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 .
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on December 26, 2025 has been entered.
Status of Claims
Claims 1, 11 and 18 have been amended and are hereby entered.
Claims 3 and 19 were cancelled.
Claims 1 - 2, 4 – 18 and 20 are pending and have been examined.
This action is made NON-FINAL.
Response to Arguments
Applicant's arguments filed December 26, 2025 have been fully considered but they are not persuasive.
Regarding the applicant's arguments against the 101 rejection of pending claims on pages 7-8: Applicant’s arguments directed to Step 2A prong 2 analysis was considered. However, these arguments are not persuasive and the Examiner respectfully disagrees. Because the claims are not reciting specific technology that is improving the computer functioning or a technological field. Rather, the claims are further describing the abstract idea of determining “the suitability of a charging station for a charging event (e.g., potential or future) associated with an electric vehicle” as asserted by the Applicant. Thus, the claim elements are not integrated into a practical application since the claim steps further describe the end result of “identifying charging stations that can provide a suitable charging event”, as asserted by the Applicant, without providing technological discussion on how the alleged “improvement” is directed to the computer functioning and/or to the existing technology of electric vehicle (EV) charge station systems. The claim limitations recited are invoking the use of a computer that ranks or scores charging stations or potential charging stations based on the feedback received from users to determine suitability of the charging station for a specific electric vehicle (EV) and “invokes” (i.e. applies) the computer with a mapping application as a tool to perform an abstract idea (see MPEP 2106.04(d)(I) and MPEP 2106.05(f)) for automating reservations as well as presenting rewards to owners of highly ranked charging stations and mapping the ranked charging stations with their scores to consumers in order to further promote business relations and the adoption of EVs. Thus, not providing an inventive concept at Step 2B. These claims, when compared at least to the Enfish case (see MPEP 2106.05(a)(I)), as asserted by the Applicant in p.8 from Remarks, does not reflect an improvement to the way the computer is working (i.e. functionality) to specifically achieve the determination of the suitability of charging stations for EVs and improve EV charge station systems. As for the the Ex Parte Desjardins case, the Applicant is misapplying such analysis since the case is directed to evaluating claims related to training machine learning (ML) or Artificial Intelligence (AI), which in the instant case such feature is not claimed (see December 5, 2025 Memorandum). Therefore, the Examiner respectfully disagrees, and maintains 35 USC § 101 rejection for these pending claims.
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.
Claims 1 - 2, 4 – 18 and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Firstly, it should be stated that claim 11 and claim 18 are being addressed separately as these are the most representative of the independent claims set 1, 11 and 18.
Step 1: the claimed invention in claims 1 – 2 and 4 – 10 falls under statutory category of a process and claims 11 – 18 and 20 are directed to a process.
At Step 2A Prong 1: the abstract idea is defined by the elements (e.g. functional steps) in the following limitations per claim:
For claim 11 (representative of claim 1):
accessing context information associated with a potential charging event at a charging station,
wherein the potential charging event is specific to an electric vehicle, and wherein the accessed context information includes information accessed…associated with the charging station and from…the electric vehicle;
determining a score for the potential charging event at the charging station based on the context information,
wherein the context information includes information associated with reviews of previous charging events at the charging station, including information based on a date-weighted average of binary station reviews obtained via check-in actions of other electric vehicles at the charging station,
wherein the score for the potential charging event at the charging station is determined for the potential charging event of charging the electric vehicle at the charging station; and
presenting a map via a mapping application associated with the electric vehicle that displays icons of multiple available charging stations, including an icon representative of the charging station along with an indicator of the score determined for the potential charging event at the charging station.
For claim 18:
accessing context information associated with a future charging event for the electric vehicle at a charging station by directly communicating…associated with the electric vehicle; and
determining a score for the future charging event at the charging station by:
accessing a score previously assigned to the charging station; and
updating the score previous assigned to the charging station based on the context information,
wherein the context information includes information associated with reviews of previous charging events at the charging station, including information that is based on a date- weighted average of binary station reviews obtained via check-in actions of other electric vehicles at the charging station.
automatically reserving the charging station for the electric vehicle by directly communicating…associated with the electric vehicle when the score for the potential charging event at the charging station is above a threshold score for reserving the electric vehicle at a charging station.
The claim(s) recite(s) limitations that describe a system and a method to rank or score charging stations or potential charging stations based on the feedback received from users to determine suitability of the charging station for a specific electric vehicle (EV) while providing a mapping application, automatic reservations and rewards to owners of highly ranked charging stations and promote business relations and the adoption of EVs. As disclosed in the specification in ¶0060, this invention includes a “charging station ranking engine 150” that “may provide drivers with dynamically determined scores or rankings of charging stations when they are deciding what charging stations to utilize in charging their vehicles.” However, the abstract idea(s) of a certain method of organizing human activity (See MPEP 2106.04(a)(2), subsection II) are/is recited in claims 1, 11 and 18 in the form of a certain method of organizing human activity in the sub-groups of “engaging in commercial or legal interactions” and “managing personal behavior or relationships or interactions between people” as these claims recite the steps of accessing “context information” that includes user and charging station feedback information (e.g. user’s “check-in actions” which is a form of monitoring social activities and user feedback representing sales activities or behaviors) for a specific electric vehicle (EV) to determine a “score for the potential charging event of charging the EV at the charging station” based on a score previously assigned, accessed and updated, render an “icon” representing a “charging station” with an “indicator of the score determined” on a map presented to the user and automatically reserve the charging station when is above a threshold score. Thus, these claims recite the management of EV charging station information and event data to promote and advertise (e.g. through rewards; see claim 9) the reservation of available EV charging stations with their respective scores (e.g. ranks) for efficient, cost-effective and/or green consumption, while monitoring the user’s related social activities and their associated feedback.
In addition, certain steps in claims 11 and 18 fall within the mental process grouping because these steps cover concepts performed in the human mind or with a pen and paper, including observation, evaluation, judgment, and opinion (See MPEP 2106.04(a)(2), subsection III). Specifically, in the steps directed to accessing context information related to charging events and their corresponding reviews to determine a potential charging event score based on the context information to present a map with icons of multiple charging stations and their score indicators (e.g. from claims 1 and 11) and/or to automatically reserve the charging station when is above a threshold score (e.g. from claim 18).
Step 2A Prong 2: For claims 1, 11 and 18, The judicial exception(s) or abstract idea previously identified is not integrated into a practical application (see MPEP 2106.04 (d)). The claims recite the additional element(s) of a non-transitory memory, one or more processors, non-transitory memory, charging interface system and a computing device (from claim 11); a computer-readable non-transitory storage medium, a computing system of an electric vehicle and a non-transitory storage medium (from claim 18); a charging station, a charging network, and a mapping application (from claims 1, 11 and 18). These additional elements, individually and in combination, merely is used as a tool to perform the abstract idea (refer to MPEP 2106.05(f)). Specifically, steps of “determining a score for the potential charging event…based on the context information” (e.g. from claims 1 and 11), “determining a score for the future charging event…” (e.g. from claim 18), “presenting a map…that displays icons of multiple available charging stations” (e.g. from claims 1 and 11), and “automatically reserving the charging station for the electric vehicle…” (e.g. from claim 18) are recited as being performed by the computer. The computer and the mapping application along with the charging station/network used are recited at a high level of generality that is being used as a tool to perform the generic computer functions for accessing, determining and presenting data related to available charging stations reviews and scores to further make reservations.
As for the “presenting a map” step in claims 1 and 11 is really nothing more than links to computer implementing the use of ordinary capacity for implementing the use of ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components for displaying data (refer to MPEP 2106.05 f (2)).
Step 2B: For claims 1, 11 and 18, these claims do not provide an inventive concept. The recited additional elements of the claim(s) are the following: a non-transitory memory, one or more processors, non-transitory memory, charging interface system and a computing device (from claim 11); a computer-readable non-transitory storage medium, a computing system of an electric vehicle and a non-transitory storage medium (from claim 18); a charging station, a charging network, and a mapping application (from claims 1, 11 and 18). These additional elements are not sufficient to amount significantly more than the judicial exception as these elements are further reciting the abstract idea (see MPEP 2106.05). Because, as indicated in Step 2A Prong 2, these additional element(s) claimed are merely, instructions to “apply” the abstract ideas, which cannot provide an inventive concept. Thus, even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer, which do not provide an inventive concept at Step 2B.
For dependent claims 2, 4 – 10, 12 – 17 and 20, these claims cover or fall under the same abstract idea of a certain method of organizing human activity and mental processes. They describe additional limitations steps of:
Claims 2, 4 – 8 and 12 – 14: further describes the abstract idea of the ranking or scoring charging stations and/or charging events or sessions method and its user’s information (e.g. context information, user’s check-in actions and charging information) to determine a score for the potential charging event at the charging station. Moreover, it further describes that the context information also includes a current state of charge of the electric vehicle; a route to be traveled from a current location of the electric vehicle to a physical location of the charging station; information identifying a cost to charge the electric vehicle at the charging station during the potential charging event and information identifying a source of renewable energy to be used by the charging station during the potential charging event. Thus, being directed to the abstract idea groups of “managing personal behavior or relationships or interactions between people” and “engaging in commercial or legal interactions” as it collecting user related data including location (e.g. managing user’s social activities) to promote the EV charging stations available to purchase for their EVs consumption.
Claims 9 – 10, 15 – 17 and 20: further describes the abstract idea of the ranking or scoring charging stations and/or charging events or sessions method and the receiving information of an occurring charging event indication to assign a score to the actual charging event and provide a reward to the EV driver (e.g. user) based on the score assigned. Similarly, the method includes updating the score for previously scored charging stations and presenting an icon representing the charging stations and their scores on a map associated to the EV which further attributes to the abstract idea group of “managing personal behavior or relationships or interactions between people” and “engaging in commercial or legal interactions” as well.
Step 2A Prong 2 and Step 2B: For dependent claims, these claims do not include additional elements. Rather, what is claimed simply further defines the same abstract idea that was set forth in independent claims 1, 11 and 18. Nothing additional is claimed that is not part of the abstract idea. Thus, these limitations further instruct one to practice the abstract idea by using general computer components that merely are used as a tool. Thus, it amounts no more than mere instructions to apply the exception using a generic computer component (MPEP 2106.05(f)). Therefore, these claim limitations amount to no more than mere instructions to apply the exception using generic computer components and or computing technologies (e.g. that are merely deployed to be used as a tool; see MPEP 2106.05 (f)).
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.
Claims 1 - 2, 4 - 8, 10 - 18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Horita (U.S. Pub No. 20130226441 A1) in view of Hodges (U.S. Pub No. 20160275400 A1).
Regarding claims 1 and 11:
Horita teaches:
a non-transitory memory storing instructions; one or more processors coupled to the non-transitory memory and operable to execute the instructions from the non-transitory memory, the execution of the instructions causing the charging interface system to perform operations, the operations comprising: (See Fig. 1 (1) and Fig. 3 (120 and 110): Refer to ¶0070 - 71 for more details regarding the computer system with memory and the “processing unit” of the “calculation unit 110”. See ¶0034 also.)
accessing context information associated with a potential charging event at a charging station, wherein the potential charging event is specific to an electric vehicle, and wherein the accessed context information includes information accessed from a charging network associated with the charging station and from a computing device of the electric vehicle; (In ¶0073; Fig. 3 (117); Fig. 5 (S7); Fig. 6 (S104 and S105); Fig. 12 (S21 and S22): teaches that “the server system 1 repeatedly or even periodically or by means of event-triggering monitors the risk of battery shortage of the vehicle 4 based on the vehicle information 122 received from the vehicle 4 which indicates the battery status of the vehicle 4, the destination location of the vehicle 4, and/or the current location 4 of the vehicle, and optionally on the further basis of other types of information including the charging station availability and position information 124, the weather information 125 and/or the traffic information 123”, in accordance to examples of information accessed in ¶0036 and ¶0041 from Applicant disclosure. See ¶0080 – 86 for more details of each type of information collected.)
determining a score for the potential charging event at the charging station based on the context information, (In ¶0077; Fig. 7 (507) and Fig. 12 (S23): teaches that “the charging station score calculation unit 114 is configured to calculate a score value for one, more or even each of the charging station candidates indicated in the charging station information 124 on the basis of availability, position, available facilities (e.g. if the charging station provides a quick charger unit for quick charge and/or normal charger units) and potentially also on the basis of user preferences for each charging station candidate indicated in the user information 121”. See ¶0140 for more details of scoring value and their basis.)
wherein the score for the potential charging event at the charging station is determined for the potential charging event of charging the electric vehicle at the charging station; and (In ¶0140; Fig. 7 (507) and Fig. 12 (S23): teaches that “the charging station score calculating unit 114 calculates, for each charging station candidate, a score value on the basis of the user preference (step S23). The respective score may be calculated as one value based on the solution preference specified in the activation request, or may be calculated as multiple values which are relevant to solution preference items, respectively, such as quick arrival, less cost and certainty (see field 507 in FIG. 7)”.)
presenting a map via a mapping application associated with the electric vehicle that displays of multiple available charging stations, (In ¶0129; Fig. 8 (512) and Fig. 14 (701): teaches “a driving assistance application including a risk monitoring service” (see ¶0119) with a “main screen 550 with an additional monitoring mode icon 509 illustrating that the monitoring mode is activated” that includes “field 512” which “shows navigational data including a navigational map and route information (such as an arrow 513)” and/or “optionally, additional information about one or more selected routes including information on a destination position” (see ¶0080) which is directed to an example of displays of multiple available charging stations.)
including an icon representative of the charging station along with an indicator of the score determined for the potential charging event at the charging station. (In ¶0143; Fig. 8 (512) and Fig. 14 (701): further teaches the display information per available charging station with their corresponding scores as shown in Fig. 14 wherein “a list 701 of the charging station candidates” is “exemplarily sorted by the calculated score but the candidates may also be listed according to other criteria, such as certainty, quick arrival and low cost”, in accordance to examples of charging stations and their indicators given in ¶0040 from Applicant disclosure.)
Horita does not explicitly teach the ability of having context information specifically associated with reviews of previous charging events at the charging station that are based on a date-weighted average of binary station reviews obtained via check-in actions of other electric vehicles visiting each station. However, Hodges teaches:
wherein the context information includes information associated with reviews of previous charging events at the charging station, including information based on a date-weighted average of binary station reviews obtained via check-in actions of other electric vehicles at the charging station, and (In ¶0055 – 56; Fig. 3 (302 and 306 - 308); Fig. 4 (402 and 404) and Fig. 5 (502 and 506 - 508): teaches this limitation considered as non-functional descriptive matter that does not hold patentable weight, under the broadest reasonable interpretation (BRI), as the “variety of other kind of information” that can also be included in “a recommendation, such as rating and comments from users, pay structure indications, distinctions as public/private, indication of other amenities associated with stations (e.g., waiting area, food availability, entertainment), and/or to name a few examples” as well as “additional detail information for the charging stations can also be included along with the locations and can be utilized by the recommendation module 206 to further individualize recommendations based on various criteria” (see ¶0056), in accordance to the definition of binary reviews and examples of date-weighted average of binary station reviews given in ¶0038 – 39 and ¶0055 from Applicant disclosure. Refer to ¶0059 – 60 wherein “data associated with an account for a particular user is collected that is indicative of factors that influence availability of charging opportunities for a computing device used to access the account (block 402)”, thus this data “be collected through interaction with client computing devices to obtain feedback 208, such as using a data collection module 202 or comparable functionality” which is directed to user’s inputted check-in actions (see ¶0067 also). See ¶0064 for collected data that is derived from “crowd-sourced information from a community of users and corresponding devices”)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify Horita to provide the ability of having context information specifically associated with reviews of previous charging events at the charging station that are based on a date-weighted average of binary station reviews obtained via check-in actions of other electric vehicles visiting each station, as taught by Hodges in order to “account for charging opportunities and provide users with indications regarding when and where they may be able to charge their devices” (¶0001; Hodges) and to further consider and take advantage of “charging opportunities using recommendations from the service, users may tailor charging to different situations and utilize their devices more efficiently, which may lead to increased device performance and extended battery life” (¶0013; Hodges), see also MPEP 2143.I.G. Further such ability provided by Hodges into Horita system would have been obvious because the claimed invention is merely applying a known technique to a known method ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 406 (2007). In other words, all of the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art at the time of the invention (i.e., predictable results are obtained by including specific context information based on crowd-sourced information criteria such as date-weighted average of binary station reviews obtained via check-in actions of other electric vehicles at the charging station to accurately recommend the best suitable charging stations to the user). See also MPEP § 2143(I)(D).
Regarding claim 18:
Horita further teaches:
accessing context information associated with a future charging event for the electric vehicle at a charging station by directly communicating with the charging station or a charging network associated with the electric vehicle; and (In ¶0073; Fig. 3 (117); Fig. 5 (S7); Fig. 6 (S104 and S105); Fig. 12 (S21 and S22): teaches that “the server system 1 repeatedly or even periodically or by means of event-triggering monitors the risk of battery shortage of the vehicle 4 based on the vehicle information 122 received from the vehicle 4 which indicates the battery status of the vehicle 4, the destination location of the vehicle 4, and/or the current location 4 of the vehicle, and optionally on the further basis of other types of information including the charging station availability and position information 124, the weather information 125 and/or the traffic information 123”, in accordance to examples of information accessed in ¶0036 and ¶0041 from Applicant disclosure. See ¶0080 – 86 for more details of each type of information collected.)
determining a score for the future charging event at the charging station by: accessing a score previously assigned to the charging station; and (In ¶0077; Fig. 7 (507); Fig. 9 (S120); Fig. 10 (515) and Fig. 12 (S23): teaches that “the charging station score calculation unit 114 is configured to calculate a score value for one, more or even each of the charging station candidates indicated in the charging station information 124 on the basis of availability, position, available facilities (e.g. if the charging station provides a quick charger unit for quick charge and/or normal charger units) and potentially also on the basis of user preferences for each charging station candidate indicated in the user information 121” directed to scores previously assigned by the user to the charging station. See ¶0140 for more details of scoring value and their basis. Another example of accessing previously assigned scores for charging station is taught as “the public charging station information, the server system 1 may also obtains user-specific charging point information (step S22), which can be pre-assigned by the user, e.g. for registering private charging points in advance, such as a private charger at the user's home, for example” (see ¶0139).)
updating the score previous assigned to the charging station based on the context information, (In ¶0137; Fig. 12 (S21 and S23); Fig. 13 (S131 and S139 – S143): teaches that in “step S21, the charging station booking control unit 116 first obtains the latest available information on charging station availability in the relevance area on the basis of information 124 stored in the memory unit 120 (which may be automatically and periodically updated) and/or on the basis of information received and/or requested from the charging station center 3.” Refer to ¶0157 wherein upon receiving “a booking result message” (in step S138) that further indicates a “booking success (S139 returns yes)”, the “communication unit 230 receives the updated risk information from the server system 1 (step S142) which was sent in step S34, and then updates the monitoring mode icon on the screen of the human machine interface unit 240 notifying the user about the current risk status (step S143).”)
automatically reserving the charging station for the electric vehicle by directly communicating with the charging station or the charging network associated with the electric vehicle when the score for the future charging event at the charging station is above a threshold score for reserving the electric vehicle at a charging station. (In ¶0079; Fig. 5 (S12 – S13); Fig. 12 (S28 and S29); Fig. 13 (S134 – S141); Fig. 15: teaches that the “charging station booking control unit 116 is configured to make a reservation and/or cancel a reservation in connection with a charging station candidate according to a request from the user, on behalf of the user, e.g. if the user selects one or more of the suggested charging station candidates” (see ¶0076 also). Also, teaches an example wherein if “the risk of the occurrence of a battery shortage is determined to exceed a pre-determined threshold”, the determination of a recommended action such as “perform a search, on the basis of the charging station information 124 and the vehicle information 122, if there exists an available charging station which can be reached by the vehicle 4 with the remaining battery level” and/or based on the determination of a risk for an EV item being higher than a specified threshold (see ¶0112 - ¶0114 and Fig. 5 (S9 – S12).)
Horita does not explicitly teach the ability of having context information specifically associated with reviews of previous charging events at the charging station that are based on a date-weighted average of binary station reviews obtained via check-in actions of other electric vehicles visiting each station. However, Hodges teaches:
wherein the context information includes information associated with reviews of previous charging events at the charging station, including information that is based on a date- weighted average of binary station reviews obtained via check-in actions of other electric vehicles at the charging station; and (In ¶0055 – 56; Fig. 3 (302 and 306 - 308); Fig. 4 (402 and 404) and Fig. 5 (502 and 506 - 508): teaches this limitation considered as non-functional descriptive matter that does not hold patentable weight, under the broadest reasonable interpretation (BRI), as the “variety of other kind of information” that can also be included in “a recommendation, such as rating and comments from users, pay structure indications, distinctions as public/private, indication of other amenities associated with stations (e.g., waiting area, food availability, entertainment), and/or to name a few examples” as well as “additional detail information for the charging stations can also be included along with the locations and can be utilized by the recommendation module 206 to further individualize recommendations based on various criteria” (see ¶0056), in accordance to the definition of binary reviews and examples of date-weighted average of binary station reviews given in ¶0038 – 39 and ¶0055 from Applicant disclosure. Refer to ¶0059 – 60 wherein “data associated with an account for a particular user is collected that is indicative of factors that influence availability of charging opportunities for a computing device used to access the account (block 402)”, thus this data “be collected through interaction with client computing devices to obtain feedback 208, such as using a data collection module 202 or comparable functionality” which is directed to user’s inputted check-in actions (see ¶0067 also). See ¶0064 for collected data that is derived from “crowd-sourced information from a community of users and corresponding devices”)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify Horita to provide the ability of having context information specifically associated with reviews of previous charging events at the charging station that are based on a date-weighted average of binary station reviews obtained via check-in actions of other electric vehicles visiting each station, as taught by Hodges in order to “account for charging opportunities and provide users with indications regarding when and where they may be able to charge their devices” (¶0001; Hodges) and to further consider and take advantage of “charging opportunities using recommendations from the service, users may tailor charging to different situations and utilize their devices more efficiently, which may lead to increased device performance and extended battery life” (¶0013; Hodges), see also MPEP 2143.I.G. Further such ability provided by Hodges into Horita system would have been obvious because the claimed invention is merely applying a known technique to a known method ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 406 (2007). In other words, all of the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art at the time of the invention (i.e., predictable results are obtained by including specific context information based on crowd-sourced information criteria such as date-weighted average of binary station reviews obtained via check-in actions of other electric vehicles at the charging station to accurately recommend the best suitable charging stations to the user). See also MPEP § 2143(I)(D).
Regarding claim 2:
The combination of Horita and Hodges, as shown in the rejection above, discloses the limitations of claim 1.
Horita further teaches:
wherein determining a score for the potential charging event at the charging station based on the context information includes dynamically determining the score after accessing the context information for the charging station (In ¶0077: teaches that “the charging station score calculation unit 114 is configured to calculate a score value” for the “charging station candidates indicated in the charging station information 124 on the basis of availability, position, available facilities” and potentially also on the basis of user preferences for each charging station candidate indicated in the user information 121 based on the user's pre-defined preferences or by means of a dynamically generated user profile (e.g. by machine learning).”)
Regarding claims 4 – 6 and 12 – 13:
The combination of Horita and Hodges, as shown in the rejection above, discloses the limitations of claims 1 and 11, respectively.
This dependent claim set is represented by claim 6 while incorporating claim language from claims 12 – 13 as shown in brackets.
Horita further teaches:
wherein the context information includes a current [accessing information identifying] state of charge of the electric vehicle [to be charged during the potential charging event.] (In ¶0082; Figs. 10 – 11: teaches collected information (see ¶0080) such as “vehicle information 122 may include information on the battery status (e.g. information on an available energy supply level and/or a deterioration of the battery 10)”. See ¶0093 and ¶0132 for more details)
and [accessing information identifying] a route to be traveled from a current location of the electric vehicle to a physical location of the charging station. (In ¶0107; Figs. 8 and 10 – 11: teaches that “the risk calculation unit 112 determines the estimated battery consumption when traveling to the destination, e.g., based on the driving distance of the route from the current position and the position of the destination and estimated travelling time from the current position of the vehicle 4 to the destination position, the electricity mileage of the vehicle 4, the electricity devices usage of the electric devices (e.g. devices 12 and 13) of the vehicle 4 and/or the weather information 125”. Refer to ¶0074, ¶0097 and ¶0129 for more details and examples.)
Regarding claims 7 and 14:
The combination of Horita and Hodges, as shown in the rejection above, discloses the limitations of claims 1 and 11, respectively.
This dependent claim set is represented by claim 14.
Horita further teaches:
the operations further comprising accessing information identifying a cost to charge the electric vehicle during the potential charging event (In ¶0143; Fig. 14 (703): teaches that the system can receive “a list 701 of the charging station candidates” that can be sorted by “low cost” and a “field 703 exemplarity shows also the costs for the charging operation at the at the selected charging station candidate taking into account the recommended charging time” and “how the battery 10 is going to recover (e.g. 30%->60%) by the recommended charging operation” (see ¶0147) as shown in Fig. 14.)
Horita does not explicitly teach the ability of having information specifically identifying a source of energy to be used at the charging station. However, Hodges further teaches:
and information identifying a source of energy to be used when charging the electric vehicle during the future, potential charging event at the charging station (In ¶0027: teaches that the “charging discovery service 138 may maintain a database having information regarding locations for the charging stations and other details, such as energy source type (fossil fuel or other fuel type), identification of clean or green sources (wind, solar, hydro, etc.), status as public or private, identification as a pay or free station, ratings and recommendations, indications of other amenities, owner/operator, hours of operations, and other relevant details”, in accordance to definition and examples of power sources given in ¶0063 and ¶0070 from Applicant disclosure.)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify Horita to provide the ability of having information specifically identifying a source of energy to be used at the charging station, as taught by Hodges in order to “form recommendations for charging station availability and to individualize the recommendations based on various factors.” (¶0027; Hodges), see also MPEP 2143.I.G. Further such ability provided by Hodges into Horita system would have been obvious because the claimed invention is merely applying a known technique to a known method ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 406 (2007). In other words, all of the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art at the time of the invention (i.e., predictable results are obtained by including specific information regarding charging stations’ energy source types to accurately recommend the best suitable charging stations to the user based on their needs and preferences). See also MPEP § 2143(I)(D).
Regarding claim 8:
The combination of Horita and Hodges, as shown in the rejection above, discloses the limitations of claim 1.
Horita further teaches:
and wherein the indicator of the score determined for the potential charging event at the charging station further displays the source of the renewable energy. (In ¶0145; Figs. 14 – 15: teaches this descriptive matter under BRI as an “example of screen 700 the field 703 indicates the name of the selected charging station candidate, the location of the selected charging station candidate, the available facilities at the selected charging station candidate (number of charging units and types of charging units)”.)
Horita does not explicitly teach the ability of having displaying context information specifically identifying a source of renewable energy to be used at the charging station. However, Hodges further teaches:
wherein the context information includes information identifying a source of renewable energy to be used by the charging station during the potential charging event, (In ¶0027: teaches the non-functional descriptive matter that the “charging discovery service 138 may maintain a database having information regarding locations for the charging stations and other details, such as energy source type (fossil fuel or other fuel type), identification of clean or green sources (wind, solar, hydro, etc.), status as public or private, identification as a pay or free station, ratings and recommendations, indications of other amenities, owner/operator, hours of operations, and other relevant details”, in accordance to definition and examples of power sources given in ¶0063 and ¶0070 from Applicant disclosure.)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify Horita to provide the ability of having displaying context information specifically identifying a source of renewable energy to be used at the charging station, as taught by Hodges in order to “form recommendations for charging station availability and to individualize the recommendations based on various factors.” (¶0027; Hodges), see also MPEP 2143.I.G. Further such ability provided by Hodges into Horita system would have been obvious because the claimed invention is merely applying a known technique to a known method ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 406 (2007). In other words, all of the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art at the time of the invention (i.e., predictable results are obtained by including specific context information regarding charging stations’ renewable energy source types to accurately recommend the best suitable charging stations to the user based on their needs and preferences). See also MPEP § 2143(I)(D).
Regarding claim 10:
The combination of Horita and Hodges, as shown in the rejection above, discloses the limitations of claim 1.
Horita further teaches:
wherein determining a score for the potential charging event at the charging station based on the context information includes dynamically updating a score previously assigned to the charging station. (In ¶0137; Fig. 12 (S21 and S23); Fig. 13 (S131 and S139 – S143): teaches that in “step S21, the charging station booking control unit 116 first obtains the latest available information on charging station availability in the relevance area on the basis of information 124 stored in the memory unit 120 (which may be automatically and periodically updated) and/or on the basis of information received and/or requested from the charging station center 3.” Refer to ¶0157 wherein upon receiving “a booking result message” (in step S138) that further indicates a “booking success (S139 returns yes)”, the “communication unit 230 receives the updated risk information from the server system 1 (step S142) which was sent in step S34, and then updates the monitoring mode icon on the screen of the human machine interface unit 240 notifying the user about the current risk status (step S143).”)
Regarding claim 15:
The combination of Horita and Hodges, as shown in the rejection above, discloses the limitations of claim 11.
Horita further teaches:
the operations further comprising updating a score previously assigned to the charging station based on the context information. (In ¶0137; Fig. 12 (S21 and S23); Fig. 13 (S131 and S139 – S143): teaches that in “step S21, the charging station booking control unit 116 first obtains the latest available information on charging station availability in the relevance area on the basis of information 124 stored in the memory unit 120 (which may be automatically and periodically updated) and/or on the basis of information received and/or requested from the charging station center 3.” Refer to ¶0157 wherein upon receiving “a booking result message” (in step S138) that further indicates a “booking success (S139 returns yes)”, the “communication unit 230 receives the updated risk information from the server system 1 (step S142) which was sent in step S34, and then updates the monitoring mode icon on the screen of the human machine interface unit 240 notifying the user about the current risk status (step S143).”)
Regarding claim 16:
The combination of Horita and Hodges, as shown in the rejection above, discloses the limitations of claim 11.
Horita further teaches:
the operations further comprising updating a score previously assigned to the charging station based on information identifying a state of charge for the electric vehicle to be charged during the potential charging event. (In ¶0137; Fig. 12 (S21 and S23); Fig. 13 (S131 and S139 – S143): teaches that in “step S21, the charging station booking control unit 116 first obtains the latest available information on charging station availability in the relevance area on the basis of information 124 stored in the memory unit 120 (which may be automatically and periodically updated) and/or on the basis of information received and/or requested from the charging station center 3.” Refer to ¶0157 wherein upon receiving “a booking result message” (in step S138) that further indicates a “booking success (S139 returns yes)”, the “communication unit 230 receives the updated risk information from the server system 1 (step S142) which was sent in step S34, and then updates the monitoring mode icon on the screen of the human machine interface unit 240 notifying the user about the current risk status (step S143).”)
Regarding claim 17:
The combination of Horita and Hodges, as shown in the rejection above, discloses the limitations of claim 11.
Horita further teaches:
the operations further comprising updating a score previously assigned to the charging station based on information identifying a predicted route currently traveled by the electric vehicle to be charged during the future, potential charging event. (In ¶0137; Fig. 12 (S21 and S23); Fig. 13 (S131 and S139 – S143): teaches that in “step S21, the charging station booking control unit 116 first obtains the latest available information on charging station availability in the relevance area on the basis of information 124 stored in the memory unit 120 (which may be automatically and periodically updated) and/or on the basis of information received and/or requested from the charging station center 3.” Refer to ¶0157 wherein upon receiving “a booking result message” (in step S138) that further indicates a “booking success (S139 returns yes)”, the “communication unit 230 receives the updated risk information from the server system 1 (step S142) which was sent in step S34, and then updates the monitoring mode icon on the screen of the human machine interface unit 240 notifying the user about the current risk status (step S143).”)
Regarding claim 20:
The combination of Horita and Hodges, as shown in the rejection above, discloses the limitations of claim 18.
Horita further teaches:
presenting a map via a mapping application associated with the electric vehicle that displays icons of multiple available charging stations, (In ¶0129; Fig. 8 (512) and Fig. 14 (701): teaches “a driving assistance application including a risk monitoring service” (see ¶0119) with a “main screen 550 with an additional monitoring mode icon 509 illustrating that the monitoring mode is activated” that includes “field 512” which “shows navigational data including a navigational map and route information (such as an arrow 513)” and/or “optionally, additional information about one or more selected routes including information on a destination position” (see ¶0080) which is directed to an example of displays of multiple available charging stations.)
including an icon representative of the charging station along with an indicator of the score determined for the future charging event at the charging station. (In ¶0143; Fig. 8 (512) and Fig. 14 (701): further teaches the display information per available charging station with their corresponding scores as shown in Fig. 14 wherein “a list 701 of the charging station candidates” is “exemplarily sorted by the calculated score but the candidates may also be listed according to other criteria, such as certainty, quick arrival and low cost”, in accordance to examples of charging stations and their indicators given in ¶0040 from Applicant disclosure.)
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Horita (U.S. Pub No. 20130226441 A1) in view of Hodges (U.S. Pub No. 20160275400 A1) in further view of Penilla (U.S. Pub No. 20120271723 A1).
Regarding claim 9:
The combination of Horita and Hodges, as shown in the rejection above, discloses the limitations of claim 1.
Horita further teaches:
receiving an indication that an actual charging event has occurred between the electric vehicle and the charging station; (In ¶0155; Fig. 12 (S31 – S32); Fig.13 (S141): teaches an example wherein “if the booking was successful (step S29 returns yes), the charging station booking control unit 116 by means of the communication unit 130 sends a booking confirmation message to the user (step S31) and updates the destination information of the user by setting the location of the newly booked charging station as the intermediate destination (step S32) for route calculation and navigation, and also for the destination used for risk monitoring items such as traffic jam, delayed arrival or battery shortage”, in accordance to the example for scoring charge events given in ¶0064 from Applicant disclosure.)
assigning a score to the actual charging event that is based on the score for the potential charging event at the charging station; and (In ¶0155; Fig. 12 (S33 – S34); Fig.13 (S142 – S143): teaches that if “the risk monitoring mode is activated, the risk calculation unit 112 calculates the risk levels for selected risk monitoring items, e.g. up to the charging station as a destination and/or up to the final destination by taking into account the battery charging in the newly booked charging station (step S33), and the calculation results are sent via the communication unit 130 to the HMI device for notifying the user about the new calculated risk levels (step S34)”. Similarly, “If the booking operation fails for any reason (step S29 of determining whether the booking was successful returns no), a booking failure message is sent to the HMI device in order to notify the user (step S30) and goes back to the step S25”. Refer to ¶0020 for another example wherein “if the destination is a booked support station such as a booked charging station (e.g. as a potential intermediate destination) for which a delayed arrival may mean that the reservation is lost and the battery shortage risk might be indirectly increased since the charging operation cannot be performed as planned at departure.” See ¶0025 – 28 for more examples and details.)
Neither Horita or Hodges explicitly teach the ability of providing rewards to the EV driver based on assigned scores corresponding to the actual charge event. However, Hodges teaches:
providing a reward to a driver of the electric vehicle that is based on the score assigned to the actual charging event. (In ¶0209; Figs. 37C and 39 – 40: teaches providing rewards to drivers based on assigned scores as “deals to friends can be provided by way of a map that identifies the location of your friends and the possibility of giving your friend special points or the receipt of loyalty points for referring friends to specific kiosk locations”, in accordance to the example for scoring charge events and providing rewards to drivers given in ¶0059 from Applicant disclosure. Refer to ¶0216 – 217 for examples wherein “the charging station can provide incentives to users to come to the particular charging station” and/or “during that particular period of time, the charging station can offer discounts or rewards to users so that drivers can decide to visit the charging station instead of another charging station”. Finally see ¶0223 wherein “dynamic adjustment of discounts can occur based on a preset number of rules (e.g., what discount, where offered, when offered, how long it lasts, incentives for fast buy, logic for combining discounts, logic for sharing costs of discounts with others, logic for reducing the cost of the charge, etc.), as set by the provider the charge and/or the sponsor”. See ¶0251 for rewards presented to the user.)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify Horita and Hodges to provide the ability of ability of providing rewards to the EV driver based on assigned scores corresponding to the actual charge event, as taught by Penilla in order to “enable tradeoffs between length of path and reward obtained” (¶0019; Penilla) as well as to allow drivers decide “to visit the charging station instead of another charging station” and “to drive traffic to or near that particular business” (¶0217; Penilla), see also MPEP 2143.I.G. Further such ability provided by Penilla into Horita and Hodges’ systems would have been obvious because the claimed invention is merely applying a known technique to a known method ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 406 (2007). In other words, all of the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art at the time of the invention (i.e., predictable results are obtained by including specific context information based on crowd-sourced information criteria such as date-weighted average of binary station reviews obtained via check-in actions of other electric vehicles at the charging station to accurately recommend the best suitable charging stations to the user). See also MPEP § 2143(I)(D).
Conclusion
The following prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Tate (U.S. Pub No. 20120233077 A1) is pertinent because it “relates to a system and method for reserving an electric charging station of the type used to recharge a battery module of a vehicle having at least one electric vehicle operating mode.”
Wu (U.S. Pub No. 20130030696 A1) is pertinent because it “relates to a system and method for reserving an electric charging station of the type used to recharge a battery module of a vehicle having at least one electric vehicle operating mode.”
Fisher (U.S. Pub No. 20130179057 A1) is pertinent because it “pertains to methods and apparatus for networked electric vehicle (EV) user support services.”
Boot (U.S. Pub No. 20120296678 A1) is pertinent because it “relate[s] generally to electric vehicles, and more particularly, to systems and methods for reservations of charging stations for electric vehicles.”
Meehan (U.S. Patent No. 9418345 B1) is pertinent because it is “generally directed to vending systems and methods and more specifically to vending systems and methods for mobility vehicles.”
Khoo (U.S. Pub No. 20130110296 A1) is pertinent because it “relate[s] to electric vehicles and systems and methods for recharging electric vehicles.”
UESUGI (U.S. Pub No. 20110193522 A1) is pertinent because it “relates to an operation managing server for charging stations and an operation managing system for charging stations.”
Park (U.S. Pub No. 20130342310 A1) is pertinent because it “relates to reservation of exchange of batteries of electric vehicles, and more particularly, to technology for reserving exchange of batteries of electric vehicles so that the batteries can be exchanged easily, conveniently, and quickly.”
Wolfson (U.S. Pub No. 20140122190 A1) is pertinent because it “relates generally to detecting whether a resource is available and facilitating the selection of a resource from a set of resources, some of which may be available or unavailable at any given time.”
Marshall (U.S. Pub No. 20150006249 A1) is pertinent because it “is in the field of alternative energy infrastructure and the operation of related alternative energy powered vehicles and/or equipment.”
Miller (U.S. Pub No. 20170087999 A1) is pertinent because it “relates to identifying acceptable vehicle charge stations along a travel route.”
Mayer (CA Pub No. 2648972 A1) is pertinent because it “enables improved and more efficient recharging of electric cars, preferably by using improved batteries and/or improved recharger arrangements in electric cars and/or in infrastructures that are used for recharging electric cars, and/or other improvements in the infrastructures, while preferably also protecting the electric grid from overload.”
De Haas (WO Pub No. 2011098195 A1) is pertinent because it “relates to methods and systems for obtaining information regarding the positions of charging locations for electric vehicles in a navigation system. The invention also extends to navigation apparatus for carrying out methods in accordance with the invention, and methods of operating navigation apparatus.”
Automobile Club of Southern California, AAA Adds EV Charging Stations to Digital Mapping Tools (March 15, 2012) is pertinent because it discusses “TripTik® Travel Planner on AAA.com or the free AAA TripTik Mobile app” to find “places to recharge electric vehicles before they run out of energy”.
Berman, Making Sense of Electric Car Apps (July 11, 2012) is pertinent because it discusses “mobile apps specifically designed to map locations of EV charging stations.”
Constine, Recargo And Xatori Merge To Make PlugShare The Essential App For Electric Car Drivers (May 22, 2013) is pertinent because it discusses the applicant’s merging with Xatori company to “promote their station map and trip planner app PlugShare.”
Big Island Video News, Hawaii rolls out EV charging station location app; You Tube Video (Jul 16, 2013) is pertinent because it discusses a new mobile application to “help drivers find electric vehicle (EV) charging stations that are open to the public around the islands” in Hawaii.
Lee, Reservation-Based Charging Service for Electric Vehicles (2011) is pertinent because it discusses “EV telematics” technology and a derived system that “must consider the EV specific requirement such as online advance booking of charging spots, remote vehicle diagnostics, and time display for next charging.” This way, the paper designs “an EV telematics service capable of providing efficient charging station selection”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ivonnemary Rivera Gonzalez whose telephone number is (571)272-6158. The examiner can normally be reached Mon - Fri 9:00AM - 5:30PM.
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/IVONNEMARY RIVERA GONZALEZ/Examiner, Art Unit 3626
/NATHAN C UBER/Supervisory Patent Examiner, Art Unit 3626