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 .
Response to Arguments
Applicant's arguments filed 05/28/2026 have been fully considered, but they are not persuasive.
Applicant argues the cited references do not teach the limitation of “the potential triggering action being associated, based on previously learned driver habits and triggering actions data, with the undesirable driving habit of the subject driver”, as Beaurepaire (US 20230039738) has been admitted not to teach this limitation Wang (US 20220335820) does not remedy this deficiency. Applicant argues Wang does not teach the recited triggering actions data or an association between a triggering action and a subject driver’s undesirable driving habit, which is defined within the specification and contrasts with Wang’s recitations. Applicant argues Wang describes travel habit data consisting of the trips a driver took during a specified time period, how the driver got from the starting point to the destination, and the associated trip and route probabilities, which is all navigation related and there is nothing within Wang about learned undesirable driving habits of the driver, as defined in the specification. Applicant argues a driver’s trip and route history cannot reasonably be characterized as undesirable driving habits, which presents at least two fundamental problems with the Final Action: such an interpretation is inconsistent with the specification even under a broadest reasonable interpretation, and this interpretation effectively reads out the word “undesirable” from the claim, which inconsistently and impermissibly violate examination procedure. Applicant continues, Wang does not say anything about the recited triggering actions or the association between a learned triggering action and a subject driver’s learned undesirable driving habit, which is a central concept to the claims. Therefore, Applicant concludes Wang does not fill the gaps of Beaurepaire. Applicant argues Beaurepaire’s negative traffic impact is not the recited undesirable driving habit of a subject driver and the mapping is incorrect. Applicant, Beaurepaire describes traffic-flow effects caused by concerning behavior of an own vehicle such as following vehicles braking hard, which is an externally caused traffic reaction or disturbance, not a previously learned undesirable driving habit of a particular subject driver, and it is incorrect to characterize a natural reaction of a following vehicle to the own vehicle’s unnecessary braking behavior as an undesirable driving habit. Therefore, Applicant concludes the combination of Beaurepaire and Wang do not teach the independent claims, and the claims are allowable.
However, Wang teaches, as the Applicant appears to readily admit, determining travel habit data of a driver over a specified time period. Wang teaches that historical driver habit and traffic data are determined and used to form a traffic model representing traffic congestion, accidents, and the like. This necessarily learns all driver habits and driver actions performed historically within at least a specified historical period, both desirable and undesirable. Were this to preclude undesirable driver habits, as the Applicant appears to suggest, unclear boundaries would be applied outside of the scope of the reference that arbitrarily remove specific teachings and features, and render the reference inoperable for its intended purpose because one of ordinary skill would be unable to determine when the reference would operate and when it would not operate. This is arbitrary and illogical and as such, this interpretation of the reference would not have been taken by one of ordinary skill in the art and instead the reference would have been read for its explicitly and implicitly disclosed features, including that each and every diving habit and action taken within the historical time period is learned by the operations of Wang. Once these driver habits and driver actions are learned, a traffic model is constructed which represents traffic congestion, accidents, and the like. This associates each and every previous action with the evolvement of the traffic model, necessarily relating the actions to the effect on traffic, including the effect on each individual vehicle within traffic.
Beaurepaire, as established, teaches a driving behavior of the reference’s own vehicle may be determined within the context of traffic and environmental conditions around the vehicle and used to determine a traffic impact index caused by the driving behavior of the own vehicle, where the reference’s “own vehicle” aligns with the claim’s “one or more connected-vehicle drivers”. The impact of the driving behavior may be negative on traffic flow, including hard braking performed by individual vehicles, which is an undesirable driving habit of that individual vehicle, which aligns with the claim’s “subject driver”. This detects a traffic situation in which one vehicle performs an action that potentially triggers an undesirable action within a different individual vehicle.
By the combination of these references, Beaurepaire, in view of Wang, one of ordinary skill arrives at the traffic situation is learned such that the potential triggering action of the Beaurepaire’s own vehicle, which again aligns with the claimed “one or more connected-vehicle drivers”, is associated with its traffic impact at least by being passed through Wang’s traffic model taking learned desired and undesired driving habits and actions and determining the change in the traffic model, for example, where traffic now includes actions increasing traffic congestion, accidents, and the like within individual vehicles, which is an association of potential triggering actions with undesirable driving habits of other vehicles.
Wang does not appear to contrast or be inconsistent with the specification’s disclosed explanation of undesirable driver habits, as Wang learns all driver habits, nor does the interpretation read out any language of the claim, but rather each and every word has been given its broadest reasonable interpretation in light of the specification as originally filed. No procedure appears to have been violated in Examination. Further, it is unclear why harder than desired braking would not be considered an undesirable driving habit. Hard braking is well known to cause excessive wear and tear and is distinguished specifically from non-hard braking, which is desired braking. Even if hard braking does avoid an accident or is a natural response, this does not make such hard braking desirable, as it is particularly braking that is more excessive, when less hard braking would do based upon the traffic situation. As such, this interpretation will not be taken, and instead one of ordinary skill in the art would have interpreted the reference using its plain language and have understood that hard braking is undesired braking and normal braking is desired braking. The application of Beaurepaire is not incorrect, despite the Applicant’s assertions.
As such, Beaurepaire and Wang do teach each and every limitation within the claims and this argument is unpersuasive.
Applicant argues the dependent claims are allowable by virtue of their dependency.
This argument is unpersuasive as each claim has been fully rejected and for the same reasons as given above.
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.
Claim 1-8 and 10-20 is rejected under 35 U.S.C. 103 as being unpatentable over Beaurepaire et al. (US 20230039738), in view of Wang et al. (US 20220335820).
In regards to claim 1, Beaurepaire teaches a system, comprising: (Fig 1, 3, 7-9.)
a processor; ([0061] processor performs operations.) and
a memory storing machine-readable instructions that, when executed by the processor, cause the processor to: ([0061] memory stores instructions performed by processor.)
detect a traffic situation in which a potential triggering action by one or more connected-vehicle drivers is predicted to trigger an undesirable driving habit of a subject driver, ([0062], [0064], [0065] a driving behavior of the own vehicle may be determined within the context of traffic and environmental conditions around the own vehicle, and used to determine a traffic impact index caused by the driving behavior of the own vehicle. The own vehicle here is one or more connected vehicle drivers. [0028], [0030], [0032] the impact of the driving behavior may particularly be a negative impact on traffic flow, such as hard braking performed by individual vehicles. These negative impacts on traffic flow by individual vehicles are undesirable driving habits of a subject driver that are predicted to be triggered by the driving behavior of the another vehicle. [0088] vehicles may be connected to a services platform as connected vehicles.)
generate guidance for the one or more connected-vehicle drivers other than the subject driver regarding control of their respective connected vehicles to prevent the one or more connected-vehicle drivers other than the subject driver from carrying out the potential triggering action; ([0068] a recommended change in the behavior of the own vehicle is determined to improve the traffic impact including by preventing the driving behavior of the own vehicle, where the own vehicle is the one or more connected vehicles and the subject driver is a driver of another individual vehicle in traffic.) and
transmit the guidance to the one or more connected-vehicle drivers other than the subject driver; ([0068] a recommended change in the behavior of the own vehicle is determined to improve the traffic impact including by preventing the driving behavior of the own vehicle and transmitted and displayed by the own vehicle’s corresponding user interface. This reduces unsafe behaviors such as hard braking by reducing, preventing, and mitigating the causes of the unsafe behaviors, where the own vehicle is the one or more connected vehicle and the subject driver is another driver of an individual vehicle.)
wherein at least one of the one or more connected-vehicle drivers other than the subject driver carries out the guidance to prevent an unsafe traffic situation by preventing triggering of the undesirable driving habit. ([0068] a recommended change in the behavior of the own vehicle is determined to improve the traffic impact including by preventing the driving behavior of the own vehicle and transmitted and displayed by the own vehicle’s corresponding user interface, where the own vehicle is the one or more connected vehicle and the subject driver is another driver of an individual vehicle. This reduces unsafe behaviors such as hard braking by reducing, preventing, and mitigating the causes of the unsafe behaviors.)
Beaurepaire does not teach:
the potential triggering action being associated, based on previously learned driver habits and triggering actions data, with the undesirable driving habit of the subject driver;
However, Wang teaches determining historical driver habit and traffic data and forming a traffic model representing traffic congestion, accidents, and the like, which is at least in part composed based upon the historical driving habits of the driver ([0037], [0070]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the vehicle control system of Beaurepaire, by incorporating the teachings of Wang, such that historical driver habits and traffic data are stored, which form previously learned data, and used to construct a traffic model reflecting traffic congestion and accidents, at least also reflecting the driving behavior of the vehicles of Beaurepaire and the impact of each driving behavior from the vehicles of traffic conditions.
The motivation to do so is that, as acknowledged by Wang, this allows for better reflecting the actual traffic situation throughout a vehicle road environment ([0003], [0004]).
In regards to claim 2, Beaurepaire, as modified by Wang, teaches the system of claim 1, wherein the undesirable driving habit of the subject driver is learned by a machine-learning-based system in a connected vehicle driven by the subject driver through automated observation of the driving of the subject driver over a period of time preceding the detected traffic situation. ([0037], [0038] contextual data such as vehicle data, traffic data, and the like is fed to machine learning models to determine the traffic impact caused by particular behavior of the own vehicle, where [0054], [0060], [0088] machine learning models may be part of driving platform and implemented as a combination of local services within each vehicle and the server. [0032] analysis is performed both in real-time and retrospectively over a recent period. This includes a machine learning model that determines the traffic impact of each behavior of each vehicle through automated observation of the driving of each particular driver over a preceding time period.)
In regards to claim 3, Beaurepaire, as modified by Wang, teaches the system of claim 1, wherein the undesirable driving habit of the subject driver is predicted through present analysis of driving behavior of the subject driver by a machine-perception-based system in at least one connected vehicle driven by the one or more connected-vehicle drivers other than the subject vehicle. ([0037], [0038], [0062] traffic impact of own vehicle behavior and the own vehicle behavior itself can be determined using machine learning system with sensors on the vehicles and infrastructure. This is a machine-perception-based system in at least one connected vehicle driven by one or more connected vehicle drivers that is analyzed to determine driving behaviors. [0088] vehicles may be connected to a services platform as connected vehicles.)
In regards to claim 4, Beaurepaire, as modified by Wang, teaches the system of claim 1, wherein an association between the potential triggering action and the undesirable driving habit is learned by a machine-learning-based system in a connected vehicle driven by the subject driver through one or more of time-series analysis, retrospective analysis, and event clustering. ([0037], [0038] traffic impact of behavior is determined through machine learning models of vehicle, [0042] which includes historical analysis to train the models, which involves at least both time-series analysis and retrospective analysis. [0088] vehicles may be connected to a services platform as connected vehicles.)
In regards to claim 5, Beaurepaire, as modified by Wang, teaches the system of claim 1, wherein the guidance includes one or more of a speed advisory, a lane-change instruction, and an instruction to permit a vehicle driven by the subject driver to proceed, at a merge, ahead of a connected vehicle driven by one of the one or more connected-vehicle drivers other than the subject driver. ([0032], [0073] recommendation may be given to drive in a slower lane, pull over, or overtake using a full lane width, which include at least a lane-change instruction.)
In regards to claim 6, Beaurepaire, as modified by Wang, teaches the system of claim 1, wherein the subject driver is human and at least one of the one or more connected-vehicle drivers other than the subject driver is an automated driving system. ([0081] vehicles in communication with server performing operations may be human driven or autonomous vehicles, which includes the case of the other vehicles exhibiting the effects of traffic impact being human drivers and the own vehicle being an autonomous vehicle.)
In regards to claim 7, Beaurepaire, as modified by Wang, teaches the system of claim 6, wherein the automated driving system carries out the guidance unconditionally. ([0068], [0081] recommendation may be given and when the vehicle is an autonomous vehicle, the recommendation may be executed by the autonomous vehicle not based on further conditions, which is carrying out the guidance unconditionally.)
In regards to claim 8, Beaurepaire, as modified by Wang, teaches the system of claim 1, wherein the system is implemented in a server that communicates with one or more connected vehicles driven by the respective one or more connected-vehicle drivers other than the subject driver and with a connected vehicle driven by the subject driver. ([0084], [0087], [0088] vehicles may be in communication with server performing operations, where behavior of vehicles forms traffic, including vehicles particularly driven by other drivers which respond with a traffic impact based on driving behavior of own vehicle.)
In regards to claim 10, Beaurepaire teaches a non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to: ([0061] memory stores instructions performed by processor.)
detect a traffic situation in which a potential triggering action by one or more connected-vehicle drivers is predicted to trigger an undesirable driving habit of a subject driver, ([0062], [0064], [0065] a driving behavior of the own vehicle may be determined within the context of traffic and environmental conditions around the own vehicle, and used to determine a traffic impact index caused by the driving behavior of the own vehicle. The own vehicle here is one or more connected vehicle drivers. [0028], [0030], [0032] the impact of the driving behavior may particularly be a negative impact on traffic flow, such as hard braking performed by individual vehicles. These negative impacts on traffic flow by individual vehicles are undesirable driving habits of a subject driver that are predicted to be triggered by the driving behavior of the another vehicle. [0088] vehicles may be connected to a services platform as connected vehicles.)
generate guidance for the one or more connected-vehicle drivers other than the subject driver regarding control of their respective connected vehicles to prevent the one or more connected-vehicle drivers other than the subject driver from carrying out the potential triggering action; ([0068] a recommended change in the behavior of the own vehicle is determined to improve the traffic impact including by preventing the driving behavior of the own vehicle, where the own vehicle is the one or more connected vehicles and the subject driver is a driver of another individual vehicle in traffic.) and
transmit the guidance to the one or more connected-vehicle drivers other than the subject driver; ([0068] a recommended change in the behavior of the own vehicle is determined to improve the traffic impact including by preventing the driving behavior of the own vehicle and transmitted and displayed by the own vehicle’s corresponding user interface. This reduces unsafe behaviors such as hard braking by reducing, preventing, and mitigating the causes of the unsafe behaviors, where the own vehicle is the one or more connected vehicle and the subject driver is another driver of an individual vehicle.)
wherein at least one of the one or more connected-vehicle drivers other than the subject driver carries out the guidance to prevent an unsafe traffic situation by preventing triggering of the undesirable driving habit. ([0068] a recommended change in the behavior of the own vehicle is determined to improve the traffic impact including by preventing the driving behavior of the own vehicle and transmitted and displayed by the own vehicle’s corresponding user interface, where the own vehicle is the one or more connected vehicle and the subject driver is another driver of an individual vehicle. This reduces unsafe behaviors such as hard braking by reducing, preventing, and mitigating the causes of the unsafe behaviors.)
Beaurepaire does not teach:
the potential triggering action being associated, based on previously learned driver habits and triggering actions data, with the undesirable driving habit of the subject driver;
However, Wang teaches determining historical driver habit and traffic data and forming a traffic model representing traffic congestion, accidents, and the like, which is at least in part composed based upon the historical driving habits of the driver ([0037], [0070]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the vehicle control instructions of Beaurepaire, by incorporating the teachings of Wang, such that historical driver habits and traffic data are stored, which form previously learned data, and used to construct a traffic model reflecting traffic congestion and accidents, at least also reflecting the driving behavior of the vehicles of Beaurepaire and the impact of each driving behavior from the vehicles of traffic conditions.
The motivation to do so is that, as acknowledged by Wang, this allows for better reflecting the actual traffic situation throughout a vehicle road environment ([0003], [0004]).
In regards to claim 11, Beaurepaire, as modified by Wang, teaches the non-transitory computer-readable medium of claim 10.
Claim 11 recites a non-transitory computer-readable medium having substantially the same features of claim 2 above, therefore claim 11 is rejected for the same reasons as claim 2.
In regards to claim 12, Beaurepaire, as modified by Wang, teaches the non-transitory computer-readable medium of claim 10.
Claim 12 recites a non-transitory computer-readable medium having substantially the same features of claim 3 above, therefore claim 12 is rejected for the same reasons as claim 3.
In regards to claim 13, Beaurepaire, as modified by Wang, teaches the non-transitory computer-readable medium of claim 10.
Claim 13 recites a non-transitory computer-readable medium having substantially the same features of claim 4 above, therefore claim 13 is rejected for the same reasons as claim 4.
In regards to claim 14, Beaurepaire teaches a method, comprising: (Fig 4.)
detecting a traffic situation in which a potential triggering action by one or more connected-vehicle drivers is predicted to trigger an undesirable driving habit of a subject driver, ([0062], [0064], [0065] in steps 401-405, a driving behavior of the own vehicle may be determined within the context of traffic and environmental conditions around the own vehicle, and used to determine a traffic impact index caused by the driving behavior of the own vehicle. The own vehicle here is one or more connected vehicle drivers. [0028], [0030], [0032] the impact of the driving behavior may particularly be a negative impact on traffic flow, such as hard braking performed by individual vehicles. These negative impacts on traffic flow by individual vehicles are undesirable driving habits of a subject driver that are predicted to be triggered by the driving behavior of the another vehicle. [0088] vehicles may be connected to a services platform as connected vehicles.)
generating guidance for the one or more connected-vehicle drivers other than the subject driver regarding control of their respective connected vehicles to prevent the one or more connected-vehicle drivers other than the subject driver from carrying out the potential triggering action; ([0068] a recommended change in the behavior of the own vehicle is determined to improve the traffic impact including by preventing the driving behavior of the own vehicle, where the own vehicle is the one or more connected vehicles and the subject driver is a driver of another individual vehicle in traffic.) and
transmitting the guidance to the one or more connected-vehicle drivers other than the subject driver; ([0068] a recommended change in the behavior of the own vehicle is determined to improve the traffic impact including by preventing the driving behavior of the own vehicle and transmitted and displayed by the own vehicle’s corresponding user interface. This reduces unsafe behaviors such as hard braking by reducing, preventing, and mitigating the causes of the unsafe behaviors, where the own vehicle is the one or more connected vehicle and the subject driver is another driver of an individual vehicle.)
wherein at least one of the one or more connected-vehicle drivers other than the subject driver carries out the guidance to prevent an unsafe traffic situation by preventing triggering of the undesirable driving habit. ([0068] a recommended change in the behavior of the own vehicle is determined to improve the traffic impact including by preventing the driving behavior of the own vehicle and transmitted and displayed by the own vehicle’s corresponding user interface, where the own vehicle is the one or more connected vehicle and the subject driver is another driver of an individual vehicle. This reduces unsafe behaviors such as hard braking by reducing, preventing, and mitigating the causes of the unsafe behaviors.)
Beaurepaire does not teach:
the potential triggering action being associated, based on previously learned driver habits and triggering actions, with the undesirable driving habit of the subject driver;
However, Wang teaches determining historical driver habit and traffic data and forming a traffic model representing traffic congestion, accidents, and the like, which is at least in part composed based upon the historical driving habits of the driver ([0037], [0070]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the vehicle control method of Beaurepaire, by incorporating the teachings of Wang, such that historical driver habits and traffic data are stored, which form previously learned data, and used to construct a traffic model reflecting traffic congestion and accidents, at least also reflecting the driving behavior of the vehicles of Beaurepaire and the impact of each driving behavior from the vehicles of traffic conditions.
The motivation to do so is that, as acknowledged by Wang, this allows for better reflecting the actual traffic situation throughout a vehicle road environment ([0003], [0004]).
In regards to claim 15, Beaurepaire, as modified by Wang, teaches the method of claim 14.
Claim 15 recites a method having substantially the same features of claim 2 above, therefore claim 15 is rejected for the same reasons as claim 2.
In regards to claim 16, Beaurepaire, as modified by Wang, teaches the method of claim 14.
Claim 16 recites a method having substantially the same features of claim 3 above, therefore claim 16 is rejected for the same reasons as claim 3.
In regards to claim 17, Beaurepaire, as modified by Wang, teaches the method of claim 14.
Claim 17 recites a method having substantially the same features of claim 4 above, therefore claim 17 is rejected for the same reasons as claim 4.
In regards to claim 18, Beaurepaire, as modified by Wang, teaches the method of claim 14.
Claim 18 recites a method having substantially the same features of claim 5 above, therefore claim 18 is rejected for the same reasons as claim 5.
In regards to claim 19, Beaurepaire, as modified by Wang, teaches the method of claim 14.
Claim 19 recites a method having substantially the same features of claim 6 above, therefore claim 19 is rejected for the same reasons as claim 6.
In regards to claim 20, Beaurepaire, as modified by Wang, teaches the method of claim 19.
Claim 20 recites a method having substantially the same features of claim 7 above, therefore claim 20 is rejected for the same reasons as claim 7.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Beaurepaire, in view of Wang, in further view of Higuchi et al. (US 20200153902).
In regards to claim 9, Beaurepaire, as modified by Wang, teaches the system of claim 1.
Beaurepaire, as modified by Wang, does not teach: wherein the system is implemented in a distributed-computing fashion among a plurality of connected vehicles that are networked in a vehicular micro cloud.
However, Higuchi teaches establishing a micro cloud of a distributed system between multiple vehicles ([0003]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the vehicle control system of Beaurepaire, as already modified by Wang, by incorporating the teachings of Higuchi, such that the vehicles and server form a micro cloud system implemented in a distributed computing fashion.
The motivation to do so is that, as acknowledged by Higuchi, this allows for accounting for increased network traffic of the connected vehicles ([0003]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ucar et al. (US 11548515) teaches learning undesirable driving habits of vehicle drivers.
Beaurepaire et al. (US 12472955) teaches determining historical vehicle conditions that have caused aggressive behaviors by vehicle drivers.
Cunningham et al. (US 20220097732) teaches determining the responses of neighboring vehicles to an own vehicle’s action and iterating through vehicle actions to determine the action with the least risk.
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/MATTHIAS S WEISFELD/Examiner, Art Unit 3661
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