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 .
Claim Status
This action is in response to the application filed on 11/15/2024. Claims 1-20 are pending and examined below.
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.
Claims 1-5, 7-12, and 14-19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 20190205842 A1 (“Starns”).
Regarding claim 1, Starns teaches receiving, at one or more processors, trip data of an EV, the trip data including ;predicting, at the one or more processors, that the EV will have a power level below a threshold power level in a location an area along a route of the EV based on the trip data (see at least [0027]);
identifying, at the one or more processors, a mobile EV charging station of a plurality of mobile EV charging stations based on one or more factors (see at least [0031])
generating, at the one or more processors, one or more driving instructions for the mobile EV charging station; and transmitting, via a communication network, the one or more driving instructions to at least one of a mobile computing device or the mobile EV charging station to cause deployment of the mobile EV charging station to the location (see at least [0048]).
Regarding claim 2, Starns teaches the trip data includes at least one of a remaining range of the EV or the route (see at least [0031]).
Regarding claim 3, Starns teaches the one or more factors include at least one of EV charging station data, a distance between each of the plurality of mobile EV charging stations and the location, how much charge remains in each of the plurality of mobile EV charging stations, or one or more environmental factors (see at least [0031])
Regarding claim 4, Starns teaches the one or more environmental factors include traffic between each of the plurality of mobile EV charging stations and the location (see at least[0048]).
Regarding claim 5, Starns teaches the one or more driving instructions include at least one of a GPS navigation instruction or an automated driving instruction (see at least [0031]).
Regarding claim 7, Starns teaches determining the location using at least one of a distance between each of the plurality of mobile EV charging stations and the location, how much charge remains in each of the plurality of mobile EV charging stations, or traffic between each of the plurality of mobile EV charging stations and the location (see at least [0048]).
Regarding claim 8, Starns teaches at least one processor; and a memory storing instructions, which when executed by the at least one processor, cause the system to: receive trip data of an EV ;predict that the EV will have a power level below a threshold power level in a location along a route of the EV based on the trip data (see at least [0027]);
identify a mobile EV charging station of a plurality of mobile EV charging stations based on one or more factors; generate one or more driving instructions for the mobile EV charging station (see at least [0031]); and
transmit the one or more driving instructions to at least one of a mobile computing device or the mobile EV charging station to cause the mobile EV charging station to move to the location (see at least [0045]).
Regarding claim 9, Starns teaches the trip data includes at least one of a range of the EV or the route (see at least [0031]).
Regarding claim 10, Starns teaches the one or more factors include at least one of EV charging station data, a distance between each of the plurality of mobile EV charging stations and the location, how much charge remains in each of the plurality of mobile EV charging stations, or one or more environmental factors (see at least [0031]).
Regarding claim 11, Starns teaches the one or more environmental factors include traffic between each of the plurality of mobile EV charging stations and the location (see at least [0048]).
Regarding claim 12, Starns teaches the one or more driving instructions include at least one of a GPS navigation instruction or an automated driving instruction (see at least [0031]).
Regarding claim 14, Starns teaches determine the location using at least one of a distance between each of the plurality of mobile EV charging stations and the location, how much charge remains in each of the plurality of mobile EV charging stations, or traffic between each of the plurality of mobile EV charging stations and the location (see at least [0048])
Regarding claim 15, Starns teaches the computer process comprising: receiving trip data of an Depredating that the EV will have a power level below a threshold power level in a location along a route of the EV based on the trip data (see at least [0027]);
identifying a mobile EV charging station of a plurality of mobile EV charging stations based on one or more factors (see at least [0031]); and
generating one or more driving instructions for the mobile EV charging station to direct the mobile EV charging station to move to the location (see at least [0048]).
Regarding claim 16, Starns teaches the trip data includes at least one of a range of the EV or the route (see at least [0031])
Regarding claim 17, Starns teaches the one or more factors include at least one of EV charging station data, a distance between each of the plurality of mobile EV charging stations and the location, how much charge remains in each of the plurality of mobile EV charging stations, or one or more environmental factors (see at least[0031])
Regarding claim 18, Starns teaches the one or more environmental factors include traffic between each of the plurality of mobile EV charging stations and the location (see at least [0048]).
Regarding claim 19, Starns teaches at least one of the plurality of mobile EV charging stations is an autonomous vehicle (see at least 0031]).
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.
Claims 6, 13, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over US 20190205842 A1 (“Starns”) in view of US 20240017628 A1 (“Woods”).
Regarding claims 6, 13, and 20 Starns is not explicit on at least one of the plurality of mobile EV charging stations is an autonomous vehicle, however,
Woods discloses at least one of the plurality of mobile EV charging stations is an autonomous vehicle (see at least [0044]).
One of ordinary skill in the art would have been motivated to combine the system disclosed by Starns with the system disclosed by Woods because there are not as many charging stations (e.g., quick-charge or fast-charge charging stations) currently available as desired, and a range such a vehicle can travel (which sometimes may not be sufficient to go from one location to another) may drop even further when, e.g., a trailer is attached to the vehicle resulting in a heavier load (Woods, [0003])
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATHEW FRANKLIN GORDON whose telephone number is (408)918-7612. The examiner can normally be reached Monday - Friday, 7:00 - 5:00 PST.
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/MATHEW FRANKLIN GORDON/Primary Examiner, Art Unit 3665