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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03 June 2026 has been entered.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 1,3,4,6,7, 9, 11,13,14, 16-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Qi (US 20220017032 A1), Peterson (US 20220246036 A1) and further in view of Carver (US 20200334762 A1).
Claim 1. Qi teaches a method comprising:
retrieving, by a processor, data associated with a set of driving sessions
([0060] Total loss module 201 may be executed by data processing block 144 of mobile device 104.
[0036] For instance, the time interval may begin at the first time before the crash event and end at a third time in which total loss module 201, using driving sensors 202, determines (from the sensor measurements) that the vehicle has come to a rest (e.g., to ensure that the time interval includes the entire crash event).);
generating, by the processor, a training dataset by:
labeling, by the processor, a first subset of data that corresponds to at least one driving session that included a first event corresponding to an insurance claim
([0039] [0039] Total loss module 201 may provide vehicle data 220 to TLE prediction model 210. Vehicle data 220 includes one or more data types... Examples of the data types of vehicle data 220 include... previous insurance claims on the vehicle);
labeling, by the processor, a second subset of the data that corresponds to at least
one driving session that included an indication of an airbag activation
([0038] Total loss module 201 may provide additional crash inputs 208 to TLE prediction model 210. Additional crash inputs 208 may include, but are not limited to, airbag deployment information,);
and training, by the processor, an artificial intelligence model using the training dataset
([0041]-[0043] The machine learning models of TLE prediction model 210 may be trained, using supervised or unsupervised learning, using data sets of particular data types... The first feature vector may include a first set of features such as, but not limited to, vehicle features (e.g., extracted from vehicle data 220)...The second feature vector may include a second set of features such as,...and an indication of airbag deployment.)
, such that the trained artificial intelligence model is configured to predict a score indicative of a
likelihood of a new driving session associated with a driver being associated with at least the
first event and airbag activation
([0041]-[0043] [0043] TLE prediction model 210 may generate multiple confidence values using one or more machine learning models as described above. Total loss module 201 may determine a total loss confidence 222 from the confidence values for each respective machine-learning model. An example of the total loss confidence 222 may be a percentage likelihood that the crash event 204 is a total loss event.
[0052] At block 310, the process 300 involves predicting a confidence of a total loss event. The TLE prediction model uses a first machine-learning model and a second machine-learning model... The confidence may be represented as a percentage (e.g., of 100), an integer, a grade (e.g., low, medium, high, A-F, or the like) or in any manner capable of identifying a confidence that a loss event is a total loss event.).
Qi teaches the use of at least two subset data but does not specifically disclose but does not specifically disclose labeling, by the processor, a third subset of the data that corresponds to at least one driving session that did not include the first event or the indication of an airbag activation.
However, Petersen teaches labeling, by the processor, a third subset of the data that corresponds to at least one driving session that did not include the first event or the indication of an airbag activation
([0169] In process 600 the facility may further generate TripLabel, PulloverLabel. In some embodiments, the facility may also generate EngineFaultLabel by looking at the correlated engine faults and determining whether or not an airbag has deployed or nearly deployed and how many impact based faults were observed... If there is no match, the label is classified as unknown.
e.g. EngineFaultLabel or unknown label is the third subset of data).
Therefore, it would have been obvious to one ordinarily skilled in the art before the effective filing date of invention to use a third subset of data as taught by Petersen within the system of Qi for the purpose of enhancing system to detect a variety of conditions related to a non-collision in addition to a vehicle impact collision.
Qi further discloses the trained artificial intelligence model is configured to predict a score indicative of a
likelihood of a new driving session associated with a driver being associated with at least the
first event and airbag activation but does not specifically disclose predict a score indicative of a likelihood of a new driving session associated with a new driver.
However, Carver teaches predicting a score indicative of a likelihood of a new driving session associated with a new driver
( [0065] In the case of new drivers for a car sharing operation, the method can use the large data set in the scoring database 107, and the computed vehicle risk index 109 and the collision level index 110 to quickly arrive at a driver safety index 108, usually within a single trip. [0082] The machine learning engine for the scoring database 107 performs the machine learning and then classifies, clusters, ranks, and/or predicts data states for the scoring result from given input data
[0098] An embodiment includes a system comprising at least one computing device implementing a vehicle trip scoring database configured to create relative driver safety indices to be used to predict collisions based on the trip to trip standard deviation of the results.[0101]-[0102] a driver safety index (DSI) 610A/B... safety grade (OSG) 630 [0061] displaying scoring indices for particular risk))
Therefore, it would have been obvious to one ordinarily skilled in the art before the effective filing date of invention to use predicting a score indicative of a likelihood of a new driving session associated with a new driver as taught by Carver within the system of Barfield for the purpose of notifying to a new driver to take remedial and corrective actions for improving driver behavior.
Claim 3. Qi teaches the method of claim 1, wherein the data is received from a set of sensors of a set of vehicles associated with each driving session
([0138] In some instances, the acceleration measurements may be used by TLE prediction model 210 may include significant changes (e.g., deviations from a typical drive or braking event) in acceleration.).
Claim 4. Qi teaches the method of claim 3, wherein at least one sensor within the set of sensors is configured to collect data associated with forward collision warnings, braking events, autonomous
driving disqualifications, autonomous steering disqualifications, or lane departures
([0138] During a drive with a mobile device positioned in a vehicle, the IMU of the mobile device may be used to obtain movement measurements from any of the accelerometer, the gyroscope, and the magnetometer, and the movement measurements to generate an input for a crash prediction engine 1024 to predict a crash.).
Claim 6. Qi teaches the method of claim 1, further comprising:
presenting, by the processor, the score to be displayed on an electronic device
([0141] Other components of electronic device 1004 may then further analyze the sensor data received during the drive to identify driver behavior, driver score, crash detection, etc. In some instances, this may be performed by an operating system of the mobile device to control data collection by sensor data block 108.).
Claim 7. Qi teaches the method of claim 6, wherein the electronic device is associated with a vehicle corresponding to the new driving session
([0141] Activity detection engine 1032 may use the activity to detect drives from sensor data. For instance, activity detection engine 1032 may analyze the data received from mobile device 104 and identify a first time when the activity indicates a high probability that mobile device 104 is in a car that is driving.).
Claim 9. Qi teaches the method of claim 1, wherein the score is calculated based on at least one
attribute of the new driver associated with the new driving session
([0141] Other components of electronic device 1004 may then further analyze the sensor data received during the drive to identify driver behavior, driver score, crash detection, etc. In some instances, this may be performed by an operating system of the mobile device to control data collection by sensor data block 108.)..
Claim 11. Qi teaches a system comprising:
a non-transitory computer-readable medium comprising instructions that when executed, cause a processor to:
retrieve data associated with a set of driving sessions
([0060] Total loss module 201 may be executed by data processing block 144 of mobile device 104.
[0036] For instance, the time interval may begin at the first time before the crash event and end at a third time in which total loss module 201, using driving sensors 202, determines (from the sensor measurements) that the vehicle has come to a rest (e.g., to ensure that the time interval includes the entire crash event).);
generate a training dataset by:
labeling a first subset of data that corresponds to at least one driving session that included a first event corresponding to an insurance claim
([0039] [0039] Total loss module 201 may provide vehicle data 220 to TLE prediction model 210. Vehicle data 220 includes one or more data types... Examples of the data types of vehicle data 220 include... previous insurance claims on the vehicle);
labeling a second subset of the data that corresponds to at least one driving
session that included an indication of an airbag activation
([0038] Total loss module 201 may provide additional crash inputs 208 to TLE prediction model 210. Additional crash inputs 208 may include, but are not limited to, airbag deployment information,);
and train an artificial intelligence model using the training dataset
([0041]-[0043] The machine learning models of TLE prediction model 210 may be trained, using supervised or unsupervised learning, using data sets of particular data types... The first feature vector may include a first set of features such as, but not limited to, vehicle features (e.g., extracted from vehicle data 220)...The second feature vector may include a second set of features such as,...and an indication of airbag deployment.)
, such that the trained artificial intelligence model is configured to predict a score indicative of a likelihood of a new driving session associated with a new driver being associated with at least the first event and airbag activation
([0041]-[0043] [0043] TLE prediction model 210 may generate multiple confidence values using one or more machine learning models as described above. Total loss module 201 may determine a total loss confidence 222 from the confidence values for each respective machine-learning model. An example of the total loss confidence 222 may be a percentage likelihood that the crash event 204 is a total loss event.
[0052] At block 310, the process 300 involves predicting a confidence of a total loss event. The TLE prediction model uses a first machine-learning model and a second machine-learning model... The confidence may be represented as a percentage (e.g., of 100), an integer, a grade (e.g., low, medium, high, A-F, or the like) or in any manner capable of identifying a confidence that a loss event is a total loss event.).
Qi teaches the use of at least two subset data but does not specifically disclose but does not specifically disclose labeling, by the processor, a third subset of the data that corresponds to at least one driving session that did not include the first event or the indication of an airbag activation.
However, Petersen teaches labeling, by the processor, a third subset of the data that corresponds to at least one driving session that did not include the first event or the indication of an airbag activation
([0169] In process 600 the facility may further generate TripLabel, PulloverLabel. In some embodiments, the facility may also generate EngineFaultLabel by looking at the correlated engine faults and determining whether or not an airbag has deployed or nearly deployed and how many impact based faults were observed... If there is no match, the label is classified as unknown.
e.g. EngineFaultLabel or unknown label is the third subset of data).
Therefore, it would have been obvious to one ordinarily skilled in the art before the effective filing date of invention to use a third subset of data as taught by Petersen within the system of Qi for the purpose of enhancing system to detect a variety of conditions related to a non-collision in addition to a vehicle impact collision.
Qi further discloses the trained artificial intelligence model is configured to predict a score indicative of a
likelihood of a new driving session associated with a driver being associated with at least the
first event and airbag activation but does not specifically disclose predict a score indicative of a likelihood of a new driving session associated with a new driver.
However, Carver teaches predicting a score indicative of a likelihood of a new driving session associated with a new driver
( [0065] In the case of new drivers for a car sharing operation, the method can use the large data set in the scoring database 107, and the computed vehicle risk index 109 and the collision level index 110 to quickly arrive at a driver safety index 108, usually within a single trip. [0082] The machine learning engine for the scoring database 107 performs the machine learning and then classifies, clusters, ranks, and/or predicts data states for the scoring result from given input data
[0098] An embodiment includes a system comprising at least one computing device implementing a vehicle trip scoring database configured to create relative driver safety indices to be used to predict collisions based on the trip to trip standard deviation of the results.[0101]-[0102] a driver safety index (DSI) 610A/B... safety grade (OSG) 630 [0061] displaying scoring indices for particular risk))
Therefore, it would have been obvious to one ordinarily skilled in the art before the effective filing date of invention to use predicting a score indicative of a likelihood of a new driving session associated with a new driver as taught by Carver within the system of Barfield for the purpose of notifying to a new driver to take remedial and corrective actions for improving driver behavior.
Claim 13. Qi teaches the system of claim 11, wherein the data is received from a set of sensors of a set of vehicles associated with each driving session
([0138] In some instances, the acceleration measurements may be used by TLE prediction model 210 may include significant changes (e.g., deviations from a typical drive or braking event) in acceleration.).
Claim 14. Qi teaches the system of claim 13, wherein at least one sensor within the set of sensors is configured to collect data associated with forward collision warnings, braking events, autonomous driving disqualifications, autonomous steering disqualifications, or lane departures
([0138] During a drive with a mobile device positioned in a vehicle, the IMU of the mobile device may be used to obtain movement measurements from any of the accelerometer, the gyroscope, and the magnetometer, and the movement measurements to generate an input for a crash prediction engine 1024 to predict a crash.).
Claim 16. Qi teaches the system of claim 11, wherein the instructions further cause the processor to: present the score to be displayed on an electronic device
([0141] Other components of electronic device 1004 may then further analyze the sensor data received during the drive to identify driver behavior, driver score, crash detection, etc. In some instances, this may be performed by an operating system of the mobile device to control data collection by sensor data block 108.).
Claim 17. Qi teaches the system of claim 16, wherein the electronic device is associated with a
vehicle corresponding to the new driving session
([0141] Activity detection engine 1032 may use the activity to detect drives from sensor data. For instance, activity detection engine 1032 may analyze the data received from mobile device 104 and identify a first time when the activity indicates a high probability that mobile device 104 is in a car that is driving.).
Claim 18. Qi teaches a system (Figs. 10 and 2) comprising:
an artificial intelligence model
(([0041]-[0043] The machine learning models of TLE prediction model 210 may be trained,);
and a server at least one processor in communication with the artificial intelligence model
(Fig. 10 [0130] electronic device 1004 may be …a server)
, the server configured to:
retrieve data associated with a set of driving sessions
([0060] Total loss module 201 may be executed by data processing block 144 of mobile device 104.
[0036] For instance, the time interval may begin at the first time before the crash event and end at a third time in which total loss module 201, using driving sensors 202, determines (from the sensor measurements) that the vehicle has come to a rest (e.g., to ensure that the time interval includes the entire crash event).);
generate a training dataset by label a first subset of data that corresponds to at least
one driving session that included a first event corresponding to an insurance claim
([0039] [0039] Total loss module 201 may provide vehicle data 220 to TLE prediction model 210. Vehicle data 220 includes one or more data types... Examples of the data types of vehicle data 220 include... previous insurance claims on the vehicle);
label a second subset of the data that corresponds to at least one driving session that
included an indication of an airbag activation
([0038] Total loss module 201 may provide additional crash inputs 208 to TLE prediction model 210. Additional crash inputs 208 may include, but are not limited to, airbag deployment information,);
and train the artificial intelligence model using the training dataset
([0041]-[0043] The machine learning models of TLE prediction model 210 may be trained, using supervised or unsupervised learning, using data sets of particular data types... The first feature vector may include a first set of features such as, but not limited to, vehicle features (e.g., extracted from vehicle data 220)...The second feature vector may include a second set of features such as,...and an indication of airbag deployment.)
, such that the trained artificial intelligence model is configured to predict a score indicative of a likelihood of a new driving session associated with a new driver being associated with at least the first event and airbag activation
([0041]-[0043] [0043] TLE prediction model 210 may generate multiple confidence values using one or more machine learning models as described above. Total loss module 201 may determine a total loss confidence 222 from the confidence values for each respective machine-learning model. An example of the total loss confidence 222 may be a percentage likelihood that the crash event 204 is a total loss event.
[0052] At block 310, the process 300 involves predicting a confidence of a total loss event. The TLE prediction model uses a first machine-learning model and a second machine-learning model... The confidence may be represented as a percentage (e.g., of 100), an integer, a grade (e.g., low, medium, high, A-F, or the like) or in any manner capable of identifying a confidence that a loss event is a total loss event.).
Qi teaches the use of at least two subset data but does not specifically disclose but does not specifically disclose labeling, by the processor, a third subset of the data that corresponds to at least one driving session that did not include the first event or the indication of an airbag activation.
However, Petersen teaches labeling, by the processor, a third subset of the data that corresponds to at least one driving session that did not include the first event or the indication of an airbag activation
([0169] In process 600 the facility may further generate TripLabel, PulloverLabel. In some embodiments, the facility may also generate EngineFaultLabel by looking at the correlated engine faults and determining whether or not an airbag has deployed or nearly deployed and how many impact based faults were observed... If there is no match, the label is classified as unknown.
e.g. EngineFaultLabel or unknown label is the third subset of data).
Therefore, it would have been obvious to one ordinarily skilled in the art before the effective filing date of invention to use a third subset of data as taught by Petersen within the system of Qi for the purpose of enhancing system to detect a variety of conditions related to a non-collision in addition to a vehicle impact collision.
Qi further discloses the trained artificial intelligence model is configured to predict a score indicative of a
likelihood of a new driving session associated with a driver being associated with at least the
first event and airbag activation but does not specifically disclose predict a score indicative of a likelihood of a new driving session associated with a new driver.
However, Carver teaches predicting a score indicative of a likelihood of a new driving session associated with a new driver
( [0065] In the case of new drivers for a car sharing operation, the method can use the large data set in the scoring database 107, and the computed vehicle risk index 109 and the collision level index 110 to quickly arrive at a driver safety index 108, usually within a single trip. [0082] The machine learning engine for the scoring database 107 performs the machine learning and then classifies, clusters, ranks, and/or predicts data states for the scoring result from given input data
[0098] An embodiment includes a system comprising at least one computing device implementing a vehicle trip scoring database configured to create relative driver safety indices to be used to predict collisions based on the trip to trip standard deviation of the results.[0101]-[0102] a driver safety index (DSI) 610A/B... safety grade (OSG) 630 [0061] displaying scoring indices for particular risk))
Therefore, it would have been obvious to one ordinarily skilled in the art before the effective filing date of invention to use predicting a score indicative of a likelihood of a new driving session associated with a new driver as taught by Carver within the system of Barfield for the purpose of notifying to a new driver to take remedial and corrective actions for improving driver behavior.
Claim 20. Qi teaches the system of claim 19, wherein the data is received from a set of sensors of a set of vehicles associated with each driving session
([0138] In some instances, the acceleration measurements may be used by TLE prediction model 210 may include significant changes (e.g., deviations from a typical drive or braking event) in acceleration.).
Claim(s) 5 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Qi, Peterson, Carver and further in view of Barfield (US 20150213555 A1).
Claim 5. Qi teaches the method of claim 1,and discloses the process of obtaining sensor information and obtaining a score but does not specifically disclose transmitting, by the processor, the score to a software application configured to receive the score and generate an insurance rate.
However, Barfield teaches transmitting, by the processor, the score to a software application configured to receive the score and generate an insurance rate
( [0022] the driver Y prediction may be used by an insurance provider for the purpose of determining an insurance cost for driver Y. [0053] a display screen of user device 210 and/or that the driver is using an application hosted by user device 210).
Therefore, it would have been obvious to one ordinarily skilled in the art before the effective filing date of invention to use the process of transmitting to a software application configured to receive the score and generate an insurance rate as taught by Barfield within the system of Qi for the purpose of enhancing the system to provide a cost based on the data sensed from the driving performance.
Claim 15. Qi teaches the system of claim 11, and discloses the process of obtaining sensor information and obtaining a score but does not specifically disclose wherein the instructions further cause the processor to: transmit the score to a software application configured to receive the score and generate an insurance rate.
However, Barfield teaches wherein the instructions further cause the processor to: transmit the score to a software application configured to receive the score and generate an insurance rate
( [0022] the driver Y prediction may be used by an insurance provider for the purpose of determining an insurance cost for driver Y. [0053] a display screen of user device 210 and/or that the driver is using an application hosted by user device 210).
Therefore, it would have been obvious to one ordinarily skilled in the art before the effective filing date of invention to use the process of transmitting to a software application configured to receive the score and generate an insurance rate as taught by Barfield within the system of Qi for the purpose of enhancing the system to provide a cost based on the data sensed from the driving performance.
Claim(s) 8 is rejected under 35 U.S.C. 103 as being unpatentable over Qi, Peterson, Carver and further in view of Konrardy (US 20210116256 A1).
Claim 8. Qi teaches the method of claim 1, and further discloses the usage of sensors and obtaining sensor but does not specifically disclose identifying, by the processor, a modification to at least one sensor.
However, Konrardy teaches identifying, by the processor, a modification to at least one sensor
([0144] he on-board computer 114 may determine that damage to multiple sensors 120 in the front-right portion of the vehicle 108 following a collision further indicates that headlights, signal lights, and the front bumper in that area are likely also damaged. ).
Therefore, it would have been obvious to one ordinarily skilled in the art before the effective filing date of invention to use the process of identifying a modification to at least one sensor as taught by Brandmaier within the system of Qi for the purpose of enhancing the system to determine status of sensors involved in an accident and notification of a replacement is required in order to optimize vehicle detection performance.
Claim(s) 10 is rejected under 35 U.S.C. 103 as being unpatentable over Qi, Peterson, Carver and further in view of Li (CN 109283107 A).
Claim 10. Qi teaches the method of claim 1, and discloses the process of generating and recording sessions but does not specifically disclose wherein the set of driving sessions belongs to a predetermined drive cycle, wherein when the processor determines that a vehicle associated with a driving session does not have network connectivity, the processor excludes the driving session from the set of driving sessions.
However, Li teaches wherein the set of driving sessions belongs to a predetermined drive cycle, wherein when the processor determines that a vehicle associated with a driving session does not have network connectivity, the processor excludes the driving session from the set of driving sessions
(Page 13- where there is no network, network signal difference and difference in GPS signal will cause the locating precision is low, the data is not accurate. for these conditions by monitoring individual locating application end will preferably exclude the low locating precision of coordinate points, while using spherical distance formula for calculation and analysis for a series of location data:).
Therefore, it would have been obvious to one ordinarily skilled in the art before the effective filing date of invention to use excludes the driving session from the set of driving sessions as taught by Li within the system of Qi for the purpose of enhancing the system to use only complete data in order to optimize data for prediction models.
Response to Arguments
Applicant’s arguments, see pages 6-7, filed 03 June 2026, with respect to the rejection(s) of claim(s) 1,3-11,13,18 and 20 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of 35 U.S.C. 103 as being unpatentable over Qi (US 20220017032 A1), Peterson (US 20220246036 A1) and further in view of Carver (US 20200334762 A1).
For claims 1, 11 and 18, Applicant states that the prior art fails to specifically teach a third subset of the data that corresponds to at least one driving session that did not include the first event or the indication of an airbag activation. However, in the broadest interpretation allowed (MPEP 2111.01), the prior art of Peterson provides obviousness improvement for teaching the third subset of data in combination with Qi. Therefore, Qi, Peterson and further in view of Carver are obvious over the claimed invention.
Conclusion
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/RUFUS C POINT/Primary Examiner, Art Unit 2689
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