DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of claims
Claims 5,7,8,16 and 19 are canceled.
Claims 1,6,9,11,12,15,17,18 and 20 are amended.
Claims 21-25 are newly added.
Response to arguments
With respect to Applicant’s remarks filed on 06/29/2026; Applicant's “Amendments and Remarks” have been fully considered. Applicant’s remarks will be addressed in sequential order as they were presented.
Applicant remarks:
Amendment should overcome 35 U.S.C. 101 rejection.
Amendment should overcome 35 U.S.C. 112(b).
Pal crash detection mode does not apply detection criteria depend on the historical data (driving performance)
Office Response:
Amendment overcomes 35 U.S.C. 101 rejection.
Amendment overcome 35 U.S.C. 112(b) rejection.
See new mapping mainly independent claims.
Applicant further argues that the other independent claims which recite similar features are allowable and the dependent claims are also allowable since they depend on allowable subject and the Office respectfully disagrees. It is the Office's stance that all of the claimed subject matter has been properly rejected; therefore, the Office's respectfully disagrees with applicant’s arguments.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-4, 6,9-15, 17-18 and 20-25 are rejected under 35 U.S.C. 102(a) (1) as being anticipated by US20240095844A1 to Salodkar et al. (herein after “Salodkar”).
Regarding claim 1, Salodkar teaches A method comprising:
receiving, by a crash detection application executing on a computing device (See Salodkar para[0025] the method produces an easy intelligible user interface (e.g., at a client application)), and from a sensor arrangement coupled to the computing device,
movement measurements indicating motion of the computing device during a drive by a driver in a vehicle (see Salodkar para[0048] receiving a movement dataset collected during a time period of vehicle movement; ), wherein the movement measurements include raw sensor data and/or processed sensor data collected using a sensor arrangement coupled to the computing device (See para[0089]The collision model preferably receives as input at least a set of sensor inputs received in S205 ); and
analyzing, by the crash detection application, the movement measurements to detect motion of the vehicle (see Salodkar para[0039] In some variations, for instance, any or all of the models 110 function to determine auxiliary information related to driver and/or the vehicle, such as, but not limited to, any or all of: motion parameters of the vehicle such as a speed (e.g., speed, speed just before the collision, speed during collision, speed just after collision, average speed during trip, etc.), acceleration (e.g., G-force associated with the vehicle), distance (e.g., braking distance), etc.; historical information (e.g., driver's driving history, number of collisions driver has been involved in, driving style and/or behaviors of driver, etc.); driver information (e.g., risk score assigned to driver);
receiving, by the crash detection application, and from a crash detection management server system (See Salodkar para[0050] a remote computing system (e.g., a server, at least one networked computing system, stateless computing system, stateful computing system, etc. ), crash detection criteria generated for a historical driving performance of the vehicle, of a driver associated with the computing device, or both (See Salodkar para[0039] historical information (e.g., driver's driving history, number of collisions driver has been involved in, driving style and/or behaviors of driver, etc.); driver information (e.g., risk score assigned to driver), para[0079]The information collected in S205 can additionally include driver information, such as any or all of: historical information associated with the driver); and
determining, by the crash detection application, that the vehicle was involved in a crash during the drive by applying the crash detection criteria to the motion of the vehicle (see Salodkar para[0053] to detect a vehicular accident event based on the received data)
executing, by the crash detection application, a crash detection model on the movement measurements, wherein the crash detection model is configured to detect whether the vehicle was involved in a crash during the drive by applying a crash detection criterion to the movement measurements (See at least Salodkar para[0044] In some variations, for instance, the set of modules includes modules configured to detect any or all of: a confidence associated with the occurrence of a collision, a severity of the collision, a direction of impact of the vehicle, fraud involved in the collision, and/or the set of modules can be configured to detect any other suitable information related to the collision.), and
wherein the crash detection criterion that is applied by the crash detection model depends on a historical driving performance of the driver during one or more previous trips that occurred prior to the drive in the vehicle. (See Salodkar para[0079] The information collected in S205 can additionally include driver information, such as any or all of: historical information associated with the driver (e.g., prior history of collisions, prior history of fraud, riskiness of driver's driving behavior as represented in a driving risk score, etc.), a driver's attention and/or distraction level, an activity of the driver (e.g., texting), a driver score (e.g., safety score, risk score, etc.), a driver behavior (e.g., during accident, prior to accident, after accident, etc.), circumstantial information (e.g., driver had a long work day beforehand and was tired, driver was in an area he had never driven in before, etc.), intoxication level, and/or any other suitable information)
Regarding claim 2, Salodkar teaches receiving previous movement measurements collected by the sensor arrangement during a plurality of drives that occurred before the drive; (see at least Salodkar para[0169] The historical preferably includes at least sensor data collected from the drivers, further preferably at mobile devices (e.g., smartphones) associated with the drivers) analyzing the previous movement measurements to detect occurrences of one or more types of driving events during each of the plurality of drives (see para[[0062] The method 200 includes collecting a set of inputs S205, which functions to collect information (e.g., data) with which to determine the occurrence of a collision and to reconstruct on or more features of the collision.) ; and
generating a historical driver score based on the occurrences of the one or more types of driving events detected during the plurality of drives; and wherein the historical driving performance comprises the historical driver score (see Salodkar para[0089] information other than sensor information (e.g., historical information, a risk score and/or prior driving behavior of the driver, environmental information, etc.), and/or any other suitable information.)
Regarding claim 3, Salodkar teaches wherein generating the historical driver score comprises: generating drive scores for each drive of the plurality of drives based on the occurrences of the one or more types of driving events detected during each drive; and aggregating a subset of the drive scores from a predefined number of most recent drives. (See Sakidkar risk score, para[0131] The severity module can produce any number of outputs, such as, but not limited to, any or all of: one or more scores (e.g., severity score, particular type of severity score, etc.), para [0160]S230 can additionally or alternatively be used to determine and/or update one or more scores associated with any or all of: a driver (e.g., through updating a driver risk score in an event of a collision), a road segment and/or road type, and/or any other suitable information.)
Regarding claim 4, Salodkar teaches wherein generating a drive score for a respective drive of the plurality of drives comprises: generating an event score (See Salodkar para[0048] detecting a vehicular accident event from processing the set of movement features with an accident detection model ) for each type of the one or more types of driving events based on a number of occurrences of each type detected during the respective drive (see Salodkar para[0044] The set of modules preferably includes multiple modules, wherein each of the multiple modules functions to reconstruct a portion); and aggregating the event score for each type of the one or more types of driving events. (See Salodkar para [0160]S230 can additionally or alternatively be used to determine and/or update one or more scores associated with any or all of: a driver (e.g., through updating a driver risk score in an event of a collision))
Regarding claim 6, Salodkar teaches wherein the crash detection criteria model includes a machine learning model (See Salodkar para[0045] The modules can include any or all of: machine learning models and/or algorithms (e.g., traditional machine learning models, deep learning models, any or all of those described above, etc.); classical models (e.g., rule-based algorithms ) and determining that the vehicle was involved in the crash comprises: executing, by the crash detection application, the machine learning model on the motion of the vehicle movement measurements collected during the subsequent drive to produce a crash classification (See Salodkar para[0035] In further additional or alternative variations, any or all of the models can implement non-machine-learning processes, such as classical processes (e.g., rule-based algorithms, dynamic equations to determine one or more motion characteristics of the vehicle, etc.).
Regarding claim 9, Salodkar teaches wherein the one or more types of driving events include at least one of: hard braking events, hard acceleration events, distracted driving events, road type events, or time of day events. (see Salodkar para[0109] S210 (and/or any other processes in the method) include determining one or more driving events, such as, but not limited to, any or all of: a hard brake event (e.g., in which the driver brakes a significant amount in a short distance and/or time, in which the driver brakes suddenly, etc.); a sudden acceleration event (e.g., in which the vehicle changes direction and/or speed suddenly and/or over a short distance or time); a mobile device usage event (e.g., indicating that the driver was using his or her mobile device while driving, a distraction event, etc.); a speeding event (e.g., indicating that the driver was speeding, indicating that the driver was speeding by at least a predetermined threshold, etc.); a turning event (e.g., indicating that the vehicle turned suddenly); and/or any other suitable driving events. )
Regarding claim 10, Salodkar teaches wherein the sensor arrangement includes at least one of an accelerometer, a gyroscope, a magnetometer, a compass, a barometer, or a Global Navigation Satellite System (GNSS) receiver. (see Salodkar para[0073]Movement data can be collected from and/or associated with any one or more of: motion sensors (e.g., multi-axis and/or single-axis accelerometers, gyroscopes, etc.), location sensors (e.g., GPS data collection components, magnetometer, compass, altimeter, etc.), and/or any other suitable components)
Regarding claim 11, Salodkar teaches wherein determining that the vehicle was involved in a crash comprises determining that the crash occurred at a first time, the method further comprising:
identifying, by the crash detection application, a subset of the movement measurements that were collected by the sensor arrangement within predefined time period before the first time, after the first time, or both (See Salodkar para[0046] In a first set of variations, the set of modules includes a 1st subset and a 2nd subset, wherein the 1st subset includes machine learning regression models and wherein the second subset includes rule-based algorithms. In specific examples, the 1st subset of modules includes any or all of: a confidence detection module, a direction of impact module, and a severity detection module, and wherein the 2nd subset includes a fraud detection module.); and
transmitting, by the crash detection application, the subset of the movement measurements to [[the]]a crash detection management server system in response to determining that the vehicle was involved in the crash (See Salodkar para[0053] In another example, a remote server can be configured to receive movement data from a vehicle and a mobile computing device, to detect a vehicular accident event based on the received data, and to automatically contact emergency services (e.g., through a telecommunications API)).
Regarding claim 12, Salodkar teaches wherein the crash detection criteria are criterion includes first crash detection criteria and the method further comprises: applying, by the crash detection management server system, second crash detection criteria to the subset of the movement measurements to verify the occurrence of the crash (See Salodkar para[0031] the collision model functions to produce outputs (e.g., probabilistic outputs, classifications, etc.) related to: whether or not a collision has occurred).
Regarding claim 13, Salodkar teaches wherein the computing device is a smartphone disposed within the vehicle. (See Salodkar para[0050] Mobile computing devices implementing at least a portion of the method 200 can include one or more of: a smartphone, a wearable computing device (e.g., head-mounted wearable computing device, a smart watch, smart glasses),)
Regarding claim 14, Salodkar teaches wherein the computing device is an integrated component of the vehicle. (See Salodkar para[0094] a collision based at least on motion information associated with the vehicle (e.g., as collected at a mobile device of the driver inside of the vehicle)).
Regarding claim 15, Salodkar teaches A crash detection system, comprising: a computing device, comprising: (See Salodkar para[0025] the method produces an easy intelligible user interface (e.g., at a client application)),
one or more first processors (See Salodkar para[0055] The computer-executable component can be a processor); and a first memory storing a first set of instructions which, when executed by the one or more first processors, cause the one or more first processors to perform first operations comprising (See Salodkar para[0055] The computer-readable medium can be stored on any suitable computer readable media such as RAMs, ROMs, flash memory, EEPROMs, optical devices (CD or DVD), hard drives, floppy drives, or any suitable device.)
receiving movement measurements indicating motion of the computing device during a drive by a driver of a vehicle, wherein the movement measurements include raw sensor data and/or processed sensor data collected using a sensor arrangement coupled to the computing device, movement measurements indicating motion of the computing device during a drive in a vehicle; (see Salodkar para[0048] receiving a movement dataset collected during a time period of vehicle movement; para[0089]The collision model preferably receives as input at least a set of sensor inputs received in S205 );
analyzing the movement measurements to detect motion of the vehicle;
receiving, from a crash detection management server system, (See Salodkar para[0050] a remote computing system (e.g., a server, at least one networked computing system, stateless computing system, stateful computing system, etc. ), first crash detection criteria generated using a historical driving performance of the vehicle, of a driver associated with the computing device, or both; (see Salodkar para[0039] In some variations, for instance, any or all of the models 110 function to determine auxiliary information related to driver and/or the vehicle, such as, but not limited to, any or all of: motion parameters of the vehicle such as a speed (e.g., speed, speed just before the collision, speed during collision, speed just after collision, average speed during trip, etc.), acceleration (e.g., G-force associated with the vehicle), distance (e.g., braking distance), etc.; historical information (e.g., driver's driving history, number of collisions driver has been involved in, driving style and/or behaviors of driver, etc.); driver information (e.g., risk score assigned to driver););
determining that the motion of the vehicle satisfies the first crash detection criteria; (see Salodkar para[0053] to detect a vehicular accident event based on the received data)
executing a crash detection model on the movement measurements, wherein the crash detection model is configured to detect whether the vehicle was involved in a crash during the drive by applying a first crash detection criterion to the movement measurements, and wherein the first crash detection criterion that is applied by the crash detection model depends on a historical driving performance of the driver during one or more previous trips that occurred prior to the drive in the vehicle(See at least Salodkar para[0044] In some variations, for instance, the set of modules includes modules configured to detect any or all of: a confidence associated with the occurrence of a collision, a severity of the collision, a direction of impact of the vehicle, fraud involved in the collision, and/or the set of modules can be configured to detect any other suitable information related to the collision.),; and
transmitting the movement measurements to a crash detection management server system in response to determining that the motion of the vehicle satisfies the first crash detection criteria the crash detection model detecting that the vehicle was involved in a crash (See para[0053] In another example, a remote server can be configured to receive movement data from a vehicle and a mobile computing device, to detect a vehicular accident event based on the received data, and to automatically contact emergency services (e.g., through a telecommunications API), it is possible to do transmit the decision whether the vehicle was involved or not to the remote server rather sending input information to determine the vehicle was involved or not); and the crash detection management server system, comprising:
one or more second processors; and a second memory storing a second set of instructions which, when executed by the one or more second processors, cause the one or more second processors to perform second operations comprising (it is possible to add a separate processing system to execute the following steps rather than same processing system)
transmitting the first crash detection criteria to the computing device;
receiving the movement measurements from the computing device (See para[0052] The system is preferably configured to facilitate reception and processing of the data and information described below, but can additionally or alternatively be configured to receive and/or process any other suitable type of data. As such, the processing system can be implemented on one or more computing systems including one or more of: a cloud-based computing system); and
confirming that the vehicle was involved in a crash during the drive by applying second crash detection criteria to the movement measurements, the second crash detection criteria being different than the first crash detection criteria criterion. (See para[0105] until a threshold condition and/or trigger condition is met (e.g., until confirmation that a collision has not occurred is determined, in response to a driver responding to a notification to confirm that he has not been in a collision, etc, para[0125]
The confidence module preferably receives as input at least the output of a collision detection algorithm in S210, but can additionally or alternatively receive the outputs from any or all of the algorithms in S210 (e.g., severity detection algorithm, direction of impact detection algorithm, etc.), auxiliary information from S210 and/or S205, other inputs (e.g., from S205), and/or any other suitable information. The confidence detection module preferably produces as an output a confidence parameter (e.g., confidence value, confirmed “yes” or “no” determination, etc.));
Regarding claim 17, Salodkar teaches wherein the second operations further comprise (See para[0049] Additionally or alternatively, the processing system can include and/or interface with any other processing systems, such as those mounted to and/or within the vehicle, those of a vehicle's on-board diagnostics (OBD) system, and/or any other suitable processing systems; para [0116] S220 is preferably performed with a processing system of the system 100 (e.g., the same processing system used in S210, a different processing system as that used in S210, etc.)
generating the second crash detection criteria from a second collection of vehicle motion training data (See para[0065] wherein the second set of inputs and/or any or all of the first set of inputs is used to reconstruct the collision.), wherein the second collection of vehicle motion training data comprises vehicle motion data collected by a second computing device during a crash involving a second vehicle or a second driver having a different historical driving performance than the second vehicle or the second driver of the vehicle (See para[0169] The information used to train the models and/or modules preferably includes historical data collected from previous drivers, such as any or all of the set of inputs collected in S205); and
determining that a match exists between the historical driving performance of the second vehicle, of the second driver, or of both (See para[0133] The set of modules further preferably includes a direction of impact detection module, which functions to determine the direction of impact of the vehicle associated with the driver (and/or another vehicle in the collision) , and
the historical driving performance of the vehicle (see para [0071]
Further additionally or alternatively, the information collected in S205 can include vehicle information associated with one or more vehicles), of the driver, or of both, wherein the first crash detection criteria are transmitted to the computing device in response to determining that the match exists (See para [0030] The system 100 preferably includes a set of one or more models no, wherein the set of models includes a collision model, wherein the collision model functions to assess (e.g., detect, classify, etc.) the occurrence of a collision (e.g., a suspected collision based on a set of inputs as described in the method below), and to optionally assess (e.g., detect, check for, determine a likelihood of, assign a probability to, etc.) one or more features associated with the collision).
Regarding claim 18, Salodkar teaches wherein first crash detection criteria the crash detection model includes a machine learning model(See Salodkar para[0045] The modules can include any or all of: machine learning models and/or algorithms (e.g., traditional machine learning models, deep learning models, any or all of those described above, etc.); classical models (e.g., rule-based algorithms ), and determining that the motion of the vehicle satisfies the first crash detection criteria comprises: executing the machine learning model on the motion of the vehicle during the subsequent drive to produce a crash classification(See Salodkar para[0035] In further additional or alternative variations, any or all of the models can implement non-machine-learning processes, such as classical processes (e.g., rule-based algorithms, dynamic equations to determine one or more motion characteristics of the vehicle, etc.).
Regarding claim 20, Salodkar teaches A non-transitory machine-readable storage medium, including instructions that, when executed by one or more processors of a crash detection system (See para[0050] a machine configured to receive a computer-readable medium storing computer-readable instructions, or by any other suitable computing system possessing any suitable component (e.g., a graphics processing unit, a communications module, etc.), cause the one or more processors to perform operations comprising: receiving, by a crash detection application executing on a computing device(See Salodkar para[0050] a remote computing system (e.g., a server, at least one networked computing system, stateless computing system, stateful computing system, etc. ), and from a sensor arrangement coupled to the computing device, movement measurements indicating motion of a computing device during a drive by a driver in a vehicle(See para[0089]The collision model preferably receives as input at least a set of sensor inputs received in S205 );
wherein the movement measurements include raw sensor data and/or processed sensor data collected using a sensor arrangement coupled to the computing device(See para[0089]The collision model preferably receives as input at least a set of sensor inputs received in S205 );; and
analyzing, by the crash detection application, the movement measurements to detect motion of the vehicle(see Salodkar para[0039] In some variations, for instance, any or all of the models 110 function to determine auxiliary information related to driver and/or the vehicle, such as, but not limited to, any or all of: motion parameters of the vehicle such as a speed (e.g., speed, speed just before the collision, speed during collision, speed just after collision, average speed during trip, etc.), acceleration (e.g., G-force associated with the vehicle), distance (e.g., braking distance), etc.; historical information (e.g., driver's driving history, number of collisions driver has been involved in, driving style and/or behaviors of driver, etc.); driver information (e.g., risk score assigned to driver);
receiving, by the crash detection application, and from a crash detection management server system, crash detection criteria generated for a historical driving performance of the vehicle, of a driver associated with the computing device, or both(See Salodkar para[0039] historical information (e.g., driver's driving history, number of collisions driver has been involved in, driving style and/or behaviors of driver, etc.); driver information (e.g., risk score assigned to driver), para[0079]The information collected in S205 can additionally include driver information, such as any or all of: historical information associated with the driver); and
determining, by the crash detection application, that the vehicle was involved in a crash during the drive by applying the crash detection criteria to the motion of the vehicle (see Salodkar para[0053] to detect a vehicular accident event based on the received data)executing a crash detection model on the movement measurements, wherein the crash detection model is configured to detect whether the vehicle was involved in a crash during the drive by applying a crash detection criterion to the movement measurements(See at least Salodkar para[0044] In some variations, for instance, the set of modules includes modules configured to detect any or all of: a confidence associated with the occurrence of a collision, a severity of the collision, a direction of impact of the vehicle, fraud involved in the collision, and/or the set of modules can be configured to detect any other suitable information related to the collision.)and wherein the crash detection criterion that is applied by the crash detection model depends on a historical driving performance of the driver during one or more previous trips that occurred prior to the drive in the vehicle(See Salodkar para[0079] The information collected in S205 can additionally include driver information, such as any or all of: historical information associated with the driver (e.g., prior history of collisions, prior history of fraud, riskiness of driver's driving behavior as represented in a driving risk score, etc.), a driver's attention and/or distraction level, an activity of the driver (e.g., texting), a driver score (e.g., safety score, risk score, etc.), a driver behavior (e.g., during accident, prior to accident, after accident, etc.), circumstantial information (e.g., driver had a long work day beforehand and was tired, driver was in an area he had never driven in before, etc.), intoxication level, and/or any other suitable information).
Regarding claim 21, Salodkar teaches wherein the crash detection model is configured to: receive historical driving data of the driver as input (See para[0079] The information collected in S205 can additionally include driver information, such as any or all of: historical information associated with the driver ); and dynamically select the crash detection criterion based on the historical driving data. (See para[0104] Additionally or alternatively, the order in which any or all of the algorithms is performed can be determined based on a priority (e.g., predetermined priority, dynamically determined priority, etc.); para[0127] Additionally or alternatively, the confidence module can be performed multiple times (e.g., as described for the collision detection algorithm above) independent of how many times the collision detection algorithm is performed, a single time, a dynamically determined number of times (e.g., continuously until a threshold confidence level is reached).
Regarding claim 22, Salodkar teaches wherein the historical driving data includes the historical driving performance of the driver. (See para[0039] historical information (e.g., driver's driving history, number of collisions driver has been involved in, driving style and/or behaviors of driver, etc.); driver information (e.g., risk score assigned to driver).
Regarding claim 23, Salodkar teaches wherein the operations further comprise: selecting the crash detection model (See Salodkar set of models ) from among a plurality of crash detection models based on historical driving data of the driver, wherein each respective crash detection model of the plurality of crash detection models(See Salodkar set of models ) is configured to use a different crash detection criterion for detecting a crash event(See Salodkar para [0042]The system preferably includes a set of reconstruction modules 120, wherein the set of reconstruction modules functions to produce outputs for use by various different entities in reconstructing and/or understanding various potential aspects of the collision.);; and based on selecting the crash detection model, executing the crash detection model on the movement measurements associated with the drive to detect whether the vehicle was involved in a crash, wherein the crash detection model is configured to use the crash detection criterion(See Salodkar para[0090] the algorithms of the collision model can function to take into account circumstantial driver information (e.g., duration of drive, driver risk score, driver's amount of phone use, driver distraction level, etc.), along with location and/or vehicle information (e.g., speed, direction, location, etc.) to detect a collision and/or determine one or more features of the collision.).
Regarding claim 24, Salodkar teaches wherein selecting the crash detection model from among the plurality of crash detection models(See Salodkar set of models) based on the historical driving data of the driver comprises(See Salodkar para[[0040] In a first set of variations, the collision model 110 includes a set of classifiers configured to determine a set of probabilistic outputs related to the collision ): classifying the driver into a particular category of a plurality of categories based on the historical driving data(See Salodkar para[0090] driver information (e.g., duration of drive, driver risk score, driver's amount of phone use, driver distraction level, etc);; and selecting the crash detection model from among the plurality of crash detection models based on the particular category into which the driver is classified (see Salodkar para[0037] The set of algorithms of the model(s) preferably function to classify the set of inputs relative to any or all of the categories (e.g., collision vs. no collision, collision features, etc.) described above. As such, the set of algorithms preferably includes one or more classification algorithms (equivalently referred to herein as classifiers), wherein the algorithms are configured to determine classifications for the set of inputs) .
Regarding claim 25, Salodkar teaches wherein the operations further comprise: receiving the crash detection criterion from a remote server system (See para[0053] In another example, a remote server can be configured to receive movement data from a vehicle and a mobile computing device, to detect a vehicular accident event based on the received data, and to automatically contact emergency services (e.g., through a telecommunications API) ), wherein the crash detection criterion is selected by the remote server system based on historical driving data of the driver (See para[0030] wherein the collision model functions to assess (e.g., detect, classify, etc.) the occurrence of a collision (e.g., a suspected collision based on a set of inputs); and applying, by the crash detection model, the received crash detection criterion to detect whether the vehicle was in a crash (See para[0143] In some variations, the fraud module can include collecting information (e.g., in S205, after the collision, etc.) associated with another driver involved in the collision (and/or any other users such as passengers and/or pedestrians), which can function to bolster the outputs of the fraud module (e.g., by looking into another driver's behavior involved in crash).).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/NAZIA AFRIN/Examiner, Art Unit 3666
/SCOTT A BROWNE/Supervisory Patent Examiner, Art Unit 3666
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