DETAILED ACTION
Status of Claims
This Office action is in response to the amendment filed on 07/29/2026. Claim 1 was previously canceled. Claims 2-21 are currently pending and are presented for examination.
Response to Amendment/Arguments
The amendment filed 07/29/2026 has been entered and applicant’s arguments filed 07/29/2026 have been fully considered.
Regarding claim objection:
Applicant has argued that the objection to claim 14 is overcome by the filed amendment. The examiner agrees and has withdrawn the objection accordingly.
Regarding double patenting rejections:
The examiner acknowledges applicant’s request to hold the double patenting rejections in abeyance. Since the double patenting rejections are the only remaining issues, the examiner requests that applicant address these rejections in the next correspondence.
Regarding claim rejections under 35 U.S.C. § 103:
Applicant has argued that the claims are not taught by the prior art because the cited portion of Corcoba teaches to consider the particular device hardware rather than the operating system as required by the instant claims. This argument is persuasive and the prior art rejections are withdrawn accordingly.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 2-21 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-18 of U.S. Patent No. US 12,247,840 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because the differences amount to minor wording changes, splitting features among multiple claims, and removing features which were present in the claims of the reference patent. The underlined differences are shown in the table below:
Instant application: 19/075,119
Reference patent: US 12,247,840 B2
2. A method for determining vehicle fuel efficiency, the method comprising:
training a prediction model based on a training data set of collected trip fuel consumption data and other ground truth data regarding driving and vehicle attributes, wherein the prediction model is penalized by utilizing a loss function to provide feedback to the prediction model to improve the prediction model until the trained prediction model is generated;
collecting sensor data from a mobile device, wherein the sensor data includes a dataset that reflects a last trip on a vehicle by the mobile device, and wherein the sensor data is collected by one or more sensors of the mobile device;
determining patterns of driving events at one or more particular locations comprising at least one of one or more braking events, one or more speeding events, and one or more acceleration events based on the sensor data and historical driving events;
determining a distance of the last trip based on the sensor data;
generating, via the trained prediction model, a first fuel consumption prediction based on the distance, the driving events, and the mobile operation system of the mobile device; and
providing, via a user interface, an indication of the first fuel consumption prediction.
1. A method for determining vehicle fuel efficiency, the method comprising:
training a prediction model based on a training data set of collected trip fuel consumption data and other ground truth data regarding driving and vehicle attributes, wherein the prediction model is penalized by utilizing a loss function to provide feedback to the prediction model to improve the prediction model until the trained prediction model is generated;
collecting sensor data from a mobile device, wherein the sensor data includes a dataset that reflects a last trip on a vehicle by the mobile device, wherein the sensor data is collected from global position system (GPS) sensor data and micro-electro-mechanical system (MEMS) sensor data of the mobile device;
determining patterns of driving events at one or more particular locations comprising at least one of one or more braking events, one or more speeding events, and one or more acceleration events based on the GPS sensor data and historical driving events;
determining a distance of the last trip based on the sensor data;
generating, via the trained prediction model, a first fuel consumption prediction based on the distance, the driving events, and the mobile operation system of the mobile device; and
generating a user interface that includes a visual map of the last trip including highlights of locations associated with instances of the driving events of the determined patterns, a fuel efficiency score indicative of an amount of wasted fuel on the last trip, and one or more fuel efficiency score factors associated with the at least one of the one or more braking events, the one or more speeding events, and the one or more acceleration events indicative of an amount by which these factors contributed to the fuel efficiency score.
3. The method of claim 2, further comprising:
generating, via the trained prediction model, a second fuel consumption prediction based on the distance without the driving events; and
calculating a fuel wasted prediction based on a difference between the first fuel consumption prediction and the second fuel consumption prediction.
2. The method of claim 1, further comprising:
generating, via the trained prediction model, a second fuel consumption prediction based on the distance without the driving events; and
calculating a fuel wasted prediction based on a difference between the first fuel consumption prediction and the second fuel consumption prediction.
4. The method of claim 3, further comprising:
determining fuel wasted per unit distance based on the fuel wasted prediction and the distance;
mapping the fuel wasted per unit distance to a distribution of a plurality of fuel wasted per unit distance data points based on a dataset of past trips for a plurality of users to set a plurality of thresholds; and
determining the fuel efficiency score for the last trip based on one or more of the thresholds.
3. The method of claim 2, further comprising:
determining fuel wasted per unit distance based on the fuel wasted prediction and the distance;
mapping the fuel wasted per unit distance to a distribution of a plurality of fuel wasted per unit distance data points based on a dataset of past trips for a plurality of users to set a plurality of thresholds; and
determining the fuel efficiency score for the last trip based on one or more of the thresholds.
5. The method of claim 4, further comprising:
determining a user efficiency score, wherein the user efficiency score is an accumulated score based on past fuel efficiency scores of trips taken by a user associated with the mobile device in one or more vehicles.
4. The method of claim 3, further comprising:
determining a user efficiency score, wherein the user efficiency score is an accumulated score based on past fuel efficiency scores of trips taken by a user associated with the mobile device in one or more vehicles.
6. The method of claim 3, further comprising:
determining a make and subtype of the vehicle based on stored data; and
looking up a coefficient associated with the make and subtype of the vehicle in a data store, wherein the generating the first fuel consumption prediction accounts for the coefficient, wherein the coefficient is one of a plurality of coefficients associated with different makes and subtypes of vehicles stored in the data store.
5. The method of claim 2, further comprising:
determining a make and subtype of the vehicle based on stored data; and
looking up a coefficient associated with the make and subtype of the vehicle in a data store, wherein the generating the first fuel consumption prediction accounts for the coefficient, wherein the coefficient is one of a plurality of coefficients associated with different makes and subtypes of vehicles stored in the data store.
7. The method of claim 3, wherein the sensor data comprises at least one of: global position system (GPS) sensor data, micro-electro-mechanical system (MEMS) sensor data, or both.
1. … wherein the sensor data is collected from global position system (GPS) sensor data and micro-electro-mechanical system (MEMS) sensor data of the mobile device…
8. The method of claim 3, further comprising:
determining at least one of speed data points throughout the trip,
wherein the generating the first fuel consumption prediction accounts for the speed data points throughout the trip.
6. The method of claim 2, further comprising:
determining at least one of speed data points throughout the trip,
wherein the generating the first fuel consumption prediction accounts for the speed data points throughout the trip.
9. The method of claim 3, further comprising:
determining road type throughout the trip based on the sensor data,
wherein the generating the first fuel consumption prediction accounts for the road type throughout the trip.
7. The method of claim 2, further comprising:
determining road type throughout the trip based on the GPS data,
wherein the generating the first fuel consumption prediction accounts for the road type throughout the trip.
10. The method of claim 3, further comprising:
determining weather conditions throughout the trip based on the sensor data and third-party data sourced via an application programming interface (API),
wherein the generating the first fuel consumption prediction is based, in part, on the weather conditions throughout the trip.
8. The method of claim 2, further comprising:
determining weather conditions throughout the trip based on the GPS data and third-party data sourced via an application programming interface (API),
wherein the generating the first fuel consumption prediction is based, in part, on the weather conditions throughout the trip.
11. The method of claim 3, further comprising:
determining elevation throughout the trip based on the sensor data,
wherein the generating the first fuel consumption prediction is based, in part, on the elevation throughout the trip.
9. The method of claim 2, further comprising:
determining elevation throughout the trip based on the GPS data,
wherein the generating the first fuel consumption prediction is based, in part, on the elevation throughout the trip.
12. The method of claim 3, further comprising:
calculating a cost of fuel waste based on the fuel wasted prediction.
10. The method of claim 2, further comprising:
calculating a cost of fuel waste based on the fuel wasted prediction.
13. The method of claim 3, further comprising:
receiving the sensor data from the mobile device;
generating the first fuel consumption prediction at a cloud-based server; and
sending the first fuel consumption prediction to the mobile device.
11. The method of claim 2, further comprising:
receiving the sensor data from the mobile device;
generating the first fuel consumption prediction at a cloud-based server; and
sending the first fuel consumption prediction to the mobile device.
14. A system for determining vehicle fuel efficiency comprising:
at least one processor; and
at least one memory, in communication with the at least one processor, and that stores instructions that, when executed by the at least one processor, cause the system to perform operations comprising:
training a prediction model based on a training data set of collected trip fuel consumption data and other ground truth data regarding driving and vehicle attributes, wherein the prediction model is penalized by utilizing a loss function to provide feedback to the prediction model to improve the prediction model until the trained prediction model is generated;
collecting sensor data from a mobile device, wherein the sensor data includes a dataset that reflects a last trip on a vehicle by the mobile device, and wherein the sensor data is collected by one or more sensors of the mobile device;
determining patterns of driving events at one or more particular locations comprising at least one of one or more braking events, one or more speeding events, and one or more acceleration events based on the sensor data and historical driving events;
determining a distance of the last trip based on the sensor data;
generating, via the trained prediction model, a first fuel consumption prediction based on the distance, the driving events, and the mobile operation system of the mobile device; and
providing, via a user interface, an indication of the first fuel consumption prediction.
12. A system for determining vehicle fuel efficiency based on a trained prediction model, the system comprising:
at least one processor configured to:
train a prediction model based on a training data set of collected trip fuel consumption data and other ground truth data regarding driving and vehicle attributes, wherein the prediction model is penalized by utilizing a loss function to provide feedback to the prediction model to improve the prediction model until the trained prediction model is generated;
receive sensor data from a mobile device, wherein the sensor data includes a dataset that reflects a last trip on a vehicle by the mobile device, wherein the sensor data is collected from global position system (GPS) sensor data and micro-electro-mechanical system (MEMS) sensor data of the mobile device;
determine patterns of driving events at one or more particular locations comprising at least one of one or more braking events, one or more speeding events, and one or more acceleration events based on the GPS sensor data and historical driving events;
determine a distance of the last trip based on the sensor data;
generate, via the trained prediction model, a first fuel consumption prediction based on the distance, the driving events, and the mobile operation system of the mobile device; and
generate a user interface that includes a visual map of the last trip including highlights of locations associated with instances of the driving events of the determined patterns, a fuel efficiency score indicative of an amount of wasted fuel on the last trip, and one or more fuel efficiency score factors associated with the at least one of the one or more braking events, the one or more speeding events, and the one or more acceleration events indicative of an amount by which these factors contributed to the fuel efficiency score.
15. The system of claim 14, wherein execution of the instructions causes the system to perform operations comprising:
determining an intensity of each of the one or more braking events, wherein the intensity is determined based on a deceleration over a period of time of each of the braking events; and
classifying each of the one or more braking events into one of two or more braking event buckets, wherein each braking event bucket is associated with a distinct intensity range.
13. The system of claim 12, wherein the at least one processor is further configured to:
determine an intensity of each of the one or more braking events, wherein the intensity is determined based on a deceleration over a period of time of each of the braking events; and
classify each of the one or more braking events into one of two or more braking event buckets, wherein each braking event bucket is associated with a distinct intensity range.
16. The system of claim 14, wherein execution of the instructions causes the system to perform operations comprising:
determining an intensity of each of the one or more acceleration events, wherein the intensity is determined based on acceleration over a period of time of each of the acceleration events; and
classifying each of the one or more acceleration events into one of two or more acceleration event buckets, wherein each acceleration event bucket is associated with a distinct intensity range.
14. The system of claim 12, wherein the at least one processor is further configured to:
determine an intensity of each of the one or more acceleration events, wherein the intensity is determined based on acceleration over a period of time of each of the braking events; and
classify each of the one or more braking events into one of two or more braking event buckets, wherein each braking event bucket is associated with a distinct intensity range.
17. The system of claim 14, wherein execution of the instructions causes the system to perform operations comprising:
determining a length of each of the one or more speeding events; and
classifying each of the one or more speeding events into one of two or more speeding event buckets, wherein each speeding event bucket is associated with a distinct length range.
15. The system of claim 12, wherein the at least one processor is further configured to:
determine a length of each of the one or more speeding events; and
classify each of the one or more speeding events into one of two or more speeding event buckets, wherein each speeding event bucket is associated with a distinct length range.
18. The system of claim 14, wherein execution of the instructions causes the system to perform operations comprising:
determining a user efficiency score, wherein the user efficiency score is an accumulated score based on past fuel efficiency scores of trips taken by a user associated with the mobile device in one or more vehicles; and
comparing the user efficiency score with user efficiency scores associated with a set of other users based on a common characteristic.
16. The system of claim 12, wherein the at least one processor is further configured to:
determine a user efficiency score, wherein the user efficiency score is an accumulated score based on past fuel efficiency scores of trips taken by a user associated with the mobile device in one or more vehicles; and
compare the user efficiency score with user efficiency scores associated with a set of other users based on a common characteristic.
19. One or more tangible non-transitory computer-readable storage media storing computer-executable instructions for performing a computer process on a computing system, the computer process comprising:
training a prediction model based on a training data set of collected trip fuel consumption data and other ground truth data regarding driving and vehicle attributes, wherein the prediction model is penalized by utilizing a loss function to provide feedback to the prediction model to improve the prediction model until the trained prediction model is generated;
collecting sensor data from a mobile device, wherein the sensor data includes a dataset that reflects a last trip on a vehicle by the mobile device, and wherein the sensor data is collected by one or more sensors of the mobile device;
determining patterns of driving events at one or more particular locations comprising at least one of one or more braking events, one or more speeding events, and one or more acceleration events based on the sensor data and historical driving events;
determining a distance of the last trip based on the sensor data;
generating, via the trained prediction model, a first fuel consumption prediction based on the distance, the driving events, and the mobile operation system of the mobile device; and
providing, via a user interface, an indication of the first fuel consumption prediction.
17. One or more tangible non-transitory computer-readable storage media storing computer-executable instructions for performing a computer process on a computing system, the computer process comprising:
training a prediction model based on a training data set of collected trip fuel consumption data and other ground truth data regarding driving and vehicle attributes, wherein the prediction model is penalized by utilizing a loss function to provide feedback to the prediction model to improve the prediction model until the trained prediction model is generated;
collecting sensor data from a mobile device, wherein the sensor data includes a dataset that reflects a last trip on a vehicle by the mobile device, wherein the sensor data is collected from global position system (GPS) sensor data and micro-electro-mechanical system (MEMS) sensor data of the mobile device;
determining patterns of driving events at one or more particular locations comprising at least one of one or more braking events, one or more speeding events, and one or more acceleration events based on the GPS sensor data and historical driving events;
determining a distance of the last trip based on the sensor data;
generating, via the trained prediction model, a first fuel consumption prediction based on the distance, the driving events, and the mobile operation system of the mobile device;
causing to display, at the mobile device, a trip summary that correlates an amount of each driving event with a known scale associated with each type of driving event; and
generating a user interface that includes a visual map of the last trip including highlights of locations associated with instances of the driving events of the determined patterns, a fuel efficiency score indicative of an amount of wasted fuel on the last trip, and one or more fuel efficiency score factors associated with the at least one of the one or more braking events, the one or more speeding events, and the one or more acceleration events indicative of an amount by which these factors contributed to the fuel efficiency score.
20. The one or more tangible non-transitory computer-readable storage media of claim 19, wherein the sensor data comprises at least one of: global position system (GPS) sensor data, micro-electro-mechanical system (MEMS) sensor data, or both.
19. … wherein the sensor data is collected from global position system (GPS) sensor data and micro-electro-mechanical system (MEMS) sensor data of the mobile device…
21. (New) The one or more tangible non-transitory computer-readable storage media of claim 19, the computer process further comprising:
receiving an indication at the mobile device that the vehicle is being driven in an autonomous mode;
classifying driving events captured during which the vehicle is being driven in the autonomous mode separately;
generating a second fuel consumption prediction based on the distance driven in the autonomous mode and the driving events captured during the autonomous mode; and
comparing the first fuel consumption prediction with the second fuel consumption prediction.
18. The one or more tangible non-transitory computer-readable storage media of claim 17, the computer process further comprising:
receiving an indication at the mobile device that the vehicle is being driven in an autonomous mode;
classifying driving events captured during which the vehicle is being driven in the autonomous mode separately;
generating a second fuel consumption prediction based on the distance driven in the autonomous mode and the driving events captured during the autonomous mode; and
comparing the first fuel consumption prediction with the second fuel consumption prediction.
Allowable Subject Matter
Claims 2-21 would be allowable if rewritten or amended to overcome the double patenting rejections set forth in this Office action, or in the case that a terminal disclaimer is filed to obviate the rejections.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Madison R Inserra whose telephone number is (571)272-7205. The examiner can normally be reached Monday - Friday: 9:30 AM - 6:30 PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Aniss Chad can be reached at 571-270-3832. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Madison R. Inserra/Primary Examiner, Art Unit 3662
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