DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Drawings
The drawings are objected to because boxes 6, 8 and 10 are not readily understood by looking at Fig. 1. It is requested that applicant add a text description, in addition to the use of reference numbers, 6, 8 and 10, for these boxes in order to provide a drawing that is more helpful and readily understood to a viewer of the figure. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 2, 4, and 6-10 are is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Starns (US 2019/0205842 A1).
Regarding claim 1, Starns teaches: A method for controlling a charging infrastructure including spatially distributed electrical charging stations (service facilities 200, including charging stations 202, see Fig. 2A and para. 0019) configured to be used by multiple vehicles (autonomous vehicles (AVs) 140) of a fleet which are powered by drive batteries, the method comprising: using a control system (AV computing environment 400, transportation management system 410, autonomous vehicle computing device 450, service facility management 460, Fig. 4 and para. 0029), wherein the control system encompasses a charging stations database (service facility data store 423), containing information about the availability of the electrical charging stations (see para. 0036), a drive batteries database (logistics module 417 and/or service facility logistics 462), containing information about states of charge of the drive batteries of the vehicles of the fleet (see para. 0035, 0043), and a fleet navigation system (route selection module 412, traffic pattern data store 418, road condition data store 420, autonomous route data store 422), containing information about the spatial distribution of the vehicles of the fleet and information about respective traffic situations in which the vehicles of the fleet are involved (see para. 0031, 0032, 0036, 0037), determining positions of vehicles of the fleet in a defined region (see para. 0031, 0032, 0036, 0037, and Fig. 5), determining states of charge of drive batteries of the vehicles of the fleet in the defined region (see para. 0035, 0043, and Fig. 5), and based on traffic situations in the defined region (see para. 0029, 0031), on availability of free charging stations and their respective positions in the defined region (see para. 0036, 0037), on positions and states of charge of drive batteries of the vehicles of the fleet in the defined region (see para. 0035, 0042, 0043), and on the respective traffic situations in the region (see para. 0029, 0031, 0047), automatically determining, by the control system, for at least one vehicle in the defined region, a charging station to carry out a charging process for the drive battery of the at least one vehicle (see the abstract, para. 0036-0039, 0049-0055, and Fig. 5).
Regarding claim 2, Starns teaches: The method according to claim 1, wherein positions of all other vehicles of the fleet which are located in the defined region around the position of the at least one vehicle are determined (each AV 140 will send location data to the transportation management system 410, see para. 0031, 0032, 0036, 0037), and the at least one charging station is determined based on the traffic situation existing between the position of the at least one vehicle and the at least one charging station in the defined region (see para. 0029, 0031, 0036, 0047).
Regarding claim 4, Starns teaches: The method according to claim 1, wherein the at least one vehicle is navigated with the fleet navigation system to the at least one proposed charging station (see para. 0038, 0048, 0054).
Regarding claim 6, Starns teaches: The method according to claim 1, wherein the control system is self-learning (machine learning, see para. 0041)
Regarding claim 7, Starns teaches: The method according to claim 1, wherein the duration (see para. 0042) of the charging process and the quantity of electrical energy provided to the drive battery (type of battery charging available (e.g., standard charger, fast charger, etc.), see para. 0036) of the at least one vehicle during the at least one charging process are taken into account in determining the charging station to carry out the charging process for the drive battery of the at least one vehicle (also see para. 0051, 0052).
Regarding claim 8, Starns teaches: The method according to claim 1, wherein the probability of observing a deadline (to be ready at a specific time, see para. 0019; predicted amounts of wait time and predicted amounts of time for service work, see para. 0040) for at least one charging process with the at least one determined charging station by the at least one vehicle is taken into account in determining the charging station to carry out the charging process for the drive battery of the at least one vehicle (also see para. 0036-0039, 0049-0055, and Fig. 5).
Regarding claim 9, Starns teaches: The method according to claim 1, wherein current positions of vehicles of the fleet and the current traffic situation are detected by the fleet navigation system (see para. 0031, 0032, 0036, 0037) and future positions (for example, requestor location information, pick-up location and travel data represent future positions of the AVs, see para. 0031), of vehicles of the fleet and a future traffic situation are predicted by the fleet navigation system (for example, a predicted surge in ride requests, see para. 0019 and 0041, represents a future traffic situation), wherein current states of charge of the drive batteries of the vehicles are ascertained by the drive batteries database and future states of charge of the drive batteries of the vehicles are predicted by the drive batteries database (see para. 0035 and 0042), and/or wherein the current availability of the charging stations is ascertained by the charging stations database and a future availability of the charging stations is predicted by the charging stations database (current status or anticipated future status of the identified service facilities, see para. 0037).
Note that with the use of “and/or” in line 6, of claim 9, Starns can be considered as meeting the requirements of claim 9 by teaching at least the limitations after “and/or”.
Regarding claim 10, Starns teaches: A control system (AV computing environment 400, transportation management system 410, autonomous vehicle computing device 450, service facility management 460, Fig. 4 and para. 0029) for controlling a charging infrastructure including spatially distributed electrical charging stations (service facilities 200, including charging stations 202, see Fig. 2A and para. 0019) configured to be used by multiple vehicles (autonomous vehicles (AVs) 140) of a fleet which are powered by drive batteries, the control system comprising: a charging stations database (service facility data store 423), containing information about the availability of the electrical charging stations (see para. 0036), a drive batteries database (logistics module 417 and/or service facility logistics 462), containing information about states of charge of the drive batteries of the vehicles of the fleet (see para. 0035 and 0042), and a fleet navigation system (route selection module 412, traffic pattern data store 418, road condition data store 420, autonomous route data store 422), containing information about the spatial distribution of the vehicles of the fleet and information about respective traffic situations in which the vehicles of the fleet are involved (see para. 0031, 0032, 0036, 0037), wherein the fleet navigation system is adapted to determine positions of vehicles of the fleet in a defined region (see para. 0032, 0036, 0037, and Fig. 5), wherein the drive batteries database is adapted to determine states of charge of drive batteries of the vehicles of the fleet in the defined region (see para. 0035, 0043, and Fig. 5), wherein the fleet navigation system is adapted to take into account traffic situations in the defined region (see para. 0029, 0031), wherein the charging stations database is adapted to determine automatically, depending on the availability of free charging stations and their respective positions in the defined region (see para. 0036, 0037), depending on positions and states of charge of drive batteries of the vehicles of the fleet in the defined region (see para. 0035, 0042, 0043), and depending on the respective traffic situations in the defined region (see para. 0029, 0031, 0047), for at least one vehicle in the defined region, a charging station to carry out a charging process for the drive battery of the at least one vehicle (see the abstract, para. 0036-0039, 0049-0055, and Fig. 5).
Claim Rejections - 35 USC § 103
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.
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Starns (US 2019/0205842 A1) in view of Maeda et al (US 12,202,372 B2).
Regarding claim 3, the teachings of Starns, as applied to claim 1, have been discussed above.
Starns does not specifically teach wherein the at least one charging station so determined is reserved for the at least one vehicle.
Maeda et al teaches a system and method for identifying and selecting a charging station for a vehicle and reserving the selected charging station for the vehicle (see Fig. 6 and col. 26, line 40 – col. 27, line 30 and col. 28, lines 3-27).
In view of Maeda et al’s teachings, it would have been obvious to one of ordinary skill in the art prior to the effective filing date, to include with the method of Starns, wherein the at least one charging station so determined is reserved for the at least one vehicle, in order to ensure that the charging station will be available for the vehicle when it arrives.
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Starns (US 2019/0205842 A1) in view of Li (CN 110696627 A).
Regarding claim 5, the teachings of Starns, as applied to claim 1, have been discussed above.
Starns does not specifically teach wherein one energy saving mode among several energy saving modes is selected and established for the at least one vehicle, and the energy saving mode is taken into account in determining the charging station to carry out the charging process for the drive battery of the at least one vehicle.
Li teaches a method for determining a charging location for a vehicle, including determining vehicle control state information to determine a travel distance and whether the vehicle can reach the target charging location (see the description of step 204, bridging pages 7-8 of the attached translation). Li teaches that the vehicle control state information can include a driving mode, such as economy mode, sport mode, standard mode, mud mode, land mode, and so on. (see paragraphs 3-5 on page 8 of the attached translation). Li can be considered as teaching several energy saving modes, since economy mode and standard mode can be considered energy saving modes, compared to sport mode.
In view of Li’s teachings, it would have been obvious to one of ordinary skill in the art prior to the effective filing date, to include with the method of Starns, wherein one energy saving mode among several energy saving modes is selected and established for the at least one vehicle, and the energy saving mode is taken into account in determining the charging station to carry out the charging process for the drive battery of the at least one vehicle, since this would provide a more accurate prediction of whether the vehicle can reach the determined charging location (see the description of steps 201-205 on pages 8-10 of the attached translation).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Donnelly et al (US 11,820,246 B2) teaches a charge control system for a vehicle fleet.
Ludwick et al (US 11,168,995 B2) teaches a system for managing a fleet of vehicles, including electric vehicles.
Chase et al (US 10,829,000 B2) teaches a control system for intelligent vehicle fleet charging.
Lindemann et al (US 10,099,569 B2) teaches an adaptive system and method for optimizing a fleet of electric vehicles.
McGrath et al (US 10,017,068 B2) teaches a method for charging a fleet of electric vehicles.
Please also see the additional references cited on the attached PTO-892, which are related to electric vehicle charging and/or operation.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jared Fureman whose telephone number is (571)272-2391. The examiner can normally be reached M-F 8:30 am - 5:00 pm.
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/JARED FUREMAN/Primary Examiner, Art Unit 2859