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 Objections
Claims 1-11, and 14-20 are objected to because of the following informalities:
Regarding Claim 1, “A system, comprising: one or more processors, coupled with memory, to:”
should read “A system, comprising: one or more processors, coupled with memory, configured to:”
Regarding Claims 2-11, “comprising the one or more processors to:” should read “wherein the one or more processors are further configured to:” or similar language.
Regarding Claims 14-17, “The method of claim 13, comprising:” should read “The method of claim 13, further comprising:”
Regarding Claim 18, “A vehicle, comprising: one or more processors, coupled with memory, to:” should read “A vehicle, comprising: one or more processors, coupled with memory, configured to:”
Regarding Claims 19-20, “comprising the one or more processors to:” should read “wherein the one or more processors are further configured to:” or similar language.
Appropriate correction is required.
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.
Claim 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1 and 18 recite a system which is a statutory category of invention. Claim 13 recites a method which is a statutory category of invention.
Step 2A, Prong 1: Exemplary Claim 1 recites “one or more processors to receive data regarding charging sessions of electric vehicles”, “generate a score for each of the plurality of chargers”, and “update a user interface to depict the path including the location” which are mental observations or evaluations and fall within the "mental processes" grouping of abstract ideas set forth in the 2019 PEG. 2019 PEG Section I, 84 Fed. Reg. at 52
Step 2A, Prong 2: Claim 1 recites one or more processors and a user interface which indicate a field of use or technological environment in which to apply a judicial exception and do not integrate a judicial exception into a practical application.
Step 2B: Claim 1 does not include additional elements when considered individually and/or as an ordered combination that are sufficient to amount to significantly more than the abstract idea. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, are not sufficient to amount to significantly more, as discussed in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984 (see MPEP $22 2106.05(f));
The one or more processors, user interface, and electric vehicle are well understood, routine and conventional in the field of electric vehicle charging. For example, Wu et al. (US 20240294088 A1) describes a processor (1004), electric vehicle (102), and user interface (800). Wu et al. is evidence that such components are well understood, routine, and conventional.
Dependent claims 2-12 fail to cure this deficiency of independent claim 1 (set forth above) and are rejected accordingly. Claims 2-12 recite limitations that represent (in addition to the limitations already noted above) either the abstract idea or an additional element that is merely extra-solution activity, mere use of instructions and/or generic computer component(s) as a tool to implement the abstract idea, and/or merely limits the abstract idea to a particular technological environment.
Independent claim 13 and dependent claims 14-17 are rejected for the same reasons as claim 1.
Independent claim 18 and dependent claims 19-20 are rejected for the same reasons as claim 1.
Claim Rejections - 35 USC § 102
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 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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-4, 7-8, 10-16 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Wu et al. (US 20240294088 A1).
Regarding Claim 1, Wu teaches a system (Fig. 10), comprising:
one or more processors (1004), coupled with memory (1006) (¶[121] “FIG. 10 illustrates an example computing device 1002 for customer-centric dynamic charging station assessment for vehicles” ¶[122] “As shown, the computing device 1002 may include a processor 1004 that is operatively connected to a storage 1006”), to:
receive data regarding charging sessions of electric vehicles at a plurality of chargers (¶[21] “The charger service 122 may also use the raw vehicle data 116 to generate charging station scores 126 descriptive of ratings of the charging stations 114. The user weights 124 and the charging station scores 126 may accordingly be used to provide charger recommendations 132” see also ¶[46] “The reliability scores 302 may be based on previous customer charging success or lack of success rates”);
generate a score for each of the plurality of chargers based at least on the data regarding the charging sessions (see ¶[21] and ¶[46] quoted above, and Fig. 8);
generate a path from a current location of a vehicle to a destination that includes a location for each of at least one of the plurality of chargers selected at least based on the scores (¶[109] “In the navigation mode, the main screen area 808 illustrates a map 810 of the surroundings of the vehicle 102. The location of the vehicle 102 itself is shown as vehicle indication 812. Moreover, as the user is seeking a charging station 114, the map 810 further illustrates the locations of a plurality of available chargers charging stations 114” see Fig. 8, the destination may be the charging station); and
update a user interface (¶[108] “FIG. 8 illustrates an example HMI 800 of a navigation view of the vehicle 102”) to depict the path including the location for each of the at least one of the plurality of chargers (¶[108] “a navigation screen (which is selected) from which maps and routing may be performed,”).
Regarding Claim 2, Wu teaches the system of claim 1.
Wu further teaches comprising the one or more processors to:
receive geolocation data of the charging sessions at the plurality of chargers (¶[36] “The charger service 122 may correlate the raw vehicle data 116 into charging visit (CV) records 204. Each CV record 204 may include one or more EV events that occurred within a specific date and time window identified as a visit arrival and departure using the vehicle 102 odometer as a key aggregator criterion … The CV record 204 may also be correlated with other information, such as a location determined from the GNSS controller 108 of the vehicle 102”); and
determine the location for each of the at least one of the plurality of chargers based on the geolocation data of charging sessions at the at least one charger (¶[51] “The relative locations of charging stations 114 may be determined based on a public charger locations database 318. The public charger locations database 318 may include a listing of the charging station 114 indexed by geographic location … ¶[53] Thus, this CACS record 310 may be associated with the charging station 114C as a location-associated CACS record 314.”).
Regarding Claim 3, Wu teaches the system of claim 1, comprising the one or more processors to:
aggregate the data regarding the charging session into a plurality of metrics for each of one or more of the plurality of chargers, the plurality of metrics comprising actual delivered power for a given temperature or an average time to begin a charging session (¶[56] “the CSRS processing 316 may be configured to utilize a CSRS algorithm to generate the reliability scores 302 for the charging stations 114 using the filtered CACS records 310 that are associated with the charging stations 114. As shown in FIG. 5, the CSRS algorithm may include an assign reliability score operation 502, an aggregate reliability score operation 504, and an define reliability score confidence operation 506”); and
generate the score for each of the one or more of the plurality of chargers based at least on the plurality of metrics aggregated for the charger (see Figs 5-6, ¶[77] “Referring back to FIG. 5, the aggregate reliability score operation 504 may include aggregating the base probability score 508 over time. For instance, an exponential moving average (EMA) may be used to aggregate the base probability scores 508 over time to obtain a more accurate and stable estimate of the reliability of the charging stations 114. The EMA may give more weight to recent data and less weight to older data, which can help to smooth out fluctuations in the base probability scores 508 and provide a more stable estimate over time. This estimate over time may be referred to herein as the reliability scores 302”).
Regarding Claim 4, Wu teaches the system of claim 1.
Wu further teaches comprising the one or more processors (1004) to:
generate the score for each of the plurality of chargers based at least on the data regarding the charging sessions and specifications for the plurality of chargers (¶[113] “At operation 906, the charger service 122 updates the charging station scores 126 … In other examples, the charger service 122 may updates other charging station scores 126 based on the vehicle data 116, such as cleanliness of the facility, available POIs, available amenities, competitive price or savings, fast speeds, and/or well-lit conditions at the charging location”).
Regarding Claim 7, Wu teaches the system of claim 1.
Wu further teaches comprising the one or more processors (1004) to:
display visual indicators for one or more of the plurality of chargers (114A-C) on the user interface (800, see Fig. 8); and
adjust the visual indicators for the one or more chargers proportional to the scores for the at least one charger (814A-C) (¶[109] “the map 810 further illustrates the locations of a plurality of available chargers charging stations 114. For each charging station 114, a callout 814 is provided including the user-specific charger score 128 for that respective charging station 114. The user-specific charger scores 128 may serve to inform the user of the desirability of the charging station 114 in terms of the user's specific requirements. For instance, for the charging station 114A, a callout 814A shows a recommendation score of 4.2, for the charging station 114B, a callout 814B shows a recommendation score of 4.88, and for the charging station 114C, a callout 814C shows a recommendation score of 3.8”).
Regarding Claim 8, Wu teaches the system of claim 1.
Wu further teaches comprising the one or more processors (1004) to:
generate a health score (reliability score) for each of the plurality of chargers based at least on the data regarding the charging sessions (¶[46] “The reliability scores 302 may be based on previous customer charging success or lack of success rates. Charging locations with lower scores may require more charge attempts or require manual intervention to begin a charging session. Very low scores may indicate that the vehicle 102 were unable to charge at the charging station 114”); and
select the at least one charger based on the health scores (¶[108] “a navigation screen (which is selected) from which maps and routing may be performed” a charger needs to be selected to be navigated to).
Regarding Claim 10, Wu teaches the system of claim 1.
Wu further teaches comprising the one or more processors (1004) to:
store the scores for the plurality of chargers in the memory (¶[3] “a storage configured to maintain, for each of a plurality of charging stations, charging station scores indicating ratings of properties of the charging stations”);
subsequent to storing the scores in the memory, receive, from the vehicle, a request to generate the path (¶[3] “The system further includes a processor configured to receive a charger request from a vehicle, the charger request including an identifier of a sender of the charger request and a location of the vehicle”)
responsive to receiving the request, retrieve the scores for the plurality of chargers from the memory (¶[3] “identify one or more charging stations in proximity to the location of the vehicle, for each identified charging station, compute a user-specific charger score using the plurality of charging station scores for the charging station weighted according to the user weights corresponding to the identifier”); and
generate the path using the retrieved scores for the plurality of chargers (¶[3] “send a charger recommendation to the vehicle responsive to the charger request, the charger recommendation including, for each of the one or more charging stations, a location of the charging station and the user-specific charger score corresponding to the charging station”, see also “a navigation screen (which is selected) from which maps and routing may be performed”).
Regarding Claim 11, Wu teaches the system of claim 1.
Wu further teaches wherein the scores are first scores and the data regarding charging sessions of electric vehicles at the plurality of chargers is first data regarding charging sessions of electric vehicles at the plurality of chargers (¶[111] “the charger service 122 determines whether to update the user weights 124 and/or the charging station scores 126 based on the receipt of new raw vehicle data 116 is received”), and comprising the one or more processors to:
receive, during a first time period, the first data regarding charging sessions of electric vehicles at the plurality of chargers (¶[36] “The charger service 122 may correlate the raw vehicle data 116 into charging visit (CV) records 204”);
generate the first score for each of the plurality of chargers based at least on the first data regarding the charging sessions (¶[21] “The charger service 122 may also use the raw vehicle data 116 to generate charging station scores 126 descriptive of ratings of the charging stations 114. The user weights 124 and the charging station scores 126 may accordingly be used to provide charger recommendations 132”);
store the first scores for the plurality of chargers in the memory (¶[3] “a storage configured to maintain, for each of a plurality of charging stations, charging station scores indicating ratings of properties of the charging stations”);
receive, during a second time period subsequent to the first time period (¶[111] “the charger service 122 may perform updates to the user weights 124 and/or the charging station scores 126 periodically, such as daily or weekly”), second data regarding charging sessions of electric vehicles at the plurality of chargers;
generate a second score for each of the plurality of chargers based at least on the second data regarding the charging sessions (see ¶[21] quoted above); and
replace the first scores for the plurality of chargers in the memory with the second scores for the plurality of chargers in the memory (see ¶[111] and ¶[3] quoted above).
Regarding Claim 12, Wu teaches the system of claim 1.
Wu further teaches wherein the data regarding the charging sessions comprises, for a charging session of the charging sessions, geolocation data (¶[36] “The charger service 122 may correlate the raw vehicle data 116 into charging visit (CV) records 204 … The CV record 204 may also be correlated with other information, such as a location determined from the GNSS controller 108 of the vehicle 102”), an indication of whether the charging session succeeded or failed (¶[46] “The reliability scores 302 may be based on previous customer charging success or lack of success rates. Charging locations with lower scores may require more charge attempts or require manual intervention to begin a charging session. Very low scores may indicate that the vehicle 102 were unable to charge at the charging station 114”), temperature sensor data, or an amount of current that was delivered.
Regarding Claim 13, Wu teaches a method, comprising:
receiving, by one or more processors (1004) (¶[121] “FIG. 10 illustrates an example computing device 1002 for customer-centric dynamic charging station assessment for vehicles”), data regarding charging sessions of electric vehicles at a plurality of chargers (¶[21] “The charger service 122 may also use the raw vehicle data 116 to generate charging station scores 126 descriptive of ratings of the charging stations 114. The user weights 124 and the charging station scores 126 may accordingly be used to provide charger recommendations 132” see also ¶[46] “The reliability scores 302 may be based on previous customer charging success or lack of success rates”);
generating, by the one or more processors, a score for each of the plurality of chargers based at least on the data regarding the charging sessions (see ¶[21] and ¶[46] quoted above, and Fig. 8);
generating, by the one or more processors, a path from a current location of a vehicle to a destination that includes a location for each of at least one of the plurality of chargers selected at least based on the scores (¶[109] “In the navigation mode, the main screen area 808 illustrates a map 810 of the surroundings of the vehicle 102. The location of the vehicle 102 itself is shown as vehicle indication 812. Moreover, as the user is seeking a charging station 114, the map 810 further illustrates the locations of a plurality of available chargers charging stations 114” see Fig. 8); and
updating, by the one or more processors, a user interface (¶[108] “FIG. 8 illustrates an example HMI 800 of a navigation view of the vehicle 102”) to depict the path including the location for each of the at least one of the plurality of chargers (¶[108] “a navigation screen (which is selected) from which maps and routing may be performed”).
Regarding Claim 14, Wu teaches the method of claim 13.
Wu further teaches comprising: receiving, by the one or more processors, geolocation data of the charging session at the plurality of chargers (¶[36] “The charger service 122 may correlate the raw vehicle data 116 into charging visit (CV) records 204. Each CV record 204 may include one or more EV events that occurred within a specific date and time window identified as a visit arrival and departure using the vehicle 102 odometer as a key aggregator criterion … The CV record 204 may also be correlated with other information, such as a location determined from the GNSS controller 108 of the vehicle 102”); and
determining, by the one or more processors, the location for each of the at least one of the plurality of chargers based on the geolocation data of charging sessions at the at least one charger (¶[51] “The relative locations of charging stations 114 may be determined based on a public charger locations database 318. The public charger locations database 318 may include a listing of the charging station 114 indexed by geographic location … ¶[53] Thus, this CACS record 310 may be associated with the charging station 114C as a location-associated CACS record 314.”).
Regarding Claim 15, Wu teaches the method of claim 13.
Wu further teaches comprising: aggregating, by the one or more processors, the data regarding the charging session into a plurality of metrics for each of one or more of the plurality of chargers, the plurality of metrics comprising actual delivered power for a given temperature, an average time to begin a charging session, or a health of a charger (¶[56] “the CSRS processing 316 may be configured to utilize a CSRS algorithm to generate the reliability scores 302 for the charging stations 114 using the filtered CACS records 310 that are associated with the charging stations 114. As shown in FIG. 5, the CSRS algorithm may include an assign reliability score operation 502, an aggregate reliability score operation 504, and an define reliability score confidence operation 506”); and
generating, by the one or more processors, the score for each of the one or more of the plurality of chargers based at least on the plurality of metrics aggregated for a charger (see Figs 5-6, ¶[77] “Referring back to FIG. 5, the aggregate reliability score operation 504 may include aggregating the base probability score 508 over time. For instance, an exponential moving average (EMA) may be used to aggregate the base probability scores 508 over time to obtain a more accurate and stable estimate of the reliability of the charging stations 114. The EMA may give more weight to recent data and less weight to older data, which can help to smooth out fluctuations in the base probability scores 508 and provide a more stable estimate over time. This estimate over time may be referred to herein as the reliability scores 302”).
Regarding Claim 16, Wu teaches the method of claim 13.
Wu further teaches generating, by the one or more processors, the score for each of the plurality of chargers based at least on the data regarding the charging sessions and specifications for the plurality of chargers (¶[113] “At operation 906, the charger service 122 updates the charging station scores 126 … In other examples, the charger service 122 may updates other charging station scores 126 based on the vehicle data 116, such as cleanliness of the facility, available POIs, available amenities, competitive price or savings, fast speeds, and/or well-lit conditions at the charging location”).
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.
Claim(s) 5-6 is rejected under 35 U.S.C. 103 as being unpatentable over Wu et al. (US 20240294088 A1) in view of Maeda et al. (US 20230039307 A1).
Regarding Claim 5, Wu teaches the system of claim 1.
Wu does not explicitly teach comprising the one or more processors to:
determine an added time for each of the plurality of chargers based at least on the score of the charger; and select the at least one of the plurality of chargers based on the added times.
Maeda teaches comprising the one or more processors to: determine an added time for each of the plurality of chargers based at least on the score of the charger; and select the at least one of the plurality of chargers based on the added times (¶[56] “The charging station map user interface 500 may present the user 122 with candidate reservations, such as a first candidate reservation 502, a second candidate reservation 504, and a third candidate reservation 506. The candidate reservations 502, 504, and 506 may also include reservation information such as the arrival time, wait time before charging, and a finish time. For example, the first candidate reservation 502 may begin at 10:06 AM with a ten-minute wait and have expected finish time of 10:35 AM” see also Fig. 5 and ¶[57]).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Wu to incorporate the teachings of Maeda to provide comprising the one or more processors to:
determine an added time for each of the plurality of chargers based at least on the score of the charger; and select the at least one of the plurality of chargers based on the added times in order to reduce the overall time for the user.
Regarding Claim 6, Wu teaches the system of claim 1.
Wu does not explicitly teach comprising the one or more processors to:
select the at least one of the plurality of chargers based at least on the scores and average amounts of current delivered during the charging sessions.
Maeda teaches comprising the one or more processors to:
select the at least one of the plurality of chargers based at least the average amounts of current delivered during the charging sessions (¶[38] “the charging station computing infrastructure 118 may receive perspective and/or real-time price data that may be provided by each respective charging stations 112 to communicate different charging rates. The perspective and/or real-time price data may include charging rates during a certain period of time (e.g., hourly, daily, weekly), charging rates to charge the EV 102 at various charging speeds (e.g., conventional electric vehicle charging speed, fast electric vehicle charging speed, charging power levels), charging rates that may be based on a customer rating that may be applied to a user 122 associated with the EV 102, and/or charging rates that may be applied to the user 122 of the EV 102 based on one or more compensation offers (e.g., incentives, discounts, credits, etc.) that may be provided to the user 122”).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Wu to incorporate the teachings of Maeda to provide comprising the one or more processors to: select the at least one of the plurality of chargers based at least the average amounts of current delivered during the charging sessions in order to improve the user convenience by choosing a charging station with a larger average current which results in a shorter charging time. The combination of Wu and Maeda teaches comprising the one or more processors to: select the at least one of the plurality of chargers based at least on the scores and average amounts of current delivered during the charging sessions.
Claim(s) 9 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Wu et al. (US 20240294088 A1) in view of North et al. (US 20150226572 A1).
Regarding Claim 9, Wu teaches the system of claim 1.
Wu does not explicitly teach comprising the one or more processors to:
identify a subset of the plurality of chargers based on the subset being between the current location of the vehicle and the destination; and
select the at least one of the plurality of chargers based at least on the scores of the subset of the plurality of chargers.
North teaches to identify a subset of the plurality of chargers based on the subset being between the current location of the vehicle and the destination; and select the at least one of the plurality of chargers based at least on the scores of the subset of the plurality of chargers (¶[45] “The station selection module 260 may identify and select a charging station that is within a travel range available to the electric vehicle 130 based on the low level of charge and is proximate to the route currently traveled by the electric vehicle to the destination” ¶[46] “The station selection module 260, may, in some embodiments, access information associated with other electric vehicles, and utilize the accessed information when selecting a charging station that is a best match for the electric vehicle 130”).
It would be obvious to one of ordinary skill in the art to before the effective filing date of the claimed invention to have modified Wu to incorporate the teachings of North to provide to identify a subset of the plurality of chargers based on the subset being between the current location of the vehicle and the destination; and select the at least one of the plurality of chargers based at least on the scores of the subset of the plurality of chargers; in order to minimize the amount of driving and the time spent driving for the user.
Regarding Claim 17, Wu teaches the method of claim 13.
Wu does not explicitly teach comparing, by the one or more processors, the scores for the plurality of chargers; and selecting, by the one or more processors, the at least one of the plurality of chargers responsive to determining the scores of the at least one of the plurality of chargers satisfy a condition.
North teaches comparing, by the one or more processors, the scores for the plurality of chargers (¶[44] “For example, the station selection module 260 may compare some or all available or proximate charging stations to information associated with the parameters of a current trip traveled by the electric vehicle 130, and determine a best matched charging station to the parameters of the current trip”); and selecting, by the one or more processors, the at least one of the plurality of chargers responsive to determining the scores of the at least one of the plurality of chargers satisfy a condition (¶[45] “As an example, the parameters of a current trip may be associated with a state of charge being at a certain low level of charge and a route currently traveled by the electric vehicle 130 to a destination. The station selection module 260 may identify and select a charging station that is within a travel range available to the electric vehicle 130 based on the low level of charge and is proximate to the route currently traveled by the electric vehicle to the destination”).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Wu to incorporate the teachings of North to provide comparing, by the one or more processors, the scores for the plurality of chargers; and selecting, by the one or more processors, the at least one of the plurality of chargers responsive to determining the scores of the at least one of the plurality of chargers satisfy a condition in order to improve by user convivence by making sure the charging station is within the driving range of the vehicle.
Claim(s) 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over North et al. (US 20150226572 A1) in view of Wu et al. (US 20240294088 A1).
Regarding Claim 18, North teaches a vehicle (130), comprising: one or more processors (135), coupled with memory (¶[32] “Aspects of the system may be stored or distributed on computer-readable media (e.g., physical and/or tangible computer-readable storage media, such as non-transitory media), including magnetically or optically readable computer discs, hard-wired or preprogrammed chips (e.g., EEPROM semiconductor chips), nanotechnology memory, biological memory, or other data storage media”), to: receive an identification of a destination (¶[16] “the systems and methods receive a request to determine a route to a destination”);
determine a path from a current location of the vehicle to the destination (¶[16] “determine a state of charge for an electric vehicle associated with the request, generate a route of travel to the destination based on the determined state of charge, and display the generated route via a mapping application associated with the electric vehicle”),
display the path including the location for each of the at least one of the plurality of chargers (¶[45] “The station selection module 260 may identify and select a charging station that is within a travel range available to the electric vehicle 130 based on the low level of charge and is proximate to the route currently traveled by the electric vehicle to the destination”).
North does not teach wherein the path is determined according to a score for each of a plurality of chargers based at least on data regarding charging sessions performed at the plurality of chargers, the path including a location for each of at least one of the plurality of chargers selected at least based on the scores.
Wu teaches wherein the path is determined according to a score for each of a plurality of chargers based at least on data regarding charging sessions performed at the plurality of chargers, the path including a location for each of at least one of the plurality of chargers selected at least based on the scores (¶[21] “The charger service 122 may also use the raw vehicle data 116 to generate charging station scores 126 descriptive of ratings of the charging stations 114. The user weights 124 and the charging station scores 126 may accordingly be used to provide charger recommendations 132” see also ¶[46] “The reliability scores 302 may be based on previous customer charging success or lack of success rates”);
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified North to incorporate the teachings of Wu to provide wherein the path is determined according to a score for each of a plurality of chargers based at least on data regarding charging sessions performed at the plurality of chargers, the path including a location for each of at least one of the plurality of chargers selected at least based on the scores in order to increase user convenience by determining the most reliable charger.
Regarding Claim 19, North in view of Wu teaches vehicle of claim 18.
North further teaches comprising the one or more processors to: display visual indicators for one or more of the plurality of chargers on the user interface (see Fig. 4A-B); and
Wu further teaches to adjust the visual indicators for the one or more chargers proportional to the scores for the at least one charger (814A-C) (¶[109] “the map 810 further illustrates the locations of a plurality of available chargers charging stations 114. For each charging station 114, a callout 814 is provided including the user-specific charger score 128 for that respective charging station 114. The user-specific charger scores 128 may serve to inform the user of the desirability of the charging station 114 in terms of the user's specific requirements. For instance, for the charging station 114A, a callout 814A shows a recommendation score of 4.2, for the charging station 114B, a callout 814B shows a recommendation score of 4.88, and for the charging station 114C, a callout 814C shows a recommendation score of 3.8”).
Regarding Claim 20, North in view of Wu teaches the vehicle of claim 18.
Wu further teaches wherein the data regarding the charging sessions comprises, for a charging session of the charging sessions, geolocation data (¶[36] “The charger service 122 may correlate the raw vehicle data 116 into charging visit (CV) records 204 … The CV record 204 may also be correlated with other information, such as a location determined from the GNSS controller 108 of the vehicle 102”), an indication of whether the charging session succeeded or failed (¶[46] “The reliability scores 302 may be based on previous customer charging success or lack of success rates. Charging locations with lower scores may require more charge attempts or require manual intervention to begin a charging session. Very low scores may indicate that the vehicle 102 were unable to charge at the charging station 114”), temperature sensor data, or an amount of current that was delivered.
Conclusion
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Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Julian Huffman can be reached at 571-272-2147. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/A.B./Examiner, Art Unit 2859
/JULIAN D HUFFMAN/Supervisory Patent Examiner, Art Unit 2859
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