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 .
Request for Continued Examination
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 3/26/2026 has been entered.
Status of Claims
Claims 6 and 13 have been previously cancelled; therefore, Claims 1-5, 7-12, and 14-16 are currently pending in application 18/699,704.
Claim Rejections - 35 USC § 102
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 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.
Claims 1-5, 7-12, and 14-16 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Pal et al. (US 10,137,889 B2).
As per independent Claims 1 and 8, Pal discloses method (system) for monitoring movement of a vehicle during a ride based on a mobile device associated with the vehicle (See at least Figs.1-9; Claim 1; C28-C31), comprising:
detecting an unusual movement of the vehicle during the monitoring, the unusual movement comprising at least one of a change in speed, routeee at least Fig.1; C4, Collecting a Movement Dataset - Movement Data and Location Data; C6, Motion Dataset; C13-C17, Detecting a Vehicular Accident Event – C16L46-53, “In examples, Block S140 can include triggering the testing for a vehicular accident event in response to movement-related data indicating rapid deceleration (e.g., which can indicate an emergency braking situation), unusual position and speed data (e.g., a stationary vehicle in the middle of a highway), high-risk locations (e.g., an intersection with a high number of accidents), and/or other suitable movement-related events.”; C17L15-21, “Block S142 functions compare at least one of movement-related data (e.g., raw movement data, movement features, extracted characteristics, etc.) and supplemental-related data (e.g., raw supplemental data, supplemental features, extracted characteristics, etc.) to threshold conditions for determining when to monitor for one or more vehicular accident events.”);
determining an overall risk level based on the unusual movement (See at least Fig.1, Accident detection model; C14);
triggering a response based on the overall risk level, the response including a request to confirm a status of an occupant of the vehicle (See at least Fig.1 and 4-8); and
triggering a followup (follow-up) response based on the status of the occupant (See at least Fig.1; C3L1-6, “Third, the technology can automatically initiate accident response actions in response to detection of a vehicular accident. Accident response actions can include any one or more of notifying emergency services, notifying loved ones, requesting roadside assistance, insurance processing, and/or other suitable accident response actions.”; C7L47-L62, “ Block S120 preferably includes passively collecting supplementary data (e.g., without requiring human intervention), but can additionally or alternatively include actively collecting supplementary data (e.g., from presenting a digital survey to the user at the mobile computing device, etc.). In a specific example, Block S120 can include prompting, at an application executing on the mobile computing device, the user to input vehicle model information. Block S120 can include collecting supplementary data at any suitable time before, during, and/or after a vehicular accident event. In a variation, Block S120 can include collecting supplementary data in response to determining an unsafe driving conditions status (e.g., from movement data, from other supplementary data, etc.). In another variation, Block S120 can include collecting supplementary data in response to detecting a vehicular accident event.”; C8L17-33, “ collecting audio data. Block S121 preferably includes collecting audio data that can be used to detect that an accident has occurred and/or the severity of an accident (e.g., by analyzing recorded audio for sounds related to or resulting from impacts). For example, audio data can provide information on the status of a vehicle before/during/after an accident (e.g., by recording engine noise), the nature, location, and/or severity of an accident (e.g., by recording impact noise), and the effect of the accident on the vehicle's occupants (e.g., by recording voice data for a vehicle's occupants). In another example, Block S121 can include recording airbag deployment audio data, and detecting a vehicular accident event and/or determining an vehicular accident characteristic based on the airbag deployment audio data, any other suitable audio data, and/or movement data.”);
wherein determining the overall risk level further comprises:
processing, by a plurality of distinct risk model
the ride (C11L50-65, Temporal Data; See also C11L50-65),
the vehicle (See at least C11L34-35, “vehicle model data (e.g., model, age, accident history, mileage, repair history, etc.)”; see also C14); See also Fig.1, C14, and C26L63-C27L9), and
occupants in the vehicle during the ride (C11L38-42, “In an example, Block S128 can include collecting driver behavior data (e.g., actively collected driver data, derived from movement data, etc.), which can be used to adjust and/or select one or more accident detection models tailored to a given driver.”; C16L15-33, “In another example, Block S140 can include generating a set of accident detection models for mobile computing devices coupled to different portions of a driver's body. In this example, a first and a second accident detection model can be generated for mobile computing devices coupled to the wrist (e.g., a smartwatch worn by the driver) and to a driver pocket (e.g., a smartphone within a pant pocket), respectively. Block S140 can accordingly include, in this example, retrieving the second accident detection model in response to mobile computing device light sensor data indicating a device position within the pocket of the driver. In another example, if it is detected or otherwise known that the navigation device is kept in a driver's pocket, the acceleration profile of the phone can be assumed to more closely match that of the vehicle than if the navigation device was set on (and not fixed to) the dashboard. However, dynamically adjusting an accident detection threshold, model, and/or reference profile can be performed in any suitable manner.”; See also C11L50-65, “In another example, Block S128 can include collecting temporal data indicating the time of day when a driving session is occurring. For example, a higher risk of vehicular accident can be correlated with temporal data indicating a driving session at a time when the driver does not usually drive. In another example, mobile computing device usage by the driver during the driving session (e.g., texting while driving) can provide insight into driver behaviors affecting the likelihood of a vehicular accident. In another example, the method 100 can include collecting a light dataset from a light sensor of the mobile computing device; determining a position of the mobile computing device in the vehicle (e.g., in a user's pocket, being held, mounted, in a cup holder, in a passenger seat, etc.); and selecting an accident detection model from a set of accident detection models, based on the position of the mobile computing device.”; C13L42-46, “Block S130 can include processing any available data (e.g., behavior models built into the navigation system, a database of known data profiles stored in the cloud, learned patterns derived from system behavior during normal non-accident operation periods, etc.).”; C15L45-65, “In examples of this variation, Block S140 can include matching patterns between one or more reference profiles and at least one of movement-related data and supplemental-related data. For example, Block S140 can include processing movement and/or supplemental data with an accident detection machine learning model (e.g., in Block S144). For example, Block S140 can include comparing accelerometer data to known impact profiles or impact models. Block S140 can also include comparing modeled results to typical results for data types; for example, stopping distance can be compared to a typical emergency stopping distance for a vehicle. If measured stopping distance is significantly shorter than the typical emergency stopping distance, that can be an indication that an accident has occurred. As an example of joint pattern recognition, Block S1340 can process movement data and microphone data simultaneously to determine potential impacts (e.g., for impact to be detected, an impact noise can need to be detected within a certain time period of a rapid acceleration change). However, generating a comparison with a reference profile can be performed in any suitable manner.”);
wherein the processing of the data by each of the plurality of distinct risk modelcorresponding specific risk, the specific risk comprising at least ONE of
risk of a driving accident (See at least Fig.2, S140 – Detecting a vehicular accident event with an accident detection model; See also Figs.1-3 and C14) and
determining in real-time, by the plurality of distinct risk modela risk level (i.e. probabilistic determination) for each specific risk [Under BRI (Broadest Reasonable Interpretation) – Could be only ONE specific risk - risk of a driving accident, and all the risk models of the plurality of distinct risk models could be based on the same one risk] based on the processed data (See at least Fig.1; C14-C16; C24-C28; See also C3L11-12, “Fourth, the technology can detect vehicular accident events in real-time”; C10L34-49, “In an example, Block S125 can include measuring, at a fitness monitoring device coupled to the vehicle driver, a blood pressure parameter of the vehicle driver during a time period in which movement data is collected. In this example, an unusually high blood pressure (e.g., relative an average blood pressure for the driver during other driving sessions) can indicate a driver's inability to focus, which can be correlated with a higher probability of vehicular accident. In another example, Block S125 can include: in response to detecting a vehicular accident event, measuring a heart rate parameter of the vehicle driver at a smart watch coupled to the vehicle driver. In this example, measuring an average heart rate that is comparable to the driver's average resting heart rate can indicate driving conditions familiar to the driver, which can be correlated with a lower probability of vehicular accident.”; C11L58-65, “In another example, the method 100 can include collecting a light dataset from a light sensor of the mobile computing device; determining a position of the mobile computing device in the vehicle (e.g., in a user's pocket, being held, mounted, in a cup holder, in a passenger seat, etc.); and selecting an accident detection model from a set of accident detection models, based on the position of the mobile computing device”; C26L63-C27L9, “In another example, Block S163 can include determining an irregular change in tire pressure during a time period in which a vehicular accident event is detected; and identifying, based on the irregular change in tire pressure, a vehicle damage characteristic of tire damage resulting from the vehicular accident event. In another example, the method 100 can include collecting horizontal vehicle movement data (e.g., parallel the road); extracting turning movement features (e.g., left turn, right turn, turn radius, etc.) form the horizontal vehicle movement data; and in response to the turning movement features indicating a sharp turn, determining a high probability of a side collision (e.g., a T-bone accident). However, determining an accident type S163 can be performed in any suitable manner.”; See also C30L20-65);
training the plurality of distinct risk models based on the status to output a revised risk level for each specific risk of the detected unusual movement, wherein the plurality of distinct risk model(See at least C3L21-32, “ The technology can continuously collect and utilize non-generic sensor data (e.g., location sensor data, motion sensor data, etc.) to provide real-time accident detection and/or determination of accident characteristics (e.g., severity, accident type, etc.) Further, the technology can take advantage of the non-generic sensor data and/or supplemental data (e.g., vehicle sensor data, weather data, traffic data, biosignal sensor data, etc.) to better improve the understanding of correlations between such data and vehicular accident events, leading to an increased understanding of vehicular-related and driver-related variables affecting vehicular accidents.”; C8L-33, “For example, audio data can provide information on the status of a vehicle before/during/after an accident (e.g., by recording engine noise), the nature, location, and/or severity of an accident (e.g., by recording impact noise), and the effect of the accident on the vehicle's occupants (e.g., by recording voice data for a vehicle's occupants). In another example, Block S121 can include recording airbag deployment audio data, and detecting a vehicular accident event and/or determining an vehicular accident characteristic based on the airbag deployment audio data, any other suitable audio data, and/or movement data.”; C13L55-58, “FIG. 3, Block S140 can additionally or alternatively include performing threshold satisfaction tests S142, and/or generating an accident detection machine learning model S144.”; C14L18-27, “ Additionally or alternately, Block S142 can include detecting a vehicular accident event based on supplemental data. For example, the method 100 can include receiving a traffic dataset describing traffic conditions proximal a vehicle location extracted from the second location dataset, where the traffic conditions include at least one of: a traffic level, a traffic law, and accident data; and detecting a vehicular accident event with the accident detection model, the traffic dataset, and a movement dataset (e.g., a location dataset, a motion dataset, etc.).”; C15L14-26, “Block S140 can include dynamically adjusting an accident detection threshold based on at least one of movement-related data and/or supplemental-related data. For example, Block S140 can include dynamically adjusting an accident detection threshold of change in speed based on weather conditions (e.g., reducing the threshold change in speed of 40 MPH in less than 2 seconds to 35 MPH in less than 3 seconds based on a high precipitation forecast). In another example, Block S140 can include dynamically increasing a change in acceleration accident detection threshold in response to retrieving traffic level supplemental data indicating light traffic at a location proximal the vehicle.”; See also C18L54-58, “Block S144 preferably includes training and/or updating one or more accident detection machine learning models with movement features (e.g., extracted in Block S130), supplementary features (e.g., extracted in Block S130), and/or any other suitable features.”);
assigning a priority to each of the one or more revised risk levels based on the specific risk associated with each of the plurality of distinct risk model
triggering the response if a revised risk level of a highest priority exceeds a threshold value (See at least C19-C24, Initiating an Accident Response Action; C24-C28), the response being at least ONE of
making a call to an occupant of the vehicle (Fig.1, Block S155),
sending a message to an occupant of the vehicle (See at least Fig.1 and 4-8; C14-C15; C23-C24, Communicating with a Mobile Computing Device),
activating audio recording of the mobile device,
communicating with a next-of-kin of an occupant of the vehicle (See at least C3; C20-C21, Presenting an Accident-Related Notification),
requesting assistance from a public service or an incident response team (See at least C21-C22, Contacting Emergency Services), and
continued monitoring of the ride, based on the overall risk level
(See at least C19-C20, Initiating an Accident response Action / Accident Response Action – Presenting an Accident Related Notification; and C24-C28; See also Figs.1-3; C3L1-6, “Third, the technology can automatically initiate accident response actions in response to detection of a vehicular accident. Accident response actions can include any one or more of notifying emergency services, notifying loved ones, requesting roadside assistance, insurance processing, and/or other suitable accident response actions.”).
As per Claims 2 and 9, Pal discloses wherein monitoring the movement of the vehicle further comprises monitoring at least ONE of a
GPS,
accelerometer or
gyroscope sensor of the mobile device (See at least C5-C6, i.e. Motion Data Set).
As per Claims 3 and 10, Pal discloses wherein determining the overall risk level for each specific risk is further based on a cumulative set of prior events and previously calculated risk levels saved for the ride being monitored (See at least C4L66-C5L13, “receiving a first location dataset collected at a location sensor of a mobile computing device during a first time period of movement of the vehicle; receiving a first motion dataset collected at a motion sensor of the mobile computing device during the first time period; in response to a vehicle motion characteristic (e.g., extracted from at least one of the first location dataset and the first motion dataset) exceeding the motion characteristic threshold: receiving a second location dataset collected at the location sensor of the mobile computing device during a second time period of the movement of the vehicle, where the second time period is after the first time period, and receiving a second motion dataset collected at the motion sensor of the mobile computing device during the second time period.”).
As per Claims 4 and 11, Pal discloses wherein the processing is further based on one or more feature vectors (See at least Fig.1; C13-C17; C24-C28), each feature vector representing at least ONE of
a contextual information and
historical information relating to the ride,
wherein contextual information comprises a duration of the unusual movement, a location at which the unusual movement occurred, start and stop time of the unusual movement, congestion level on road segment when the unusual movement occurred, booking information of the ride, behaviour (behavior) of occupants of the vehicle, and past events that occurred on the ride (See at least C11-C13); and
historical information comprises historical data about occupants of the vehicle and information about similar past bookings, stops and past unusual movement relating to a location at which the unusual movement occurred (See at least Fig.1; C13-C17; C24-C28).
As per Claims 5 and 12, Pal discloses wherein determining the overall risk level further comprises:
processing, by a decision model,
the risk levels determined by the plurality of distinct risk models,
business goals,
existing resources and
possible tradeoffs;
and determining the overall risk level based on the processing (See at least Fig.1; C13-C17; C24-C28).
As per Claims 7 and 14, Pal discloses wherein triggering the followup (follow-up) response comprises at least ONE of
making a call to an occupant of the vehicle,
communicating with a next-of-kin of an occupant of the vehicle,
requesting assistance from a public service or an incident response team, and
continued monitoring of the ride,
based on the status of the occupant (See at least Fig.1; C13-C17; C24-C28).
As per Claims 15 and 16, Pal discloses wherein determining the overall risk level further comprises processing historical information associated with one or more prior rides (See at least C15L27-65, “Block S140 can include generating a comparison with one or more reference profiles. Reference profiles can include accident reference profiles (e.g., movement profiles and/or supplemental profiles indicative of a vehicular accident event), non-accident reference profiles, and/or any other suitable reference profiles for illuminating the presence of a vehicular accident event. One or more reference profiles are preferably compared to movement-related data collected for a user driving the vehicle, but can additionally or alternatively be compared to supplemental-related data. In an example, Block S140 can include generating a comparison between an expected non-accident reference profile for a large vehicle performing a sharp U-turn and movement-related data collected for a commercial truck performing a sharp U-turn. In this example, Block S140 can include detecting a vehicular accident event in response to a similarity score (e.g., between the reference profile and the movement-related data) below a similarity score threshold. In examples of this variation, Block S140 can include matching patterns between one or more reference profiles and at least one of movement-related data and supplemental-related data. For example, Block S140 can include processing movement and/or supplemental data with an accident detection machine learning model (e.g., in Block S144). For example, Block S140 can include comparing accelerometer data to known impact profiles or impact models. Block S140 can also include comparing modeled results to typical results for data types; for example, stopping distance can be compared to a typical emergency stopping distance for a vehicle. If measured stopping distance is significantly shorter than the typical emergency stopping distance, that can be an indication that an accident has occurred. As an example of joint pattern recognition, Block S1340 can process movement data and microphone data simultaneously to determine potential impacts (e.g., for impact to be detected, an impact noise can need to be detected within a certain time period of a rapid acceleration change). However, generating a comparison with a reference profile can be performed in any suitable manner.”).
Response to Arguments
Applicant's arguments filed on 3/16/2026, with respect to Claims 1-5, 7-12, and 14-16, have been considered but are not persuasive. The claimed limitations are found in the prior art as stated/mapped in the rejection above. The rejection will remain as NON-FINAL, based on the rejection above.
The Applicant has made the following argument:
“First, Pal, at best, describes only a single accident detection model configured to detect one specific type of risk - a vehicular accident - based solely on motion derived features. However, Pal does not disclose the claimed processing, by a plurality of distinct risk models, nor does Pal disclose that each model is based on a different specific safety risk category such as sexual crime risk, driver related risk, passenger related risk, crime occurrence risk, accident risk, or abnormal ride progress risk. Instead, Pal is limited to processing movement features through only one accident detection model, with no disclosure of multiple distinct risk models or multi risk analysis.”
The prior art of Pal does explicitly describe several models and modeling techniques for determining specific risks involved with a car accident (See rejection above). However, the Applicant(s) are reminded that the independent claims require, “wherein the processing of the data by each of the plurality of distinct risk model at least ONE of a risk of sexual crime, risk associated with a driver of the vehicle, risk associated with a passenger of the vehicle, risk of occurrence of a crime, risk of a driving accident , and risk of an abnormal progress of the ride.” Furthermore, the Applicant(s) are reminded that optional or conditional elements do not narrow the claims because they can always be omitted (“at least ONE” or “One or More” Choice provided ten time in Independent claims – Under Broadest Reasonable Interpretation of the Claims, the Examiner only required one option when comparing the claims to the prior art). See e.g. MPEP §2106 II C: “Language that suggest or makes optional but does not require steps to be performed or does not limit a claim to a particular structure does not limit the scope of a claim or claim limitation. [Emphasis in original.]”; and In re Johnston, 435 F.3d 1381, 77 USPQ2d 1788, 1790 (Fed. Cir. 2006) “As a matter of linguistic precision, optional elements do not narrow the claim because they can always be omitted.” In re Johnston, 435 F.3d 1381, 77 USPQ2d 1788, 1790 (Fed. Cir. 2006)(where the Federal Circuit affirmed the Board’s claim construction of “further including that said wall may be smooth, corrugated, or profiled with increased dimensional proportions as pipe size is increased” since “this additional content did not narrow the scope of the claim because these limitations are stated in the permissive form ‘may.’”).
In order to overcome the sited prior art of Pal, the Examiner suggests claiming the distinct risk models and the corresponding distinct specific risks separately, for example: “wherein the processing of the data by each of the plurality of distinct risk models is based on a corresponding distinct risk, the distinct risks comprising: a risk of sexual crime, risk associated with a driver of the vehicle, risk associated with a passenger of the vehicle, risk of occurrence of a crime, risk of a driving accident, and risk of an abnormal progress of the ride …”.
Applicant’s remaining arguments are addressed in the rejection above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure can be found in the previous PTO-892 Notice of References cited 2/11/2026. The Examiner suggests the applicant review all of these documents before submitting any amendments; especially the following:
Liu et al (L. Liu, X. Zhang, M. Qiao and W. Shi, "SafeShareRide: Edge-Based Attack Detection in Ridesharing Services," 2018 IEEE/ACM Symposium on Edge Computing (SEC), Seattle, WA, USA, 2018, pp. 17-29.) – See at least Pgs.19-21, Driving Behavior Detection.
Meroux et al. (US 11,658,830 B2) – Describes a system/ method for vehicle sensing and analysis (See at least C10L14-33, “ In some examples, the disclosed systems may handle certain special cases related to ridesharing. For example, the disclosed systems can determine a change in the real-time pickup or drop-off location from the designated pickup or drop-off location. Accordingly, the disclosed systems can trigger a safety mechanism to request support by law enforcement. For safety and emergency situations, the disclosed systems can include panic buttons. The safety mechanism and/or the panic buttons can send a packet of data through the blockchain ledger indicating the status as an emergency and the type as either a medical emergency or a police emergency. In other examples, the disclosed systems can configure a physical and/or a digital panic button in the vehicle (for example on displays of an infotainment system, and/or on the mobile application) that can be used in cases where medical support or police support may be needed. For example, the physical and/or a digital panic button can be used during when a rideshare user is having a medical emergency and the driver may need to quickly contact emergency services such as an ambulance.”; See also Fig.3, C12L23-38).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JONATHAN P OUELLETTE whose telephone number is (571)272-6807. The examiner can normally be reached on M-F 8am-6pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Lynda C Jasmin, can be reached at telephone number (571) 272-6782. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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April 26, 2026
/JONATHAN P OUELLETTE/Primary Examiner, Art Unit 3629
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