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 AMENDMENTS
Claims 1, 2, and 4-21 are presented for examination.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1 and 13 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claims contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor, at the time the application was filed, had possession of the claimed invention.
Claim 1 claims, in part:
“where the first hidden driver state is not directly observable based upon driver inputs of one or more of an accelerator, brake system and steering system…”
The applicant’s published application at Wang et al., U.S. 2025/0319881 states:
[0063] Next, from the acceleration data and determined aggression ratings, it can be seen that the driver aggression level varies in different circumstances and at different times. Additionally, the driver may have a state of mind or mood, which may be affected by driving conditions or other factors, and these moods affect the driving behavior of the driver. For example, if the driver is excited or upset, the driver might drive more aggressively than if the driver is tired or distracted (e.g. on a phone call or listening to a pod cast or other audio). Various driver states may be modeled or considered in the model, the driver states may be considered as hidden because they are not directly observable from the acceleration data, and a Hidden Markov Model may be utilized to predict the hidden driver states from observable acceleration data and/or aggression ratings determined at least in part base on the acceleration data.
The operation of the following are control inputs: accelerator, steering system, and brake system, which are respectively related to acceleration, turning, and braking data. The functions of each control input are not the same as these respective parameters, parameters used in measuring characteristics of a vehicle in motion. The former are functional inputs. The latter are parameters. Accordingly, claiming the former is tantamount to claiming patentable subject matter not originally taught in the original disclosure.
Claims 2, 4-12, and 14-21 are rejected for the same reasons due to their dependency on rejected claims 1 and 13.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of the first paragraph of 35 U.S.C. 112(b):
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 21 recites the limitation "the second hidden driver state.” There is insufficient antecedent basis for this limitation in the claim. Claim 13, from which claim 21 depends, does not disclose the limitation “a second hidden driver state.”
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103(a) are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, and 6-11 are rejected under 35 U.S.C. 103 as being unpatentable over Straub et al., U.S. WO 2019/174932 in view of Naitou et al., U.S. 2008/0120025.
On claim 1, Straub cites except as underlined:
A method of determining energy use during operation of a vehicle,
comprising:
determining an aggression rating associated with current use of a vehicle;
Straub, page 2, [0004-5] discloses:
According to the invention, a proactive display of an optimal (er) en F ahr (er) behavior,
such as for example an optimized travel speed, is thus carried out. The total travel time
and/or the costs can be reduced by such early and optimal action recommendations. In
particular, unnecessary charging stops can be avoided by reducing the total travel time
and/or the energy consumption. In typical embodiments, the anticipated driver behavior
may be derived from a learned driver model. In particular, the vehicle can collect data
about the driver's driving (er), such as, for example, via acceleration processes,
deceleration processes, travel speeds and the like. These data can be used to create an
individual (behavior) profile of the driver. For example, an expected speed value may be
predicted with which the driver will travel on a determined route section, such as a
highway.
determining a first hidden driver state as a function of the determined aggression rating over time,
As Straub disclosed previously:
the vehicle can collect data about the driver's driving (er), such as, for example, via acceleration processes, deceleration processes, travel speeds and the like. These data can be used to create an individual (behavior) profile of the driver. For example, an expected speed value may be predicted with which the driver will travel on a determined route section, such as a highway. (Inherently, the driver’s recorded behavior is done over time).
where the first hidden driver state is not directly observable based upon driver inputs of one or more of an accelerator, brake system and steering system;
determining an energy use level as a function of the first hidden driver state;
As Straub above: The total travel time and/or the costs can be reduced by such early and optimal action recommendations. In particular, unnecessary charging stops can be avoided by reducing the total travel time and/or the energy consumption
determining a prevalence of the first hidden driver state;
Straub, page 2, [0004-5] discloses: the anticipated driver behavior may be derived from a learned driver model. In particular, the vehicle can collect data about the driver's driving (er), such as, for example, via acceleration processes, deceleration processes, travel speeds and the like.
(the claimed "prevalence" is time involved to determine the driver behavior. The learned driver model inherently includes a time to learn the characteristics and behaviors of the driver)
and
providing a prediction of future energy use based at least in part on the prevalence of the first hidden driver state.
[0006] The term "anticipated driver behavior" as used in the present disclosure relates to a
prediction of a behavior of a particular driver based on a driver model. For example, it can
be determined that the driver will drive a certain speed on a specific route at a certain
speed. Thus, a probable energy consumption can be derived and an energy prediction can
be created for the route that is tailored to this particular driver. In other words, the energy
prediction takes into account an individual
Regarding the excepted: where the first hidden driver state is not directly observable based upon driver inputs of one or more of an accelerator, brake system and steering system, Straub, as disclosed, included the following:
In particular, the vehicle can collect data
about the driver's driving (er), such as, for example, via acceleration processes,
deceleration processes, travel speeds and the like. These data can be used to create an
individual (behavior) profile of the driver. For example, an expected speed value may be
predicted with which the driver will travel on a determined route section, such as a
highway.
Straub didn’t disclose the excepted claim limitations.
In the same art of driver monitoring, Naitou discloses.
[0004] In the driving behavior prediction apparatus described in the patent document 1, for example, the driving behavior of a driver is predicted by a statistical method using a hidden Markov model which is generally used for voice recognition. To be concrete, the driving behavior prediction apparatus is provided with plural models which correspond to driving behaviors (for example, turning right or left, and running straight) to be objects of recognition. Each of such models outputs an occurrence probability of the corresponding driving behavior by having driving data inputted, the driving data being represented by, for example, the depth to which the accelerator pedal is depressed, the depth to which the brake pedal is depressed, and the vehicle speed and acceleration. Thus, such models are generated based on driving data collected when driving behaviors to be objects of driving behavior prediction are practiced. The models calculate the occurrence probabilities of driving behaviors corresponding to them, respectively, by having actual driving data inputted, and the driving behavior corresponding to the model with the highest occurrence probability is predicted to be the driving behavior the driver will actually practice. In the driving behavior prediction apparatus using a hidden Markov model, to make the apparatus compatible with the driving behavior characteristics of various drivers, driving behavior patterns are recognized using models generated based on plural driving data. In the driving behavior prediction apparatus, unless the models are generated using driving data not much different from actual driving data, the result of predicting a driving behavior may differ from the driving behavior actually practiced by the driver, as a result, causing the driving behavior prediction accuracy to decline. Therefore, to accurately predict the driving behavior of each of plural drivers, it is necessary to generate and use models corresponding to individual drivers.
It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention modify Straub’s embodiment, using the disclosed features of Naitou to form an embodiment meeting the claimed invention.
Naitou, as above, discloses using driver inputs analyzed via a Hidden Markov Model to issue a driver prediction. In short, Naitou teaches the use of Hidden Markov Models to enable future prediction of driver behavior.
One of ordinary skill, apprised with the features disclosed in Naitou, would have implemented its known features into Straub to provide an embodiment meeting the claimed invention.
On claim 6, Straub cites except as underlined:
The method of claim 1 which also includes comparing an actual energy use to
the predicted future energy use over a period of time relating to at least part of the duration of the predicted future energy use and applying a correction factor when the actual energy use differs from the predicted future energy use by more than a threshold.
Straub cites:
Page 2, [0005-0008] In typical embodiments, the anticipated driver behavior may be derived from a learned driver model. In particular, the vehicle can collect data about the driver's driving (er), such as, for example, via acceleration processes, deceleration
processes, travel speeds and the like. These data can be used to create an individual (behavior) profile of the driver. For example, an expected speed value may be predicted with which the driver will travel on a determined route section, such as a highway. This action recommendation may indicate a behavior deviating from this anticipated behavior, such as a reduced speed. By predicting the driver behavior and the proactive action recommendation, an energy consumption can be reduced, as a result of which, for example, costs and a total travel time can be reduced and unnecessary charging or tank stops can be avoided. According to some embodiments, the optimized driving behavior, such as the optimized energy consumption, is determined on the basis of a fleet dynamics of a plurality of vehicles, i.e. a fleet. The fleet dynamics may comprise dynamic parameters (e.g. speeds and accelerations) and/or energy consumption data of the plurality of vehicles for the route or route sections of the route. The data can be collected centrally. In particular, for each route section (or for each segment of a route section divided into a plurality of segments), a W probability distribution of the dynamic parameters and/or of the energy consumption of the vehicles of the fleet can be created. The optimized energy consumption can be determined from the fleet data, and in particular the probability distribution, and the action recommendation for the driver or the driving function can be derived therefrom.
Straub doesn't disclose the above excepted claimed invention.
However, it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to try and include into Straub the claimed features.
As stated in Straub, there is "anticipated driving behavior derived from a learned
driving model." In a previous example, the "anticipated driving behavior" would be a
behavior recorded on the basis of the driver prone to accelerations and driving patterns.
This would be the claimed "predicted future energy use." Additionally, Straub includes
an example of optimum energy consumption based on fleet dynamics, which is taken to
be synonymous with the claimed "actual energy use." Already, Straub is using these
different parameters to show a difference between the two driver performances, sans
the claimed "correction factors." The cited "correction factors" are functions used to
mathematically calculate a relationship between one known set of data to another and
extrapolate future data based on the mathematical relationship between the two known
sets of data.
On claim 7, Straub cites except the underlined:
The method of claim 1 wherein the aggression rating is determined at a desired frequency or continually within a rolling period of time.
However, Straub discloses:
Page 2, [0005] Action recommendation may indicate a behavior deviating from this anticipated behavior, such as a reduced speed. By predicting the driver behavior and the proactive action recommendation, an energy consumption can be reduced, as a result of which, for example, costs and a total travel time can be reduced and unnecessary charging or tank stops can be avoided.
It would have been obvious to one of ordinary skill in the art at the time of the claimed invention to cite Straub such that the cited embodiment meets the claimed limitations. The claimed the aggression rating is determined at a desired frequency or continually within a rolling period of time is met where clearly discloses a condition of a type of aggression, that is, a lower "aggression rating" where the driver isn't behaving in a manner where the vehicle is accelerating such that unnecessary fuel consumption is taking place. Hence, the claimed "the aggression rating is determined at a desired frequency or continually within a rolling period of time" is when the driver is driving where energy consumption is lowest over a period of time. One of ordinary skill would have observed the plain meaning of Straub's embodiment would meet the claimed invention.
On claim 8, Straub cites except as underlined:
The method of claim 7 wherein the prevalence of the first hidden driver state is
determined at least in part with regard to duration of the rolling period of time.
Regarding the excepted: the rolling period of time, Straub discloses:
Straub, page 2, [0004-5] discloses: According to the invention, a proactive display of an optimal (er) en F ahr (er) behavior, such as for example an optimized travel speed, is thus carried out. The total travel time and/or the costs can be reduced by such early and optimal action recommendations. In particular, unnecessary charging stops can be avoided by reducing the total travel time and/or the energy consumption. In typical embodiments, the anticipated driver behavior may be derived from a learned driver model. In particular, the vehicle can collect data about the driver's driving (er), such as, for example, via acceleration processes, deceleration processes, travel speeds and the like.
The cited "learned drive model" presumes the driver behavior is learned over a period of time to ascertain a reasonable driver behavior "snapshot." Straub doesn't disclose the excepted claim limitations. However, it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to include into Straub the use of a "rolling period of time." One of ordinary skill would expect that while driver data is collected over time and used, changes in the driver's behavior can be expected and therefore, the driver's behavior is 'rerecord' to ensure the latest driver data is up to date and not based on stale information.
Regarding the excepted “first hidden driver state,”
Straub discloses:
Page 2, [0005] Action recommendation may indicate a behavior deviating from this anticipated behavior, such as a reduced speed. By predicting the driver behavior and the proactive action recommendation, an energy consumption can be reduced, as a result of which, for example, costs and a total travel time can be reduced and unnecessary charging or tank stops can be avoided.
Straub didn’t disclose the excepted:
wherein the prevalence of the first hidden driver state is determined at least in part with regard to duration of the rolling period of time.
In the same art of driver monitoring, Naitao cites:
[0004] In the driving behavior prediction apparatus described in the patent document 1, for example, the driving behavior of a driver is predicted by a statistical method using a hidden Markov model which is generally used for voice recognition. To be concrete, the driving behavior prediction apparatus is provided with plural models which correspond to driving behaviors (for example, turning right or left, and running straight) to be objects of recognition. Each of such models outputs an occurrence probability of the corresponding driving behavior by having driving data inputted, the driving data being represented by, for example, the depth to which the accelerator pedal is depressed, the depth to which the brake pedal is depressed, and the vehicle speed and acceleration.
It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to include into Straub the features disclosed in Naitau such that the claimed “first hidden driver state” is realized.
Clearly, as was previously inferred in Straub, lower energy consumption infers reduced speed, and therefore, reduced acceleration. In contrast, a higher energy consumption infers a higher speed, and therefore, higher acceleration. Naitau’s hidden Markov Modeling feature would have picked up on the higher speed and acceleration suggested in Straub such that the claimed “first hidden driver state” is met. One of ordinary skill, apprised with the known driving detection and analysis features of Naitau, would have provided the condition of a “first hidden driver state” meeting the claimed invention.
On claim 9, Straub cites except as underlined:
The method of claim 1 wherein the first driver state is one of multiple driver states
that are each determined as a function of the determined aggression rating over time, and a prevalence of each of the multiple driver states is determined, and wherein the prediction of future energy use is based at least in part on the prevalence of each of the multiple driver states.
Straub discloses:
Page 2, [0005] Action recommendation may indicate a behavior deviating from this
anticipated behavior, such as a reduced speed. By predicting the driver behavior and the
proactive action recommendation, an energy consumption can be reduced, as a result of
which, for example, costs and a total travel time can be reduced and unnecessary
charging or tank stops can be avoided.
Straub doesn't specifically disclose the excepted claim limitations.
However, it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to include into Straub an embodiment meeting the claimed invention.
As disclosed above, Straub asserts there is a commensurate energy
consumption associated with particular driving behaviors. Accordingly, the different driver behaviors disclosed in Straub could be construed to be the same as the claimed "multiple driver states." Each driver state, or driver behavior, carries with it an innate energy consumption. Thus, when driver behavior is analyzed over a period of time, driver behavior can be linked to energy consumption depending on the determined driving behavior. For this reason, one of ordinary skill would have derived energy consumption based on driver behavior based on the Straub's embodiment such that the claimed invention is realized.
On claim 10, Straub cites except as underlined:
The method of claim 1 wherein the aggression rating is determined as a function
of at least a first acceleration threshold that is associated with an increasing vehicle speed.
Straub discloses:
Page 2, [0005-0008] In typical embodiments, the anticipated driver behavior may be
derived from a learned driver model. In particular, the vehicle can collect data about the
driver's driving (er), such as, for example, via acceleration processes, deceleration
processes, travel speeds and the like. These data can be used to create an
individual (behavior) profile of the driver.
For example, an expected speed value may be
predicted with which the driver will travel on a determined route
section, such as a highway. This
Action recommendation may indicate a behavior deviating from this anticipated behavior,
such as a reduced speed. By predicting the driver behavior and the
proactive action recommendation, an energy consumption can be reduced, as a result of
which, for example, costs and a total travel time can be reduced
and unnecessary charging or tank stops can be avoided.
According to some embodiments, the optimized driving behavior, such as the optimized
energy consumption,
(Per the applicant's specification: [0046] In generally, more aggressive driving uses greater energy and reduces the effective range of the vehicle, and can wear out tires, brakes and other vehicle components more quickly than less aggressive driving).
Straub doesn't disclose the excepted claim limitations. However, it would have been obvious to one of ordinary skill in the art at the time of the claimed invention to infer the claimed invention via the following: if reduced speed is one driver behavior, clearly, increasing velocity is associated with another driver behavior. Thus, one of ordinary skill would have derived the claimed aggressive rating based on the applicant's specification and on Straub's driver behavior analysis embodiment.
On claim 11, Straub cites:
The method of claim 10 which also includes determining a second acceleration of the vehicle, wherein the second acceleration is associated with a decreasing speed of the vehicle, and wherein the aggression rating is determined at least in part as a function of a magnitude of the second acceleration. Straub cites:
Page 2, [0005] Action recommendation may indicate a behavior deviating from this anticipated behavior, such as a reduced speed. By predicting the driver behavior and the proactive action recommendation, an energy consumption can be reduced, as a result of which, for example, costs and a total travel time can be reduced and unnecessary charging or tank stops can be avoided.
Claims 12 are rejected under 35 U.S.C. 103 as being unpatentable over Straub et al., U.S. WO 2019/174932 in view of Naitou et al., U.S. 2008/0120025 and Chandra et al., U.S. 2023/0271618.
On claim 12, Straub cites except as underlined:
The method of claim 11 which also includes determining a third acceleration of
the vehicle, wherein the third acceleration is a lateral acceleration associated with turning of the vehicle, and wherein the aggression rating is determined at least in part as a function of a magnitude of the third acceleration.
As previously disclosed, Straub includes driver behavior deemed to be
aggressive driving behavior. The driving behavior was disclosed as having a having
either an increased acceleration/speeding or reduced acceleration with a lower energy consumption. However, Straub did not disclose the excepted claim limitations. In the same art of driver analysis, Chandra discloses;
[0050] The method 200 is further preferably configured to detect driving behavior which includes lateral events (a.k.a. lateral movement events), with lateral events preferably herein referring to behaviors and/or actions which involve the vehicle moving, accelerating, steering, or otherwise deviating in a lateral direction (e.g., non-parallel with respect to the vehicle's course, perpendicular or substantially perpendicular with respect to the vehicle's heading/trajectory, etc.). These can include, but are not limited to: lane change behaviors and/or actions (e.g., aggressive and/or unsafe lane changing, fast lane changing, high speed and/or high acceleration lane changing, lane changing without signaling, etc.), swerving behaviors and/or actions, driving off-center of a lane (e.g., at the edge of a lane, between lanes, etc.), drifting within and/or out of a lane, and/or any other events involving lateral motion. Additionally or alternatively, the method 200 can detect any other driving behavior.
It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to include into Straub, Chandra's driver analysis system wherein the driving analysis includes lateral acceleration driving monitoring. One of ordinary skill would have added this feature to Straub to further characterize a driver's driving habits.
Claims 2-5 are rejected under 35 U.S.C. 103 as being unpatentable over Straub et al., U.S. WO 2019/174932 in view of Naitou et al., U.S. 2008/0120025 and Moustafa et al., WO 2020/205648 A1.
On claim 2, Struab cites except as underlined:
The method of claim 1 which also includes determining a second
hidden driver state as a function of the determined aggression rating over time, wherein the second hidden driver state is associated with a lower aggression rating than the first hidden driver state
and wherein the second hidden driver state is not directly observable based upon driver inputs of one or more of an accelerator, brake system and steering system,
See claim 1 regarding the claimed “hidden driver state.” Nothing is observable unless the observation is provided under the cited “Hidden Markov Model.”
and
which also includes determining an energy use level as a function of the second hidden driver state, and
determining a prevalence of the second hidden driver state,
and wherein the prediction of future energy use is based at least in part on the prevalence of the second hidden driver state.
Regarding the excepted: determining a second
hidden driver state as a function of the determined aggression rating over time, wherein the second hidden driver state is associated with a lower aggression rating than the first hidden driver state,
Straub disclosed:
Page 2, [0005] Action recommendation may indicate a behavior deviating from this anticipated behavior, such as a reduced speed. By predicting the driver behavior and the proactive action recommendation, an energy consumption can be reduced, as a result of which, for example, costs and a total travel time can be reduced and unnecessary charging or tank stops can be avoided.
Straub, while suggesting a lower energy consumption is attributed by driving behavior that includes reduction of speeds (and therefore, less accelerations), Straub doesn’t attribute determining the aggression rating over time due to a lower rating due to the hidden driver state. In the same art of vehicle monitoring,
Moustafa cites:
[00138] The following disclosure provides various possible embodiments, or examples, for implementing a fault and intrusion detection system 1800 for highly automated and autonomous vehicles as shown in FIG. 18. In one or more embodiments, vehicle motion prediction events and control commands, which are both a higher level of abstraction, are monitored. Based on the current state of vehicle motion parameters and road parameters, a vehicle remains within a certain motion envelope. A temporal normal behavior model 1841 is constructed to maintain adherence to the motion envelope. In at least one embodiment, at least two algorithms are used to build the temporal normal behavior model. The algorithms include a vehicle behavior model 1842 (e.g., based on a Hidden Markov Model (HMM)) for learning normal vehicle behavior and a regression model 1844 to find the deviation from the vehicle behavior model. In particular, the regression model is used to determine whether the vehicle behavior model correctly detects a fault, where the fault could be a vehicle system error or a malicious attack on the vehicle system.
It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to include into Straub and Naitau the disclosed features of Moustafa such that the claimed invention is realized.
Moustafa discloses a known feature of using Hidden Markov Modeling to determine normal (and thus, “lower aggression” driving”). One of ordinary skill would have included such a feature into Straub and Naitau to form a baseline reference for non-aggressive driving for which to compare that driving against monitored driving that shows aggressive tendencies.
On claim 4, Straub and Naitau cites except as underlined:
The method of claim 1 wherein the first hidden driver state is determined at least in part with a Hidden Markov Model.
(The above excepted claim limitations mean the “first hidden driver state” is one of driver aggression that exceeds normal driving)
Straub, Naitau, and Moustafa, as previously disclosed in the rejection of claim 2, included an embodiment where the Hidden Markov Modeling feature was used to determine normal vehicle behavior (which is analogous the claimed second hidden driver state). Furthermore, Straub disclosed:
Page 2, [0005] Action recommendation may indicate a behavior deviating from this anticipated behavior, such as a reduced speed. By predicting the driver behavior and the proactive action recommendation, an energy consumption can be reduced, as a result of which, for example, costs and a total travel time can be reduced and unnecessary charging or tank stops can be avoided.
And Naitau disclosed:
[0004] In the driving behavior prediction apparatus described in the patent document 1, for example, the driving behavior of a driver is predicted by a statistical method using a hidden Markov model which is generally used for voice recognition. To be concrete, the driving behavior prediction apparatus is provided with plural models which correspond to driving behaviors (for example, turning right or left, and running straight) to be objects of recognition. Each of such models outputs an occurrence probability of the corresponding driving behavior by having driving data inputted, the driving data being represented by, for example, the depth to which the accelerator pedal is depressed, the depth to which the brake pedal is depressed, and the vehicle speed and acceleration.
None of the above references specifically disclose using a Hidden Markov Model to determine a first hidden driver state. However, it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention, using the information derived from Straub, Naitau, and Moustafa to provide an embodiment meeting the claimed invention.
Straub at least discloses predicting driver behavior based on monitoring and recommending vehicle energy consumption. That is, to lower energy consumption, the driver is admonished to reduce speed. By inference, higher energy consumption is garnered when the driver drives the vehicle at a higher speed, or higher acceleration.
Naitau teaches the use of a Hidden Markov Model to determine driver behavior, to include monitoring when a vehicle’s accelerator is depressed.
Moustafa discloses providing a normal driving baseline using Hidden Markov Modeling. Accordingly, one of ordinary skill, apprised with an embodiment disposed to monitoring and predicting normal driving behavior, would also include abnormal driving behavior that includes the inferred higher speeds and accelerations such that those monitored driving characteristics would results in data meeting the claimed ‘first hidden driver state.”
Accordingly, it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to try, based on the known features disclosed in the references, to construct an embodiment meeting the claimed invention.
On claim 5, Straub, Naitau, and Moustafa cites:
The method of claim 2 wherein the first hidden driver state and the second hidden driver state are determined at least in part with a Hidden Markov Model. See the rejection of claim 4 which discloses the same subject matter as claim 5 and is rejected for the same reasons.
Claims 13-15 are rejected under 35 USC 103 as being unpatentable over Straub et al., WO 2019/174932 (English translation) in view of Naitou et al., U.S. 2008/0120025 and Cha et al., U.S. 2022/0028183.
On claim 13, Straub cites except as underlined:
A system for determining energy use during operation of a vehicle, comprising: one or more processors.
Page 3, paragraph 2, Typically, the driver assistance method is executed entirely by the vehicle, such as a processor in the vehicle, or a server external to the vehicle.
a memory; and
one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, the one or more programs including instructions to: determine an aggression rating associated with current use of a vehicle;
Straub, page 2, [0004-5] discloses: According to the invention, a proactive display of an optimal (er) en F ahr (er) behavior, such as for example an optimized travel speed, is thus carried out. The total travel time and/or the costs can be reduced by such early and optimal action recommendations. In particular, unnecessary charging stops can be avoided by reducing the total travel time and/or the energy consumption. In typical embodiments, the anticipated driver behavior may be derived from a learned driver model. In particular, the vehicle can collect data about the driver's driving (er), such as, for example, via acceleration processes, deceleration processes, travel speeds and the like. These data can be used to create an individual (behavior) profile of the driver. For example, an expected speed value may be predicted with which the driver will travel on a determined route section, such as a highway. determine a first driver state as a function of the determined aggression rating over time; as above: anticipated driver behavior may be derived from a learned driver mode And the anticipated driver behavior may be derived from a learned driver model. In particular, the vehicle can collect data about the driver's driving (er), such as, for example, via acceleration processes, deceleration processes, travel speeds and the like. determine an energy use level as a function of the first driver state;
As above:
The total travel time and/or the costs can be reduced by such early and optimal action recommendations. In particular, unnecessary charging stops can be avoided by reducing the total travel time and/or the energy consumption
And
Page 3 and [0005] The F driver assistance method 100 comprises, in block 110, a creation of an energy forecast for a route based on an anticipated driver behavior, in block 120 a determination of a driving behavior optimized with respect to the energy forecast and/or optimized energy consumption for the route and in block 130 an output of an action recommendation on the basis of the optimized driving behavior or optimized energy consumption. Typically, the optimized driving behavior is determined on the optimized energy consumption or corresponds to the optimized energy consumption.
According to the invention, a proactive determination and display of an optimal driving behavior, such as, for example, an optimum travel speed, takes place. The total travel time and/or the costs can result from such early and optimum determine a prevalence of the first driver state;
Straub, page 2, [0004-5] discloses: the anticipated driver behavior may be derived from a
learned driver model. In particular, the vehicle can collect data about the driver's driving
(er), such as, for example, via acceleration processes, deceleration processes, travel speeds and the like. And provide a prediction of future energy use based at least in part on the prevalence of the first driver state.
([0006] The term "anticipated driver behavior" as used in the applicant’s disclosure relates to a prediction of a behavior of a particular driver based on a driver model. For example, it can be determined that the driver will drive a certain speed on a specific route at a certain speed. Thus, a probable energy consumption can be derived and an energy prediction can be created for the route that is tailored to this particular driver. In other words, the energy prediction takes into account an individual’s driving behavior).
Regarding the excepted: determining an aggression rating associated with
current use of a vehicle, Straub, as previously disclosed, cites "anticipated driver
behavior may be derived from a learned driving mode." Further, Straub disclosed "
In particular, the vehicle can collect data about the driver's driving (er), such as, for example, via acceleration processes, deceleration processes, travel speeds and the like."
Straub doesn't specifically disclose an "aggressive rating" associated with the
current use of a vehicle. However, it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to include into Straub an embodiment wherein the claimed "aggressive rating" is measured in accordance with Straub's embodiment.
Straub is agnostic to the claimed "aggressive driving." "Aggressive driving" is a
type of behavior that may include different aspects of driving such as increased
acceleration/speeding that likely leads to a decrease in gas mileage and an increase in
energy usage. The claimed "aggressive rating" is a the level of which the driver exhibits the "aggressive driving" behavior. Per Straub,
“In particular, the vehicle can collect data about the driver's driving (er), such as, for example, via acceleration processes, deceleration processes, travel speeds and the like.”
Based on Straub's teachings, recorded driving behavior includes the driver
acceleration and deceleration of a vehicle.
Furthermore, Straub discloses:
an anticipated driver behavior, in block 120 a determination of a driving behavior
optimized with respect to the energy forecast and/or optimized energy consumption for
the route and in block 130 an output of an action recommendation on the basis of the
optimized driving behavior or optimized energy consumption. Typically, the optimized
driving behavior is determined on the optimized energy consumption or corresponds to
the optimized energy consumption. According to the invention, a proactive determination and display of an optimal driving behavior, such as, for example, an optimum travel speed, takes place. The total travel time and/or the costs can result from such early and optimum
In other words, depending on the driver's state, "aggressive driving"/"aggressive
rating" is a subjective term, and one of ordinary skill would attribute increased accelerations as an attribute of "aggressive driving." Thus, one of ordinary skill, apprised of the known organic definitions disclosed in Straub, would have included acceleration processes attributed to identified driver data and associated energy consumption outside of optimum energy usage to grade a driver in a manner to the claimed "aggression rating."
Regarding the excepted: a memory; and one or more programs, wherein the one
or more programs are stored in the memory and are configured to be executed by the one or more processors, the one or more programs, as indicated above, Straub discloses at least a processor/server. Staub doesn't disclose the claimed memory nor program. In the same art of driver assistance systems, Cha discloses the following:
[0076] The controller 130 may be implemented as a memory (not shown) that stores an algorithm for controlling the operation of components in a vehicle or a data about a program that reproduces the algorithm, and a processor (not shown) that performs the above-described operation using data stored in the memory. In this case, the memory and the processor may be implemented as separate chips, respectively. Alternatively, the memory and processor may be implemented as a single chip.
It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to include into Straub's processor the memory and program features of Cha such that the claimed invention is realized. One of ordinary skill would have included memory and programs from Cha into the processor/server of Straub to provide the claimed processing system as known in the art.
Regarding the excepted:
determine a first hidden driver state as a function of the determined aggression rating over time, where the first hidden driver state is not directly observable based upon driver inputs of one or more of an accelerator, brake system and steering system;
Straub, as disclosed, included the following:
In particular, the vehicle can collect data
about the driver's driving (er), such as, for example, via acceleration processes,
deceleration processes, travel speeds and the like. These data can be used to create an
individual (behavior) profile of the driver. For example, an expected speed value may be
predicted with which the driver will travel on a determined route section, such as a
highway.
Straub didn’t disclose the excepted claim limitations.
In the same art of driver monitoring, Naitou discloses.
[0004] In the driving behavior prediction apparatus described in the patent document 1, for example, the driving behavior of a driver is predicted by a statistical method using a hidden Markov model which is generally used for voice recognition. To be concrete, the driving behavior prediction apparatus is provided with plural models which correspond to driving behaviors (for example, turning right or left, and running straight) to be objects of recognition. Each of such models outputs an occurrence probability of the corresponding driving behavior by having driving data inputted, the driving data being represented by, for example, the depth to which the accelerator pedal is depressed, the depth to which the brake pedal is depressed, and the vehicle speed and acceleration. Thus, such models are generated based on driving data collected when driving behaviors to be objects of driving behavior prediction are practiced. The models calculate the occurrence probabilities of driving behaviors corresponding to them, respectively, by having actual driving data inputted, and the driving behavior corresponding to the model with the highest occurrence probability is predicted to be the driving behavior the driver will actually practice. In the driving behavior prediction apparatus using a hidden Markov model, to make the apparatus compatible with the driving behavior characteristics of various drivers, driving behavior patterns are recognized using models generated based on plural driving data. In the driving behavior prediction apparatus, unless the models are generated using driving data not much different from actual driving data, the result of predicting a driving behavior may differ from the driving behavior actually practiced by the driver, as a result, causing the driving behavior prediction accuracy to decline. Therefore, to accurately predict the driving behavior of each of plural drivers, it is necessary to generate and use models corresponding to individual drivers.
It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention modify Straub’s embodiment, using the disclosed features of Naitou to form an embodiment meeting the claimed invention.
Naitou, as above, discloses using driver inputs analyzed via a Hidden Markov Model to issue a driver prediction. In short, Naitou teaches the use of Hidden Markov Models to enable future prediction of driver behavior.
One of ordinary skill, apprised with the features disclosed in Naitou, would have implemented its known features into Straub to provide an embodiment meeting the claimed invention.
Regarding the excepted:
determine an energy use level as a function of the first hidden driver state;
determine a prevalence of the first hidden driver state; and
provide a prediction of future energy use based at least in part on the prevalence of the first hidden driver state.
Straub discloses:
Page 2, [0005-0008] In typical embodiments, the anticipated driver behavior may be
derived from a learned driver model. In particular, the vehicle can collect data about the
driver's driving (er), such as, for example, via acceleration processes, deceleration
processes, travel speeds and the like. These data can be used to create an
individual (behavior) profile of the driver.
For example, an expected speed value may be
predicted with which the driver will travel on a determined route
section, such as a highway. This
Action recommendation may indicate a behavior deviating from this anticipated behavior,
such as a reduced speed. By predicting the driver behavior and the
proactive action recommendation, an energy consumption can be reduced, as a result of
which, for example, costs and a total travel time can be reduced
and unnecessary charging or tank stops can be avoided.
According to some embodiments, the optimized driving behavior, such as the optimized
energy consumption,
(Per the applicant's specification: [0046] In generally, more aggressive driving uses greater energy and reduces the effective range of the vehicle, and can wear out tires, brakes and other vehicle components more quickly than less aggressive driving).
Straub doesn't disclose the excepted claim limitations. However, it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to infer the claimed invention via the following: if reduced speed is one driver behavior, clearly, increasing velocity is associated with another driver behavior. Thus, one of ordinary skill would have derived the claimed aggressive rating and commensurate energy usage based on the applicant's specification and on Straub's driver behavior analysis embodiment. Furthermore, a driver continually operating in the inferred increased aggressive rating would have a proportional energy consumption associated with aggressive driving.
On claim 14, Straub cites except as underlined:
The system of claim 13 wherein providing a prediction of future energy use includes providing on a display of the vehicle an estimated distance that the vehicle can travel on some or all of the energy for propulsion remaining in the vehicle.
Straub discloses:
Straub, page 2, [0004-5] discloses: According to the invention, a proactive display of an optimal (er) en F ahr (er) behavior such as for example an optimized travel speed, is thus carried out.
Straub doesn't disclose the excepted claim limitations. In the same art of driver assistance, Cha discloses:
[0112] Referring to FIG. 5, when the load mass is coupled to the vehicle, the controller 130 may immediately update the distance to empty to provide accurate information to the driver.
It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to include into Straub's display the distance to empty indication feature disclosed in Cha such that the claimed invention is realized. Cha discloses a feature of indicating a distance-to-empty, that is, the distance a vehicle can travel until the fuel is empty and one of ordinary skill would have provided this feature to Straub as an improved driver analysis and assistance system for the driver.
On claim 15, Straub cites: The system of claim 13 wherein the one or more processors are part of a vehicle control system. Page 3, paragraph 2, Typically, the driver assistance method is executed entirely by the vehicle, such as a processor in the vehicle, or a server external to the vehicle
Claims 16 and 17 are rejected under 35 USC 103 as being unpatentable over Straub et al., WO 2019/174932 (English translation) in view of Naitou et al., U.S. 2008/0120025 and Cha et al., U.S. 2022/0028183 and Ucar et al., U.S. 2023/0073151.
On claim 16, Straub cites except as underlined:
The system of claim 13 which includes one or more accelerometers that are
responsive to accelerations and are communicated with the one or more processors to provide acceleration data to the one or more processors.
Page 3, paragraph 2, Typically, the driver assistance method is executed entirely by the vehicle, such as a processor in the vehicle, or a server external to the vehicle.
And
Straub, page 2, [0004-5] discloses: According to the invention, a proactive display of an optimal (er) en F ahr (er) behavior, such as for example an optimized travel speed, is thus carried out. The total travel time and/or the costs can be reduced by such early and optimal action recommendations. In particular, unnecessary charging stops can be avoided by reducing the total travel time and/or the energy consumption. In typical embodiments, the anticipated driver behavior may be derived from a learned driver model. In particular, the vehicle can collect data about the driver's driving (er), such as, for example, via acceleration processes, deceleration processes, travel speeds and the like.
Regarding the excepted claim limitations, Straub discloses measuring
accelerations and decelerations processes. However, Straub doesn't disclose using accelerometers to measure these accelerations and decelerations.
In the same art of driver analysis, Ucar discloses:
Figure 1 and [0239] In some embodiments, the sensor set 126 may include one or more of
the following sensors: an altimeter; a gyroscope; a proximity sensor; a microphone; a
microphone array; an accelerometer
It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to include into Straub the accelerators disclosed in Ucar such that the claimed invention is realized. Ucar discloses a known device used to monitor driver performances in a vehicle. One of ordinary skill would have included this known device to monitor driver accelerations in a vehicle.
On claim 17, Straub cites except as underlined:
The system of claim 16 wherein the aggression rating is determined as a function
of at least a first acceleration threshold that is associated with an increasing vehicle
speed, and wherein acceleration causing increasing vehicle speed is sensed by the one
or more accelerometers.
Straub discloses:
Page 2, [0005-0008] In typical embodiments, the anticipated driver behavior may be
derived from a learned driver model. In particular, the vehicle can collect data about the
driver's driving (er), such as, for example, via acceleration processes, deceleration
processes, travel speeds and the like. These data can be used to create an individual (behavior) profile of the driver. For example, an expected speed value may be predicted with which the driver will travel on a determined route section, such as a highway. This Action recommendation may indicate a behavior deviating from this anticipated behavior, such as a reduced speed. By predicting the driver behavior and the proactive action recommendation, an energy consumption can be reduced, as a result of which, for example, costs and a total travel time can be reduced and unnecessary charging or tank stops can be avoided. According to some embodiments, the optimized driving behavior, such as the optimized energy consumption,
(Per the applicant's specification: [0046] In generally, more aggressive driving uses greater energy and reduces the effective range of the vehicle, and can wear out tires, brakes and other vehicle components more quickly than less aggressive driving)
Straub doesn't disclose the excepted claim limitations. However, it would have been obvious to one of ordinary skill in the art at the time of the claimed invention to infer the claimed invention via the following: if reduced speed is one driver behavior, clearly, increasing velocity is associated with another driver behavior. Thus, one of ordinary skill would have derived the claimed aggressive rating based on the applicant's specification and on Straub's driver behavior analysis embodiment.
Claim 18 are rejected under 35 USC 103 as being unpatentable over Straub et al., WO 2019/174932 (English translation) in view of Naitou et al., U.S. 2008/0120025 and Cha et al., U.S. 2022/0028183 and Moustafa et al., WO 2020/205648 A1.
On claim 18, Straub cites except as underlined:
The system of claim 13 wherein the instructions also relate to determining a
second hidden driver state as a function of the determined aggression rating over time, wherein the second hidden driver state is associated with a lower aggression rating than the first hidden driver state, determining an energy use level as a function of the second hidden driver state, and determining a prevalence of the second hidden driver state, and wherein the prediction of future energy use is based at least in part on the prevalence of the second hidden driver state.
Straub cites:
Page 2, [0005] Action recommendation may indicate a behavior deviating from this
anticipated behavior, such as a reduced speed. By predicting the driver behavior and the
proactive action recommendation, an energy consumption can be reduced, as a result of
which, for example, costs and a total travel time can be reduced and unnecessary
charging or tank stops can be avoided.
Straub, while suggesting a lower energy consumption is attributed by driving behavior that includes reduction of speeds (and therefore, less accelerations), Straub doesn’t attribute determining the aggression rating over time due to a lower rating due to the hidden driver state. In the same art of vehicle monitoring,
Moustafa cites:
[00138] The following disclosure provides various possible embodiments, or examples, for implementing a fault and intrusion detection system 1800 for highly automated and autonomous vehicles as shown in FIG. 18. In one or more embodiments, vehicle motion prediction events and control commands, which are both a higher level of abstraction, are monitored. Based on the current state of vehicle motion parameters and road parameters, a vehicle remains within a certain motion envelope. A temporal normal behavior model 1841 is constructed to maintain adherence to the motion envelope. In at least one embodiment, at least two algorithms are used to build the temporal normal behavior model. The algorithms include a vehicle behavior model 1842 (e.g., based on a Hidden Markov Model (HMM)) for learning normal vehicle behavior and a regression model 1844 to find the deviation from the vehicle behavior model. In particular, the regression model is used to determine whether the vehicle behavior model correctly detects a fault, where the fault could be a vehicle system error or a malicious attack on the vehicle system.
It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to include into Straub and Naitau the disclosed features of Moustafa such that the claimed invention is realized.
Moustafa discloses a known feature of using Hidden Markov Modeling to determine normal (and thus, “lower aggression” driving”), which is considered the “second hidden driver state.” One of ordinary skill would have included such a feature into Straub and Naitau to form a baseline reference for non-aggressive driving for which to compare that driving against monitored driving that shows aggressive tendencies.
Claims 19 are rejected under 35 U.S.C. 103 as being unpatentable over Straub et al., U.S. WO 2019/174932 in view of Naitou et al., U.S. 2008/0120025 and Moustafa et al., WO 2020/205648 A1 and Werner et al., U.S. 2019/0185009.
On claim 19, Straub cites except as underlined:
The method of claim 2 wherein the first hidden driver state is a first state of mind
in which the driver is excited or upset, and
the second hidden driver state is a second state of mind in which the driver is distracted or tired.
In the rejection of claim 1, Straub and Naitau asserts the claimed “first hidden driver state” is a driver behavior predicted by a Hidden Markov Model.
In the rejection of claim 2, Straub, Naitau, and Moustafa asserts the claimed “second hidden driver state” is a normal vehicular behavior model predicted by a Hidden Markov Model. However, none of the above references disclose the excepted claim limitations.
In the same art of driver monitoring, Werner cites:
[0059] In embodiments of the invention, the driver state module 410, vehicle state module 420, and the ML correlation engine 430 can be implemented as machine learning algorithms or classifiers configured and arranged to classify data. Examples of suitable classifiers include but are not limited to neural networks, support vector machines (SVMs), logistic regression, decision trees, hidden Markov Models (HMMs), etc. The end result of the classifier's operations, i.e., the “classification,” is to predict a class for the data. For example, predicting the driver's mood (angry, distracted, focused, sad, excited) based on data showing that driver's hand wave in the air in a manner that might have been a hostile gesture, or that might have been an animated response to a favorite song playing on the radio.
It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention include into Straub, Naitau, and Moustafa the embodiment disclosed in Werner such that the claimed “first hidden driver state” includes Werner’s HMM detected “angry” state while the claimed “second hidden driver state” is met with Werner’s “distracted” state.
Werner discloses known driver moods detectable by Hidden Markov Models and one of ordinary skill would have included Werner’s embodiment into Straub, Naitau, and Moustafa and the results of the modification would have made an expanded Hidden Markov Model to detect additional driver states
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Straub et al., U.S. WO 2019/174932 in view of Naitou et al., U.S. 2008/0120025 and Moustafa et al., WO 2020/205648 A1 and Shea et al., CA 3105835.
On claim 20, Straub cites except as underlined:
The method of claim 5 wherein the Hidden Markov Model uses inputs that include one or more of the following inputs to the Hidden Markov Model: 1) a time series of vehicle speed history within a certain period of time; 2) a time series of vehicle longitudinal and lateral acceleration history within a certain period of time; 3) a time series of vehicle propulsion torque history within a certain period of time; and 4) a time series of vehicle propulsive power history within a certain period of time.
In the rejection of claim 5, Straub, Naitau, and Moustafa disclosed an embodiment in which a Hidden Markov Model was used to determine a first hidden driver state. However, none of the above references discloses the excepted claim limitations.
In the related art of crash monitoring, Shea, figure 5, page 17, L 25-31, and page 18, L1-2, discloses the use of Hidden Markov Modeling to determine relevant speeds and velocities recorded at specific timelines during a crash event.
It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention include into Straub, Naitau, and Moustafa the Hidden Markov Modeling categorizing feature of Shea to produce an embodiment in which relevant acceleration and velocity features are presented in accordance with Hidden Markov Modeling. Shea discloses a known embodiment for presenting Hidden Markov Modeling filtered data and one of ordinary skill would have applied this embodiment to Straub, Naitau, and Moustafa to determine relevant hidden driver parameters.
Claims 21 is rejected under 35 USC 103 as being unpatentable over Straub et al., WO 2019/174932 (English translation) in view of Naitou et al., U.S. 2008/0120025 and Moustafa et al., WO 2020/205648 A1 and Cha et al., U.S. 2022/0028183 and Shea et al., CA 3105835.
On claim 21, Straub cites except as underlined:
The system of claim 13 wherein the first hidden driver state and the second hidden driver state are determined at least in part with a Hidden Markov Model, and wherein the Hidden Markov Model uses inputs that include one or more of the following inputs to the Hidden Markov Model: 1) a time series of vehicle speed history within a certain period of time; 2) a time series of vehicle longitudinal and lateral acceleration history within a certain period of time; 3) a time series of vehicle propulsive power history within a certain period of time.
In the rejection of claim 13, Straub, Naitou, and Cha disclosed an embodiment in which a Hidden Markov Model was used to determine first and second hidden driver states. However, none of the above references discloses the excepted claim limitations.
In the related art of crash monitoring, Shea, figure 5, page 17, L 25-31, and page 18, L1-2, discloses the use of Hidden Markov Modeling to determine relevant speeds and velocities recorded at specific timelines during a crash event.
It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention include into Straub, Naitou, and Cha the Hidden Markov Modeling categorizing feature of Shea to produce an embodiment in which relevant acceleration and velocity features are presented in accordance with Hidden Markov Modeling. The first hidden driver states respectively included driver data such as acceleration, deceleration, travel speeds and the like while the second hidden driver states include lower aggression or normal driving (as discussed in Moustafa). Shea, figure 1, discloses at least several lateral and vertical peaks of acceleration along an acceleration figures that do not have these peaks (suggesting normal driving). Accordingly, Shea discloses a known embodiment for presenting Hidden Markov Modeling filtered data and one of ordinary skill would have applied this embodiment to Straub, Naitou, Moustafa,and Cha to determine relevant hidden driver parameters.
Remarks
The claimed “hidden driver state” is considered to be driver characteristics and qualities not readily detected using normal sensors or observations. The Hidden Markov Model is a function or algorithm used to analyze a driver nuanced actions and from the analysis, predict a likely outcome based on those actions.
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 extension fee 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 CAL EUSTAQUIO whose telephone number is (571) 270-7229. The examiner can normally be reached on Mon -Thu 9:00 Am-5:30Pm. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Nabil H. Syed, whose telephone number is (571) 270-3028. The fax phone number for the organization where this application or proceeding is assigned is 571-270-8229. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/CAL J EUSTAQUIO/Examiner, Art Unit 2686
/BRIAN A ZIMMERMAN/Supervisory Patent Examiner, Art Unit 2686
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