Office Action 2: Notice of Allowance (2025-05-20)

CLAIMS:
AMENDMENTS TO THE CLAIMS

The following listing of claims replaces all prior versions and listings of claims in the application:

Listing of Claims

CLAIMS

Amendment dated March 4, 2025 Reply to Office Action dated November 27, 2024

1\. (Currently Amended) A method, comprising:capturing by a first telematics device coupled to a first vehicle:a vehicle-provided reversing indication, over an interface port of the first vehicle; and
training acceleration data, from a 3-axis accelerometer installed in the first telematics device, the training acceleration data representing accelerometer data corresponding to the first vehicle moving in a reverse direction as indicated by the vehicle-provided reversing indication;
sending, by the first telematics device, to a telematics server, the training acceleration data and the vehicle-provided reversing indication;
receiving, at \[\[a\]\]the telematics server\[\[:\]\],\_the training acceleration data andthe \[\[a\]\] vehicle-provided reversing indication;
training, by the telematics server, a central reversing determination machine-learning model using the training acceleration data and the vehicle-provided reversing indication as a known output label;
generating and sending, by the telematics server, a reversing determination model update including updated values of model parameters to a second telematics device coupled to a second vehicle which does not provide a reverse gear indication;

updating, by the second telematics device, an edge reversing determination machine- learning model using the updated values of model parameters of the reversing determination model update;
capturing, by the second telematics device, captured acceleration data for the second vehicle from athree-axis accelerometer installed in the second telematics device; \[\[and\]\] determining, by the edge reversing determination machine-learning model, a machine-learning-determined reversing indication for the second vehicle based on the captured acceleration data; and
sending, by the second telematics device, the machine-learning\-determined reversing indication to the telematics server.

2\. (Original) The method of claim 1, further comprising generating an alert of a reversing event at the second telematics device in response to determining the machine-learning-determined reversing indication.

3\. (Original) The method of claim 1, wherein the vehicle-provided reversing indication comprises:a vehicle speed, provided by the first vehicle to the first telematics device, that is greater than about zero, and
a reverse gear indication, provided by the first vehicle to the first telematics device.

4\. (Cancelled).

5\. (Original) The method of claim 1, wherein sending the reversing determination model update to the second telematics device is done periodically.

6\. (Original) The method of claim 1, wherein sending the reversing determination model update to the second telematics device is done in response to an

improvement in an output confidence level of the central reversing determination machine-learning model.

7\. (Original) The method of claim 1, wherein sending the reversing determination model update to the second telematics device is done in response to an update request from the second telematics device.

8\. (Original) The method of claim 1, wherein capturing the captured acceleration data is done in response to a synchronous event on the second telematics device.

9\. (Original) The method of claim 8, wherein capturing the captured acceleration data comprises copying sampled accelerometer readings from accelerometer First-In- First-Out buffers after the synchronous event.

10\. (Original) The method of claim 8, wherein capturing the captured acceleration data comprises copying sampled accelerometer readings from accelerometer First-In- First-Out buffers prior to the synchronous event.

11\. (Original) The method of claim 1, further comprising capturing, at the first telematics device, the training acceleration data from a three-axis accelerometer installed in the first telematics device.

12\. (Original) The method of claim 1, wherein capturing the training acceleration data is in response to a trigger event.

13\. (Original) The method of claim 1, wherein capturing the training acceleration data comprises capturing post-trigger event acceleration data.

14\. (Original) The method of claim 1, wherein capturing the training acceleration data comprises capturing pre-trigger event acceleration data and post-trigger event acceleration data.

15\. (Original) The method of claim 12, wherein capturing pre-trigger event acceleration data comprises copying sampled accelerometer readings from accelerometer First- In-First-Out buffers prior to the trigger event.

16\. (Original) The method of claim 12, wherein capturing post-trigger event acceleration data comprises copying sampled accelerometer readings from accelerometer First- In-First-Out buffers after the trigger event and before an expiry of a post-trigger duration timer.

17\. (Original) The method of claim 12, wherein the trigger event comprises the vehicle- provided reversing indication.

18\. (Original) The method of claim 1, further comprising sending, by the second telematics device, the machine-learning-determined reversing indication for the second vehicle to the telematics server.

19\. (Original) The method of claim 1, further comprising logging, by the telematics server, the machine-learning-determined reversing indication for the second vehicle in a telematics database coupled to the telematics server.

20\. (Currently Amended) A telematics system, comprising:a telematics server running a central reversing determination machine-learning model;
a network;

a first telematics device coupled to a first vehicle and in communication with the telematics server over the network; and
a second telematics device coupled to a second vehicle which does not provide a reverse gear indication over the network;
wherein:
the first telematics device captures:a vehicle-provided reversing indication, over an interface port of the first vehicle; and
training acceleration data, from a 3-axis accelerometer installed in the first telematics device, the training acceleration data representing accelerometer data corresponding to the first vehicle moving in a reverse direction as indicated by the vehicle-provided reversing indication;
the first telematics device sends the training acceleration data and the vehicle- provided reversing indication to the telematics server;
the telematics server receives the training acceleration data and \[\[a\]\]the vehicle- provided reversing indication from the first telematics device;
the telematics server trains the central reversing determination machine-learning model and generates a reversing determination model update using the training acceleration data and the vehicle-provided reversing indication as a known output label;
the telematics server sends the reversing determination model update including updated values of model parameters to the second telematics device;
the second telematics device updates an edge reversing determination machine- learning model using updated values of model parameters of the reversing determination model update;

Amendment dated March 4, 2025 Reply to Office Action dated November 27, 2024 the second telematics device captures captured acceleration data for the second vehicle from athree-axis accelerometer installed in the second telematics device;\[\[andl\] the edge reversing determination machine-learning model determines a machine-learning-determined reversing indication for the second vehicle based on the captured acceleration data; andthe second telematics device sends the machine-learning\-determined reversing indication to the telematics server.

OFFICE ACTION:
Notice of Pre-AIA  or AIA  Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This Office Action is in response to the amendment filed 03/04/2025. Claims 1-3 and 5-20 are presently pending and are presented for examination. Claim 4 is canceled.
Response to Amendment
The Amendment filed 03/04/2025 has been entered. Claims 1-3 and 5-20 remain pending in the application. Claim 4 is canceled. Applicant’s arguments directed to the 112(b) and 101 rejections are convincing and therefore the claims overcome the rejections under 35 U.S.C. 112(b) and 35 U.S.C. 101 previously set forth in the Non-Final Office Action mailed 11/27/2024.
Allowable Subject Matter
The following is an examiner’s statement of reasons for allowance:
Regarding claim 20 and similarly with respect to claim 1
The recitation “the telematics server trains the central reversing determination machine-learning model and generates a reversing determination model update using the training acceleration data and the vehicle-provided reversing indication as a known output label; the telematics server sends the reversing determination model update including updated values of model parameters to the second telematics device; the second telematics device updates an edge reversing determination machine- learning model using updated values of model parameters of the reversing determination model update; the second telematics device captures captured acceleration data for the second vehicle from a three-axis accelerometer installed in the second telematics device; the edge reversing determination machine-learning model determines a machine-learning-determined reversing indication for the second vehicle based on the captured acceleration data;” overcomes the art of record, rendering the claims in a manner specific enough to overcome the methods disclosed in the closest prior art. The claims specifically overcome the art of record, because of the limitation directed to using a trained model to generate a reversing model update, and sending the trained model to a second device, which captures acceleration data, and uses the update and acceleration data to determine a reverse indication. The closest prior art for example, discloses using a machine learning model for determining a reverse indication of a vehicle, however , no single prior art reference that fully discloses the claimed invention was found, nor was there determined a reasonable combination of prior art references that would have made the claimed invention obvious to one of ordinary skill in the art before the effective filing date of the invention. The subject matter of the claims is therefore allowable.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee.  Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any 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.

APPLICANT REBUTTAL: REMARKSThis paper is responsive to the Office Action dated November 27, 2024. A petition for extension of time under 37 CFR 1.136(a) and the appropriate fee are included with this response.Claim Status Claims 1-20, of which claims 1 and 20 are independent, were examined in the Office Action dated November 27, 2024.Independent claim 1 has been amended to recite capturing by a first telematics device coupled to a first vehicle:a vehicle-provided reversing indication, over an interface port of the first vehicle; and training acceleration data, from a 3-axis accelerometer installed in the first telematics device, the training acceleration data representing accelerometer data corresponding to the first vehicle moving in a reverse direction as indicated by the vehicle-provided reversing indication;sending, by the first telematics device, to a telematics server, the training acceleration data and the vehicle-provided reversing indication Support for the amendment can be found in paragraph [87], Figure 8, and paragraph [129], Figure 20.Independent claim 1 has also been amended to include the limitations of claims 2 and 4.Analogous amendments have been made to independent claim 20.The amendments to the claims have been made without prejudice and without acquiescing to any of the Examiner's objections. The amendments do not contain new subject matter and their entry is respectfully requested.Allowable Subject matter The Applicant thanks the Examiner for indicating that Claims 1-20 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C.112 (pre-AIA), 2nd paragraph, set forth in this Office action.Rejections under 35 U.S.C. § 112 Claim 3 was rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. The Office Action further stated that "The term "that is greater than about zero" in claim 3 is a relative term which renders the claim indefinite. The term "about" is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. For example, it is unclear to what degree a speed greater than zero "about" includes.".The Applicant respectfully disagrees and directs the Examiner's attention to paragraphs [83] and [84] of the specification. In paragraph [83], the specification states:"In the context of this disclosure, about 0 is defined as a speed which is above 0 and above a very low threshold. For example, about 0 may be defined as a speed of 2 Km/h." Furthermore, in paragraph [84] of the specification, it is recited that:"In some embodiments, a telematics device 200 may combine a vehicle speed 115 which is greater than about 0 (e.g., 3 Km/h or higher) with a reverse gear indication to conclude that the vehicle is reversing." Hence, the specification defines "about 0" as a speed of 2 Km/h and also defines "greater than about 0" as 3 Km/h or higher. The Applicant respectfully requests that the rejection under 35 USC 112 be withdrawn.Rejections under 35 U.S.C. § 101 Claims 1-20 were rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter for allegedly being directed to a mental process that can be practicably performed in the human mind, and therefore an abstract idea. While the Applicant respectfully disagrees, the Applicant has amended claims 1 and 20 for the sole purpose of advancing the prosecution of the application. The Applicant requests reconsideration in view to the amendments to the claims and the remarks below.The Applicant first would like to discuss the problem being solved for the purpose of highlighting how the claims are integrated into a practical application.As indicated in paragraph [83] many fatalities and injuries are caused by back-up accidents each year. Accordingly, fleet owners and managers wish to monitor and track reversing events in vehicles of their fleets. Telematics devices may capture reverse gear indications from a vehicle transmission ECU over an interface port of a vehicle. Then as indicated in paragraph [84], the telematics device derives a vehicle-provided reversing indication from the vehicle speed and the reverse gear indication. The telematics device includes such vehicle-provided reversing indication to telematics data that the telematics device sends to a telematics server for recording and analysis.A problem arises when vehicles do not report a reverse gear indication, as discussed in paragraph [85]. An early '700 patent proposed using acceleration data to determine when a vehicle is reversing. However, as discussed in paragraph [86], the method in the '700 patent requires having an accelerometer oriented along the longitudinal axis of the vehicle. Practically, a telematics device may have any orientation within the vehicle as the telematics device may be attached to the end of a harness.The present application provides a system by which an edge machine-learning model running on a telematics device installed in a vehicle that does not provide a reverse gear indication, can make a determination that the vehicle is reversing. As a result, the telematics device can send a reversing indication to the telematics server for recording an analysis. The edge machine-learning model has parameters that need to be adjusted and updated so that it can accurately determine the reversing indication.The invention as claimed is not directed to an abstract idea. A first telematics device captures a vehicle-provided reversing indication over an interface port of a first vehicle.The first telematics device also captures training acceleration data from a 3-axis accelerometer installed in the first telematics device. The first telematics device sends the training acceleration data and vehicle-providing reversing indication to a telematics server on which a central reversing determination machine-learning model runs. The training acceleration data serves as training data for the model and the vehicle-provided reversing indication serves as the known output label. As is known in the art, a machine-learning model has model parameters that are adjusted to improve the prediction performance of the model. The telematics server thus trains the central reversing determination machine- learning model and generates a reversing determination model update including update values of model parameters. A second telematics device runs on a second vehicle, the does not provide a vehicle-provided reversing indication. The second telematics device thus needs to determine that the second vehicle reversing using only acceleration data captured by a 3-axis accelerometer of the second telematics device. The second telematics device runs an edge reversing determination machine-learning model and uses the edge reversing determination machine-learning model to determine a machine- learning-determined reversing indication for the second vehicle. However, the edge reversing determination machine-learning model needs to be updated with updated values of the model parameters, so the edge reversing determination machine-learning model can accurately determine the machine-learning-determined reversing indication.Updating the model parameters of the edge reversing determination machine-learning model is done by the telematics server sending a model update including updated values of model parameters to the second telematics device. With the edge reversing determination machine-learning model updated with the new parameter values, the edge reversing determination machine-learning model can determine the machine-learning- determined reversing indication from the captured acceleration data captured by the second telematics device. The Applicant submits that the claims as amended are not directed to an abstract idea as the claims recite explicit technical steps that are coupled to hardware components.If the claims recite an abstract idea (which the Applicant does not concede), the abstract idea is integrated into a practical application. As indicated above, the claims are directed to solving a problem with vehicles that do not include a reversing gear indication on the asset communications bus (e.g. CANbus) thereof. Hence a telematics device cannot determine whether a vehicle is reversing except from captured acceleration data.However, since the orientation of the telematics device relative to the vehicle is not known, determining that the vehicle is reversing is not straightforward. Utilizing machine learning and training a centralized model, then updating an edge model to predict, based solely on acceleration data, that a vehicle is reversing integrates the allegedly abstract idea into a practical application.Conclusion The Applicant respectfully submits that the present application is in condition for allowance and requests early and favorable disposition.Notwithstanding any argument or claim amendment herein presented in response to any rejection or objection in the Office Action, any argument submitted herein is not to be assumed to be exhaustive. Unless clearly indicated, the Applicant does not consent to, or acquiesce in, any rejection or objection made in the Office Action. The Applicant expressly reserves the right to submit such further or other arguments or amendments as may advance the case, at such time and in such manner as the Applicant may choose in accordance with law, whether in prosecution herein, in a continuation, divisional, re- examination or re-issue proceeding, or such other proceeding as may be.Any questions regarding this submission or this application should be directed to Sherif Abdel-Kader at 437-352-4070 or sherifAbdel-kader@geotab.com.If additional fees are required, please charge the fees to our Deposit Account No. 604022.Respectfully submitted, /Sherif A. Abdel-Kader/ Sherif Abdel-Kader Reg. No. 79295 Tel: 437-352-4070 Geotab Inc.2440 Winston Park Drive Oakville, ON Canada L6H 7V2