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 .
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-14 are rejected under 35 U.S.C. sec. 103 as being unpatentable as obvious in view of United States Patent App. Pub. No.: US 2018/0247207 A1 to Ristovski and in view of U.S. Patent Application No.: US 2018/0170349 A1 to Edward JOBSON et al. and in view of U.S. Patent No.: US 2018/349785 A1 to Zheng et al.
In regard to claim 1, and claim 11, Ristovski discloses “...1. A method for operating a plurality of vehicles, each vehicle comprising an
associated local self-learning model and wherein the plurality of vehicles are connected to a global self-learning model,
said method comprising: (See paragraph 21-31)
receiving a first set of vehicle data and a second set of vehicle data, the vehicle data comprising information about each vehicle of the plurality of vehicles, each vehicle operating along at least one fixed route; (see paragraph 21-35)”
The primary reference is silent but JOBSON teaches “...receiving a first set of environmental data and a second set of environmental data, the
environmental data comprising information about each fixed route; (see paragraph 14 where the vehicle control strategy can be used based on environmental restrictions about the route from other vehicles including 1. Emission 2. Noise, 3. Weight, 4. Speed, and 5, safety)
Ristovski discloses “... estimating, by means of the global self-learning model and each local-self learning
model, a schedule parameter for each vehicle of the plurality of vehicles based on the received
first set of vehicle data, (see paragraph 56-59)”.
JOBSON teaches “.. the received first set of environmental data, the received second set of vehicle data, the received second set of environmental data, and a predefined interaction model
between the global self-learning model and each local-self learning model, (see paragraph 14 where the vehicle control strategy can be used based on environmental restrictions about the route from other vehicles including 1. Emission 2. Noise, 3. Weight, 4. Speed, and 5, safety and see claims 1- 11 where the vehicle can adapt the strategy based on the historical data from the previous passage from the zone )
wherein each of the local self-learning models make an initial estimation of the schedule parameter for the associated vehicle, where after the initial estimation is used as an input to the global self learning model;
receiving a measured schedule parameter for each vehicle;
comparing the estimated schedule parameter with the received measured schedule parameter; and (see claims 1 to 12 where the future delay based on the historical data can be provided and the time schedule can update based on the historical data)”...
It would have been obvious for one of ordinary skill in the art before the effective filing date of the present disclosure to combine the teachings of JOBSON with the disclosure of RISTOVSKI since JOBSON teaches that one or more environmental sensor data points for vehicles can be collected and provides as historical data including various sensed parameters. These can include energy consumption, wheel speed, throttle data, actuator data sensors, camera sensors and lidar sensors. The vehicles can operate through zones via a schedule that is a time schedule. Based on the environmental restrictions and sensed values, the environment can be delayed due to road parameters, or delays in charging that can be observed by the vehicle agents. Then the future predictions can be made and then a time schedule can be updated. For example, a road may be very rough and causing delays and a battery charging line also may cause a delay. The vehicle and then be modulated to avoid the delays. See paragraph 1-29 and claims 1-18.
The primary reference is silent but Zheng teaches “...updating the global self-learning model and each local self-learning model based on the comparison of the estimated schedule parameter with the received measured schedule parameter (See paragraph 51-61 where the global model update server can includes sensors to provide an update of the models)
operating the plurality of vehicles based on the estimated schedule parameters. ”. (see paragraph 77-83 where the vehicle can operate based on the updated model and motion tracking and depth detection)
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It would have been obvious for one of ordinary skill in the art before the effective filing date of the present disclosure to combine the teachings of ZHENG with the disclosure of RISTOVSKI since ZHENG teaches that one or more so called “events of interest” can be provided from the perception systems 140-1 to 140-n to the global model update cloud 160. This is from “real time sensors” that are active sensors 130-1 to 130n which are from a vehicle fleet that can include challenges or errors. See paragraph 76. For example, there can be different weather events that are encountered or different weather events and poor driving conditions or a significant error that is being encountered. Independently, events of interest for global model update are selected at 560. Once selected, the events of interest for global update are transmitted, at 580, to the global model update cloud 160. When the in situ perception system 110 receives, at 590, the updated model package from the global model update cloud 160, it updates the models stored locally accordingly. The updated model package may include information to update the global model 410 and/or any class models stored in class models 420 (see FIG. 4A). After the models are updated based on the received updated model package, the process goes back to 510 to continue the operation. This can provide an improved operation of the fleet based on real time improved models. See paragraph 70-
In regard to claim 2 and 12, Jobson teaches “...2. The method according to claim 1, wherein the predefined interaction model comprises:
making a first estimation of the schedule parameter for each vehicle by means of the global self-learning model based on the received first set of vehicle data and the received first 30 set of environmental data; (see paragraph 65-72 where the sensor data can take road quality data and wheel speed sensor data, and in paragraph 70 can provide data to a database for improved future predictions of any operating parameter such as an actuator or emission or energy use)
making a second estimation of the schedule parameter by means of each local self learning model for each corresponding vehicle based on the received second set of vehicle data and the received second set of environmental data, and the first estimation of the schedule
parameter for each vehicle, the second estimation being the estimated schedule parameter. . (see paragraph 65-72 where the sensor data can take road quality data and wheel speed sensor data, and in paragraph 70 can provide data to a database for improved future predictions of any operating parameter such as an actuator or emission or energy use)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the present disclosure to combine the teachings of JOBSON with the disclosure of RISTOVSKI since JOBSON teaches that one or more environmental sensor data points for vehicles can be collected and provides as historical data including various sensed parameters. These can include energy consumption, wheel speed, throttle data, actuator data sensors, camera sensors and lidar sensors. The vehicles can operate through zones via a schedule that is a time schedule. Based on the environmental restrictions and sensed values, the environment can be delayed due to road parameters, or delays in charging that can be observed by the vehicle agents. Then the future predictions can be made and then a time schedule can be updated. For example, a road may be very rough and causing delays and a battery charging line also may cause a delay. The vehicle and then be modulated to avoid the delays. See paragraph 1-29 and claims 1-18.
In regard to claim 3 and 13, Jobson teaches “...3. The method according to claim 1, wherein the predefined interaction model
comprises:
making a first estimation of the schedule parameter for each vehicle by means of the associated local self-learning model based on the received second set of vehicle data and the
received second set of environmental data;
making a second estimation of the schedule parameter by means of means of the global self-learning model based on the received first set of vehicle data and the received first set of environmental data, and the first estimation of the schedule parameter for each vehicle, the second estimation being the estimated schedule parameter. . (see paragraph 19-21, 34-43 where future parameters can be predicted being based on the historical data and the time schedule and see paragraph 65-72 where the sensor data can take road quality data and wheel speed sensor data, and in paragraph 70 can provide data to a database for improved future predictions of any operating parameter such as an actuator or emission or energy use)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the present disclosure to combine the teachings of JOBSON with the disclosure of RISTOVSKI since JOBSON teaches that one or more environmental sensor data points for vehicles can be collected and provides as historical data including various sensed parameters. These can include energy consumption, wheel speed, throttle data, actuator data sensors, camera sensors and lidar sensors. The vehicles can operate through zones via a schedule that is a time schedule. Based on the environmental restrictions and sensed values, the environment can be delayed due to road parameters, or delays in charging that can be observed by the vehicle agents. Then the future predictions can be made and then a time schedule can be updated. For example, a road may be very rough and causing delays and a battery charging line also may cause a delay. The vehicle and then be modulated to avoid the delays. See paragraph 1-29 and claims 1-18.
In regard to claim 4 and claim 14, Zheng teaches “..4. The method according to claim 1, further comprising:
comparing vehicle data of the new vehicle with the vehicle data of each vehicle of the plurality of vehicles; (see paragraph 60-64 and 66 and 72-76)
selecting a local-self learning model of one of the vehicles of the plurality of based on 20 the comparison and at least one predefined constraint; and (see paragraph 66 and 72-76)
implementing the selected local self-learning model into a new vehicle to be added to the plurality of vehicles”. (See paragraph 50-61 and 72-76)”
It would have been obvious for one of ordinary skill in the art before the effective filing date of the present disclosure to combine the teachings of ZHENG with the disclosure of RISTOVSKI since ZHENG teaches that one or more so called “events of interest” can be provided from the perception systems 140-1 to 140-n to the global model update cloud 160. This is from “real time sensors” that are active sensors 130-1 to 130n which are from a vehicle fleet that can include challenges or errors. See paragraph 76. For example, there can be different weather events that are encountered or different weather events and poor driving conditions or a significant error that is being encountered. Independently, events of interest for global model update are selected at 560. Once selected, the events of interest for global update are transmitted, at 580, to the global model update cloud 160. When the in situ perception system 110 receives, at 590, the updated model package from the global model update cloud 160, it updates the models stored locally accordingly. The updated model package may include information to update the global model 410 and/or any class models stored in class models 420 (see FIG. 4A). After the models are updated based on the received updated model package, the process goes back to 510 to continue the operation. This can provide an improved operation of the fleet based on real time improved models. See paragraph 70-81.
Ristovski discloses “...5. The method according to claim 1, wherein the vehicle data comprises at least one of a geographical position of each vehicle, an acceleration request of each vehicle, a brake
request of each vehicle, a cargo load of each vehicle, a transmission type of each vehicle, a state of charge of a traction battery of each vehicle, a state of health of the traction battery of each
vehicle, and an axle load of each vehicle” (see paragraph 17-21).
Jobson teaches “...6. The method according to claim 1, wherein the environmental data comprises at
least one of weather along each fixed route, route data of each fixed route, a road curvature of each fixed route, an inclination profile of each fixed route, operational data for each fixed route,
infrastructural data for each fixed route, a time of day, and calendar data. (see paragraph 18-29)”.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the present disclosure to combine the teachings of JOBSON with the disclosure of RISTOVSKI since JOBSON teaches that one or more environmental sensor data points for vehicles can be collected and provides as historical data including various sensed parameters. These can include energy consumption, wheel speed, throttle data, actuator data sensors, camera sensors and lidar sensors. The vehicles can operate through zones via a schedule that is a time schedule. Based on the environmental restrictions and sensed values, the environment can be delayed due to road parameters, or delays in charging that can be observed by the vehicle agents. Then the future predictions can be made and then a time schedule can be updated. For example, a road may be very rough and causing delays and a battery charging line also may cause a delay. The vehicle and then be modulated to avoid the delays. See paragraph 1-29 and claims 1-18.
Jobson teaches “..7. The method according to claim 1, wherein the schedule parameter is an arrival time to a destination, a fuel consumption, or a power consumption. (see paragraph 18-29).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the present disclosure to combine the teachings of JOBSON with the disclosure of RISTOVSKI since JOBSON teaches that one or more environmental sensor data points for vehicles can be collected and provides as historical data including various sensed parameters. These can include energy consumption, wheel speed, throttle data, actuator data sensors, camera sensors and lidar sensors. The vehicles can operate through zones via a schedule that is a time schedule. Based on the environmental restrictions and sensed values, the environment can be delayed due to road parameters, or delays in charging that can be observed by the vehicle agents. Then the future predictions can be made and then a time schedule can be updated. For example, a road may be very rough and causing delays and a battery charging line also may cause a delay. The vehicle and then be modulated to avoid the delays. See paragraph 1-29 and claims 1-18.
Ristovski discloses “..8. The method according to claim 1, wherein the vehicle data and/or the environmental data is retrieved from each vehicle of the plurality of vehicles”. (see paragraph 32).
JOBSON teaches “...9. The method according to claim 1, wherein the vehicle data and/or the environmental data is retrieved from a data storage unit connected to the plurality of vehicles. .(see paragraph 72)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the present disclosure to combine the teachings of JOBSON with the disclosure of RISTOVSKI since JOBSON teaches that one or more environmental sensor data points for vehicles can be collected and provides as historical data including various sensed parameters. These can include energy consumption, wheel speed, throttle data, actuator data sensors, camera sensors and lidar sensors. The vehicles can operate through zones via a schedule that is a time schedule. Based on the environmental restrictions and sensed values, the environment can be delayed due to road parameters, or delays in charging that can be observed by the vehicle agents. Then the future predictions can be made and then a time schedule can be updated. For example, a road may be very rough and causing delays and a battery charging line also may cause a delay. The vehicle and then be modulated to avoid the delays. See paragraph 1-29 and claims 1-18.
Jobson teaches “...10. A non-transitory computer-readable storage medium storing one or more programs configured to be executed by one or more processors of a vehicle fleet management system, the one or more programs comprising instructions for performing the method according to claim 1. (See paragraph 67-72)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the present disclosure to combine the teachings of JOBSON with the disclosure of RISTOVSKI since JOBSON teaches that one or more environmental sensor data points for vehicles can be collected and provides as historical data including various sensed parameters. These can include energy consumption, wheel speed, throttle data, actuator data sensors, camera sensors and lidar sensors. The vehicles can operate through zones via a schedule that is a time schedule. Based on the environmental restrictions and sensed values, the environment can be delayed due to road parameters, or delays in charging that can be observed by the vehicle agents. Then the future predictions can be made and then a time schedule can be updated. For example, a road may be very rough and causing delays and a battery charging line also may cause a delay. The vehicle and then be modulated to avoid the delays. See paragraph 1-29 and claims 1-18.
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 §§ 706.02(l)(1) - 706.02(l)(3) 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 USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-14 are rejected under obviousness double patenting in view of claim 1-14 of U.S. Patent No.: 12198080 that recites “ [a] method for operating a plurality of vehicles, each vehicle comprising
an associated local self-learning model and wherein the plurality of vehicles are connected to a global self-learning model, said method comprising:
receiving a first set of vehicle data and a second set of vehicle data, the vehicle data comprising information about each vehicle of the plurality of vehicles, each vehicle operating along at least one fixed route;
receiving a first set of environmental data and a second set of environmental data, the environmental data comprising information about each fixed route;
estimating, by means of the global self-learning model and each local-self learning model, a schedule parameter for each vehicle of the plurality of vehicles based on the received first set of vehicle data, the received first set of environmental data, the received second set of
vehicle data, the received second set of environmental data, and a predefined interaction model between the global self-learning model and each local-self learning model;
receiving the estimated schedule parameter for each vehicle;
receiving a measured schedule parameter for each vehicle;
comparing the estimated schedule parameter with the received measured schedule parameter;
updating the global self-learning model and each local self-learning model based on the comparison of the estimated schedule parameter with the received measured schedule parameter;
[[and]]
operating the plurality of vehicles based on the estimated schedule parameters; wherein
the schedule parameter is a fuel consumption or a power consumption[[.]L
comparing vehicle data of a new vehicle to be added to the plurality of vehicles with the vehicle data of each vehicle of the plurality of vehicles;
selecting a new local self-learning model of the new vehicle based on the comparison and at least one predefined constraint, such that the new local self-learning model comprises a combination of one or more local self-learning models of the plurality of vehicles; and
implementing the new local self-learning model into the new vehicle based on the selection. .... ”.
The only feature different in claim of the present claims is it recites causing selecting a new self-learning model that is local of the vehicle based on a constraint. These are obvious variants to select a new model based on a faster resolution or a new model based on the desire of the operator. Zheng teaches selecting new models into a global model. See elements 410 and 420.
The claims are otherwise identical.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JEAN PAUL CASS whose telephone number is (571)270-1934. The examiner can normally be reached Monday to Friday 7 am to 7 pm; Saturday 10 am to 12 noon.
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/JEAN PAUL CASS/Primary Examiner, Art Unit 3666