Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 4, 11, and 18 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The present claims recite “based on the predicted charging duration of the profile of the user.” However, claims 1, 8, and 15, upon which the present claims depend, respectively, recite “a predicted charging duration” and “a profile of a user” as separate entities. There is no suggestion that the predicted charging duration is “of the profile of the user,” and it is unclear what it would mean for the predicted charging duration to be “of the profile of the user.” For the purposes of examination under prior art, the examiner will interpret the present claim as though it recited “executing a first AI model based on the predicted charging duration of the profile or the user to determine a list of charging stations for the EV.”
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3, 5-10, 12-17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Gell et al. (U.S. 2025/0162449, hereinafter “Gell”) in view of Mittal, Rohin, and Vaibhav Sinha (“A personalized time-bound activity Recommendation System,” 2017 IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC). IEEE, 2017; hereinafter “Mittal”).
Regarding Claim 1, Gell teaches a method (fig. 19; Abstract), comprising:
determining a predicted charging duration for a rechargeable battery of an electric vehicle (EV) at a plurality of charging stations (fig. 6; ¶ [0111] – [0113]—a display shows estimated charging durations for multiple charging stations);
determining an activity based on the predicted charging duration and a profile of a user of the EV (figs. 4 and 6; ¶ [0099], [0102], and [0119]—activities and amenities, such as a restaurant and tennis courts, are determined based on charging duration, user profile preferences, and proximity to the charging stations); and
determining a charging station of the plurality of charging stations proximate the activity and notifying the EV of the charging station and the activity (figs. 7A, and 7B; ¶ [0120]—a user selects one of the charging station options, thus notifying the EV of the charging station and the activity).
Gell does not specifically teach:
the determining an activity is by an artificial intelligence (AI) model;
collecting data of the user during the activity by one or more of the EV and a device associated with the user; and
training the AI model based on the data of the user.
However, Mittal teaches:
determining, by an artificial intelligence (AI) model, an activity based on a predicted duration and a profile of a user (section III—an activity is determined based on a user profile and an available duration of time. Section III. A describes user profiling, as does the “1) Initial User Vectors” section two pages later in section III. The “2) User Feedback and User Vector Tuning” section below that describes the use of a Naïve Bayes classifier, which is an AI model);
collecting data of the user during the activity by one or more of the EV and a device associated with the user (the “2) User Feedback and User Vector Tuning” section describes collecting user feedback about activities, thus comprising collecting data of the user during the activity); and
training the AI model based on the data of the user (the “2) User Feedback and User Vector Tuning” section describes tuning {i.e. training} the AI model based on the feedback from the user).
All of the claimed elements were known in Gell and Mittal and could have been combined by known methods with no change in their respective functions. It therefore would have been obvious to a person of ordinary skill in the art at the time of filing of the applicant’s invention to combine the AI model and training of Mittal with the method and EV of Gell to yield the predictable result of determining, by an artificial intelligence (AI) model, an activity based on the predicted charging duration and a profile of a user of the EV; collecting data of the user during the activity by one or more of the EV and a device associated with the user; and training the AI model based on the data of the user. One would be motivated to make this combination for the purpose of improving recommendations to a user based on their preferences, mood, and time available (Mittal, section I).
Regarding Claim 8, Gell teaches an apparatus (fig. 18; ¶ [0081]) comprising:
a memory (fig. 18; memory 121; ¶ [0081]); and
a processor coupled to the memory (fig. 18; processor 120; ¶ [0081]). Gell and Mittal teach the processor configured to perform the operations of the present claim in the same manner as for claim 1, above.
Regarding Claim 15, Gell teaches a computer-readable storage medium comprising instructions stored therein (fig. 18; ¶ [0081] and [0170]). Gell and Mittal teach the instructions, when executed by a processor cause the processor to perform the operations of the present claim in the same manner as for claim 1, above.
Regarding Claims 2, 9, and 16, Gell/Mittal teaches wherein the determining the predicted charging duration for the rechargeable battery comprises receiving a state of charge of the rechargeable battery from the EV and a charge capacity of the rechargeable battery via an electronic message transmitted from the EV, and determining the predicted charging duration based on the state of charge and the charge capacity (Gell, fig. 6; ¶ [0113] – [0114]—the predicted charge duration is determined based on a current amount of charge in the battery {i.e. state of charge}, battery capacity, charging speed of each charging station, and other data).
Regarding Claims 3, 10, and 17, Gell/Mittal teaches wherein the method further comprises displaying one or more user interfaces on a display device of the EV, receiving input from the user via the one or more user interfaces associated with activity preferences of the user, and generating the profile of the user based on the input from the user (Gell, fig. 4; ¶ [0099] – [0102]).
Regarding Claims 5, 12, and 19, Gell/Mittal teaches wherein the method further comprises receiving feedback about the activity via a user interface displayed on one or more of a display system of the EV and a mobile device of the user, wherein the training comprises retraining the AI model based on the feedback about the activity (Mittal, “2) User Feedback and User Vector Tuning” portion of section III—user feedback about the activity is used for tuning {i.e. retraining} the AI model. Gell shows and describes receiving user input on a display system of the EV in figs. 7A and 7B, described in ¶ [0120]).
Regarding Claims 6 and 13, Gell/Mittal teaches wherein the method further comprises tracking activities performed by the user via the EV, storing data about the activities in the profile of the user, and training the AI model based on the data about the activities in the profile of the user (Mittal, section IV. A—activities and feedback of users are tracked and used to train the AI model).
Regarding Claims 7, 14, and 20, Gell/Mittal teaches wherein the method further comprises receiving real-time traffic data at the plurality of charging stations proximate the activity, wherein the determining the charging station of the plurality of charging stations comprises executing the AI model based on the real-time traffic data (Gell, ¶ [0122]).
Allowable Subject Matter
Claims 4, 11, and 18 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims. None of the prior art of record teaches “wherein the determining the charging station comprises executing a first AI model based on the predicted charging duration of the profile of the user to determine a list of charging stations for the EV, executing a second AI model based on status information of the plurality of charging stations and status information of a plurality of EVs to generate a list of EVs for a target charging station, and matching the target charging station to the EV based on the list of charging stations for the EV and the list of EVs for the target charging station” as recited by the present claims. Mittal uses only a single AI model, so it does not teach “executing a second AI model . . .” as recited by the present claims. No other prior art of record would make an obvious combination with Gell and Mittal to teach these limitations. None of the prior art cited below makes use of an AI model.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. This art includes:
Quint et al. (U.S. 2023/03898) teaches an EV system and method that recommends nearby activities when the EV is being charged or serviced based on a user profile and a duration of the charging or service
Eylander et al. (U.S. 2024/0219191) teaches an EV system that learns user mobility patterns to recommend multi-tasking charging stops
Maeda et al. (U.S. 2023/0234468) teaches selecting an EV charging station in consideration of points of interest frequented by an EV
Chikkannanavar et al. (U.S. 2017/0308948) teaches ranking activity centers that a user has visited when selecting an EV charging station
DeLuca et al. (U.S. 2020/0217679) teaches guiding an EV to a charging station near a user’s preferred points of interest
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HAL W SCHNEE whose telephone number is (571) 270-1918. The examiner can normally be reached M-F 7:30 a.m. - 6:00 p.m.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michael Huntley can be reached at 303-297-4307. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/HAL SCHNEE/Primary Examiner, Art Unit 2129
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