DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This Office Action is in response to the application filed on May 22, 2026. Claims 1, 2, 4, 8, 9, 15, and 19-21 have been amended. Claims 1-21 are presently pending and are presented for examination.
Response to Amendments
In response to Applicant's Amendments dated May 22, 2026, Examiner withdraws the previous claims objections, withdraws the previous 35 U.S.C. 101 rejection, and withdraws the previous prior art rejections.
Response to Arguments
Applicant's arguments filed on May 22, 2026 have been fully considered, but they are moot in view of the new ground(s) of rejections.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AlA 35 U.S.C. 102 and 103 is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically discloses 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 are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-9, 12, and 15-21 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pub. No. 20170329332 (hereinafter, "Pilarski"; newly of record), in view of U.S. Pat. No. 9535878 (hereinafter, "Brinkmann"; previously of record), in further view of U.S. Pat. No. 9147353 (hereinafter, "Slusar"; previously of record) and in further view of U.S. Pub. No. 20150161913 (hereinafter, "Brian"; previously of record).
Regarding claim 1, Pilarski discloses a method for monitoring driving, comprising:
detecting, by at least one processor of a computing device (“the perception logic 118 can be centralized, such as residing with a processor or combination of processors in a central portion of the vehicle” (para 0027)), an appearance of an object based on visual data from a camera (“The perceptions 123 can correspond to interpreted sensor data, such as (i) image, sonar or other electronic sensory-based renderings of the environment, (ii) detection and classification of objects in the environment, and/or (iii) state information associated with individual objects (e.g., whether object is moving, pose of object, direction of object)” (para 0027)), wherein the camera is attached to a vehicle (“the vehicle 410 uses one or more sensor views 403 (e.g., field of view of camera) to scan a road segment on which the vehicle 410 is about to traverse as part of a trip. The vehicle 410 can process image data, corresponding to the sensor views 403 of one or more cameras in order to detect objects that are moving or can move into the path of the vehicle 10” (para 0087));
predicting, by the at least one processor, a future path of travel of the vehicle mapped to a camera view based on determined object locations (“The perceptions 123 and the predictions 139 can provide input into the motion planning component 124. The motion planning component 124 includes logic to detect dynamic objects of the vehicle's environment from the perceptions. When dynamic objects are detected, the motion planning component 124 determines a response trajectory 125 of the vehicle” (para 0029));
generating, by the at least one processor, using an input from the camera, an inference representing an occurrence of a driving event comprising an interaction between the predicted path of travel and the detected object (“the prediction analysis 226 can utilize input from the sensor processing component 210 and the route planner 122 in order to anticipate a likelihood or probability that an object of one or more predetermined classes (e.g., persons, bicycles, other vehicles) will interfere or collide with the path of the autonomous vehicle 10” (para 0059));
However, Pilarski does not explicitly teach
determining, by the at least one processor executing a machine learned model and based on the inference, that the driving event does not decrease a safety score for a safety factor; and
adjusting a data record value based on an aggregated safety score for a time period, wherein the aggregated safety score increases as the safety score for the safety factor increases, and wherein a higher aggregated safety score corresponds to a lower data record value.
Brinkmann, in the same field of endeavor, teaches
determining, by the at least one processor executing a machine learned model and based on the inference (“the driving analysis module 221 and the driver score calculation module 222 may include one or more driving event analysis/driver score calculation algorithms, which may be executed by one or more software applications running on generic or specialized hardware within the driving analysis server 220” (Col. 7, lines 38-55)), that the driving event does not decrease a safety score for a safety factor (“a driver score may be positively adjusted based on the image, video, and proximity data analysis associated with the driving event. For instance, if an image, video, and proximity data analysis in step 304 indicates that a driver was not speeding or tailgating based on the current road conditions, and that the driver reacted quickly and appropriately to an unforeseen driving event (e.g., by quickly swerving or braking to avoid a pedestrian), then the driver score may be positively adjusted (e.g., raised) based on the driving event” (Col. 14, lines 37-46)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Pilarski with the teachings of Brinkmann in order to determine whether or not to adjust a driver score in response to a potentially high-risk or unsafe driving event; see Brinkmann at least at (Col. 8, lines 32-33); and
Slusar, in the same field of endeavor, teaches
adjusting a data record value based on an aggregated safety score for a time period (“determining driving behaviors of vehicles, and calculating driver scores based on the determined driving behaviors” (Col. 1, lines 58-60) and “all occurrences of all determined positive and negative driving behaviors may be accumulated and stored over a period of time, such a week, month, year, or for an insurance term, and the accumulated set of driving behaviors may be used to calculate insurance rate adjustments or discounts, along with other factors such as … driving record” (Col. 14, lines 17-23)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Pilarski with the teachings of Slusar in order to calculate insurance rate adjustments or discounts along with driving record based on accumulated driver scores over a period of time; see Slusar at least at (Col. 14, lines 17-23),
Brian, in the same field of endeavor, teaches
wherein the aggregated safety score increases as the safety score for the safety factor increases (“the method 200 calculates a score for the driver based on the monitoring. Any scoring system may be used to calculate the score. In one embodiment, the scoring may be cumulative” (para 0039)), and wherein a higher aggregated safety score corresponds to a lower data record value (“The automobile insurance company may use the driver's score to determine an insurance premium or rate for the driver. For example, the higher the driver's score, the lower the insurance premium will be set” (para 0017)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Pilarski with the teachings of Brian in order to determine an insurance premium such that a higher cumulative score corresponds to a lower insurance premium; see Brian at least at [0017].
Regarding claim 2, Pilarski discloses the method of claim 1. However, Pilarski does not explicitly teach wherein determining that the detected driving event does not decrease the safety score comprises characterizing, by the at least one processor, the driving event as positive, the method further comprising:
lowering the data record value in response to the characterization of the driving event as positive.
Brinkmann, in the same field of endeavor, teaches
wherein determining that the detected driving event does not decrease the safety score comprises characterizing, by the at least one processor, the driving event as positive (“a driver score may be positively adjusted based on the image, video, and proximity data analysis associated with the driving event” (Col. 8, lines 32-33), the method further comprising:
lowering the data record value in response to the characterization of the driving event as positive (“an insurance company server 101 may periodically calculate driver scores for one or more of the insurance company's customers, and may use the driver scores to perform insurance analyses and determinations (e.g., determine coverage, calculate premiums and deductibles, award safe driver discounts, etc.)...if a driver consistently drives within posted speed limits, wears a seatbelt, and keeps the vehicle in good repair, the driver score may be positively adjusted (e.g., increased)” (Col. 4, lines 38-50).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Pilarski with the teachings of Brinkmann in order to perform insurance analyses and determinations; see Brinkmann at least at (Col. 4, lines 38-50).
Regarding claim 3, Pilarski discloses the method of claim 1. However, Pilarski does not explicitly teach wherein determining that the detected driving event does not decrease the safety score comprises:
recognizing, by the at least one processor, an at-risk driving situation which does not result in a negative driving event.
Brinkmann, in the same field of endeavor, teaches
wherein determining that the detected driving event does not decrease the safety score comprises:
recognizing, by the at least one processor, an at-risk driving situation which does not result in a negative driving event (“if an image, video, and proximity data analysis in step 304 indicates that a driver was not speeding or tailgating based on the current road conditions, and that the driver reacted quickly and appropriately to an unforeseen driving event (e.g., by quickly swerving or braking to avoid a pedestrian), then the driver score may be positively adjusted (e.g., raised) based on the driving event” (Col. 14, lines 40-46)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Pilarski with the teachings of Brinkmann in order to determine whether or not to adjust a driver score in response to a potentially high-risk or unsafe driving event; see Brinkmann at least at (Col. 8, lines 32-33).
Regarding claim 4, Pilarski discloses the method of claim 1. However, Pilarski does not explicitly teach wherein determining that the detected driving event does not decrease a safety score of the driver comprises:
determining, by the at least one processor, that the driving event was caused by a factor outside of the driver's control.
Brinkmann, in the same field of endeavor, teaches
wherein determining that the detected driving event does not decrease a safety score of the driver comprises:
determining, by the at least one processor, that the driving event was caused by a factor outside of the driver's control (“the driving analysis server 220 may analyze the retrieved image, video, and/or proximity data and may attempt to identify an external cause for the sudden swerving, sudden braking, or vehicle impact by the vehicle 210. For example, an image or video analysis by the driving analysis server 220 of the front-facing camera data of the vehicle 210, or other image or video data, may indicate that a pedestrian, animal, cyclist, disabled vehicle, or other obstruction was an external cause of the sudden swerving, braking, or impact by the vehicle 210” (Col. 15, lines 46-55)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Pilarski with the teachings of Brinkmann in order to determine whether or not to adjust a driver score in response to a potentially high-risk or unsafe driving event; see Brinkmann at least at (Col. 8, lines 32-33).
Regarding claim 5, Pilarski discloses the method of claim 4. However, Pilarski does not explicitly teach wherein determining that the driving event was caused by a factor outside of the driver's control comprises:
identifying, by the at least one processor, the driving event as having risk, and
determining, by the at least one processor, that the driving event is not caused by the drive.
Brinkmann, in the same field of endeavor, teaches
wherein determining that the driving event was caused by a factor outside of the driver's control comprises:
identifying, by the at least one processor, the driving event as having risk (“the image, video, and proximity data retrieved in step 303 may allow the driving analysis server 220 to identify external causes and/or justifications for potentially high-risk or unsafe driving events or behaviors” (Col. 10, lines 32-36)), and
determining, by the at least one processor, that the driving event is not caused by the driver (“the driving analysis server 220 may analyze the retrieved image, video, and/or proximity data and may attempt to identify an external cause for the sudden swerving, sudden braking, or vehicle impact by the vehicle 210. For example, an image or video analysis by the driving analysis server 220 of the front-facing camera data of the vehicle 210, or other image or video data, may indicate that a pedestrian, animal, cyclist, disabled vehicle, or other obstruction was an external cause of the sudden swerving, braking, or impact by the vehicle 210” (Col. 15, lines 46-55)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Pilarski with the teachings of Brinkmann in order to determine whether or not to adjust a driver score in response to a potentially high-risk or unsafe driving event; see Brinkmann at least at (Col. 8, lines 32-33).
Regarding claim 6, Pilarski discloses the method of claim 1. However, Pilarski does not explicitly teach further comprising:
detecting a plurality of driving events in the time period, each driving event of the plurality of driving events corresponding to a safety factor of a plurality of safety factors;
wherein the plurality of safety factors comprises the safety factor, and wherein each safety factor of the plurality of safety factors corresponds to a driving event that has a higher probability of leading to an accident.
Brinkmann, in the same field of endeavor, teaches
further comprising:
detecting a plurality of driving events in the time period (“The driving analysis server 220 may be configured to receive the periodic transmissions, and then to perform periodic driving event analyses and driver score calculations for one or more vehicles and drivers” (Col. 9, lines 29-32)), each driving event of the plurality of driving events corresponding to a safety factor of a plurality of safety factors (“the driving analysis server 220 may determine in step 405 if the vehicle 210 was tailgating (using one or more tailgating thresholds), speeding (using one or more speed thresholds), driving impatiently or recklessly (using one or more braking and acceleration rate thresholds, lane change or lane departure thresholds, etc.), or driving while distracted (using one or more reaction time thresholds, noise threshold, and/or driver distraction thresholds, etc.), and so on” (Col. 16, lines 2-10));
wherein the plurality of safety factors comprises the safety factor, and wherein each safety factor of the plurality of safety factors corresponds to a driving event that has a higher probability of leading to an accident (“the driving analysis server 220 may identify one or more potentially high-risk or unsafe driving events (or driving behaviors) within the operation data of the vehicle 210. The driving events identified in step 302 may correspond to specific occurrences or patterns of high-risk, unsafe, or illegal driving activities that have the potential to affect the driver score of the vehicle 210 or a driver of the vehicle 210” (Col. 9, lines 33-40)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Pilarski with the teachings of Brinkmann in order to perform insurance analyses and determinations; see Brinkmann at least at (Col. 4, lines 38-50).
Regarding claim 7, Pilarski discloses the method of claim 1. However, Pilarski does not explicitly teach further comprising:
determining, by the at least one processor, a number of minutes of active driving in the time period, wherein the safety score for the safety factor is based on the number of minutes of active driving.
Slusar, in the same field of endeavor, teaches
determining, by the at least one processor, a number of minutes of active driving in the time period, wherein the safety score for the safety factor is based on the number of minutes of active driving (“a driver score may be calculated using various equations or algorithms that take into account one or more of a personal driving score (e.g., a measurement of the direct driving behaviors of the driver), a personal exposure value (e.g., a measurement of time, mileage, etc” (Col. 17, lines 63-67)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Pilarski with the teachings of Slusar in order to calculate a driver score based on a personal exposure value (e.g., a measurement of time, mileage, etc; see Slusar at least at (Col. 17, lines 63-67).
Regarding claim 8, Pilarski discloses the method of claim 1. However, Pilarski does not explicitly teach further comprising:
determining, by the at least one processor, how much a driver drives in the time period; and
characterizing, by the at least one processor, a number of minutes of driving as one having a driving event characterized as dangerous; wherein the calculated data record value increases as the number of minutes having events characterized as dangerous increases.
Slusar, in the same field of endeavor, teaches
further comprising:
determining, by the at least one processor, how much a driver drives in the time period (“a driver score may be calculated using various equations or algorithms that take into account one or more of a personal driving score (e.g., a measurement of the direct driving behaviors of the driver), a personal exposure value (e.g., a measurement of time, mileage, etc” (Col. 17, lines 63-67)); and
characterizing, by the at least one processor, a number of minutes of driving as one having a driving event characterized as dangerous (“a driving analysis module 214 within the vehicle 210 may calculate or update its own driver scores (e.g., for the driver of the vehicle 210) and/or for the other nearby vehicles by using V2V communications to detect “social interactions” between vehicles that may characterize positive or negative driving behaviors, such as tailgating and cutting-off (negative), or yielding and defensive avoidance (positive).” (Col. 16, lines 9-15)); wherein the calculated data record value increases as the number of minutes having events characterized as dangerous increases (“all occurrences of all determined positive and negative driving behaviors may be accumulated and stored over a period of time, such a week, month, year, or for an insurance term, and the accumulated set of driving behaviors may be used to calculate insurance rate adjustments or discounts, along with other factors such as accidents, vehicle maintenance, and driving record” (Col. 14, lines 17-23)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Pilarski with the teachings of Slusar in order to calculate a driver score based on a personal exposure value (e.g., a measurement of time, mileage, etc and adjust insurance premiums based on the accumulated set of driving behaviors; see Slusar at least at (Col. 14, lines 17-28).
Regarding claim 9, Pilarski discloses the method of claim 1. However, Pilarski does not explicitly teach further comprising:
determining, by the at least one processor, how much a driver drives in the time period; and
characterizing, by the at least one processor, a number of minutes of driving as one having a driving event characterized as above-and-beyond driving; wherein the calculated data record value decreases as the number of minutes having events characterized as above-and-beyond driving increases.
Slusar, in the same field of endeavor, teaches
further comprising:
determining, by the at least one processor, how much a driver drives in the time period (“a driver score may be calculated using various equations or algorithms that take into account one or more of a personal driving score (e.g., a measurement of the direct driving behaviors of the driver), a personal exposure value (e.g., a measurement of time, mileage, etc” (Col. 17, lines 63-67)); and
characterizing, by the at least one processor, a number of minutes of driving as one having a driving event characterized as above-and-beyond driving (“a driving analysis module 214 within the vehicle 210 may calculate or update its own driver scores (e.g., for the driver of the vehicle 210) and/or for the other nearby vehicles by using V2V communications to detect “social interactions” between vehicles that may characterize positive or negative driving behaviors, such as tailgating and cutting-off (negative), or yielding and defensive avoidance (positive).” (Col. 16, lines 9-15)); wherein the calculated insurance premium decreases as the number of minutes having events characterized as above-and-beyond driving increases (“all occurrences of all determined positive and negative driving behaviors may be accumulated and stored over a period of time, such a week, month, year, or for an insurance term, and the accumulated set of driving behaviors may be used to calculate insurance rate adjustments or discounts, along with other factors such as accidents, vehicle maintenance, and driving record” (Col. 14, lines 17-23)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Pilarski with the teachings of Slusar in order to calculate a driver score based on a personal exposure value (e.g., a measurement of time, mileage, etc and adjust insurance premiums based on the accumulated set of driving behaviors; see Slusar at least at (Col. 14, lines 17-28).
Regarding claim 12, Pilarski discloses the method of claim 1. However, Pilarski does not explicitly teach further comprising:
assessing, by the at least one processor, a driving behavior of a driver in real time; wherein assessing the driving behavior of the driver comprises: determining a cause of the driving event as it occurs.
Brinkmann, in the same field of endeavor, teaches
further comprising:
assessing, by the at least one processor, a driving behavior of a driver in real time; wherein assessing the driving behavior of the driver comprises: determining a cause of the driving event as it occurs (“telematics devices 216, vehicle operation systems 225, and other data sources may transmit vehicle operation data for a vehicle 210 to the driving analysis server 220 in real-time (or near real-time). The driving analysis server 220 may be configured to receive the vehicle operation data, and then perform real-time (or near real-time) driving analyses and driver score calculations for the vehicle 210” (Col. 9, lines 16-23)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Pilarski with the teachings of Brinkmann in order to perform insurance analyses and determinations; see Brinkmann at least at (Col. 4, lines 38-50).
Regarding claim 15, Pilarski discloses the method of claim 1. However, Pilarski does not explicitly teach further comprising:
detecting, by the at least one processor, a plurality of driving events in the time period, each driving event of the plurality of driving events corresponding to a safety factor of a plurality of safety factors; wherein the plurality of safety factors comprises the safety factor; and wherein each of the plurality of safety factors corresponds to a driver assessment module of a plurality of driver assessment modules; wherein the plurality of driver assessment modules comprise:
speed assessment; safe following distance; and hard accelerations including turns
Brinkmann, in the same field of endeavor, teaches
further comprising:
detecting, by the at least one processor, a plurality of driving events in the time period (“Based on the vehicle operational data, the driving analysis server may be configured to identify one or more potentially high-risk or unsafe driving events at a vehicle, for example, an occurrence of sudden braking or swerving, an impact to the vehicle, speeding, or a moving violation, etc” (Col. 2, lines 1-5)), each driving event of the plurality of driving events corresponding to a safety factor of a plurality of safety factors; wherein the plurality of safety factors comprises the safety factor (“a driving analysis server or system, configured as described herein for receiving and analyzing vehicle driving data and calculating driver scores based on identified driving events” (Col. 3, lines 29-32)); and wherein each of the plurality of safety factors corresponds to a driver assessment module of a plurality of driver assessment modules (“other types of safe/unsafe driving thresholds (e.g., vehicle speed thresholds, vehicle maintenance/operational condition thresholds, tailgating thresholds, lane change thresholds, lane departure thresholds, reaction time thresholds, weather condition thresholds, road condition thresholds, traffic condition thresholds, road visibility thresholds, and thresholds relating to the driver distractions and the use of vehicle controls, etc.) may be used in the analyses of step 304” (Col. 13, lines 35-43)); wherein the plurality of driver assessment modules comprise:
speed assessment; safe following distance; and hard accelerations including turns (“the driving analysis server 220 may determine in step 405 if the vehicle 210 was tailgating (using one or more tailgating thresholds), speeding (using one or more speed thresholds), driving impatiently or recklessly (using one or more braking and acceleration rate thresholds, lane change or lane departure thresholds, etc.), or driving while distracted (using one or more reaction time thresholds, noise threshold, and/or driver distraction thresholds, etc.), and so on” (Col. 16, lines 2-10)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Pilarski with the teachings of Brinkmann in order to perform insurance analyses and determinations; see Brinkmann at least at (Col. 4, lines 38-50).
Regarding claim 16, Pilarski discloses the method of claim 1. However, Pilarski does not explicitly teach wherein the safety factor corresponds to a safe following distance, wherein the detected object is another vehicle; and wherein a safe following distance is based on giving the driver enough time to react to the other vehicle.
Brinkmann, in the same field of endeavor, teaches
wherein the safety factor corresponds to a safe following distance, wherein the detected object is another vehicle; and wherein a safe following distance is based on giving the driver enough time to react to the other vehicle (“the driving analysis server 220 may use the image, video, and/or proximity data to determine the following distance of the trailing vehicle just before the accident. Based on the following distance, the driving analysis server 220 may use the speed of the vehicles to calculate a tailgating time metric for the trailing vehicle, which may be compared to a tailgating safety threshold (e.g., 2 seconds) to determine whether or not tailgating by the trailing vehicle will be classified as a cause of the accident… if the analysis of the image, video, and proximity data indicates that the trailing vehicle was tailgating closer than the tailgating safety threshold for the current road/weather/visibility conditions, then the driving analysis server 220 may determine that tailgating by the trailing vehicle was a cause of the accident” (Col. 13, lines 2-22)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Pilarski with the teachings of Brinkmann in order to determine whether or not tailgating by the trailing vehicle will be classified as a cause of the accident; see Brinkmann at least at (Col. 13, lines 15-22).
Regarding claim 17, Pilarski discloses the method of claim 16. However, Pilarski does not explicitly teach wherein driving at higher speeds in inclement weather or on slick road surfaces requires a greater following distance to allow sufficient time to react safely.
Brinkmann, in the same field of endeavor, teaches
wherein driving at higher speeds in inclement weather or on slick road surfaces requires a greater following distance to allow sufficient time to react safely (“the driving analysis server 220 may use the image, video, and/or proximity data to determine the following distance of the trailing vehicle just before the accident. Based on the following distance, the driving analysis server 220 may use the speed of the vehicles to calculate a tailgating time metric for the trailing vehicle, which may be compared to a tailgating safety threshold (e.g., 2 seconds) to determine whether or not tailgating by the trailing vehicle will be classified as a cause of the accident… Additional data metrics relating to weather conditions, road conditions, and visibility may be calculated using image data and video data received in step 303, and may be used in combination with other data metrics to determine causes of driving events. For instance, a first tailgating safety threshold (e.g., 2 seconds) may be applied in good weather and high visibility conditions, while a second tailgating safety threshold (e.g., 5 seconds) may be applied in poor weather and low visibility conditions such as rain, fog, icy roads, etc” (Col. 13, lines 2-16)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Pilarski with the teachings of Brinkmann in order to determine whether or not tailgating by the trailing vehicle will be classified as a cause of the accident; see Brinkmann at least at (Col. 13, lines 15-22).
Regarding claim 18, Pilarski discloses the method of claim 1. Additionally, Pilarski discloses wherein the object is a pedestrian, a cyclist, a traffic sign or a traffic light (“the prediction engine 126 determines possible events relating to different types or classes of dynamic objects, such as other vehicles, bicyclists or pedestrians” (para 0043)).
Regarding claim 19, Pilarski discloses the method of claim 1. However, Pilarski does not explicitly teach further comprising:
transmitting, by the at least one processor, data to a remote device, wherein the data comprises an indication that a driving behavior of a driver during the driving event is a positive driving behavior.
Brinkmann, in the same field of endeavor, teaches
further comprising:
transmitting, by the at least one processor, data to a remote device (“The driving analysis server 220 may be configured to receive the periodic transmissions, and then to perform periodic driving event analyses and driver score calculations for one or more vehicles and drivers” (Col. 9, lines 29-32)), wherein the data comprises an indication that a driving behavior of a driver during the driving event is a positive driving behavior (“a driver score may be positively adjusted based on the image, video, and proximity data analysis associated with the driving event” (Col. 14, lines 37-39)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Pilarski with the teachings of Brinkmann in order to perform insurance analyses and determinations; see Brinkmann at least at (Col. 4, lines 38-50).
Regarding claim 20, Pilarski discloses a system, comprising:
at least one camera (Fig. 5, #522“an autonomous vehicle 410 includes various sensors, such as …, front cameras 424 and radar or sonar 430, 432” (para 0086));
at least one memory unit; and at least one processor coupled to the at least one memory unit and coupled to the at least one camera (Fig. 5, #504 and #506, and “A processing center 425, comprising a combination of one or more processors and memory units can be positioned in a trunk of the vehicle 410” (para 0086)), in which the at least one processor (Fig. 5, #504) is configured to:
detect an appearance of an object based on visual data from a camera, wherein the camera is attached to a vehicle(“The perceptions 123 can correspond to interpreted sensor data, such as (i) image, sonar or other electronic sensory-based renderings of the environment, (ii) detection and classification of objects in the environment, and/or (iii) state information associated with individual objects (e.g., whether object is moving, pose of object, direction of object)” (para 0027) and “the vehicle 410 uses one or more sensor views 403 (e.g., field of view of camera)” (para 0087));
predict a future path of travel of the vehicle mapped to a camera view based on determined object locations (“The perceptions 123 and the predictions 139 can provide input into the motion planning component 124. The motion planning component 124 includes logic to detect dynamic objects of the vehicle's environment from the perceptions. When dynamic objects are detected, the motion planning component 124 determines a response trajectory 125 of the vehicle” (para 0029));
generate using an input from the camera, an inference representing an occurrence of a driving event comprising an interaction between the predicted path of travel and the detected object (“the prediction analysis 226 can utilize input from the sensor processing component 210 and the route planner 122 in order to anticipate a likelihood or probability that an object of one or more predetermined classes (e.g., persons, bicycles, other vehicles) will interfere or collide with the path of the autonomous vehicle 10” (para 0059));
However, Pilarski does not explicitly teach
determine, using a machine learned model based on the inference, that the driving event does not decrease a safety score for a safety factor; and
adjust a data record value based on an aggregated safety score for a time period, wherein the aggregated safety score increases as the safety score for the safety factor increases, and wherein a higher aggregated safety score corresponds to a lower data record value.
Brinkmann, in the same field of endeavor, teaches
determine, using a machine learned model based on the inference, that the driving event does not decrease a safety score for a safety factor (“a driver score may be positively adjusted based on the image, video, and proximity data analysis associated with the driving event. For instance, if an image, video, and proximity data analysis in step 304 indicates that a driver was not speeding or tailgating based on the current road conditions, and that the driver reacted quickly and appropriately to an unforeseen driving event (e.g., by quickly swerving or braking to avoid a pedestrian), then the driver score may be positively adjusted (e.g., raised) based on the driving event” (Col. 14, lines 37-46)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Pilarski with the teachings of Brinkmann in order to determine whether or not to adjust a driver score in response to a potentially high-risk or unsafe driving event; see Brinkmann at least at (Col. 8, lines 32-33); and
Slusar, in the same field of endeavor, teaches
adjust a data record value based on an aggregated safety score for a time period (“determining driving behaviors of vehicles, and calculating driver scores based on the determined driving behaviors” (Col. 1, lines 58-60) and “all occurrences of all determined positive and negative driving behaviors may be accumulated and stored over a period of time, such a week, month, year, or for an insurance term, and the accumulated set of driving behaviors may be used to calculate insurance rate adjustments or discounts, along with other factors such as …, and driving record” (Col. 14, lines 17-23)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Pilarski with the teachings of Slusar in order to calculate insurance rate adjustments or discounts along with driving record based on accumulated driver scores over a period of time; see Slusar at least at (Col. 14, lines 17-23),
Brian, in the same field of endeavor, teaches
wherein the aggregated safety score increases as the safety score for the safety factor increases (“the method 200 calculates a score for the driver based on the monitoring. Any scoring system may be used to calculate the score. In one embodiment, the scoring may be cumulative” (para 0039)), and wherein a higher aggregated safety score corresponds to a lower data record value (“The automobile insurance company may use the driver's score to determine an insurance premium or rate for the driver. For example, the higher the driver's score, the lower the insurance premium will be set” (para 0017)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Pilarski with the teachings of Brian in order to determine an insurance premium such that a higher cumulative score corresponds to a lower insurance premium; see Brian at least at [0017].
Regarding claim 21, Pilarski discloses a machine-readable storage medium having computer-executable instructions stored thereon that, when executed by one or more processors, cause the one or more processors to perform operations comprising:
detecting an appearance of an object based on visual data from a camera (“The perceptions 123 can correspond to interpreted sensor data, such as (i) image, sonar or other electronic sensory-based renderings of the environment, (ii) detection and classification of objects in the environment, and/or (iii) state information associated with individual objects (e.g., whether object is moving, pose of object, direction of object)” (para 0027)), wherein the camera is attached to a vehicle (“the vehicle 410 uses one or more sensor views 403 (e.g., field of view of camera)” (para 0087));
predicting a future path of travel of the vehicle mapped to a camera view based on determined object locations (“The perceptions 123 and the predictions 139 can provide input into the motion planning component 124. The motion planning component 124 includes logic to detect dynamic objects of the vehicle's environment from the perceptions. When dynamic objects are detected, the motion planning component 124 determines a response trajectory 125 of the vehicle” (para 0029));
generating using an input from the camera, an inference representing an occurrence of a driving event comprising an interaction between the predicted path of travel and the detected object (“the prediction analysis 226 can utilize input from the sensor processing component 210 and the route planner 122 in order to anticipate a likelihood or probability that an object of one or more predetermined classes (e.g., persons, bicycles, other vehicles) will interfere or collide with the path of the autonomous vehicle 10” (para 0059));
However, Pilarski does not explicitly teach
determining, executing a machine learned model based on the inference, that the driving event does not decrease a safety score for a safety factor; and
adjusting a data record value based on an aggregated safety score for a time period, wherein the aggregated safety score increases as the safety score for the safety factor increases, and wherein a higher aggregated safety score corresponds to a lower data record value.
Brinkmann, in the same field of endeavor, teaches
determining, executing a machine learned model based on the inference, that the driving event does not decrease a safety score for a safety factor (“a driver score may be positively adjusted based on the image, video, and proximity data analysis associated with the driving event. For instance, if an image, video, and proximity data analysis in step 304 indicates that a driver was not speeding or tailgating based on the current road conditions, and that the driver reacted quickly and appropriately to an unforeseen driving event (e.g., by quickly swerving or braking to avoid a pedestrian), then the driver score may be positively adjusted (e.g., raised) based on the driving event” (Col. 14, lines 37-46)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Pilarski with the teachings of Brinkmann in order to determine whether or not to adjust a driver score in response to a potentially high-risk or unsafe driving event; see Brinkmann at least at (Col. 8, lines 32-33); and
Slusar, in the same field of endeavor, teaches
adjusting a data record value based on an aggregated safety score for a time period (“determining driving behaviors of vehicles, and calculating driver scores based on the determined driving behaviors” (Col. 1, lines 58-60) and “all occurrences of all determined positive and negative driving behaviors may be accumulated and stored over a period of time, such a week, month, year, or for an insurance term, and the accumulated set of driving behaviors may be used to calculate insurance rate adjustments or discounts, along with other factors such as …, and driving record” (Col. 14, lines 17-23)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Pilarski with the teachings of Slusar in order to calculate insurance rate adjustments or discounts along with driving record based on accumulated driver scores over a period of time; see Slusar at least at (Col. 14, lines 17-23),
Brian, in the same field of endeavor, teaches
wherein the aggregated safety score increases as the safety score for the safety factor increases (“the method 200 calculates a score for the driver based on the monitoring. Any scoring system may be used to calculate the score. In one embodiment, the scoring may be cumulative” (para 0039)), and wherein a higher aggregated safety score corresponds to a lower data record value (“The automobile insurance company may use the driver's score to determine an insurance premium or rate for the driver. For example, the higher the driver's score, the lower the insurance premium will be set” (para 0017)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Pilarski with the teachings of Brian in order to determine an insurance premium such that a higher cumulative score corresponds to a lower insurance premium; see Brian at least at [0017].
Claims 10-11 and 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pub. No. 20170329332 (hereinafter, "Pilarski"; newly of record), in view of U.S. Pat. No. 9535878 (hereinafter, "Brinkmann"; previously of record), in further view of U.S. Pat. No. 9147353 (hereinafter, "Slusar"; previously of record) and in further view of U.S. Pub. No. 20150161913 (hereinafter, "Brian"; previously of record) as applied to claim 1 above, and in further view of U.S. Pub. No. 20090284361 (hereinafter, "Boddie"; previously of record).
Regarding claim 10, Pilarski discloses the method of claim 1. However, Pilarski does not explicitly teach further comprising:
displaying a detailed view of the safety score, wherein the detailed view comprises markings, wherein each marking indicates that the driver was driving and whether the driver's time was spent in compliance with safety guidelines.
Boddie, in the same field of endeavor, teaches
displaying a detailed view of the safety score, wherein the detailed view comprises markings, wherein each marking indicates that the driver was driving and whether the driver's time was spent in compliance with safety guidelines (“The driver scoring information calculated by display and processing unit 8 includes event score, total score, average score, and scoring trend of the driver. Display and processing unit 8 can display live scores for a current driving period or can display a history of scores sorted by driver. The driver scoring information may be visually displayed and audibly played by the display and processing unit 8 to alert the driver to proper or poor driver behavior” (para 0035) and “While the car is still running, indicating the driving period may continue, certain scores can be displayed. Step 402 displays the point value of the last event detected, either z0 (if points were added) or z (if points were subtracted). Step 403 displays the accumulated series score M_series, or total score, which is the value of the no_of_points_current field 260 in the most recent scoring interval record” (para 0072)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Pilarski with the teachings of Boddie in order to visually display, audibly present driver scoring information via the display and alert the driver to proper or poor driver behavior based on live scores for a current driving period; see Boddie at least at [0035].
Regarding claim 11, Pilarski discloses the method of claim 1. However, Pilarski does not explicitly teach further comprising:
generating, by the at least one processor, an alert notification based on the determination that a driving behavior of a driver during the driving event is a positive driving behavior; and
communicating, by the at least one processor, the alert notification for the positive driving behavior to the driver of the vehicle.
Boddie, in the same field of endeavor, teaches
further comprising:
generating, by the at least one processor, an alert notification based on the determination that a driving behavior of a driver during the driving event is a positive driving behavior; and
communicating, by the at least one processor, the alert notification for the positive driving behavior to the driver of the vehicle (“The driver scoring information calculated by display and processing unit 8 includes event score, total score, average score, and scoring trend of the driver. Display and processing unit 8 can display live scores for a current driving period or can display a history of scores sorted by driver. The driver scoring information may be visually displayed and audibly played by the display and processing unit 8 to alert the driver to proper or poor driver behavior” (para 0035)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Pilarski with the teachings of Boddie in order to visually display, audibly present driver scoring information via the display and alert the driver to proper or poor driver behavior based on live scores for a current driving period; see Boddie at least at [0035].
Regarding claim 13, Pilarski discloses the method of claim 1. However, Pilarski does not explicitly teach further comprising:
displaying a detailed view of the aggregated safety score, wherein the detailed view comprises markings, wherein each marking indicates the aggregated summary score for a driver plotted by time of day.
Boddie, in the same field of endeavor, teaches
further comprising:
displaying a detailed view of the aggregated safety score, wherein the detailed view comprises markings, wherein each marking indicates the aggregated summary score for a driver plotted by time of day (“At the end of each driving period, a score total will be displayed to the driver and stored in memory” (para 0014) and “Then in step 357 event table 330 is queried to obtain the number of events 359 accumulated in the Y-minute scoring interval. The number of events being designated by the variable x. Step 360 determines if any events occurred during the current scoring interval and adds or subtracts points accordingly. If there are no events during the current scoring interval, the driver is awarded positive points which are added to his score. If at least one event has occurred during the current scoring interval, points are subtracted from the driver's score” (para 0069)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Pilarski with the teachings of Boddie such that a score total will be displayed to the driver; see Boddie at least at [0014].
Regarding claim 14, Pilarski discloses the method of claim 1. However, Pilarski does not explicitly teach further comprising:
displaying a summary view of the aggregated safety score, wherein the summary view comprises an aggregated summary score calculated for a previous month.
Boddie, in the same field of endeavor, teaches
further comprising:
displaying a summary view of the aggregated safety score, wherein the summary view comprises an aggregated summary score calculated for a previous month (“Display and processor unit 105 may show current performance data 110 incorporating data for the current driving period and historical performance data 112 incorporating data from multiple previous driving periods sortable by driver” (para 0038) and “wherein a history of scores is saved in the data storage and the history of scores is displayed on the display screen” (claim 9)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Pilarski with the teachings of Boddie in order to show historical performance data incorporating data from multiple previous driving periods sortable by driver; see Boddie at least at [0038].
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 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ADAM ALHARBI whose telephone number is (313)446-6621. The examiner can normally be reached M-F 10am-6:30pm.
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/ADAM M ALHARBI/Primary Examiner, Art Unit 3663
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