Office Action 1: Non-Final Rejection (2024-11-27)
Rejection types: 101 - Patent-Eligible Subject Matter, 112 - Written Description/Enablement

CLAIMS:
CLAIMS

1\. A method, comprising:
receiving, at a telematics server:
training acceleration data from a first telematics device coupled to a first vehicle, and
a vehicle-provided reversing indication from the first telematics device;
training, by the telematics server, a central reversing determination machine- learning model using the training acceleration data and the vehicle-provided reversing indication;
generating and sending, by the telematics server, a reversing determination model update 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 reversing determination model update;
capturing, by the second telematics device, captured acceleration data for the second vehicle; 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.

2\. 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\. 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\. The method of claim 1, wherein the captured acceleration data is captured from a three-axis accelerometer installed in the second telematics device.

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

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

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

14\. 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\. 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\. 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\. The method of claim 12, wherein the trigger event comprises the vehicle- provided reversing indication.

18\. 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\. 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\. 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 telematics server receives training acceleration data and a 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;
the telematics server sends the reversing determination model update to the second telematics device;
the second telematics device updates an edge reversing determination machine-learning model using the reversing determination model update;
the second telematics device captures captured acceleration data for the second vehicle; and
the edge reversing determination machine-learning model determines a machine-learning-determined reversing indication for the second vehicle based on the captured acceleration data.

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 application filed 01/09/2023. Claims 1-20 are presently pending and are presented for examination.
Information Disclosure Statement
The Information Disclosure Statement filed on 01/09/23; 05/26/23; 08/18/23; 10/19/23; 05/21/24; and 11/18/24 have been considered. An initialed copy of the Form 1449 is enclosed herewith.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b)  CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 3 is 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 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.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101.
Claim 20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to a mental process that can be practicably performed in the human mind, and therefore an abstract idea. The claim recites “and the edge reversing determination machine-learning model determines a machine-learning-determined reversing indication for the second vehicle based on the captured acceleration data.” which, under the broadest reasonable interpretation, is a mental process that could reasonably be done in the human mind provided acceleration data. For example, nothing outside of the recitation of a “the edge reversing determination machine-learning model determines” or “a machine-learning-determined” prevents a human from determining a vehicle reversing indication from acceleration data. A human could observe a vehicle accelerating and associate the movement with a reverse maneuver, and then make a determination that a second vehicle is performing a reverse maneuver on the basis of this observation. See Berkheimer v. HP, Inc., 881 F.3d 1360, 125 USPQ2d 1649 (Fed. Cir. 2018) & In re Brown, 645 Fed. App'x 1014, 1016-17 (Fed. Cir. 2016).
Under step 2A, prong 2 claim 1 does not include additional elements that are sufficient enough to amount the abstract idea into a practical application, because the recitations “A telematics system, comprising: a telematics server running a central reversing determination machine-learning model;”, “a network;” or “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;” are directed to generic linking, merely defining an environment of the abstract idea with generically recited elements, provided for linking the abstract idea to a particular technological environment, and lacking an element to incorporate a result of performing the abstract idea with the recited structures in a meaningful way see MPEP, 2106.05(f) & 2106.05(e).
Further under step 2A, prong 2 “wherein: the telematics server receives training acceleration data and a 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;”, “the telematics server sends the reversing determination model update to the second telematics device;”, “the second telematics device updates an edge reversing determination machine-learning model using the reversing determination model update;”, & “the second telematics device captures captured acceleration data for the second vehicle;” is directed to insignificant extra solution activity data gathering/outputting, merely reciting steps for gathering and transmitting the data used for performing steps of the mental process with generically recited elements. For example, reciting steps for merely transmitting acceleration data and reversal indications used for updating machine learning model, and lacking any recitation to incorporate the output of the models as a control step, see MPEP 2106.05(g).
Under step 2B, the claim does not include additional elements that are sufficient enough to amount to significantly more than the judicial exception because for example “A telematics system, comprising: a telematics server running a central reversing determination machine-learning model;”, “a network;” or “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;” again is merely applying and generically linking the abstract idea to a particular technological environment, MPEP, 2106.05(f) & 2106.05(e), and does not impose any other meaningful limitation beyond generally linking the use of the judicial exception to a particular technological environment. For example, the recitations of “A telematics system, comprising: a telematics server running a central reversing determination machine-learning model;”, “a network;” or “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;” are only provided to define the generic structures provided to define an environment for applying the steps of the mental process and perform no additional functions.
Further, the recitations “wherein: the telematics server receives training acceleration data and a 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;”, “the telematics server sends the reversing determination model update to the second telematics device;”, “the second telematics device updates an edge reversing determination machine-learning model using the reversing determination model update;”, & “the second telematics device captures captured acceleration data for the second vehicle;” would still be directed to insignificant extra solution activity data gathering and outputting under step 2B, as the transmitting of data pertaining acceleration data and a vehicle- provided reversing indication, and the updating and training of machine learning models, requires no more than ordinary skill in the art, and therefore is directed to well understood, routine, and conventional activity in the art, see MPEP 2106.05(d), II,  i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); & iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93. & see MPEP 2106.05(g), Mere Data Gathering v. Consulting and updating an activity log, Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754;
Accordingly, the claims are not patent eligible.
Claim 1 is rejected under 35 U.S.C 101 under similar rationale as claim 20.
Additionally, claims 2-19 are rejected under 35 U.S.C. 101 by virtue of their dependency on claim 1.
Claims 2-3, 9-10, 13-16, and 18-19 do not include additional elements that are sufficient enough to amount to significantly more than the judicial exception because the claims are directed to insignificant extra solution activity data gathering and outputting steps, for example reciting steps to further define the data which is output as an alert, as recited in claim 2, or defining the data which is gathered for performing the abstract idea, such as a vehicle speed/reverse gear indication/copied sampled accelerometer readings/post-trigger event acceleration data, as recited in claims 3, 9-10, and 13-16, or outputting/storing a machine-learning-determined reversing indication, as recited in claims 18-19, each merely recites data gathering and output steps, the data amounting to nothing more than a determination of if a second vehicle is performing a reversal maneuver, determined as a result of performing the abstract idea. The mere recitation of when/how/what data is gathered/output, however again lacks a specific element reciting how the result of performing the mental process is applied or integrated as a specific control step, and therefore is directed to well understood routine and conventional activity in the art, see MPEP 2106.05(g) iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016); & See 2106.05(a) iii. Gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48.
Claims 4-8, 11-12, and 17 do not include additional elements that are sufficient enough to amount the abstract idea into a practical application, because claims recite steps to defining additional structure and data in the environment of the abstract idea, and therefore directed to generic linking. See claims 4 & 11 defining what structure is provided to capture the acceleration data, claims 5-7 reciting the frequency for updating the second device with the reversing determination model, claims 8 & 12 defining when to capture acceleration data, and claim 17 defining a trigger event. Defining a technological environment of the mental process, merely links the process to generic steps or structure, lacking an element reciting how an output of performing the process is integrated to impact the operation of the vehicle, see MPEP, 2106.05(f) & 2106.05(e). These additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Accordingly, these claims are not patent eligible.
Allowable Subject Matter
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.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding claim 20 and similarly with respect to claim 1.
The closest prior art of record taken either individually or in combination with other prior art of record fails to teach or suggest the subject matter of independent claims 20 and 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; the telematics server sends the reversing determination model update to the second telematics device; the second telematics device updates an edge reversing determination machine-learning model using the reversing determination model update; the second telematics device captures captured acceleration data for the second vehicle; and 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.
Claims 2-19, these claims would be allowable on the basis of the dependency of claim 1.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Isaac (U.S. Publication No. 2021/0319332) discloses determining driver and vehicle characteristics based on an edge-computer device, which may determine vehicle location, speed, direction and other basic driving data without needing to communicate with vehicle sensors, see Abstract, [0089], and Figs. 4-5.


NEXT 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.

CITED REFERENCE SUMMARIES: Ref US20210319332A1: Claims:
A method comprising: collecting, in real-time and via an edge-computing device located in a vehicle, vehicle driving event data comprising data indicative of driving characteristics associated with an operation of the vehicle; analyzing, by the edge-computing device and in real-time and based on a machine learning model, one or more characteristics of the vehicle driving event data; determining, by the edge-computing device and in real-time and based on the machine learning model, at least one of: a driving behavior, a driver rating, an occurrence of a collision, and vehicle diagnostics; and displaying, to a user in the vehicle and via a graphical user interface, information related to the at least one of: the driving behavior, the driver rating, the occurrence of a collision, and the vehicle diagnostics.
collecting, in real-time and via an edge-computing device located in a vehicle, vehicle driving event data comprising data indicative of driving characteristics associated with an operation of the vehicle;
analyzing, by the edge-computing device and in real-time and based on a machine learning model, one or more characteristics of the vehicle driving event data;
determining, by the edge-computing device and in real-time and based on the machine learning model, at least one of: a driving behavior, a driver rating, an occurrence of a collision, and vehicle diagnostics; and
displaying, to a user in the vehicle and via a graphical user interface, information related to the at least one of: the driving behavior, the driver rating, the occurrence of a collision, and the vehicle diagnostics.
The method of claim 1 , further comprising: collecting, in real-time and via the edge-computing device and during the operation of the vehicle, telematics information comprising one or more of driver data, vehicle data, and environmental data; and wherein the analyzing comprises analyzing one or more characteristics of the telematics information.
collecting, in real-time and via the edge-computing device and during the operation of the vehicle, telematics information comprising one or more of driver data, vehicle data, and environmental data; and
wherein the analyzing comprises analyzing one or more characteristics of the telematics information.
The method of claim 2 , wherein the environmental data comprises at least one of traffic condition data, weather condition data, or road condition data.
The method of claim 2 , further comprising: training the machine learning model to identify the telematics information and the vehicle event driving data to be collected.
training the machine learning model to identify the telematics information and the vehicle event driving data to be collected.
The method of claim 1 , wherein the edge-computing device is communicatively coupled to at least one sensor of a plurality of sensors arranged on the vehicle, and the method further comprising: receiving the vehicle driving event data from the at least one sensor.
receiving the vehicle driving event data from the at least one sensor.
The method of claim 1 , wherein the edge-computing device is communicatively coupled to an on-board telematics device of the vehicle, and the method further comprising: receiving the telematics information from the telematics device.
receiving the telematics information from the telematics device.
The method of claim 1 , further comprising: training the machine learning model to identify the at least one of the driving behavior, the driver rating, the occurrence of a collision, and the vehicle diagnostics.
training the machine learning model to identify the at least one of the driving behavior, the driver rating, the occurrence of a collision, and the vehicle diagnostics.
The method of claim 1 , further comprising: determining, based on an available network, portions of the vehicle driving event data to be transmitted to a central server.
determining, based on an available network, portions of the vehicle driving event data to be transmitted to a central server.
The method of claim 1 , further comprising: receiving, by the edge-computing device and from a central server, configuration data; and dynamically updating, based on the configuration data, the edge-computing device.
receiving, by the edge-computing device and from a central server, configuration data; and
dynamically updating, based on the configuration data, the edge-computing device.
The method of claim 1 , further comprising: receiving, by the edge-computing device and from a central server, trained data; and dynamically updating, based on the trained data, the machine learning model.
receiving, by the edge-computing device and from a central server, trained data; and
dynamically updating, based on the trained data, the machine learning model.
The method of claim 1 , further comprising: communicating, in real-time and by the edge-computing device, with additional computing devices in neighboring vehicles.
communicating, in real-time and by the edge-computing device, with additional computing devices in neighboring vehicles.
The method of claim 1 , further comprising: causing a central server to apply machine learning techniques to normalize driving behavior patterns for a plurality of drivers.
causing a central server to apply machine learning techniques to normalize driving behavior patterns for a plurality of drivers.
An apparatus, comprising: a processor; a memory unit storing computer-executable instructions, which when executed by the processor, cause the apparatus to: collect, in real-time and via an edge-computing device located in a vehicle and during an operation of the vehicle, telematics information comprising one or more of driver data, vehicle data, and environmental data; analyze, by the edge-computing device and in real-time and based on a machine learning model, one or more characteristics of the telematics information; determine, by the computing device and in real-time based on the machine learning model, at least one of: a driving behavior, a driver rating, occurrence of a collision, and vehicle diagnostics; and display, to a user in the vehicle and via a graphical user interface, information related to the at least one of: the driving behavior, the driver rating, the occurrence of a collision, and the vehicle diagnostics.
a memory unit storing computer-executable instructions, which when executed by the processor, cause the apparatus to: collect, in real-time and via an edge-computing device located in a vehicle and during an operation of the vehicle, telematics information comprising one or more of driver data, vehicle data, and environmental data; analyze, by the edge-computing device and in real-time and based on a machine learning model, one or more characteristics of the telematics information; determine, by the computing device and in real-time based on the machine learning model, at least one of: a driving behavior, a driver rating, occurrence of a collision, and vehicle diagnostics; and display, to a user in the vehicle and via a graphical user interface, information related to the at least one of: the driving behavior, the driver rating, the occurrence of a collision, and the vehicle diagnostics.
collect, in real-time and via an edge-computing device located in a vehicle and during an operation of the vehicle, telematics information comprising one or more of driver data, vehicle data, and environmental data;
analyze, by the edge-computing device and in real-time and based on a machine learning model, one or more characteristics of the telematics information;
determine, by the computing device and in real-time based on the machine learning model, at least one of: a driving behavior, a driver rating, occurrence of a collision, and vehicle diagnostics; and
display, to a user in the vehicle and via a graphical user interface, information related to the at least one of: the driving behavior, the driver rating, the occurrence of a collision, and the vehicle diagnostics.
The apparatus of claim 13 , wherein the computer-executable instructions, when executed by the processor, cause the apparatus to: collect, in real-time and via the edge-computing device, vehicle driving event data comprising data indicative of driving characteristics associated with the operation of the vehicle; and wherein the analyzing comprises analyzing one or more characteristics of the vehicle driving event data.
collect, in real-time and via the edge-computing device, vehicle driving event data comprising data indicative of driving characteristics associated with the operation of the vehicle; and
wherein the analyzing comprises analyzing one or more characteristics of the vehicle driving event data.
The apparatus of claim 14 , wherein the computer-executable instructions, when executed by the processor, cause the apparatus to: train the machine learning model to identify the telematics information and the vehicle driving event data to be collected.
train the machine learning model to identify the telematics information and the vehicle driving event data to be collected.
The apparatus of claim 13 , wherein the computer-executable instructions, when executed by the processor, cause the apparatus to: train the machine learning model to identify the at least one of the driving behavior, the driver rating, the occurrence of a collision, and the vehicle diagnostics.
train the machine learning model to identify the at least one of the driving behavior, the driver rating, the occurrence of a collision, and the vehicle diagnostics.
The apparatus of claim 13 , wherein the computer-executable instructions, when executed by the processor, cause the apparatus to: determining, based on an available network, portions of the telematics information to be transmitted to a central server.
determining, based on an available network, portions of the telematics information to be transmitted to a central server.
The apparatus of claim 13 , wherein the computer-executable instructions, when executed by the processor, cause the apparatus to: receive, by the edge-computing device and from a central server, configuration data; and dynamically update, based on the configuration data, the edge-computing device.
receive, by the edge-computing device and from a central server, configuration data; and
dynamically update, based on the configuration data, the edge-computing device.
The apparatus of claim 13 , wherein the computer-executable instructions, when executed by the processor, cause the apparatus to: receiving, by the edge-computing device and from a central server, training data; and dynamically update, based on the training data, the machine learning model.
receiving, by the edge-computing device and from a central server, training data; and
dynamically update, based on the training data, the machine learning model.
One or more non-transitory computer-readable media storing instructions that, when executed by a computing device, cause the computing device to: collect, in real-time and via an edge-computing device located in a vehicle, vehicle driving event data comprising data indicative of driving characteristics associated with one or more vehicles proximate to the vehicle; collect, in real-time and via the edge-computing device, telematics information comprising one or more of driver data, vehicle data, and environmental data; analyze, by the edge-computing device and based on a first machine learning model, one or more first characteristics of the telematics information and the vehicle driving event data; receive, by the edge-computing device and from a central server and based on a second machine learning model, one or more second characteristics of the telematics information and the vehicle driving event data; determining, by the edge-computing device and in real-time based on the one or more first characteristics and the one or more second characteristics, at least one of: a driving behavior, a driver rating, occurrence of a collision, and vehicle diagnostics; and displaying, to a driver of the vehicle and via a graphical user interface, information related to the at least one of: the driving behavior, the driver rating, the occurrence of a collision, and the vehicle diagnostics.
collect, in real-time and via an edge-computing device located in a vehicle, vehicle driving event data comprising data indicative of driving characteristics associated with one or more vehicles proximate to the vehicle;
collect, in real-time and via the edge-computing device, telematics information comprising one or more of driver data, vehicle data, and environmental data;
analyze, by the edge-computing device and based on a first machine learning model, one or more first characteristics of the telematics information and the vehicle driving event data;
receive, by the edge-computing device and from a central server and based on a second machine learning model, one or more second characteristics of the telematics information and the vehicle driving event data;
determining, by the edge-computing device and in real-time based on the one or more first characteristics and the one or more second characteristics, at least one of:
a driving behavior, a driver rating, occurrence of a collision, and vehicle diagnostics; and
displaying, to a driver of the vehicle and via a graphical user interface, information related to the at least one of: the driving behavior, the driver rating, the occurrence of a collision, and the vehicle diagnostics.
Ref US20210319332: Claims:
What is claimed is: 1. A method comprising: collecting, in real-time and via an edge-computing device located in a vehicle, vehicle driving event data comprising data indicative of driving characteristics associated with an operation of the vehicle; analyzing, by the edge-computing device and in real-time and based on a machine learning model, one or more characteristics of the vehicle driving event data; determining, by the edge-computing device and in real-time and based on the machine learning model, at least one of: a driving behavior, a driver rating, an occurrence of a collision, and vehicle diagnostics; and displaying, to a user in the vehicle and via a graphical user interface, information related to the at least one of: the driving behavior, the driver rating, the occurrence of a collision, and the vehicle diagnostics. 2. The method of claim 1, further comprising: collecting, in real-time and via the edge-computing device and during the operation of the vehicle, telematics information comprising one or more of driver data, vehicle data, and environmental data; and wherein the analyzing comprises analyzing one or more characteristics of the telematics information. 3. The method of claim 2, wherein the environmental data comprises at least one of traffic condition data, weather condition data, or road condition data. 4. The method of claim 2, further comprising: training the machine learning model to identify the telematics information and the vehicle event driving data to be collected. 5. The method of claim 1, wherein the edge-computing device is communicatively coupled to at least one sensor of a plurality of sensors arranged on the vehicle, and the method further comprising: receiving the vehicle driving event data from the at least one sensor. 6. The method of claim 1, wherein the edge-computing device is communicatively coupled to an on-board telematics device of the vehicle, and the method further comprising: receiving the telematics information from the telematics device. 7. The method of claim 1, further comprising: training the machine learning model to identify the at least one of the driving behavior, the driver rating, the occurrence of a collision, and the vehicle diagnostics. 8. The method of claim 1, further comprising: determining, based on an available network, portions of the vehicle driving event data to be transmitted to a central server. 9. The method of claim 1, further comprising: receiving, by the edge-computing device and from a central server, configuration data; and dynamically updating, based on the configuration data, the edge-computing device. 10. The method of claim 1, further comprising: receiving, by the edge-computing device and from a central server, trained data; and dynamically updating, based on the trained data, the machine learning model. 11. The method of claim 1, further comprising: communicating, in real-time and by the edge-computing device, with additional computing devices in neighboring vehicles. 12. The method of claim 1, further comprising: causing a central server to apply machine learning techniques to normalize driving behavior patterns for a plurality of drivers. 13. An apparatus, comprising: a processor; a memory unit storing computer-executable instructions, which when executed by the processor, cause the apparatus to: collect, in real-time and via an edge-computing device located in a vehicle and during an operation of the vehicle, telematics information comprising one or more of driver data, vehicle data, and environmental data; analyze, by the edge-computing device and in real-time and based on a machine learning model, one or more characteristics of the telematics information; determine, by the computing device and in real-time based on the machine learning model, at least one of: a driving behavior, a driver rating, occurrence of a collision, and vehicle diagnostics; and display, to a user in the vehicle and via a graphical user interface, information related to the at least one of: the driving behavior, the driver rating, the occurrence of a collision, and the vehicle diagnostics. 14. The apparatus of claim 13, wherein the computer-executable instructions, when executed by the processor, cause the apparatus to: collect, in real-time and via the edge-computing device, vehicle driving event data comprising data indicative of driving characteristics associated with the operation of the vehicle; and wherein the analyzing comprises analyzing one or more characteristics of the vehicle driving event data. 15. The apparatus of claim 14, wherein the computer-executable instructions, when executed by the processor, cause the apparatus to: train the machine learning model to identify the telematics information and the vehicle driving event data to be collected. 16. The apparatus of claim 13, wherein the computer-executable instructions, when executed by the processor, cause the apparatus to: train the machine learning model to identify the at least one of the driving behavior, the driver rating, the occurrence of a collision, and the vehicle diagnostics. 17. The apparatus of claim 13, wherein the computer-executable instructions, when executed by the processor, cause the apparatus to: determining, based on an available network, portions of the telematics information to be transmitted to a central server. 18. The apparatus of claim 13, wherein the computer-executable instructions, when executed by the processor, cause the apparatus to: receive, by the edge-computing device and from a central server, configuration data; and dynamically update, based on the configuration data, the edge-computing device. 19. The apparatus of claim 13, wherein the computer-executable instructions, when executed by the processor, cause the apparatus to: receiving, by the edge-computing device and from a central server, training data; and dynamically update, based on the training data, the machine learning model. 20. One or more non-transitory computer-readable media storing instructions that, when executed by a computing device, cause the computing device to: collect, in real-time and via an edge-computing device located in a vehicle, vehicle driving event data comprising data indicative of driving characteristics associated with one or more vehicles proximate to the vehicle; collect, in real-time and via the edge-computing device, telematics information comprising one or more of drive
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