perceives the road ahead with sensing devices (e.g., camera, radar and/or lidar) and V2X communication [See at least Matthews, 0108]. Matthews further discloses that Computer vision, machine learning and V2X communication are used to classify types and trajectory of obstacles located within range of the intended pathway of the host vehicle [See at least Matthews, 0108]), and input the image data to the AI model during the execution of the AI model (Matthews further discloses that Computer vision, machine learning and V2X communication are used to classify types and trajectory of obstacles located within range of the intended pathway of the host vehicle [See at least Matthews, 0108]. Matthews further discloses that ).
However, Matthews does not explicitly teach the apparatus wherein the data received is image data and where the sensor is a camera and wherein either of these are collected by a device located at a location ahead of the vehicle.
However, Moustafa does teach an apparatus wherein the data received is image data and where the sensor is a camera and wherein either of these are collected by a device located at a location ahead of the vehicle (Moustafa teaches that an autonomous driving system of a vehicle may access data collected by other remote sensors devices (e.g., other autonomous vehicles, drones, road side units, weather monitors, etc.) to determine, preemptively likely conditions on upcoming stretches of road [See at least Moustafa, 0251]. Moustafa further teaches the data sent by the autonomous vehicles comprises Image Data and Sensor Data and may also have some associated metadata [See at least Moustafa, 0315]. Moustafa further teaches that Both of the data sources can be used in conjunction or in isolation to extract and generate metadata/tags related to location [See at least Moustafa, 0315]. Moustafa further teaches that The cumulative location specific metadata can be information like geographic coordinates for example: “45° 31′ 22.4256” N and 1220 59′ 23.3880″ W″ [See at least Moustafa, 0315]). Both Moustafa and Matthews teach methods for determining the presence of upcoming road conditions based on V2X data. However, only Moustafa explicitly teaches where the data received by the ego vehicle may be image data, and where the device (another vehicle, an RSU, etc.) which sends the data collected the data that it sent at a location ahead of the ego vehicle.
It would have been obvious to anyone of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the upcoming road condition determination method of Matthews to also make the data received by the ego vehicle include image data, wherein the device (another vehicle, an RSU, etc.) which sends the data collected the data that it sent at a location ahead of the ego vehicle, as in Moustafa. Doing so improves safety by providing additional useful information to the ego vehicle to detect and respond to environmental conditions.
Regarding claims 7 and 15, The method of claim 1, comprising collecting additional sensor data of the abnormal situation via one or more sensors of the vehicle as the vehicle travels through the area of the road ahead (See at least Fig. 10A in Matthews: Matthews discloses that At 1002, the AR module scans for and detects objects based on the path of the host vehicle [See at least Matthews, 0084]. Matthews further discloses that V2V may be used for object detection [See at least Matthews, 0085]. Also see at least Fig. 5 in Matthews: Matthews illustrates that the road area for which the vehicle is detecting data is indeed the road on which the vehicle is traveling [See at least Matthews, 0079]. The rest of the pictorial figures also illustrate this).
However, Matthews does not explicitly teach training the AI model based on the maneuver autonomously performed by the vehicle and the additional sensor data.
However, Moustafa does teach an method further comprising training the AI model based on the maneuver autonomously performed by the vehicle and the additional sensor data (Moustafa teaches that a variety of sensors may provide data to cloud-based systems to aggregate and process this collection of data to provide information to multiple autonomous vehicles concerning sections of roadway and conditions affecting these routes [See at least Moustafa, 0251]. Moustafa further teaches that As noted above, in some cases, cloud-based systems and other systems may receive inputs associated with previous pullover and remote valet handover events and may detect characteristics common to these events [See at least Moustafa, 0251]. Moustafa further teaches that machine learning models may be built and trained from this information and such machine learning models may be deployed on and executed by roadside units, cloud-based support systems, remote valet computing systems, or the in-vehicle systems of the autonomous vehicles themselves to provide logic for predictively determining potential remote valet handoffs [See at least Moustafa, 0251]. Moustafa further teaches that through sensor data accessed by a given autonomous vehicle, the vehicle may determine in advance the areas along each road, where frequent pull-overs have occurred and/or remote valet handoffs are common [See at least Moustafa, 0251]. Moustafa further teaches that the autonomous vehicle may determine (e.g., from a corresponding machine learning model) that conditions reported for an upcoming section of road suggest a likelihood of a pull-over and/or remote valet handover [See at least Moustafa, 0251]). Both Moustafa and Matthews teach methods for receiving additional sensor data from multiple sources and using AI to autonomously perform maneuvers in response. However, only Moustafa explicitly teaches where the AI model is trained to execute the maneuver based on the additional sensor data.
It would have been obvious to anyone of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the AI model of Matthews to also be trained to execute the maneuver based on the additional sensor data, as in Moustafa. Doing so improves the accuracy and effectiveness of the model by allowing it to tailor its approach to actual real-world scenarios.
Regarding claims 8 and 16, The apparatus of claim 9, wherein the processor is configured to collect additional data of at least one of the abnormal situation and the maneuver (See at least Fig. 10A in Matthews: Matthews discloses that At 1002, the AR module scans for and detects objects based on the path of the host vehicle [See at least Matthews, 0084]. Matthews further discloses that V2V may be used for object detection [See at least Matthews, 0085]. Also see at least Fig. 5 in Matthews: Matthews illustrates that the road area for which the vehicle is detecting data is indeed the road on which the vehicle is traveling [See at least Matthews, 0079]. The rest of the pictorial figures also illustrate this).
However, Matthews does not explicitly teach wherein the processor is further configured to train the AI model based on execution of the AI model on the additional data of at least one of the abnormal situation and the maneuver.
However, Moustafa does teach an ADAS wherein the processor is further configured to train the AI model based on execution of the AI model on the additional data of at least one of the abnormal situation and the maneuver (Moustafa teaches that a variety of sensors may provide data to cloud-based systems to aggregate and process this collection of data to provide information to multiple autonomous vehicles concerning sections of roadway and conditions affecting these routes [See at least Moustafa, 0251]. Moustafa further teaches that As noted above, in some cases, cloud-based systems and other systems may receive inputs associated with previous pullover and remote valet handover events and may detect characteristics common to these events [See at least Moustafa, 0251]. Moustafa further teaches that machine learning models may be built and trained from this information and such machine learning models may be deployed on and executed by roadside units, cloud-based support systems, remote valet computing systems, or the in-vehicle systems of the autonomous vehicles themselves to provide logic for predictively determining potential remote valet handoffs [See at least Moustafa, 0251]. Moustafa further teaches that through sensor data accessed by a given autonomous vehicle, the vehicle may determine in advance the areas along each road, where frequent pull-overs have occurred and/or remote valet handoffs are common [See at least Moustafa, 0251]. Moustafa further teaches that the autonomous vehicle may determine (e.g., from a corresponding machine learning model) that conditions reported for an upcoming section of road suggest a likelihood of a pull-over and/or remote valet handover [See at least Moustafa, 0251]). Both Moustafa and Matthews teach methods for receiving additional sensor data pertaining to a maneuver from multiple sources and using AI to autonomously perform maneuvers in response. However, only Moustafa explicitly teaches where the AI model is trained to execute the maneuver based on the additional sensor data.
It would have been obvious to anyone of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the AI model of Matthews to also be trained to execute the maneuver based on the additional sensor data, as in Moustafa. Doing so improves the accuracy and effectiveness of the model by allowing it to tailor its approach to actual real-world scenarios.
Examiner’s Suggestion to Help Applicant Overcome the Prior Art of Record
To expedite prosecution, examiner includes the below suggestion for applicant’s consideration:
None of the prior art references investigated this far appear to teach, “Context-based authorization to determine validity if a process can be executed allows ECUs to maintain secure operation and prevent unauthorized access from elements such as the vehicle’s CAN Bus [wherein the context includes one or more of] proximity contexts, nearby objects, distance to approaching objects, speed, and trajectory relative to other moving objects, and operational contexts such as an indication of whether the vehicle is moving or parked, the vehicle’s current speed, the transmission state,… cruise control, parking assist, driving assist, location-based contexts” as disclosed in paragraph [00145] of applicant’s specification. So amending the independent claims to include the following limitation would overcome the prior art of record:
“wherein the vehicle employs context-based authorization to prevent unauthorized access from a CAN bus of the vehicle based on one or more of: a nearby object, a parked state of the vehicle, a moving state of the vehicle, a current speed of the vehicle, a transmission state of the vehicle, a driving operation of the vehicle, or a location of the vehicle.”
The above amendment would overcome the prior art of record, although further search and consideration would be required before a determination of allowability could be made.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NAEEM T ALAM whose telephone number is (571)272-5901. The examiner can normally be reached M-F, 9am-5pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, FADEY JABR can be reached at (571) 272-1516. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NAEEM TASLIM ALAM/ Examiner, Art Unit 3668
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