DETAILED ACTION
This Office Action is taken in response to Applicant’s Amendment and Remarks filed on 01/14/2026 regarding Application No. 18/115,626 originally filed on 02/28/2023. Claims 1-5, 7, 9-11, 13-15, 17-19, 21-25 are pending for consideration:
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
The applicant argues “Aly, instead, discloses a low value and a high value of acceleration in one direction (e.g., an x-acceleration). Specifically, and as depicted in Fig. 6 of Aly, reproduced below, Aly discloses a low value of x-acceleration and then a higher value, corresponding to a left lane change… High and low values are not equivalent to a pair of opposite sign lateral accelerations because the opposite signs are in opposing directions, e.g., left and right or driver-side and passenger-side… Thus, Aly fails to teach at least "the motion signature comprises a pair of opposite sign lateral accelerations."” [Remarks, p. 7-8]. The examiner respectfully disagrees.
Aly’s lane-change motion signature is characterized by a max and a min in x-acceleration within a lane-change window, and Aly expressly discloses that the x-acceleration pattern is reversed for opposite lane-change directions. (as per “Assuming that the vehicle is making a left-lane change (Fig. 6), then the x-acceleration reading first decreases to a low value and then increases back to a higher value.” in pg. 9, Section 5.1, as per “Fig. 6… The x-acceleration pattern is reversed when doing right lane-change” in pg. 11, Section 5.1). Further, as shown in Aly’s right lane-change plot of Fig. 6:
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the x-acceleration trace crosses zero (positive peak followed by a negative dip), i.e., opposite sign x-accelerations are present in that lane-change example, meeting the “pair of opposite sign lateral accelerations” limitation under BRI. Therefore, applicant’s traversal is therefore unpersuasive.
The applicant argues “Aly fails to disclose the pair of opposite sign lateral accelerations occur consecutively and within a frequency bandwidth… Above the frequency bandwidth may be filtered out (e.g., high frequencies such as may be generated from a phone vibrating within a cupholder or in response to receiving a text message,… Below the frequency bandwidth may also be filtered out, as those lateral acceleration signals may be associated with the vehicle tracking the road curvature… Accordingly, Aly fails to disclose "wherein the pair of opposite sign lateral accelerations occur consecutively and within a frequency bandwidth."… Thus, Kim fails to teach wherein the motion signature comprises higher-frequency peaks, within the frequency bandwidth, which occur along a lower-frequency signal below the frequency bandwidth.” [Remarks, p. 9-10]. The examiner respectfully disagrees.
Karnik expressly discloses filtering sensor measurements using a bandpass filter to eliminate high frequency sensor measurements occurring at particular frequencies, i.e., operating with a bounded frequency range (frequency bandwidth) after filtering. (as per “by filtering, with a bandpass filter… The bandpass filter filters out a portion of the sensor measurements that occur at particular frequencies.” in ¶164). Aly discloses the lane-change signature as a consecutive low-to-high (or reversed) x-acceleration pattern during a lane change, and thus, in view of Karnik’s express bandpass filtering, the consecutive opposite-sign lane-change accelerations are treated as occurring within the remaining frequency bandwidth after filtering. Therefore, applicant’s traversal is therefore unpersuasive.
Further, Applicant’s arguments directed to Kim regarding “wherein the motion signature comprises higher-frequency peaks, within the frequency bandwidth, which occur along a lower-frequency signal below the frequency bandwidth” are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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 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.
Claim(s) 1-5, 7, 9-11, 13-15, 23, and 25 are rejected under 35 U.S.C. 103 as being unpatentable over Cordova (US Pub. No. 20210055325) in view of Karnik (US Pub. No. 20210049837) in view of Pozo (US Pub. No. 20160016590) in view of Cordova (US Pub. No. 20170349182) in further view of Aly (NPL Title: Robust and ubiquitous smartphone-based lane detection).
As per Claim 1, Cordova discloses of a method, the method comprising:
detecting a vehicle trip for a vehicle with a mobile device; (as per "the present invention utilize mobile devices to provide information on a user's behaviors during transportation. For example, a mobile device carried by a user could be used to analyze driving habits, which is of interest for insurance coverage and the like." in ¶30)
at the mobile device, determining a first dataset, the first dataset comprising movement data collected with at least one inertial sensor of the mobile device; (as per Fig. 2, as per "The movement measurements can be obtained, for example, using sensor data block 105 in mobile device 101, e.g., a smart phone or other suitable mobile device. The collected data can include location data (e.g., GPS receiver 110 data) as a function of time, accelerometer 112 data, gyroscope 116 data, combinations thereof, or the like." in ¶39)
Cordova fails to expressly disclose:
using a first model, extracting features from the first dataset, comprising filtering out of the first dataset, high frequency vibrations above a frequency bandwidth;
based on the extracted features, determining a lateral acceleration metric corresponding to vehicle lane change behavior during the vehicle trip;
based on the lateral acceleration metric satisfying a set of lateral deviation criteria, determining an update to a driver score;
wherein the lateral acceleration metric satisfies the set of lateral deviation criteria
based on detecting a motion signature, wherein the motion signature comprises a pair of opposite sign lateral accelerations; and
triggering an action at the mobile user device based on the update to the driver score.
Karnik discloses of vehicle speed estimation (as per Abstract), comprising:
using a first model, (as per “generating a speed prediction by executing a trained neural network using the set of features; generating a vehicle crash prediction using the speed prediction” in ¶5) extracting features from the first dataset, (as per “Features, for example statistical features, may be extracted from some or all of the filtered signals.” In ¶57) comprising filtering out of the first dataset, high frequency vibrations above a frequency bandwidth; (as per “by filtering, with a bandpass filter, the set of sensor measurements to eliminate high frequency sensor measurements from the set of sensor measurements (block 2112). The bandpass filter filters out a portion of the sensor measurements that occur at particular frequencies. For example, frequencies associated with signal noise sensor measurements that do not correspond to the vehicle (e.g., movement of the mobile device independent from movement of the vehicle)” in ¶164)
In this way, Karnik operates to eliminate high frequency sensor measurements from the set of sensor measurements (¶5). Like Cordova, Karnik is concerned with driving detection.
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the pattern-based identification system as taught by Cordova with the vehicle speed estimation of Karnik to enable another standard means of removing high frequency vibrations above a frequency bandwidth (¶5). Such modification allows for the system to remove high frequency vibrations above a frequency bandwidth (¶5).
Karnik and Cordova fail to expressly disclose:
based on the extracted features, determining a lateral acceleration metric corresponding to vehicle lane change behavior during the vehicle trip;
based on the lateral acceleration metric satisfying a set of lateral deviation criteria, determining an update
determining an update to a driver score;
wherein the lateral acceleration metric satisfies the set of lateral deviation criteria
based on detecting a motion signature, wherein the motion signature comprises a pair of opposite sign lateral accelerations; and
triggering an action at the mobile user device based on the update to the driver score.
Pozo discloses of a method, the method comprising:
based on the extracted features, determining a lateral acceleration metric corresponding to vehicle lane change behavior during the vehicle trip; (as per "The driving events may be selected from the following types: manipulation events, turn events, acceleration events, stop events and zig-zag events." in ¶28, as per "Zig zag events: change lanes in a short period of time means that a vehicle is maybe avoiding an obstacle. A zig zag event (at least two changes) or a simple change may be identified by registering lateral accelerations without significant angular velocities." in ¶80)
based on the lateral acceleration metric (as per “The driving events may be selected from the following types: manipulation events, turn events, acceleration events, stop events and zig-zag events" in ¶28) satisfying a set of lateral deviation criteria (as per “Zig-zag events (35) are identified as two or more change lanes from the significant measurements of the accelerometer (lateral acceleration) not accompanied by any angular velocity” in ¶45-¶51, as per “the detection of any of these significant events is done using similar energy-based event detection algorithms, where energy can be computed from global accelerations or global angular velocities” in ¶65), determining an update (as per “The output of this stage is a set of significant driving events. From this complete sequence of significant events, the Aggressive Events Detection component (42) may be applied to identify those related to aggressive driving maneuvers” in ¶51)
wherein the lateral acceleration metric satisfies the set of lateral deviation criteria (as per “comparing, in each time segment corresponding to a driving event, a maximum energy value of the at least one motion data signal with a second threshold; in the case of said the second threshold is exceeded in a certain time segment, determining an aggressive driving event for said time segment.” in ¶19-¶20, as per “Zig-zag events (35) are identified as two or more change lanes from the significant measurements of the accelerometer (lateral acceleration) not accompanied by any angular velocity” in ¶45-¶51, as per “the detection of any of these significant events is done using similar energy-based event detection algorithms, where energy can be computed from global accelerations or global angular velocities” in ¶65),
In this way, Pozo operates to detect driving events of a vehicle based on a smartphone (Abstract). Like Cordova and Karnik, Pozo is concerned with driving detection.
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the pattern-based identification system as taught by Cordova and the vehicle speed estimation of Karnik with the driving event detection method of Pozo to enable another standard means of classifying lateral acceleration as a metric. Such modification also enables the method to track the severity and frequency of the lane changes from the lateral acceleration metric (as per ¶80). Such modification allows for the system to take into account lateral acceleration of a vehicle (¶28).
Cordova, Karnik, and Pozo fail to expressly disclose:
determining an update to a driver score;
based on detecting a motion signature, wherein the motion signature comprises a pair of opposite sign lateral accelerations; and
triggering an action at the mobile user device based on the update to the driver score.
Cordova discloses scoring driving trips, comprising:
determining an update to a driver score; (as per “this measured movement can be minor (e.g., a mobile device sliding sideways within a cup holder), or more substantial (e.g., the mobile device being picked up out of the cup holder and held to the ear of a driver). The method includes analyzing the movement measurements to determine whether they are indicative of a particular type of event occurring with respect to the mobile device in a vehicle at decision block 720. In some embodiments, this particular type of event is associated with use by the driver of mobile device, such that the driver of the vehicle is potentially not paying attention to driving tasks (e.g., the driver is distracted from driving tasks by the mobile device). For convenience, as used herein, inattentiveness, distraction, failing to pay attention, mobile device usage, and/or other similar terms and phrases broadly signify a driver not paying proper attention to tasks associated with safely operating the vehicle” in ¶80, as per “systems and methods for scoring driving trips based on sensor measurements from a mobile device” in ¶4, as per “detect and analyze driving behavior. The mobile device can further be used to provide a driving score based on the driving behavior, which may encourage modification of future driving behavior” in ¶5)
triggering an action at the mobile user device based on the update to the driver score. (as per “Notification block 140 may report the results of analysis of sensor data performed by the data processing block 120 to a user of the mobile device 101 via a display (not shown). For example, notification block 140 may display or otherwise report a score for a trip or for a plurality of trips to a user of the mobile device 101. In one embodiment, mobile device 101 may further include a scoring block (not shown) to score individual or collective trips, as described further herein with respect to FIG. 3” in ¶30, as per “updating the measurement rate for the sensor measurements from the one or more sensors of the mobile device for a subsequent trip in the vehicle based on the trip score (335)” in ¶51)
In this way, Cordova operates to assign and display scores related to driving scores detected by a mobile device (Abstract). Like Cordova, Karnik, and Pozo, Cordova is concerned with driving event detection.
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the pattern-based identification system as taught by Cordova, the vehicle speed estimation of Karnik, and the driving event detection method of Pozo with the method for scoring driving trips of Cordova to enable another standard means of assigning and displaying scores related to the driving behaviors. Such modification also allows the system to analyze driving behaviors and present the driving score to be displayed via the mobile device (¶6, ¶30).
Cordova, Karnik, Pozo, and Cordova fail to expressly disclose:
based on detecting a motion signature, wherein the motion signature comprises a pair of opposite sign lateral accelerations; and
Aly discloses of ubiquitous smartphone-based lane detection, comprising:
based on detecting a motion signature, wherein the motion signature comprises a pair of opposite sign lateral accelerations; and (as per "Assuming that the vehicle is making a left-lane change (Fig. 6), then the x-acceleration reading first decreases to a low value and then increases back to a higher value. It also minimally affects the phone’s orientation." in pg. 9, Section 5.1 - Lane Change Detection, as per "Fig. 6. Left-lane change causes a specific pattern on the x-acceleration and just a small peak on the orientation. The x-acceleration pattern is reversed when doing right lane-change" in pg. 11, Section 5.1 - Lane Change Detection, as per Fig. 6 - pictured below & Fig. 7)
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In this way, Aly operates to detect lane change events of a vehicle based on a smartphone (Abstract). Like Cordova, Karnik, Cordova, and Pozo, Aly is concerned with detecting user driving events.
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the system(s)/method(s) of Cordova, Karnik, and Pozo with the smartphone-based lane detection system of Aly to enable another standard means of detecting a lane change. Such modification also enables the method to classify right-to-left and left-to-right lane changes and filter the smartphone data (as per Fig. 6, as per Section 5.1 - Lane Change Detection).
As per Claim 2, the combination of Cordova, Karnik, Pozo, Cordova, and Aly teaches or suggests all limitations of Claim 1. Cordova and Karnik fail to expressly disclose wherein the lateral acceleration metric is associated with a frequency and severity of lane changes.
See Claim 1 for teachings of Pozo. Pozo further discloses wherein the lateral acceleration metric is associated with a frequency and severity of lane changes. (as per "Zig zag events: change lanes in a short period of time means that a vehicle is maybe avoiding an obstacle. A zig zag event (at least two changes) or a simple change may be identified by registering lateral accelerations without significant angular velocities. This is the main difference against a turn because, when a vehicle changes lanes sharply, the gyroscope barely observes any angular velocity." in ¶80)
In this way, Pozo operates to detect driving events of a vehicle based on a smartphone (Abstract). Like Cordova, Karnik, Cordova, and Aly, Pozo is concerned with driving detection.
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the system(s)/method(s) of Cordova, Karnik, and Aly with the driving event detection method of Pozo to enable another standard means of classifying lateral acceleration as a metric. Such modification also enables the method to track the severity and frequency of the lane changes from the lateral acceleration metric (as per ¶80). Such modification allows for the system to take into account lateral acceleration of a vehicle (¶28).
As per Claim 3, the combination of Cordova, Karnik, Pozo, Cordova, and Aly teaches or suggests all limitations of Claim 1. Cordova further discloses based on the first dataset, detecting a set of in-hand motion events associated with user interaction with the mobile device; and filtering, out of the first dataset, portions of the first dataset associated with the detected set of in-hand motion events. (as per "when mobile device collecting data during a drive is moved, the data collected during the time the mobile device was moved, is removed from the data set." in ¶9)
As per Claim 4, the combination of Cordova, Karnik, Pozo, Cordova, and Aly teaches or suggests all limitations of Claim 1. Cordova further discloses wherein the features are extracted for portions of the vehicle trip in which the mobile device is classified as stationary relative to the vehicle. (as per "When the mobile device is stationary, the direction of gravity vector remains unchanged relative to the position of the mobile device (i.e., in the “reference frame of the mobile device”). When the orientation of the mobile device is changed, relative to the reference frame of the mobile device, the gravity vector changes relative to the mobile device. Some embodiments take the angle difference between the gravity vectors that are one second (or other predetermined period) apart in time. If the angle difference is above a certain threshold, a determination can be made that the orientation of the mobile device was changed during that time." in ¶32, as per "a plurality of movement measurements collected from a mobile device that was estimated to be stationary relative to a vehicle when the measurements were collected." in ¶53)
As per Claim 5, the combination of Cordova, Karnik, Pozo, Cordova, and Aly teaches or suggests all limitations of Claim 1. Cordova further discloses wherein the lateral acceleration metric is determined with a pretrained machine learning (ML) classifier or tree-based heuristic classifier. (as per "a classifier is trained with sample data from drives to recognize the driving behavior of a particular user." in ¶13, as per "the training of classifier 1014 can be “supervised” by data being provided to annotate, comment on, and/or “tag” movement information. This receiving of driving activity tags (1140), can be done at the time of the activity, e.g., by an interface provided by mobile device 101 (e.g., driver change during trip, weather changes during trip, mechanical problems with vehicle, and/or other similar tags)." in ¶83)
As per Claim 7, the combination of Cordova, Karnik, Pozo, Cordova, and Aly teaches or suggests all limitations of Claim 1. Cordova and Karnik fail to expressly disclose wherein the lateral acceleration metric is further determined based on an angular velocity or a heading.
See Claim 1 for teachings of Pozo. Pozo further discloses wherein the lateral acceleration metric is determined based on an angular velocity or a heading. (as per "A zig zag event (at least two changes) or a simple change may be identified by registering lateral accelerations without significant angular velocities. This is the main difference against a turn because, when a vehicle changes lanes sharply, the gyroscope barely observes any angular velocity." in ¶80)
In this way, Pozo operates to detect driving events of a vehicle based on a smartphone (Abstract). Like Cordova, Karnik, Cordova, and Aly, Pozo is concerned with driving detection.
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the system(s)/method(s) of Cordova, Karnik, and Aly with the driving event detection method of Pozo to enable another standard means of classifying lateral acceleration as a metric. Such modification also enables the method to track the severity and frequency of the lane changes from the lateral acceleration metric (as per ¶80). Such modification allows for the system to take into account lateral acceleration of a vehicle (¶28).
As per Claim 9, Cordova discloses a method, the method comprising:
with sensors of a mobile device, collecting a sensor dataset comprising movement data from at least one inertial sensor of the mobile device; (as per "a mobile device can be configured to measure driving behaviors using sensors such as the GPS receiver, accelerometer, and gyroscope." in ¶30)
with the sensor dataset, detecting a vehicle trip based on longitudinal vehicle movement substantially aligned with a longitudinal axis of a vehicle; (as per "the longitudinal acceleration, as shown on FIG. 8, is acceleration determined to be in the direction of travel of the vehicle by an embodiment." in ¶71, as per "the present invention utilize mobile devices to provide information on a user's behaviors during transportation. For example, a mobile device carried by a user could be used to analyze driving habits, which is of interest for insurance coverage and the like." in ¶30)
Cordova fails to expressly disclose:
at the mobile device, extracting a set of data features from the movement data during a period of the vehicle trip comprising filter out of the sensor dataset, high frequency vibrations above a frequency bandwidth;
detecting a set of lateral movement events based on the set of data features, the set of lateral movement events associated with lateral deviations relative to the longitudinal vehicle movement;
wherein the set of lateral movement events are detected based on a motion signature of lane change behavior,
the motion signature comprising a pair of opposite sign lateral accelerations;
based on the set of lateral movement events satisfying a set of lateral deviation criteria, determining an update to a driver score;
triggering an action based on the update to the driver score.
Karnik discloses of vehicle speed estimation (as per Abstract), comprising:
at the mobile device, extracting a set of data features from the movement data during a period of the vehicle trip (as per “set of sensor measurements are received from a mobile device” in Abstract, as per “Features, for example statistical features, may be extracted from some or all of the filtered signals.” In ¶57) comprising filter out of the sensor dataset, high frequency vibrations above a frequency bandwidth; (as per “by filtering, with a bandpass filter, the set of sensor measurements to eliminate high frequency sensor measurements from the set of sensor measurements (block 2112). The bandpass filter filters out a portion of the sensor measurements that occur at particular frequencies. For example, frequencies associated with signal noise sensor measurements that do not correspond to the vehicle (e.g., movement of the mobile device independent from movement of the vehicle)” in ¶164)
In this way, Karnik operates to eliminate high frequency sensor measurements from the set of sensor measurements (¶5). Like Cordova, Karnik is concerned with driving detection.
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the pattern-based identification system as taught by Cordova with the vehicle speed estimation of Karnik to enable another standard means of removing high frequency vibrations above a frequency bandwidth (¶5). Such modification allows for the system to remove high frequency vibrations above a frequency bandwidth (¶5).
Karnik and Cordova fail to expressly disclose:
detecting a set of lateral movement events based on the set of data features, the set of lateral movement events associated with lateral deviations relative to the longitudinal vehicle movement;
based on the set of lateral movement events satisfying a set of lateral deviation criteria, determining an update;
determining an update to a driver score;
wherein the set of lateral movement events are detected based on a motion signature of lane change behavior,
the motion signature comprising a pair of opposite sign lateral accelerations;
triggering an action based on the update to the driver score.
Pozo discloses a method, the method comprising:
detecting a set of lateral movement events based on the set of data features, the set of lateral movement events associated with lateral deviations relative to the longitudinal vehicle movement; (as per "The driving events may be selected from the following types: manipulation events, turn events, acceleration events, stop events and zig-zag events." in ¶28, as per "Zig zag events: change lanes in a short period of time means that a vehicle is maybe avoiding an obstacle. A zig zag event (at least two changes) or a simple change may be identified by registering lateral accelerations without significant angular velocities." in ¶80)
based on the set of lateral movement events (as per “The driving events may be selected from the following types: manipulation events, turn events, acceleration events, stop events and zig-zag events" in ¶28) satisfying a set of lateral deviation criteria (as per “Zig-zag events (35) are identified as two or more change lanes from the significant measurements of the accelerometer (lateral acceleration) not accompanied by any angular velocity” in ¶45-¶51, as per “the detection of any of these significant events is done using similar energy-based event detection algorithms, where energy can be computed from global accelerations or global angular velocities” in ¶65), determining an update (as per “The output of this stage is a set of significant driving events. From this complete sequence of significant events, the Aggressive Events Detection component (42) may be applied to identify those related to aggressive driving maneuvers” in ¶51)
In this way, Pozo operates to detect driving events of a vehicle based on a smartphone (Abstract). Like Cordova and Karnik, Pozo is concerned with driving detection.
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the pattern-based identification system as taught by Cordova and the vehicle speed estimation of Karnik with the driving event detection method of Pozo to enable another standard means of classifying lateral acceleration as a metric. Such modification also enables the method to track the severity and frequency of the lane changes from the lateral acceleration metric (as per ¶53). Such modification allows for the system to take into account lateral acceleration of a vehicle (¶28).
Cordova, Karnik, and Pozo fail to expressly disclose:
determining an update to a driver score;
wherein the set of lateral movement events are detected based on a motion signature of lane change behavior,
the motion signature comprising a pair of opposite sign lateral accelerations;
triggering an action based on the update to the driver score.
Cordova discloses scoring driving trips, comprising:
determining an update to a driver score; (as per “this measured movement can be minor (e.g., a mobile device sliding sideways within a cup holder), or more substantial (e.g., the mobile device being picked up out of the cup holder and held to the ear of a driver). The method includes analyzing the movement measurements to determine whether they are indicative of a particular type of event occurring with respect to the mobile device in a vehicle at decision block 720. In some embodiments, this particular type of event is associated with use by the driver of mobile device, such that the driver of the vehicle is potentially not paying attention to driving tasks (e.g., the driver is distracted from driving tasks by the mobile device). For convenience, as used herein, inattentiveness, distraction, failing to pay attention, mobile device usage, and/or other similar terms and phrases broadly signify a driver not paying proper attention to tasks associated with safely operating the vehicle” in ¶80, as per “systems and methods for scoring driving trips based on sensor measurements from a mobile device” in ¶4, as per “detect and analyze driving behavior. The mobile device can further be used to provide a driving score based on the driving behavior, which may encourage modification of future driving behavior” in ¶5)
triggering an action based on the update to the driver score. (as per “Notification block 140 may report the results of analysis of sensor data performed by the data processing block 120 to a user of the mobile device 101 via a display (not shown). For example, notification block 140 may display or otherwise report a score for a trip or for a plurality of trips to a user of the mobile device 101. In one embodiment, mobile device 101 may further include a scoring block (not shown) to score individual or collective trips, as described further herein with respect to FIG. 3” in ¶30, as per “updating the measurement rate for the sensor measurements from the one or more sensors of the mobile device for a subsequent trip in the vehicle based on the trip score (335)” in ¶51)
In this way, Cordova operates to assign and display scores related to driving scores detected by a mobile device (Abstract). Like Cordova, Karnik, and Pozo, Cordova is concerned with driving event detection.
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the pattern-based identification system as taught by Cordova, the vehicle speed estimation of Karnik, and the driving event detection method of Pozo with the method for scoring driving trips of Cordova to enable another standard means of assigning and displaying scores related to the driving behaviors. Such modification also allows the system to analyze driving behaviors and present the driving score to be displayed via the mobile device (¶6, ¶30).
Cordova, Karnik, Pozo, and Cordova fail to expressly disclose:
wherein the set of lateral movement events are detected based on a motion signature of lane change behavior,
the motion signature comprising a pair of opposite sign lateral accelerations;
Aly discloses of ubiquitous smartphone-based lane detection, comprising:
wherein the set of lateral movement events are detected based on a motion signature of lane change behavior, (as per "Assuming that the vehicle is making a left-lane change (Fig. 6), then the x-acceleration reading first decreases to a low value and then increases back to a higher value. It also minimally affects the phone’s orientation." in pg. 9, Section 5.1 - Lane Change Detection)
the motion signature comprising a pair of opposite sign lateral accelerations; (as per "Assuming that the vehicle is making a left-lane change (Fig. 6), then the x-acceleration reading first decreases to a low value and then increases back to a higher value. It also minimally affects the phone’s orientation." in pg. 9, Section 5.1 - Lane Change Detection, as per "Fig. 6. Left-lane change causes a specific pattern on the x-acceleration and just a small peak on the orientation. The x-acceleration pattern is reversed when doing right lane-change" in pg. 11, Section 5.1 - Lane Change Detection, as per Fig. 6 - pictured below & Fig. 7)
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In this way, Aly operates to detect lane change events of a vehicle based on a smartphone (Abstract). Like Cordova, Karnik, Cordova, and Pozo, Aly is concerned with detecting user driving events.
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the system(s)/method(s) of Cordova, Karnik, and Pozo with the smartphone-based lane detection system of Aly to enable another standard means of detecting a lane change. Such modification also enables the method to classify right-to-left and left-to-right lane changes and filter the smartphone data (as per Fig. 6, as per Section 5.1 - Lane Change Detection).
As per Claim 10, the combination of Cordova, Karnik, Pozo, Cordova, and Aly teaches or suggests all limitations of Claim 9. Cordova further discloses:
wherein the movement data comprise GPS data and inertial data, (as per "The collected data can include location data (e.g., GPS receiver 110 data) as a function of time, accelerometer 112 data, gyroscope 116 data, combinations thereof, or the like." in ¶39)
wherein set of data features comprises a vehicle heading estimate, estimated by fusing the GPS data with the inertial data, (as per "Using this accelerometer signal and the assumed longitudinal acceleration value, the direction vector of the movement of the vehicle in the reference frame of the mobile device is determined (630)." in ¶62, as per "In some embodiments, in order to prolong battery life, only location/GPS data is utilized, whereas in other embodiments, the location data is supplemented with accelerometer data." in ¶39)
wherein the lateral deviations comprise heading adjustments estimated with the inertial data. (as per “Smart watches can also provide useful information about hand/arm movements during the trip, such information being an indicator of steering (e.g., the wearer is driving).” in ¶82, as per “the transverse acceleration, as shown on FIG. 9, is acceleration determined to be in the direction lateral to the direction of travel of the vehicle by an embodiment… FIG. 10 is a is a simplified system diagram illustrating a system 1000 for pattern-based identification of the driver of a vehicle… Classifier 1014 is a system component that can analyze movement information received by mobile device 101 sensors and identify driving features of a driver during a trip. As discussed below with FIGS. 11 and 12, in addition to analyzing movement information…” in ¶73-¶75)
As per Claim 11, the combination of Cordova, Karnik, Pozo, Cordova, and Aly teaches or suggests all limitations of Claim 9. Cordova further discloses:
based on the sensor dataset, classifying the mobile device as stationary relative to the vehicle during at least one portion of the vehicle trip, (as per "whether the mobile device remained stationary within the vehicle during a time interval can be estimated by comparing the determined gravity angle difference, and the determined gravity magnitude difference to one or more thresholds (245)." in ¶44)
wherein the features are extracted for the at least one portion of the vehicle trip in which the mobile device is classified as stationary relative to the vehicle. (as per "wherein the subset of movement measurements corresponds to one or more time periods where the mobile device is stationary with respect to the vehicle." in Claim 4)
As per Claim 13, the combination of Cordova, Karnik, Pozo, Cordova, and Aly teaches or suggests all limitations of Claim 9. Cordova, Karnik, and Pozo fail to expressly disclose wherein the action comprises providing driver feedback via the mobile device, the driver feedback comprising the update to the driving score.
Cordova further discloses wherein the action comprises providing driver feedback via the mobile device, the driver feedback comprising the update to the driving score. (as per “The driving behaviors may be used by scoring engine 290 to assign a driving score to a trip or to a plurality of trips based on driving behaviors. Notifications of driving behavior, such as display of a driving score, can be made via notification block 140 of mobile device 101.” in ¶36)
In this way, Cordova operates to assign and display scores related to driving scores detected by a mobile device (Abstract). Like Cordova, Karnik, Pozo, and Aly, Cordova is concerned with driving event detection.
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the system(s) of Cordova, Karnik, Pozo, and Aly with the method for scoring driving trips of Cordova to enable another standard means of assigning and displaying scores related to the driving behaviors. Such modification also allows the system to analyze driving behaviors and present the driving score to be displayed via the mobile device (¶6, ¶30).
As per Claim 14, the combination of Cordova, Karnik, Pozo, Cordova, and Aly teaches or suggests all limitations of Claim 9. Cordova and Karnik fail to expressly disclose:
determining a severity score for the set of lateral movement events based on a magnitude of the lateral deviations, wherein the action is triggered based on the severity score satisfying a threshold.
See Claim 9 for teachings of Pozo. Pozo further discloses:
determining a severity score for the set of lateral movement events based on a magnitude of the lateral deviations (as per “Once an event is detected, it is represented by the following information: type of event (manipulation, turn, acceleration or stop) start time, end time, maximum value of total acceleration (accH) within segment…” in ¶71-75), wherein the action is triggered based on the severity score satisfying a threshold. (as per “comparing, in each time segment corresponding to a driving event, a maximum energy value of the at least one motion data signal with a second threshold; in the case of said the second threshold is exceeded in a certain time segment, determining an aggressive driving event for said time segment.” in ¶19-¶20)
In this way, Pozo operates to detect driving events of a vehicle based on a smartphone (Abstract). Like Cordova, Karnik, Cordova, and Aly, Pozo is concerned with driving detection.
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the system(s)/method(s) of Cordova, Karnik, and Aly with the driving event detection method of Pozo to enable another standard means of classifying lateral acceleration as a metric. Such modification also enables the method to track the severity and frequency of the lane changes from the lateral acceleration metric (as per ¶53). Such modification allows for the system to take into account lateral acceleration of a vehicle (¶28).
As per Claim 15, the combination of Cordova, Karnik, Pozo, and Cordova teaches or suggests all limitations of Claim 9. Cordova, Karnik, Pozo, and Cordova fail to expressly disclose wherein the set of lateral movement events comprises a set of lane change events.
See Claim 9 for teachings of Aly. Aly further discloses wherein the set of lateral movement events comprises a set of lane change events. (as per "Assuming that the vehicle is making a left-lane change (Fig. 6), then the x-acceleration reading first decreases to a low value and then increases back to a higher value. It also minimally affects the phone’s orientation." in pg. 9, Section 5.1 - Lane Change Detection)
In this way, Aly operates to detect lane change events of a vehicle based on a smartphone (Abstract). Like Cordova, Karnik, Cordova, and Pozo, Aly is concerned with detecting user driving events.
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the system(s)/method(s) of Cordova, Karnik, and Pozo with the smartphone-based lane detection system of Aly to enable another standard means of detecting a lane change. Such modification also enables the method to classify right-to-left and left-to-right lane changes and filter the smartphone data (as per Fig. 6, as per Section 5.1 - Lane Change Detection).
As per Claim 23, the combination of Cordova, Karnik, Pozo, Cordova, and Aly teaches or suggests all limitations of Claim 1. Cordova ‘182 further discloses wherein triggering the action at the mobile device comprises causing a driver score alert to be presented to a user at the mobile device, and the driver score alert comprises the update to the driver score. (as per “For example, notification block 140 may display or otherwise report a score for a trip or for a plurality of trips to a user of the mobile device 101. In one embodiment, mobile device 101 may further include a scoring block (not shown) to score individual or collective trips, as described further herein with respect to FIG. 3” in ¶30, as per “Notifications of driving behavior, such as display of a driving score, can be made via notification block 140 of mobile device 101” in ¶36, as per ¶53-¶56)
In this way, Cordova operates to assign and display scores related to driving scores detected by a mobile device (Abstract). Like Cordova, Karnik, Pozo, and Aly, Cordova is concerned with driving event detection.
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the system(s) of Cordova, Karnik, Pozo, and Aly with the method for scoring driving trips of Cordova to enable another standard means of assigning and displaying scores related to the driving behaviors. Such modification also allows the system to analyze driving behaviors and present the driving score to be displayed via the mobile device (¶6, ¶30).
As per Claim 25, the combination of Cordova, Karnik, Pozo, Cordova, and Aly teaches or suggests all limitations of Claim 9. Cordova further discloses wherein the set of lateral movement events based
on the set of data features is detected (as per “a classifier analyzes movement information received by mobile device sensors and identifies driving features of a driver during a trip,” in ¶12) with a pretrained (as per “a classifier is trained with sample data from drives to recognize the driving behavior of a particular user” in ¶13) machine learning (ML) classifier (as per “principle component analysis (PCA) is used to identify three different components of the movement measurements collected the accelerometer for a time interval (410)” in ¶54) or tree-based heuristic classifier. (as per “illustrating a method of training a driver-identification classifier using a mobile device (1100). The method begins with a user (e.g., a user having a mobile device) being registered, and components (e.g., classifier 1014) collecting demographic and location information (1110) from the user. In some embodiments, this information can provide additional points of analysis for classifier 1014 to identify the registered user. For example, when a trip begins from a location identified by a user to be their home, this is a contextual feature that has a high likelihood of identifying a driver, and can be used by the processes described below. When time of day is also considered (e.g., in the morning or afternoon), the likelihood can be even higher. Demographic information can provide additional points of analysis to link a known driver with a collection of measured driving behaviors (features). A driver's age may be determined to be likely to cause different driving behaviors (e.g., higher speed, faster cornering)” in ¶77)
Claim(s) 17 is rejected under 35 U.S.C. 103 as being unpatentable over Cordova (US Pub. No. 20210055325) in view of Karnik (US Pub. No. 20210049837) in view Pozo (US Pub. No. 20160016590) in view of Cordova (US Pub. No. 20170349182) in view of Aly (NPL Title: Robust and ubiquitous smartphone-based lane detection) in further view of Oyama (US Pub. No. 20050065663).
As per Claim 17, the combination of Cordova, Karnik, Pozo, Cordova, and Aly teaches or suggests all limitations of Claim 9. Cordova, Karnik, and Pozo fail to expressly disclose:
wherein the pair of opposite sign lateral accelerations occur consecutively and within a frequency bandwidth, wherein the motion signature comprises higher-frequency peaks, within the frequency bandwidth, which occur along a lower-frequency signal below the frequency bandwidth.
See Claim 9 for teachings of Aly. Aly further discloses
wherein the pair of opposite sign lateral accelerations occur consecutively and within a frequency bandwidth; ; (as per "Assuming that the vehicle is making a left-lane change (Fig. 6), then the x-acceleration reading first decreases to a low value and then increases back to a higher value. It also minimally affects the phone’s orientation." in pg. 9, Section 5.1 - Lane Change Detection, as per "Fig. 6. Left-lane change causes a specific pattern on the x-acceleration and just a small peak on the orientation. The x-acceleration pattern is reversed when doing right lane-change" in pg. 11, Section 5.1 - Lane Change Detection, as per Fig. 6 - pictured below & Fig. 7)
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In this way, Aly operates to detect lane change events of a vehicle based on a smartphone (Abstract). Like Cordova, Karnik, Pozo, and Cordova, Aly is concerned with detecting user driving events.
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the system(s)/method(s) of Cordova, Karnik, and Pozo with the smartphone-based lane detection system of Aly to enable another standard means of detecting a lane change. Such modification also enables the method to classify right-to-left and left-to-right lane changes and filter the smartphone data (as per Fig. 6, as per Section 5.1 - Lane Change Detection).
Cordova, Karnik, Pozo, and Aly fail to expressly disclose wherein the motion signature comprises higher-frequency peaks, within the frequency bandwidth, which occur along a lower-frequency signal below the frequency bandwidth.
Oyama discloses of a wakefulness estimating apparatus (as per Abstract), wherein the motion signature comprises higher-frequency peaks (as per “the quantities of components on the side of relatively high frequencies (the quantities of high frequency components) tend to steadily appear over a frequency range” in ¶28) within the frequency bandwidth (as per “the quantities P[1] to P[16] of sixteen frequency power components are calculated at intervals of 0.02 Hz in a frequency range from 0.03 to 0.3 Hz. The reason for neglecting the frequency range lower than the 0.03 Hz is the fact that power in that range tends to increase during a travel on curves and has no direct relationship with the level of wakefulness of the driver. The frequency range higher than 0.3 Hz is neglected to reduce the amount of calculations required to calculate wakefulness H because power in that frequency range is normally negligibly small” in ¶61), which occur along a lower-frequency signal (as per “comparing the peak of the power in the neighborhood of the stagger frequency f1 and the state of power in other frequency ranges taking such a tendency into consideration” in ¶66, as per “relatively high frequencies (the quantities of high frequency components) tend to steadily appear over a frequency range” in ¶28, as per “the quantities of components on the side of relatively low frequencies (the quantities of low frequency components) tend to appear in a state of driving at a low level of wakefulness” in ¶29) below the frequency bandwidth. (as per “The frequency range is a low frequency band including a stagger frequency, the band being set based on the staggering frequency” in ¶29, as per “in that only power in the low frequency range including the stagger frequency f1 increases while levels in other ranges are low” in ¶66)
In this way, Oyama operates to prevent an erroneous determination of wakefulness attributable to road shapes (¶8). Like Cordova, Karnik, Pozo, Cordova, and Aly, Oyama is concerned with detecting user driver events.
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the system(s)/method(s) of Cordova, Karnik, Pozo, and Aly with the wakefulness estimating apparatus of Oyama to enable another standard means of analyzing lateral deviation signals in the frequency domain using a bounded frequency range (“frequency bandwidth”) and distinguishing higher-frequency components from lower-frequency components attributable to roadway curvature/road shape (¶61, ¶29, ¶66, ¶157). Such modification allows the system to treat the lane-change motion signature (pair of opposite-sign lateral accelerations) as a frequency-bounded signal component while also recognizing that a lower-frequency component below the bandwidth corresponds to road curvature, thereby improving robustness of lane-change detection in the presence of curved-road/road-shape effects (¶61, ¶66, ¶157).
Claim(s) 18 is rejected under 35 U.S.C. 103 as being unpatentable over Cordova (US Pub. No. 20210055325) in view of Karnik (US Pub. No. 20210049837) in view Pozo (US Pub. No. 20160016590) in view of Cordova (US Pub. No. 20170349182) in view of Aly (NPL Title: Robust and ubiquitous smartphone-based lane detection) in further view of Kim (US Pat. No. 10479371).
As per Claim 18, the combination of Cordova, Karnik, Pozo, Cordova, and Aly teaches or suggests all limitations of Claim 9. Cordova, Karnik, Pozo, Cordova, and Aly fail to expressly disclose wherein at least one lateral movement event is detected as a higher-frequency peak within a lower-frequency lateral acceleration signal.
Kim discloses of determining driver distraction based on jerk (as per Abstract), wherein at least one lateral movement event is detected as a higher-frequency peak within a lower-frequency lateral acceleration signal. (as per “when lateral acceleration information is received via the communicator 130, the information collector 150 may detect a low frequency component of lateral acceleration 311, for example, a frequency component of the 0.5-2 Hz band of resulting in poor riding quality of a driver, using a first frequency filter 151” in C6L30-40, as per “each of lateral and longitudinal acceleration signals may include a high frequency noise component. A high frequency component of each of the lateral and longitudinal acceleration signals may be generated by white noise of the sensor itself, vibration of a vehicle 1 of FIG. 1 by an engine, or shaking of the vehicle 1 by a bump in the road” in C8L1525, as per “the information collector 150 may detect a noise component of lateral acceleration 311 or longitudinal acceleration 313, that is, a high frequency component using a second frequency filter 155. In this case, the high frequency component of the lateral acceleration 311 or the longitudinal acceleration 313 may include, for example, a frequency component higher than the 2 Hz band. Although a frequency component which occurs by motion of a vehicle in the general driving situation is difficult to be changed to a higher frequency than approximately 2 Hz, when the vibration of the vehicle occurs due to bumps in the road, road payment fault, and/or the like, a higher frequency component than approximately 2 Hz may occur” in C6L55-67 & C7L1-5)
In this way, Kim operates to determine driver distraction based on a jerk and vehicle system (C1L10-20). Like Cordova, Karnik, Pozo, Cordova, and Aly, Kim is concerned with detecting user driver events.
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the system(s)/method(s) of Cordova, Karnik, Pozo, and Aly with the driver distraction determination of Kim to enable another standard means of defining a motion signature taking into account low frequency and high frequency events (C2L20-50). Such modification also allows the system to take into account higher-frequency peaks, which occur along a lower-frequency signal (in C6L55-67 & C7L1-5)
Claim(s) 19 is rejected under 35 U.S.C. 103 as being unpatentable over Cordova (US Pub. No. 20210055325) in view of Karnik (US Pub. No. 20210049837) in view Pozo (US Pub. No. 20160016590) in view of Cordova (US Pub. No. 20170349182) in view of Aly (NPL Title: Robust and ubiquitous smartphone-based lane detection) in view of Kim (US Pat. No. 10479371) in further view of Oyama (US Pub. No. 20050065663) in further view of Markkula (US Pub. No. 20110320163).
As per Claim 19, the combination of Cordova, Karnik, Pozo, Cordova, Aly, and Kim teaches or suggests all limitations of Claim 18. Cordova, Karnik, Pozo, Cordova, Aly, and Kim fail to expressly disclose wherein the lower-frequency lateral acceleration signal corresponds to a roadway curvature, wherein the higher-frequency peak corresponds to a lane change maneuver.
Oyama discloses of a wakefulness estimating apparatus (as per Abstract), wherein the lower-frequency lateral acceleration signal corresponds to a roadway curvature. (as per “the quantities P[1] to P[16] of sixteen frequency power components are calculated at intervals of 0.02 Hz in a frequency range from 0.03 to 0.3 Hz. The reason for neglecting the frequency range lower than the 0.03 Hz is the fact that power in that range tends to increase during a travel on curves and has no direct relationship with the level of wakefulness of the driver.” in ¶61, as per “a correction is made to decrease the quantity P′slp of low frequency components that is associated with wakefulness based on a determination that the vehicle is traveling on a highway having consecutive curves when steering is successively performed at small steering angles. It is therefore possible to avoid erroneous determinations attributable to road shapes during a travel on successive curves at a high speed.” in ¶157)
In this way, Oyama operates to prevent an erroneous determination of wakefulness attributable to road shapes. Like Cordova, Karnik, Pozo, Cordova, Aly, and Kim, Oyama is concerned with detecting user driver events.
It would have been obvious for one of ordinary skill in the art before the effective filing date to have the system(s)/method(s) of Cordova, Karnik, Pozo, Aly, and Kim with the wakefulness estimating method of Oyama to enable another standard means of relating a low frequency to a roadway curve (¶61 & ¶157). Such modification allows the system to classify a lower-frequency lateral acceleration with a roadway curvature (¶61).
Cordova, Karnik, Pozo, Aly, Kim, and Oyama fail to expressly disclose wherein the higher-frequency peak corresponds to a lane change maneuver.
Markkula discloses of determining road data (as per Abstract), wherein the higher-frequency peak corresponds to a lane change maneuver. (as per “the sensor data can comprise vehicle position data, vehicle yaw angle data and/or longitudinal/lateral acceleration data” in ¶18, as per “it is preferable to detect such maneuvers where the driver intentionally induces lateral vehicle movements that are different from the lateral movements occurring during normal attentive driving when the vehicle is following a single lane. Two examples of such maneuvers are lane changes and takeovers. If performed quickly, such maneuvers may include lateral movements that could be interpreted by the system as unintentional deviations from a desired path (Since the resulting actual trajectories have higher bend curvatures than typical roads). Therefore, it is advantageous to detect and to identify these intended maneuvers, e.g. to discard the corresponding portions of data, or to report a corresponding lowered confidence in system output” in ¶56, as per “Lane change Turn indicator activity Yaw rate profile (large amplitude followed by similar amplitude with sign change)” in ¶58)
In this way, Markkula operates to determine if actual trajectory is in a normal range or if the offset between an idea and actual trajectory is the indication of deteriorated lateral control performance of the driver. Like Cordova, Karnik, Pozo, Cordova, Aly, Kim, and Oyama, Markkula is concerned with detecting driver events.
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the system(s)/method(s) of Cordova, Karnik, Pozo, Aly, Kim, and Oyama with the determining road data of Markkula to enable another standard means of relating high amplitude peak to a lane change (¶58). Such modification allows the system to classify a high-frequency lateral acceleration with a lane change (¶56).
Claim(s) 21 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Cordova (US Pub. No. 20210055325) in view of Karnik (US Pub. No. 20210049837) in further view of Pozo (US Pub. No. 20160016590) in view of Cordova (US Pub. No. 20170349182) in view of Aly (NPL Title: Robust and ubiquitous smartphone-based lane detection) in further view of Shin (US Pub. No. 20160311442).
As per Claim 21, the combination of Cordova, Karnik, Pozo, Cordova, and Aly teaches or suggests all limitations of Claim 9. Cordova, Karnik, Pozo, Cordova, and Aly fail to expressly disclose wherein checking for a set of lateral deviation criteria comprises determining an angle associated with the lateral deviations is below a full turn threshold.
Shin discloses of detecting vehicle maneuvers with mobile phones (as per Abstract), wherein checking for a set of lateral deviation criteria comprises determining an angle associated with the lateral deviations is below a full turn threshold. (as per “the change in the vehicle's heading from time 0 to NTs can be expressed as: θfinal=Σn=1 NYnTs. For example, after making a left/right turn at the intersection, 0final≈±90°; whereas, after making a U-turn, 0final≈±180°. Thus, by exploiting the derived values, turns can be further classified as left/right turn or a U-turn.” In ¶52, as per “If the magnitude of the horizontal displacement exceeds the first threshold, the steering maneuver is classified as a curvy road; whereas, if the magnitude of the horizontal displacement is less than (or equal to) the first threshold, the steering maneuver is classified as a lane change.” In ¶55)
In this way, Shin operates to detect vehicle maneuvers using a mobile phone. Like Cordova, Karnik, Pozo, Cordova, and Aly, Shin is concerned with detecting driver events.
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the system(s)/method(s) of Cordova, Karnik, Pozo, and Aly with the vehicle maneuver detection of Shin to enable another standard means of classifying a turn as either a right turn, a left turn or a U-turn based on a difference in vehicle heading angle between start and end of vehicle maneuver (¶11). Such a modification allows the system to determine an angle associated with lateral deviations (¶52).
As per Claim 22, the combination of Cordova, Karnik, Pozo, Cordova, and Aly teaches or suggests all limitations of Claim 9. Cordova, Karnik, Pozo, and Aly fail to expressly disclose wherein checking for a set of lateral deviation criteria comprises determining an angle associated with the lateral deviations is above a minor correction threshold.
Shin discloses of detecting vehicle maneuvers with mobile phones (as per Abstract), wherein checking for a set of lateral deviation criteria comprises determining an angle associated with the lateral deviations is above a minor correction threshold. (as per “Four systems parameters are defined: δs, δh, TBUMP, and TNEXT _ DELAY. Δs represents starting point or ending point for a bump, δh represents height of a bump, TBUMP represents minimum duration for a bump… To reduce false positives and differentiate the bumps from jitters, a bump should satisfy the following three constraints for its validity: (1) all the readings during a bump should be larger than δs, (2) the largest value of a bump should be no less than δh” in ¶43)
In this way, Shin operates to detect vehicle maneuvers using a mobile phone. Like Cordova, Karnik, Pozo, Cordova, and Aly, Shin is concerned with detecting driver events.
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the system(s)/method(s) of Cordova, Karnik, Pozo, and Aly with the vehicle maneuver detection of Shin to enable another standard means of classifying a turn as either a right turn, a left turn or a U-turn based on a difference in vehicle heading angle between start and end of vehicle maneuver (¶11). Such a modification allows the system to determine an angle associated with lateral deviations (¶52).
Claim(s) 24 are rejected under 35 U.S.C. 103 as being unpatentable over Cordova (US Pub. No. 20210055325) in view of Karnik (US Pub. No. 20210049837) in view of Pozo (US Pub. No. 20160016590) in view of Cordova (US Pub. No. 20170349182) in view of Aly (NPL Title: Robust and ubiquitous smartphone-based lane detection) in further view of Slusar (US Pat. No. 11068998).
As per Claim 24, the combination of Cordova, Karnik, Pozo, Cordova, and Aly teaches or suggests all limitations of Claim 1. Cordova, Karnik, Pozo, and Aly fail to expressly disclose wherein triggering the action at the mobile device comprises updating a location score indicating a riskiness of a location associated with the vehicle trip.
Slusar discloses of risk maps, wherein triggering the action at the mobile device comprises updating a location score indicating a riskiness of a location associated with the vehicle trip. (as per “the computing devices 306 and/or 308 may assign a new risk score to the road segment and notify the user that the risk score has changed” in C16L20-30, as per “At step 417, the computing device may calculate a modified road segment risk score based on applying the additional data. For example, the computing device may use the determined risk scores from step 411 and use the multivariable equation from step 415 to use the received additional data (e.g., geographic location information, weather information, and/or environmental information) to calculate a modified road segment risk score or scores… the computing device may adjust a risk score due to a new condition (e.g. snow on the road). Due to the snow, the computing device may use the multivariable equation to determine that the previous risk score needs to be increased” in C19L20-40)
In this way, Slusar operates to calculate a risk score for each road segment forming the route, and generate a risk map based on the risk score and the route (Abstract). Like Cordova, Karnik, Pozo, Cordova, and Aly, Slusar is concerned with driving event detection.
It would have been obvious for one of ordinary skill in the art before the effective filing date to have modified the system(s)/method(s) of Cordova, Karnik, Pozo, and Aly with the polynomial risk maps of Slusar to enable another standard means of updating a location score (i.e. road segment) indicating riskiness of the vehicle trip (C16L20-30). Such modification also allows the system to determine the riskiness of a vehicle trip based on a location associated with the vehicle trip (C16L2-30).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Eskandarian (US Pub. No. 20100109881) discloses an unobtrusive driver drowsiness detection system and method.
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/T.R.R./Examiner, Art Unit 3658
/Ramon A. Mercado/Supervisory Patent Examiner, Art Unit 3658