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 .
Status of the Claims
This Office Action is in response to the Applicant’s amendments and remarks filed December 29, 2025. Claims 1, 11, and 20 have been amended. Claims 2, and 12 have been canceled. Claims 1, 3-11, and 13-20 are pending.
Response to Remarks/Arguments
Applicant’s arguments and amendments filed December 29, 2025 with respect to the previous 35 U.S.C. 101 rejections have been fully considered.
With respect to the previous rejections under 35 U.S.C. 101, Applicant argues the claimed invention is eligible subject matter as the claimed invention cannot be considered a mental process because it cannot be practically performed in the human mind, and analogizes to Example 39 to support Applicant’s argument that the claimed invention does not recite a judicial exception, as the claimed invention similarly uses a machine learning model to generate predictions and relies on a training process performed for the machine learning model with particular training data. Applicant further argues, even if the claimed invention can be construed as an abstract idea, the claimed invention is integrated into a practical application because the claimed invention improves the technical field of automated electric vehicle charge time estimation. Applicant points to the recent Desjardins rehearing decision as instructive regarding cautions against overly broad application of the Alice guidance and the eligibility of particular machine learning inventions that comprise an improvement to the functioning of a machine learning model.
Examiner respectfully disagrees. As to Applicant’s argument that the claimed invention that the claimed invention ** Examiner maintains the broadest reasonable interpretation practically can be performed by a person, either in their mind or on pen and paper, because a person can reasonably perform determining a predicted charge time increment based on collected data. The use of machine learning in the claimed invention is an example of the “apply it” standard and does not preclude the claimed invention from being construed as a mental process.
With respect to Example 39 mentioned above, the court in that case held the claimed invention eligible because the claimed invention directly addressed a known issue specific to facial recognition, specifically, the inability to robustly detect human faces due to shifts, distortions, and variations, and solves this issue by applying a specific set of transformations to each digital image of a collected set, and using first and second stages of training to specifically address known issues with using expanded training sets. This is distinguishable from the presently claimed invention because Applicant’s invention is not addressing a known issue specific to a data set analogous to the facial recognition of Example 39. Applicant’s invention uses machine learning in a routine and conventional way, i.e., performing learning based on a set of inputs, updating a model, and outputting model results, by training a model to assist in performing a mental process.
With respect to Applicant’s argument regarding an improvement, Examiner asserts the stated improvement is an improvement to the abstract idea itself, rather than to any specific technology. This is supported by Applicant’s own statement that the claimed invention recites a technical improvement to the field of automated electric vehicle charge time predictions. This alleged improvement is analogous to what the court in Recentive Analytics Inc. v. Fox Corp described as applying established methods of machine learning to a new data set, i.e., vehicle charge time prediction data. Similarly, the claimed invention is distinguishable form Desjardins because Desjardins was drawn to improvements to machine learning, whereas the presently claimed invention is directed to improvements to electric vehicle charge time predictions, not machine learning itself. Applicant’s Specification further supports a finding that the claimed invention is not directed to improvements to machine learning because Applicant’s Specification discusses the machine learning model at a high level and indicates the model may be any suitable type of machine learning model (See at least ¶34 of Applicant’s PGPUB) rather than being drawn to a specific model and discussing improvements to machine learning.
For at least the above reasons, the claimed invention is not eligible subject matter and the previous rejections under 35 U.S.C. are maintained.
Applicant’s arguments and amendments filed December 29, 2025 with respect to the previous 35 U.S.C. 103 rejections have been fully considered.
With respect to the previous rejection under 35 U.S.C. 103 of claim 1, Applicant argues the cited art of Hourunranta, US 20250290769 A1, and Heino et al., US 20250128635 A1, hereinafter referred to as Hourunranta, and Heino, respectively, fails to explicitly disclose all of the features of claim 1, as presently amended, specifically, “receiving, from the machine learning model in response to the inputs, a set of incremental charge time predictions corresponding to a plurality of increments of the given increment size between a current charge level of the battery of the vehicle and a target charge level of the battery of the vehicle.” Applicant argues Hourunranta fails to explicitly disclose similar incremental charge time predictions corresponding to a plurality of increments of a given increment size between a current charge level of a battery and a target charge level of the battery.
Examiner respectfully disagrees. Applicant appears to argue a more narrow interpretation of incremental charge time predictions than claimed. Examiner notes time is an increment, and Hourunranta’s estimates expressed in terms of time, therefore, meet the broadest reasonable interpretation of the claimed increments. If Applicant intends the claimed increment size to represent something more specific than a generalized increment, like amounts of charge, Examiner recommends amending the claims accordingly assuming such amendments are supported in Applicant’s original disclosure.
Hourunranta fails to explicitly disclose the use of machine learning to implement its charge time prediction. However, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Hourunranta and include the feature of using a machine learning model to predict charging times as claimed, with a reasonable expectation of success, because Heino teaches it is well-known and routine to use a machine learning model to predict charging times given the ubiquity of machine learning in all computing environments including estimating charge times (Machine learning model for predicting charging times – See at least Abstract of Heino). Applicant is merely combining the charge time prediction of Hourunranta with well-known and routine machine learning.
For at least the above reasons, the above subject matter is rendered obvious by the combination of Hourunranta and Heino.
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, 3-11, and 13-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claims 1, 3-11, and 13-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claim 1, 3-11, and 13-20 are directed to the abstract idea of estimating a charging time, as explained in detail below. The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional computer elements, i.e., processors, memory, and display, which are recited at a high level of generality, and using machine learning consistent with the court’s “apply it” principle, provide conventional computer functions that do not add meaningful limits to practicing the abstract idea.
Claim 1 recites a method for electric vehicle charge time prediction, comprising: providing, as inputs to a machine learning model, one or more attributes of a battery of a vehicle and one or more attributes of a vehicle charger, wherein the machine learning model has been trained, based on training data comprising particular attributes associated with past incremental charge times corresponding to a given increment size, through a supervised learning process comprising: providing the particular attributes as training inputs to the machine learning model; receiving, from the machine learning model based on the training inputs, predicted incremental charge times corresponding to given increment size between starting charge levels and completed charge levels; and updating one or more parameters of the machine learning model based on comparing the predicted incremental charge times to the past incremental charge times; receiving, from the machine learning model in response to the inputs, a set of incremental charge time predictions corresponding to a plurality of increments of the given increment size between a current charge level of the battery of the vehicle and a target charge level of the battery of the vehicle; providing via a user interface screen, based on the set of incremental charge time predictions, a charge time estimate for charging the battery of the vehicle using the vehicle charger; and providing, via the user interface screen, based on the set of incremental charge time predictions and without receiving any additional outputs from the machine learning model, an alternative charge time estimate for charging the battery of the vehicle to a different target charge level using the vehicle charger.
These features describe the concept of performing a judgment, observation, or evaluation as to estimating a charging time for an electric vehicle which corresponds to concepts identified as abstract ideas by the courts, such as the collection, analysis, and display of information of Electric Power Group, LLC v. Alstom. The invention described in claim 1 is not meaningfully different than the concept found by the court to be an abstract idea because Applicant’s invention is directed to receiving data, performing data analysis and processing using generalized machine learning, and displaying the results of the preceding data analysis and processing, which is ineligible subject-matter because the invention is a collection of abstract ideas that do not require anything but entirely conventional, generic technology.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception, because the claim recites insignificant pre-solution data gathering, insignificant post-solution displaying via a user interface, and generalized use of machine learning to implement the abstract idea, which does not render the claimed invention eligible subject matter.
Claims 3-10 depend on claim 1 but do not render the claimed invention eligible because they are directed to additional considerations related to the mental process outlined above, i.e., additional vehicle attributes, etc., using further machine learning examples, i.e., supervised learning, a gradient boosted tree model, and physics-based algorithm, to implement the abstract idea, which do not render the claimed invention eligible.
Independent claims 11, and 20 are rejected under the same rationale as claim 1 because the claims recite nearly identical subject matter but for minor differences due to the claims being drawn to different statutory classes of invention.
Claims 13-19 depend from claim 11, and are rejected under the same rationale as claims 2-10 because the claims recite nearly identical subject matter.
Claims 1, 3-11, and 13-20 are therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
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 1-6, 8, 11-16, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Hourunranta, US 20250290769 A1, in view of Heino et al., US 20250128635 A1, and in view of Heino et al., US 20240391343 A1, hereinafter referred to as Hourunranta, Heino ‘635, and Heino ‘343, respectively.
As to claims 1, Hourunranta discloses a method for electric vehicle charge time prediction, comprising:
providing, as inputs, one or more attributes of a battery of a vehicle and one or more attributes of a vehicle charger, (Estimate charge time based in part on battery level and other attributes – See at least ¶28, 58, and 60; Estimate charge time based in part on specific information for vehicle charging station, i.e., “vehicle charger attributes” – See at least ¶28);
receiving, in response to the inputs, a set of incremental charge time predictions corresponding to a plurality of increments between a current charge level of the battery of the vehicle and a target charge level of the battery of the vehicle (Plurality of estimates selectable by user – See at least ¶28, and 59; Examiner notes estimated charging times are expressed in terms of time which is incremental.); and
providing via a user interface screen, based on the set of incremental charge time predictions, a charge time estimate for charging the battery of the vehicle using the vehicle charger (Display estimate charge time – See at least ¶5),
Hourunranta fails to explicitly disclose using a machine learning model to predict charging times as claimed. However, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Hourunranta and include the feature of using a machine learning model to predict charging times as claimed, with a reasonable expectation of success, because Heino ‘635 teaches it is well-known and routine to use a machine learning model to predict charging times given the ubiquity of machine learning in all computing environments including estimating charge times (Machine learning model for predicting charging times – See at least Abstract of Heino ‘635). Applicant is merely implementing the invention of Hourunranta using well-known and routine machine learning principles like performing training based on inputs, and updating a model to produce provide the same recommendation as Hourunranta regarding a charge time prediction.
The combination of Hourunranta and Heino ‘635 fails to explicitly disclose providing, via the user interface screen, based on the set of incremental charge time predictions, an alternative charge time estimate for charging the battery of the vehicle to a different target charge level using the vehicle charger. However, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Hourunranta and Heino ‘635 and include the feature of providing, via the user interface screen, based on the set of incremental charge time predictions, an alternative charge time estimate for charging the battery of the vehicle to a different target charge level using the vehicle charger, with a reasonable expectation of success, because Heino ‘343 teaches it is well-known and routine to determine charge times for different total charges amounts, i.e., 80% full versus 100% (Estimate times to 80% and 100% – See at least ¶35 of Heino ‘343), because a person of ordinary skill in the art understands electric vehicle users routinely charge their vehicles to 80% rather than a full 100% due to the nature of vehicle charging efficiencies.
Independent claims 11, and 20 are rejected under the same rationale as claim 1 because the claims recite nearly identical subject matter but for minor differences due to the claims being directed to different statutory categories of invention.
As to claims 3, and 13, Hourunranta fails to explicitly disclose the machine learning model comprises a gradient boosted tree model. However, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Hourunranta and include the feature of the machine learning model comprises a gradient boosted tree model, with a reasonable expectation of success, because Heino ‘635 teaches it is well-known and routine to use a supervised learning model to predict charging times given the ubiquity of machine learning in all computing environments including estimating charge times (Gradient boosted tree model – See at least ¶5 of Heino ‘635).
As to claims 4, and 14, Hourunranta discloses the one or more attributes of the battery of the vehicle comprise one or more of: a voltage; a current; a temperature; or a state of health (Temperature – See at least ¶71).
As to claims 5, and 15, Hourunranta fails to explicitly disclose the one or more attributes of the vehicle charger comprise one or more of: a current limit; a target current; or a pin temperature. However, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Hourunranta and include the feature of the one or more attributes of the vehicle charger comprise one or more of: a current limit; a target current; or a pin temperature, with a reasonable expectation of success, because Heino ‘635 teaches pin temperature is a well-known and routine consideration when estimating charge times (Gradient boosted tree model – See at least ¶43 of Heino ‘635).
As to claims 6, and 16, Hourunranta discloses the inputs provided to the machine learning model further comprise one or more attributes of the vehicle (Estimate charge time based in part on battery level and other attributes – See at least ¶28, 58, and 60; Examiner notes battery attributes meet the broadest reasonable interpretation of vehicle attributes as the battery is part of the vehicle.).
As to claims 8, and 18, Hourunranta discloses the charge time estimate is provided based on a first charge time estimate request relating to a first target charge amount, and wherein the method further comprises providing, via the user interface screen, based on the set of incremental charge time predictions, an updated charge time estimate based on a second charge time estimate request relating to a second target charge amount that is different than the first target charge amount (User can select charging level they want to achieve, i.e., 80% vs. 100% – See at least ¶60).
Claims 7, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Hourunranta, US 20250290769 A1, in view of Heino et al., US 20250128635 A1 as applied to claims 6 and 16 above, and further in view of Choi, US 20230311684 A1, hereinafter referred to as Hourunranta, Heino, and Choi, respectively.
As to claims 7, and 17, the combination of Hourunranta and Heino fails to explicitly disclose the one or more attributes of the vehicle comprise one or more of: a model type; an ownership type; or a mileage. However, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Hourunranta and Heino and include the feature of the one or more attributes of the vehicle comprise one or more of: a model type; an ownership type; or a mileage, with a reasonable expectation of success, because Choi teaches it is well-known and routine for vehicle charging information to include various data including type of vehicle, model year, and/or mileage (See at least ¶13 of Choi).
Claims 9, 10, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Hourunranta, US 20250290769 A1, in view of Heino et al., US 20250128635 A1, and in view of Heino et al., US 20240391343 A1, as applied to claims 1 and 11 above, and further in view of Carlson, US 20200338959 A1, hereinafter referred to as Hourunranta, Heino ‘635, Heino ‘343, and Carlson, respectively.
As to claims 9, and 19, the combination of Hourunranta, Heino ‘635, and Heino ‘343 fails to explicitly disclose determining an alternate charge time prediction using a physics-based algorithm based on the one or more attributes of the battery of the vehicle and the one or more attributes of the vehicle charger, wherein the providing of the charge time estimate is further based on the alternate charge time prediction. However, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Hourunranta, Heino ‘635, and Heino ‘343 and include the feature of determining an alternate charge time prediction using a physics-based algorithm based on the one or more attributes of the battery of the vehicle and the one or more attributes of the vehicle charger, wherein the providing of the charge time estimate is further based on the alternate charge time prediction, with a reasonable expectation of success, because Carlson teaches physics-based algorithms are well-known and routine approaches to managing electric batteries (See at least ¶65 of Carlson).
As to claim 10, the combination of Hourunranta, Heino ‘635, Heino ‘343, and Carlson fails to explicitly disclose determining to use the set of incremental charge time predictions rather than the alternate charge time prediction for determining the charge time estimate based on a confidence level associated with the set of incremental charge time predictions. However, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Hourunranta, Heino ‘635, Heino ‘343, and Carlson and include the feature of determining an alternate charge time prediction using a physics-based algorithm based on the one or more attributes of the battery of the vehicle and the one or more attributes of the vehicle charger, wherein the providing of the charge time estimate is further based on the alternate charge time prediction, with a reasonable expectation of success, because a person of ordinary skill in the art would readily recognize the value of choosing an option with higher confidence is self-evident.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Lail Kleinman whose telephone number is (571)272-6286. The examiner can normally be reached M-F 8:00-5:00.
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/LAIL A KLEINMAN/Primary Examiner, Art Unit 3668
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