DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Applicant(s) Response to Official Action
The response filed on 11/04/2025 has been entered and made of record.
Response to Arguments/Amendments
Presented arguments have been fully considered, but are rendered moot in view of the new ground(s) of rejection necessitated by amendment(s) initiated by the applicant(s).
Claim Rejections - 35 USC § 112
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.
Claim 25 is 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 claim(s) 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(s), at the time the application was filed, had possession of the claimed invention. Applicant has not pointed out where the new (or amended) claim is supported, nor does there appear to be a written description of the claim limitation ‘comparing behavior of the driver before triggering delivery of the message with behavior of the driver following triggering delivery of the message to the driver’ in the application as filed.
When an amendment is filed in reply to an objection or rejection based on 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph, a study of the entire application is often necessary to determine whether or not "new matter" is involved. Applicant should therefore specifically point out the support for any amendments made to the disclosure. MPEP 2163.06 I.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-3, 6-9, 11, 13, 16-18, 21, 23 and 25-27 are rejected under 35 U.S.C. 102(a) (1) as being anticipated by Michael Campos et al. {[US 20200166897 A1] along with incorporated by reference WO2017123665A1, WO2017165627A1, WO2018026733A1: all already of record }.
Regarding claim 1, Michael teaches:
1. A method of reducing a processing burden in identifying driving risk for a driver of a motor vehicle (i.e. Systems and methods are provided for detecting a driving action that mitigates risk- Abstract), comprising:
capturing image data of an environment of the motor vehicle while the motor vehicle is in motion (i.e. In one embodiment, a DRIVERI™ system may continuously record video and other sensor data while a vehicle is running. In one example, the video and other data may be segmented into 1 minute durations. Based on a 1 min video duration and 100% duty cycle, an eight-hour driving day may generate 480 1-minute videos- ¶0116); and
processing, by a computing system, segments of the captured image data to produce a risk assessment of the driver (i.e. In the example driving scenario illustrated in FIG. 3, the driver performed one action (a lane change) that had an effect of mitigating risk in the environment. This action, however, was sandwiched between two risky traffic events that could also be attributed to the driver. A system or method for determining actions that mitigate risk may determine that the driver is alert and responding to the environment but may further determine that the behavior of the driver is aggressive. To determine whether the driver's behavior should be recognized with positive reinforcement, such as with a “Star Alert”, a comparison of driving risk after a driver's action may be compared with the predicted risk of a typical driver. A typical driver, for example, may not be an aggressive driver. In this example, at the time corresponding to the scene illustrated in FIG. 3C, a system may determine that a typical driver would not only have changed lanes, but would have also created more space between himself and other cars on the road. In some embodiments, a driving action that is at least as safe as a typical driver in the same scenario may be a criterion for the awarding of a DriverStar- ¶0080);
wherein capturing of up to 25% of the image data used in producing the risk assessment is triggered by an adverse driving event (i.e. described herein are atypical events that do not occur frequently (e.g. one out of a hundred minutes of driving may contain an atypical event for moderately rare events), but that may lead to unsafe conditions with relatively high frequency once they occur- ¶0057).
Regarding claim 2, Michael teaches all the limitations of claim 1 and Michael further teaches:
wherein the computing system is at least partially distal to the motor vehicle (i.e. The device 100 may include input sensors (which may include a forward-facing camera 102- ¶0038).
Regarding claim 3, Michael teaches all the limitations of claim 1 and Michael further teaches:
wherein at least first and second ones of the segments processed to produce the risk assessment, have different lengths (i.e. a driver who experiences one negative driving event in twenty minutes of driving may be considered as safe as a driver who experiences one negative driving event over the course of an eight-hour driving shift- ¶0109… In some embodiments, the processing capabilities of embedded processors may not be able to analyze all the recorded video as fast as it is collected. In this case, some of the recorded minutes may be ignored- ¶0116… based on the provided accident report, one may make inferences that may place a Driver in a better position for success. For example, one may conclude that this driver is not a good “morning-person”, but that he is an above average driver at night. In addition, or alternatively, the information selected for the report may be used to determine that the driver was having an unusually bad day. In this case, the traffic incident may have been avoided if there was an intervention (maybe 30 minutes prior), around the time that the Driver's rolling summary driving score can be observed to have decreased- ¶0123), and wherein the first and second ones of the segments are discontinuous with each other (i.e. A driver may not be active for eight hours continuously. In these cases, the number of recorded videos may be less- ¶0116).
Regarding claim 6, Michael teaches all the limitations of claim 1 and Michael further teaches:
wherein a processor in or on the vehicle at least partially determines when at least one of the processed segments begins (i.e. A recorded video may be analyzed using processors embedded within a device in the vehicle and/or by one or more processors in the cloud- ¶0116).
Regarding claim 7, Michael teaches all the limitations of claim 1 and Michael further teaches:
wherein at least one of the processed segments comprises video data(i.e. In one embodiment, a DRIVERI™ system may continuously record video and other sensor data while a vehicle is running. In one example, the video and other data may be segmented into 1 minute durations. Based on a 1 min video duration and 100% duty cycle, an eight-hour driving day may generate 480 1-minute videos. A driver may not be active for eight hours continuously. In these cases, the number of recorded videos may be less. The recorded videos may be analyzed with a DRIVERI™ service. A recorded video may be analyzed using processors embedded within a device in the vehicle and/or by one or more processors in the cloud. In some embodiments, the processing capabilities of embedded processors may not be able to analyze all the recorded video as fast as it is collected. In this case, some of the recorded minutes may be ignored. In another embodiment, a processor embedded with the vehicle may process the visual data in a streaming fashion- ¶0116)
Regarding claim 8, Michael teaches all the limitations of claim 1 and Michael further teaches:
wherein each of the processed segments comprises a still photograph (i.e. FIGS. 5A-D illustrate annotated images captured by a camera located inside of a truck and facing forward- ¶0089)
Regarding claim 9, Michael teaches all the limitations of claim 1 and Michael further teaches:
wherein the processing comprises a neural network taking as input the segments (i.e. a video caption generation system may be trained on a series of frames. The video capture generation system may be based on a Recurrent Neural Network (RNN) structure, which may use Long Short-Term Memory (LSTM) modules to capture temporal aspects of a traffic event- ¶0102), wherein the neural network is trained to predict a probability of an accident. a near-accident (i.e. Unsafe driving behavior may also lead to accidents, which may cause physical harm, and which may, in turn, lead to an increase in insurance rates for operating a vehicle. Inefficient driving, which may include hard accelerations, may increase the costs associated with operating a vehicle- ¶0041… Although any act or event while driving a vehicle may be characterized as an event, atypical traffic events as described herein are notable because they may lead to some unsafe condition that has a higher probability of leading to an accident- ¶0057). or an event correlated with the accident or the near accident based on the segments (i.e. a machine learning model, such as a deep neural network, may be used to determine the distance based on the input pixels corresponding to the vehicle ahead- ¶0041, ¶0044… According to certain aspects of the present disclosure, detecting a driving action that mitigates risk may be rule-based, and/or may be based on the output of a neural network trained on labeled data. For example, the output of a neural network may be used to identify other cars in the vicinity. FIGS. 3A-D and FIGS. 4A-D illustrate examples of systems and methods of detecting driving actions that mitigate risk based on rules in combination with outputs of a neural network. Determinations of cause of traffic events based on rules and/or neural networks may also be used to train a second neural network to detect and/or characterize traffic events and/or determine cause of a traffic event- ¶0066… The data used to train a learned model may be generated by a rule-based approach, such as described above. These labels may be accepted, rejected, or corrected by a human labeler. According to certain aspects, inputs from fleet safety officers may be utilized. For example, a fleet safety officer may correct a given action responsivity label, or may agree with labels that are provided by a rule-based and/or neural network based system. These labels may then be used to bootstrap from the rule based approach to a machine learned model that exhibits improved performance- ¶0102-0103, ¶0128).
Regarding claim 11, Michael teaches all the limitations of claim 1 and Michael further teaches:
wherein the computing system derives the risk assessment from at least 3 hours of accumulated lengths of the provided segments (i.e. In one embodiment, a DRIVERI™ system may continuously record video and other sensor data while a vehicle is running. In one example, the video and other data may be segmented into 1 minute durations. Based on a 1 min video duration and 100% duty cycle, an eight-hour driving day may generate 480 1-minute videos- ¶0116).
Regarding claim 13, Michael teaches all the limitations of claim 1 and Michael further teaches:
wherein a processor of the computing system, disposed in or on the vehicle, at least partially derives the risk assessment (i.e. A system for detecting a driving action that mitigates risk, in accordance with certain aspects of the present disclosure, may assess the driver's behavior in real-time. For example, an in-car monitoring system, such as the device 100 illustrated in FIG. 1 that may be mounted to a car, may perform analysis in support of a driver behavior assessment in real-time, may determine cause of traffic events as they occur, and may recognize that a driver's action was responsive to an existing or pending traffic situation that served to reduce risk. In this example, the system, in comparison with a system that does not include real-time processing, may avoid storing large amounts of sensor data since it may instead store a processed and reduced set of the data. Similarly, or in addition, the system may incur fewer costs associated with wirelessly transmitting data to a remote server- ¶0039).
Regarding claim 16, Michael teaches all the limitations of claim 1 and Michael further teaches:
wherein the computing system produces the risk assessment at least partially based upon analysis of the processed segments with respect to at least three of ambient traffic density(i.e. an existing or pending traffic situation- ¶0039), off-road hazard, on-road hazard (i.e. may be caused by something in the environment (such as road debris)- ¶0063), complexity of a roadway upon which the vehicle is being driven (i.e. to road conditions 272- ¶0004), behavior of a vehicle within sight range of a driver of the vehicle (i.e. the relative speeds of the Driver's vehicle and another vehicle- ¶086), and existence of pedestrians within sight range of the driver (i.e. presence of a pedestrian or a bicyclist- ¶0087).
Regarding claim 17, Michael teaches all the limitations of claim 1 and Michael further teaches:
wherein the adverse driving event is selected from the list consisting of speeding, driver distraction, hard braking, swerving, collision, and near collision (i.e. a sudden deceleration associated with a hard-stopping event- ¶0005… traffic event may be an inertial event (such as a hard-braking event, a fast acceleration, a swerving maneuver, and the like), may be a traffic violation (such as failing to come to a complete stop at a stop sign, running a red light, crossing a double yellow line on a road, and the like), may be defined by a person (such as a fleet safety manager defining a traffic event through the specification of a time and/or place of interest, a Driver indicating that unsafe driving is occurring in his or her vicinity, a traffic officer viewing a video feed remotely, and the like). In one example, a safety officer may specify a traffic event as a period of time when a specified automobile passed through a specific intersection, the specification of which may be based on a report of unsafe driving- ¶0062).
Regarding claim 18, Michael teaches all the limitations of claim 1 and Michael further teaches:
comprising detecting a change in behavior of the driver following triggering delivery of the message to the driver (i.e. According to certain aspects of the present disclosure, the Driver may be alerted that a car in an adjacent lane (for example, car ID 0 in FIG. 4A) is exhibiting unsafe driving behavior (tailgating), and/or may receive a positive reinforcement or positive driving assessment for avoiding the unsafe driver- ¶0082); and
Regarding claim 21, Michael teaches all the limitations of claim 18 and Michael further teaches:
wherein the message comprises an image visible to the driver (i.e. clicking on the cartoon image may enable the user to view a cartoon video of the event- ¶0120).
Regarding claim 23, Michael teaches all the limitations of claim 18 and Michael further teaches:
further comprising triggering delivery of the message to the driver while the driver is driving the motor vehicle (i.e. According to certain aspects of the present disclosure, the Driver may be alerted that a car in an adjacent lane (for example, car ID 0 in FIG. 4A) is exhibiting unsafe driving behavior (tailgating), and/or may receive a positive reinforcement or positive driving assessment for avoiding the unsafe driver. The Driver may avoid and unsafe driver, for example, by slowing down and thereby increasing the following distance to that car, even though the that car is in an adjacent lane- ¶0082).
Regarding claim 25, Michael teaches all the limitations of claim 18 and Michael further teaches:
further comprising comparing behavior of the driver before triggering delivery of the message with behavior of the driver following triggering delivery of the message to the driver (i.e. In the example driving scenario illustrated in FIG. 3, the driver performed one action (a lane change) that had an effect of mitigating risk in the environment. This action, however, was sandwiched between two risky traffic events that could also be attributed to the driver. A system or method for determining actions that mitigate risk may determine that the driver is alert and responding to the environment but may further determine that the behavior of the driver is aggressive. To determine whether the driver's behavior should be recognized with positive reinforcement, such as with a “Star Alert”, a comparison of driving risk after a driver's action may be compared with the predicted risk of a typical driver. A typical driver, for example, may not be an aggressive driver. In this example, at the time corresponding to the scene illustrated in FIG. 3C, a system may determine that a typical driver would not only have changed lanes, but would have also created more space between himself and other cars on the road. In some embodiments, a driving action that is at least as safe as a typical driver in the same scenario may be a criterion for the awarding of a DriverSt - ¶0082…According to certain aspects of the present disclosure, the Driver may be alerted that a car in an adjacent lane (for example, car ID 0 in FIG. 4A) is exhibiting unsafe driving behavior (tailgating), and/or may receive a positive reinforcement or positive driving assessment for avoiding the unsafe driver. The Driver may avoid and unsafe driver, for example, by slowing down and thereby increasing the following distance to that car, even though the that car is in an adjacent lane- ¶0082…The report illustrated in FIG. 7 also includes a timeline of green-minutes and alerts by this driver on a day. This information panel may correspond to a day-long time-scale. The report illustrated in FIG. 7 also includes a trend of the daily driver score for this week and summary of alerts. This information panel may correspond to a week-long time-scale. The report illustrated in FIG. 7 also includes a summary of this driver's behavior benchmarked against himself from the previous week, and against the fleet average. The benchmarks may include comparisons based on fine-grained assessments of driving conditions, such as a comparison versus fleet-wide statistics at certain times of day (at night, in the morning heavy-traffic, and the lie). In some embodiments, the text of an accident report may be made editable- ¶0121).
Regarding claim 26, Michael teaches all the limitations of claim 17 and Michael further teaches:
further comprising providing the risk assessment to an insurer, and the insurer using the risk assessment as a factor in determining an insurance premium offered or charged to the drive (i.e. An accident report generated in accordance with certain aspects of the present disclosure may be useful for enabling timely notifications that may prevent avoidable accidents. In addition, the strength of risk, and or number of moderate incident counts may be used for predicting accidents which may enable for personalized insurance premiums.)
Regarding claim 27, apparatus claim 27 is drawn to the apparatus using/performing the same method as claimed in claim 1. Therefore, apparatus claim 27 corresponds to method claim 1, and is rejected for the same rationale as used 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 of this title, 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 applie1d for establishing a background for determining obviousness under 35 U.S.C. 103 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 4-5 are rejected under 35 U.S.C. 103 as being unpatentable over Michael Campos et al. [US 20200166897 A1: already of record] in view of John Matsumura et al. [US 20190113354 A1: already of record].
Regarding claim 4, Michael teaches all the limitations of claim 1.
wherein at least first, second, and third ones of the segments are processed to produce the risk assessment, and a first interval between the first and second segments has a different length from a second interval between the second and third segments
In the same field of endeavor, John teaches:
wherein at least first, second, and third ones of the segments are processed to produce the risk assessment, and a first interval between the first and second segments has a different length from a second interval between the second and third segments (i.e. the collected data are sampled to divide a trip into random length segments (rather than based on segments extending between intersections)- ¶0077… In one embodiment, the training data is divided into segments where each segment has a random length between 0.001 miles and 0.1 miles- ¶0086).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Michael with the teachings of John to improve the energy efficiency of vehicles through routing (John- ¶0004).
Regarding claim 5, Michael teaches all the limitations of claim 1.
wherein a start time of at least one of the segment processed is at least partially random.
In the same field of endeavor, John teaches:
wherein a start time of at least one of the segment processed is at least partially random (i.e. the collected data are sampled to divide a trip into random length segments (rather than based on segments extending between intersections)- ¶0077… In one embodiment, the training data is divided into segments where each segment has a random length between 0.001 miles and 0.1 miles- ¶0086).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Michael with the teachings of John to improve the energy efficiency of vehicles through routing (John- ¶0004).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Michael Campos et al. [US 20200166897 A1: already of record] in view of SU Guan-Ming et al. [US 20210150812 A1].
Regarding claim 10, Michael teaches all the limitations of claim 1.
wherein training the neural network comprises training on a sub-sample of the segments.
In the same field of endeavor, Su teaches:
wherein training the neural network comprises training on a sub-sample of the segments (i.e. In other embodiments, computational complexity in all systems may be decreased by employing pixel subsampling both spatially and temporally. For example, in video sequences, the neural networks may be solved using sub-sampled frames and/or the results may be used for multiple consecutive frames. Furthermore, at the NN level, for each frame, initialization values may be a simple copy of the solutions from the previous frame- ¶0093).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Su to accommodate the computational constrains of a particular implementation (Su- ¶0118).
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Michael Campos et al. [US 20200166897 A1: already of record] in view of Colin D Warkentin et al. [US 8626568 B2: already of record].
Regarding claim 12, Michael teaches all the limitations of claim 1.
However, Michael does not teach explicitly:
wherein the computing system derives the risk assessment from at least 10 hours of accumulated lengths of the segments processed.
In the same field of endeavor, Colin teaches:
wherein the computing system derives the risk assessment from at least 10 hours of accumulated lengths of the segments processed (i.e. Furthermore, an indicator 420 displays the amount of drive time the driver has accrued in a given day, which (in this embodiment) has an upper limit of "11 hours" of total drive time before he or she is in violation of a predetermined drive time limit. Thus, the time values in indicators 412 and 420 will add up to the maximum drive time limit ("11 hours" in this embodiment). When the driver's total drive time in indicator 420 exceeds the upper limit, the indicator may change colors, for example, to a red color to warn the driver of the violation. Optionally, an indicator 422 displays the amount of on-duty time the driver has accrued in a given day, which (in this embodiment) has an upper limit of "14 hours" of total on-duty time before he or she is in violation of a predetermined on-duty time limit. Thus, the time values in indicators 422 and 414 will add up to the maximum on-duty time limit ("14 hours" in this embodiment). When the driver's total drive time in indicator 422 exceeds the upper limit, the indicator may change colors, for example, to a red color to warn the driver of the violation. Further, an indicator 424 displays the amount of total on-duty time the driver has accrued in a period of consecutive days (e.g., "8 days" in this embodiments). For example, the total on-duty time the driver has accrued in an eight-day period may have an upper limit of "70 hours" in this embodiment. When the driver's total drive time in indicator 424 exceeds the upper limit, the indicator may change colors, for example, to a red color to warn the driver of the violation- Col 15, line 14-41).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Michael with the teachings of Colin to warn the driver of the violation of a predetermined drive time limit (Colin- Col 15, line 14-41).
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Michael Campos et al. [US 20200166897 A1: already of record] in view of Kazuki Kozuka et al. [US 20170262750 A1: already of record].
Regarding claim 14, Michael teaches all the limitations of claim 1.
However, Michael does not teach explicitly:
wherein the computing system does not use an image of driver distraction to produce the risk assessment.
In the same field of endeavor, Kazuki teaches:
wherein the computing system does not use an image of driver distraction to produce the risk assessment (i.e. Next, the learning data generator 20 determines a risk area included in the video data (S112). More specifically, the learning data generator 20 determines a risk area that is included at least in part of the acquired images (video data) and that is an area where there is a possibility that a moving object may appear into a travelling path of a vehicle and there is a possibility that the vehicle having the in-vehicle camera by which the plurality of images are taken may collide with this moving object if the vehicle simply continues the running.- ¶0093, fig. 1).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Michael with the teachings of Kazuki to avoid the predicted risk area, which makes it possible to perform driving more safely (Kazuki- ¶0077).
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Michael Campos et al. [US 20200166897 A1: already of record] in view of Todd Binion et al. [US 20140257869 A1].
Regarding claim 15, Michael teaches all the limitations of claim 1.
However, Michael does not teach explicitly:
wherein the computing system produces the risk assessment at least partially based upon analysis of the processed segments with respect to an off-road hazard.
In the same field of endeavor, Todd teaches:
wherein the computing system produces the risk assessment at least partially based upon analysis of the processed segments with respect to an off-road hazard (i.e. In some embodiments, the processor may also execute an instruction to determine if any other risk factors are present and execute an instruction to assign a risk score to the one or more other risk factors. For example, other risk factors may include sensor data received from the vehicle such as a check engine light being active, current weather patterns, etc. Other risk factors may also include, a traversed intersection, a road type, a road hilliness, a road curviness, a road shoulder access, a number of stoplights, a number of stop signs, a roadside hazard and wherein at least one of the risk factors corresponds to at least one of the start point, the end point or one or more points in between the start point and the end point of the common driving route- ¶0027).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Michael with the teachings of Todd instruction to process one or more insurance options, including pricing and underwriting, based at least in part on the risk level of the common driving route (Todd- ¶0028).
Claims 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Michael Campos et al. [US 20200166897 A1: already of record] in view of Lawrence W. Hill et al. [US 20100188265 A1: already of record].
Regarding claim 19, Michael teaches all the limitations of claim 18.
However, Michael does not teach explicitly:
wherein the message comprises a non-verbal sound.
In the same field of endeavor, Lawrence teaches:
wherein the message comprises a non-verbal sound (i.e. The PND or other devices that contribute to implementing the disclosed system and method can produce an indication that can alert the driver to the slowed traffic conditions ahead. For example, the indication may be a displayed message, an audio prompt, such as a beep or verbalized message, a tactile prompt such as a vibratory output, or other types of indications that can alert the driver to the upcoming traffic conditions. With the implementation of these measures, drivers can be alerted to upcoming slowdowns or congestion, and increase vehicle spacing, thereby reducing the possibility of a shock wave being formed or reducing the effects of such phenomena- ¶0042).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Michael with the teachings of Lawrence to reduce possibility of a shock wave being formed or reducing the effects of such phenomena (Lawrence- ¶0042).
Regarding claim 20, Michael teaches all the limitations of claim 18.
However, Michael does not teach explicitly:
wherein the message comprises a verbal phrase.
In the same field of endeavor, Lawrence teaches:
wherein the message comprises a verbal phrase (i.e. The PND or other devices that contribute to implementing the disclosed system and method can produce an indication that can alert the driver to the slowed traffic conditions ahead. For example, the indication may be a displayed message, an audio prompt, such as a beep or verbalized message, a tactile prompt such as a vibratory output, or other types of indications that can alert the driver to the upcoming traffic conditions. With the implementation of these measures, drivers can be alerted to upcoming slowdowns or congestion, and increase vehicle spacing, thereby reducing the possibility of a shock wave being formed or reducing the effects of such phenomena- ¶0042).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Michael with the teachings of Lawrence to reduce possibility of a shock wave being formed or reducing the effects of such phenomena (Lawrence- ¶0042).
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Michael Campos et al. [US 20200166897 A1: already of record] in view of Martin Nespolo et al. [US 20170158117 A1: already of record].
Regarding claim 22, Michael teaches all the limitations of claim 18.
However, Michael does not teach explicitly:
wherein the message comprises sounding of a horn of the vehicle.
In the same field of endeavor, Martin teaches:
wherein the message comprises sounding of a horn of the vehicle (i.e. The warning device 28 can also include any suitable audible warning device, such as a horn, siren, or speaker configured to broadcast an audible alert from of the subject vehicle 10. The audible alert can include any suitable alert/warning, such as a semantic alert, a horn, or a speech tacton alert, which includes speech warnings combined with tactile driver alerts- ¶0020).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Michael with the teachings of Martin to alert drivers of secondary vehicles nearby the subject vehicle 10 that the subject vehicle 10 has identified a hazard.
Claim 24 is rejected under 35 U.S.C. 103 as being unpatentable over Michael Campos et al. [US 20200166897 A1: already of record] in view of Stuart C. Salter et al. [US 20230256909 A1: already of record].
Regarding claim 24, Michael teaches all the limitations of claim 18.
However, Michael does not teach explicitly:
further comprising triggering delivery of the message to the driver while the driver is not driving the motor vehicle.
In the same field of endeavor, Stuart teaches:
further comprising triggering delivery of the message to the driver while the driver is not driving the motor vehicle (i.e. In this disclosure, the running board assembly 12 is configured to hold the running board 14 in a particular deployed position by actively monitoring the position of the running board 14, using sensor 52, and applying torque to the motor 32 as necessary to maintain the position. If a user is standing on the running board 14 for a period of time such that a thermal breaker of the motor 32 is about to trip, an alert, such as an audible and/or visual alert, may be presented to the user asking the user to step off the running board 14. In this regard, the vehicle 10 may include a speaker or sound exciter configured to issue the alert. When holding one of the stowed or deployed positions, a torque limit of the motor 32 may be overridden for a period of time. Optionally, the motor 32 may include a brake configured to selectively lock the motor 32, and in turn the running board 14, in a desired one of the stowed or deployed positions. Another approach to preventing motor over stress may be to slowly lower the running board 14 to position D.sub.1 which would place the least torque on the motor or linkages- ¶0052).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Michael with the teachings of Stuart to preventing motor over stress may be to slowly lower the running board 14 to position D.sub.1 which would place the least torque on the motor or linkages (Stuart- ¶0052).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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CLIFFORD HILAIRE
Primary Examiner
Art Unit 2488
/CLIFFORD HILAIRE/Primary Examiner, Art Unit 2488