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 .
Response to Arguments
Regarding the previous claim objections, the previous claim objections are withdrawn in light of the present claim amendments.
Regarding the previous Double Patenting rejection, Applicant’s remarks requests that the double patenting rejection be held in abeyance. Accordingly, an updated Double Patenting rejection is maintained.
Regarding the previous 35 USC 101 rejections, Applicants arguments have been fully considered but are not persuasive. The claim amendments include determining parameters for an environment based on the regenerable elevation index, the parameters comprising a horizon length and a speed range; generating a speed profile based on the parameters; and generating control actions for the vehicle based on the speed profile. However, it is interpreted that the limitations recite an abstract idea of a person determining parameters for an environment, determining a speed profile, and determining control actions. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. As similarly recited on page 16 of the non-final office action 04/21/2026, the generating does not appear to require controlling the movement of the vehicle. It appears Applicant would be in favor of incorporating causing, by the computing system, control of at least one of braking, acceleration, and steering of the vehicle to generate movement based on the control actions as recited on page 17 of non-final office action 04/21/2026. Accordingly, the previous 35 USC 101 rejection is maintained.
Regarding the previous 35 USC 103 rejections, Applicant’s arguments have been fully considered but are not persuasive. Applicants argue that prior arts do not explicitly disclose “determining, by the computing system parameters for an environment based on the regenerable elevation index, the parameters comprising a horizon length and a speed range; generating, by the computing system, a speed profile based on the parameters; and generating, by the computing system, control actions for the vehicle based on the speed profile”. Vajedi discloses the simulation results of the cruise controller that include speed profile, distance, elevation, and the optimum predicted horizon and discloses maintaining the vehicle speed in a low rate (page 119), which discloses a relationship between the speed, distance, elevation, and the optimum predicted horizon. In light of Applicant’s arguments, Vajedi does not explicitly disclose deriving the speed range and horizon based on the prediction horizon. Maleki discloses determining, by the computing system, parameters for the environment based on a minimized regenerable elevation index by determining a current or future speed profile of the vehicle that will reduce the energy use of the vehicle and a prediction window but, in light of Applicant’s arguments, Maleki does not explicitly disclose generating the regenerable elevation index and then deriving the horizon length based on the previously generated regenerable elevation index (see at least [0216]: predictive enhanced cruise controller 602 can work on the principle of minimizing energy consumption over a distance preview window or a time preview window by intelligently driving the control commands to optimal states for a given set of vehicle and environment operating conditions). Accordingly, the previous 35 USC 103 rejection is withdrawn.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-8, 10-21 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12246747. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims recite similar subject matter with only minor variations.
As per claim(s) 1, claims 1-20 of U.S. Patent No. 12246747 disclose a computer-implemented method comprising:
determining, by a computing system, training data comprising at least one of horizon length, road elevation, and a speed range (see at least claim 1: machine learning model…elevation indexes for distances ahead of a vehicle in an environment and speed ranges for the vehicle…speed profile…, claim 2: parameters include a horizon length and a speed range, claim 5: determining the parameters for the environment comprises: providing, by the computing system, a set of parameters, including the parameters for the environment, and metrics for the environment to a machine learning model, wherein the set of regenerable elevation indexes is determined based on the machine learning model, and wherein the machine learning model is trained based on simulations of vehicle performance in environments and fuel economy performance improvements determined based on the simulations, claim 8: elevations…, claim 6: wherein the fuel economy performance improvements are relative to a baseline fuel economy performance based on a vehicle at a constant speed, claim 7: providing, by the computing system, the parameters associated with the highest regenerable elevation index as an input of an input-output pair for training);
training, by the computing system, a machine learning model based on the training data (see at least claim 5: wherein the machine learning model is trained, claim 7: input-output pair for training); and
generating, by the computing system, a regenerable elevation index (see at least claim 1: based on a machine learning model, determining, by a computing system, a set of regenerable elevation indexes for distances ahead of a vehicle in an environment and speed ranges for the vehicle);
determining, by the computing system parameters for an environment based on the regenerable elevation index, the parameters comprising a horizon length and a speed range (see at least claim 1: based on a machine learning model, determining, by a computing system, a set of regenerable elevation indexes for distances ahead of a vehicle in an environment and speed ranges for the vehicle…determining, by the computing system, parameters for the environment based on the highest regenerable elevation index of the set of regenerable elevation indexes; generating, by the computing system, a speed profile for the environment based on the parameters);
generating, by the computing system, a speed profile based on the parameters (see at least claim 1: based on a machine learning model, determining, by a computing system, a set of regenerable elevation indexes for distances ahead of a vehicle in an environment and speed ranges for the vehicle…determining, by the computing system, parameters for the environment based on the highest regenerable elevation index of the set of regenerable elevation indexes; generating, by the computing system, a speed profile for the environment based on the parameters); and
generating, by the computing system, control actions for the vehicle based on the speed profile (see at least claim 1: generating, by the computing system, control actions for the vehicle in the environment based on the speed profile).
Claims 11 (system) and 16 (non-transitory computer-readable storage medium) recite similar limitations and are rejected under the same rationale as claim 1. Further, claims 12 and 17 of U.S. Patent No. 12246747 recite a system and non-transitory computer-readable storage medium, respectively.
As per claim 2, claims 1-20 of U.S. Patent No. 12246747 disclose wherein the horizon length is a distance ahead of the vehicle, the road elevation is elevations in an environment of the vehicle, and the speed range is a lower speed limit and an upper speed limit within which to maintain the vehicle (see at least claim 1, claim 2: wherein the horizon length provides a distance ahead of the vehicle, and wherein the speed range provides a lower speed limit and an upper speed limit, claim 8: elevations in the environment). Claim(s) 12 and 17 recite similar limitations and are rejected under the same rationale.
As per claim 3, claims 1-20 of U.S. Patent No. 12246747 disclose wherein the generating the regenerable elevation index based on the machine learning model (see at least claim 1: based on a machine learning model, determining, by a computing system, a set of regenerable elevation indexes for distances ahead of a vehicle in an environment and speed ranges for the vehicle). Claim(s) 13 and 18 recite similar limitations and are rejected under the same rationale.
As per claim 4, claims 1-20 of U.S. Patent No. 12246747 disclose wherein the training data further comprises fuel economy performance improvement (see at least claim 5: wherein the machine learning model is trained based on simulations of vehicle performance in environments and fuel economy performance improvements determined based on the simulations). Claim(s) 14 and 19 recite similar limitations and are rejected under the same rationale.
As per claim 5, claims 1-20 of U.S. Patent No. 12246747 disclose wherein the fuel economy performance improvement is determined relative to baseline fuel economy performance in a simulation in which the vehicle maintains a constant speed (see at least claim 6: wherein the fuel economy performance improvements are relative to a baseline fuel economy performance based on a vehicle at a constant speed). Claim(s) 15 and 20 recite similar limitations and are rejected under the same rationale.
As per claim 6, claims 1-20 of U.S. Patent No. 12246747 disclose wherein the training data includes a first instance of training data associated with a fuel economy performance that is higher than a fuel economy performance associated with a second instance of training data (see at least claim 7: providing, by the computing system, the parameters associated with the highest regenerable elevation index as an input of an input-output pair for training; and providing, by the computing system, fuel economy performance improvements determined based on performance of the vehicle as associated with an output of the input-output pair, claim 11: wherein a regenerable elevation index for a hilly terrain is relatively higher than a regenerable elevation index for a flat terrain). Claim(s) 21 recites similar limitations and is rejected under the same rationale.
As per claim 7, claims 1-20 of U.S. Patent No. 12246747 disclose wherein the training the machine learning model comprises training the machine learning model to generate a first regenerable elevation index based on the first instance of training data that is higher than a second regenerable elevation index based on the second instance of training data (see at least claim 1: machine learning model, claim 5: training, claim 11: wherein a regenerable elevation index for a hilly terrain is relatively higher than a regenerable elevation index for a flat terrain).
As per claim 8, claims 1-20 of U.S. Patent No. 12246747 disclose wherein the generating the regenerable elevation index comprises generating a plurality of regenerable elevation indices based on a plurality of horizon lengths and speed ranges, the method further comprising;
generating a speed profile based on a horizon length and a speed range of the plurality of horizon lengths and speed ranges associated with the highest regenerable elevation index of the plurality of regenerable elevation indices (see at least claim 1: based on a machine learning model, determining, by a computing system, a set of regenerable elevation indexes for distances ahead of a vehicle in an environment and speed ranges for the vehicle…determining, by the computing system, parameters for the environment based on the highest regenerable elevation index of the set of regenerable elevation indexes; generating, by the computing system, a speed profile for the environment based on the parameters).
As per claim 10, claims 1-20 of U.S. Patent No. 12246747 disclose wherein the training data further comprises an input-output pair, wherein an input of the input-output pair includes the horizon length and the speed range associated with the highest regenerable elevation index and an output of the input-output pair includes an actual fuel economy performance of the vehicle (see at least claim 1, claim 2: wherein the parameters include a horizon length and a speed range associated with the highest regenerable elevation index; claim 6: wherein the fuel economy performance improvements are relative to a baseline fuel economy performance based on a vehicle at a constant speed, claim 7: providing, by the computing system, the parameters associated with the highest regenerable elevation index as an input of an input-output pair for training; and providing, by the computing system, fuel economy performance improvements determined based on performance of the vehicle as associated with an output of the input-output pair).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1-8, 10-21 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
A claim that recites an abstract idea, a law of nature, or a natural phenomenon is directed to a judicial exception. Abstract ideas include the following groupings of subject matter, when recited as such in a claim limitation: (a) Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations; (b) Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions); and (c) Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion). See MPEP 2106.
Even when a judicial element is recited in the claim, an additional claim element(s) that integrates the judicial exception into a practical application of that exception renders the claim eligible under §101. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. The following examples are indicative that an additional element or combination of elements may integrate the judicial exception into a practical application:
the additional element(s) reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
the additional element(s) that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
the additional element(s) implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
the additional element(s) effects a transformation or reduction of a particular article to a different state or thing; and
the additional element(s) applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Examples in which the judicial exception has not been integrated into a practical application include:
the additional element(s) merely recites the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea;
the additional element(s) adds insignificant extra-solution activity to the judicial exception; and
the additional element does no more than generally link the use of a judicial exception to a particular technological environment or field of use.
See MPEP 2106.
101 Analysis – Step 1
Claims 1, 11, 16 are directed to a method, system, and non-transitory computer readable medium. Therefore, the claims are within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis in the MPEP 2106, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claim(s) 1 includes limitations that recite an abstract idea (emphasized below) and will be used as a representative claim for the remainder of the 101 rejection.
Claim 1 recites:
determining, by a computing system, training data comprising at least one of horizon length, road elevation, and a speed range;
training, by the computing system, a machine learning model based on the training data;
generating, by the computing system, a regenerable elevation index;
determining, by the computing system, parameters for an environment based on the regenerable elevation index, the parameters comprising a horizon length and a speed range;
generating, by the computing system, a speed profile based on the parameters; and
generating, by the computing system, control actions for the vehicle based on the speed profile.
The examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. For example, the limitation(s) in the context of this claim encompasses a person determining and generating input-output pairing of input environment data and output index intended for vehicle control.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis in the MPEP 2106, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract idea into a practical application. As noted in the MPEP 2106, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitation” while the bolded portions continue to represent the abstract idea):
Claim 1 recites:
determining, by a computing system, training data comprising at least one of horizon length, road elevation, and a speed range;
training, by the computing system, a machine learning model based on the training data; and
generating, by the computing system, a regenerable elevation index;
determining, by the computing system, parameters for an environment based on the regenerable elevation index, the parameters comprising a horizon length and a speed range;
generating, by the computing system, a speed profile based on the parameters; and
generating, by the computing system, control actions for the vehicle based on the speed profile.
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations, the examiner submits that these limitations are additional elements that do not integrate the judicial exception into a practical application and amount to no more than mere instructions to apply the exception using generic computer components or insignificant extra-solution activities that merely use a computer to perform the process. The additional elements are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using generic computer components. The limitations training by a machine learning model based on the training data are recited at a high level of generality and provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f) Mere Instructions to Apply An Exception, which provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. The additional limitation steps are recited at a high level of generality (i.e. as a general means of gathering data, transmitting signals, outputting), and amounts to mere data gathering and storing and transmitting do not add a meaningful limitation to the process (MPEP 2106.05(g) v. Consulting and updating an activity log, Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754), which are forms of insignificant extra-solution activities. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than drafting effort designed to monopolize the exception (MPEP 2106.05). The additional limitations merely describe how to generally apply the otherwise mental judgements in a generic or general purpose vehicle environment. The additional limitations are recited at a high level of generality and merely automates the steps. Accordingly additional limitation(s) do/does not integrate the abstract into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
Regarding Step 2B of the MPEP 2106, representative independent claim does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to nothing more than applying the exception using generic computer components. Generally applying an exception using a generic computer component cannot provide an inventive concept.
Further, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field. The additional limitations do not provide any indication that the additional elements are anything other than a conventional computer within a vehicle. Also, MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, INC., 788 F.3d 1359, 1363 (Fed. Cir. 2015), and Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93 indicate that mere collection or receipt of data over a network, receiving or transmitting data over a network, and storing and retrieving information in memory are a well-understood, routine, and conventional functions when claimed in a merely generic manner (as it is here). Further, the Federal Circuit in Trading Techs. Int’l v. IBGLLC, 921 F.3d1084,1093(Fed. Cir.2019), and Intellectual Ventures I LLC v. Erie Indemnity Co., 850 F.3d1315, 1331 (Fed. Cir. 2017), for example, indicated that the mere displaying of data is a well understood, routine, and conventional function.
The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements are recited at a high level of generality and amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim(s) is/are not patent eligible.
Dependent claims 2-8, 10, 12-15, 17-21 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or additional elements that amount to no more than mere instructions to apply the exception using generic computer components that do not integrate the judicial exception into a practical application sufficient to amount to significantly more than the judicial exception. The additional elements are recited at a high level of generality and merely automates the steps such as claim 9 reciting generating control actions for the vehicle in the environment based on the speed profile; however, the generating does not appear to require causing, by the computing system, control of at least one of braking, acceleration, and steering of the vehicle to generate movement based on the control actions. The additional limitations are recited at a high level of generality and amounts to mere data gathering, which is a form of insignificant extra-solution activity; the additional limitations are well-understood, routine, and conventional activity because the specification does not provide any indication that the additional elements are anything other than a conventional computer components. The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements are recited at a high level of generality and amount to no more than mere instructions to apply the exception using generic computer components. Further, MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, INC., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well-understood, routine, and conventional function when it is claimed in a merely generic manner. Furthermore, the Federal Circuit in Trading Techs. Int’l v. IBGLLC, 921 F.3d1084,1093(Fed. Cir.2019), and Intellectual Ventures I LLC v. Erie Indemnity Co., 850 F.3d1315, 1331 (Fed. Cir. 2017), for example, indicated that the mere displaying of data is a well understood, routine, and conventional function. Moreover, mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Therefore, the dependent claims are not patent eligible under the same rationale as provided for in the rejection of the independent claim.
For purposes of compact prosecution, the independent claims may be amended to incorporate causing, by the computing system, control of at least one of braking, acceleration, and steering of the vehicle to generate movement based on the control actions to overcome the instant 35 USC 101 rejection.
Therefore, claim(s) 1-8, 10-20 is/are ineligible under 35 USC 101.
Allowable Subject Matter
Claim(s) 1-8, 10-21 would be allowable if the Double Patenting and 35 U.S.C. 101 rejection is overcome, set forth in this Office action.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US 20230194281 (“Lane”) ([0086]: train a learning system using the training data to generate a plurality of projected amounts of energy usage for a particular type machine having particular physical and operational characteristics as it travels over a particular travel route segment based on real time values for the input data using a learning function including at least one learning parameter, [0090]: Once trained, the machine learning engine can generate predicted amounts of energy usage based on a second set of input conditions by applying the mapping or classification to the second set of input conditions).
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 ANGELINA M SHUDY whose telephone number is (571)272-6757. The examiner can normally be reached M - F 10am - 6pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Fadey Jabr can be reached at 571-272-1516. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Angelina Shudy
Primary Examiner
Art Unit 3668
/Angelina M Shudy/Primary Examiner, Art Unit 3668
