CTNF 18/083,865 CTNF 101947 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claims 1-15 have been presented for examination based on the amendment filed on 12/19/2022. Claim 1-15 provisionally rejected on the ground of nonstatutory double patenting Claims 9-15 are rejected under 35 U.S.C. 101 Claims 1-8 are objected under 35 U.S.C. 112(f) 07-21-aia AIA Claim (s) 1-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2023/0196846 A1 By Braunstein et al. further in view of NPL: Parameterized Cloud-Connected Electro-Thermal Modelling of a Battery Electric Vehicle By Chakraborty et al . This action is made Non-Final . Double Patenting 08-33 AIA 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. 08-35 AIA Claim 1-15 provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1-12 of copending Application No. 18/085208 (US20240060786A) . Although the claims at issue are not identical, they are not patentably distinct from each other because Application 18/085208 claims a system and method for "learning" an energy efficient model for EV "learning data." The current application (18/083865) merely limits this "learning" to a specific, predictable species: "averaging local "parameter sets." . This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented. Instant Claim Co-Pending Mapping Claim 1 Preamble: A system for modeling energy consumption efficiency of an electric vehicle, the system comprising: a communication device configured to communicate with a plurality of electric vehicles; and a controller configured to: receive a parameter set of an energy consumption efficiency model [A1] from the plurality of electric vehicles, determine an average of received parameter sets as an optimal parameter set [A2], transmit the determined optimal parameter set [A3] to the plurality of electric vehicles. (None) Claim 1 Preamble: A system for modeling energy consumption efficiency of an electric vehicle, the system comprising: a communication device configured to communicate with a plurality of electric vehicles; and a controller configured to: receive learning data from the plurality of electric vehicles, learn an energy consumption efficiency model [A1] based on the received learning data, transmit the energy consumption efficiency model in which the learning is completed to the plurality of electric vehicles, wherein the electric vehicle is configured to: obtain an energy consumption prediction curve for a preset time by inputting driving data for the preset time to a basic energy consumption efficiency model [A1]; determine an energy consumption actual measurement curve for the preset time based on an output current and an output voltage of a battery; and determine the driving data for the preset time [A3] and the energy consumption actual measurement curve for the preset time as learning data when mean square error (MSE) values of the energy consumption prediction curve and the energy consumption actual measurement curve exceed a threshold value. [A2] Preamble: The technical field and general system purpose are identical The required hardware component and its configuration are identical Identical structural component for data processing. "Parameter sets" are a specific form of "learning data" used in distributed model optimization. energy consumption efficiency model [A1] is mapped to Co-pending limitation (d). Averaging parameters is a standard technique for "learning" a global model in a distributed (federated) system. optimal parameter set [A2] is mapped to Co-pending limitation (f). (MSE is a statistical metric that measures the average squared difference between the estimated values and the actual observed values.) The optimal parameter set is the specific structural output of the learning process triggered by the MSE threshold condition described in (f). Sending optimized parameters is equivalent to sending a "completed" model to the fleet. optimal parameter set [A3] is mapped to Co-pending limitation (f). The optimal parameter set serves as the optimized global version of the model that results from the data generation and learning cycle triggered by the batter-derived curves and driving data. Claim 2 Claim 2 Nearly identical Claim 3 Claim 1 Nearly identical Claim 4 Claim 1 Nearly Identical Claim 5 Claim 4 Identical Claim 6 Claim 5 Identical Claim 7 Claim 5 Nearly Identical Claim 8 Claim 6 Identical Claim 9 Claim 7 Method version of claim 1, rejected with co-pending claim 7, in the same manner as claim 1 Claim 10 Claim 8 Nearly Identical Claim 11 Claim 7 Method version of claim 1, rejected with co-pending claim 7, in the same manner as claim 1 Claim 12 Claim 7 Method version of claim 1, rejected with co-pending claim 7, in the same manner as claim 1 Claim 13 Claim 10 Identical Claim 14 Claim 11 Identical Claim 15 Claim 12 Identical Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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. 07-103 AIA The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action. Claim s 9-15 are rejected under 35 U.S.C. 101 because the claims are directed to an mental process without significantly more without any additional elements that provides a practical application or amount to significantly more than the abstract idea. Claims [ 1 ] : Step 1: the claims are drawn to a method and system respectively, falling under one of the four statutory categories of invention. Step 2A, Prong 1: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim. The limitations are bolded for abstract idea/judicial exception identification. Claim 1 Mapping Under Step 2A Prong 1 A method of modeling energy consumption efficiency of an electric vehicle, the method comprising: receiving, by a communication device, a parameter set of an energy consumption efficiency model from a plurality of electric vehicles; determining, by a controller, an average of received parameter sets as an optimal parameter set; and transmitting, by the controller, the determined optimal parameter set to the plurality of electric vehicles. Mental Process: The steps of "learning" a model and "determining" whether driving data should be designated as learning data based on an error threshold are evaluations and judgments that can be performed in the human mind (or via pen and paper(see MPEP § 2106.04(a)(2), subsection III). Mental Process: The steps of "learning" a model and "determining" whether driving data should be designated as learning data based on an error threshold are evaluations and judgments that can be performed in the human mind (or via pen and paper). (see MPEP § 2106.04(a)(2), subsection III). Mathematical Concepts: "determining... an average," is a mathematical calculation. (as in 2106.04(a)(2) Abstract Idea Groupings) Mental Process: The steps of "learning" a model and "determining" whether driving data should be designated as learning data based on an error threshold are evaluations and judgments that can be performed in the human mind (or via pen and paper). (see MPEP § 2106.04(a)(2), subsection III). The claim recite the abstract idea of mathematical concepts and mental processes. Under its BRI, these cover a mental process including an observation, evaluation, judgement or opinion that could be performed in. the human mind or with the aid of pencil and paper. Step 2A, Prong 2: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d). As per (1) the additional elements are identified as bolded parts of the limitations in column 1 of the table below, and as per (2) the evaluation is shown in the mapping section of the table. In accordance with this step, the judicial exception is not integrated into a practical application. Claim 1 Mapping Under Step 2A Prong 2 A method of modeling energy consumption efficiency of an electric vehicle, the method comprising: receiving, by a communication device, a parameter set of an energy consumption efficiency model from a plurality of electric vehicles; determining, by a controller, an average of received parameter sets as an optimal parameter set; and transmitting, by the controller, the determined optimal parameter set to the plurality of electric vehicles. Additional Elements: Receiving and transmitting data via a "communication device" and using a "controller" to find an average. Evaluation : These elements are generic computer components performing their ordinary functions of data transfer and basic math. (See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016)) Limiting the application of "averaging" to the field of electric vehicles is considered a field-of-use limitation, which does not integrate the exception. See 2106.05(h) Field of Use and Technological Environment Additional Elements: Receiving and transmitting data via a "communication device" and using a "controller" to find an average. Evaluation : These elements are generic computer components performing their ordinary functions of data transfer and basic math. (See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016)) Limiting the application of "averaging" to the field of electric vehicles is considered a field-of-use limitation, which does not integrate the exception. See 2106.05(h) Field of Use and Technological Environment Additional Elements: Receiving and transmitting data via a "communication device" and using a "controller" to find an average. Evaluation: These elements are generic computer components performing their ordinary functions of data transfer and basic math. (See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016)) Limiting the application of "averaging" to the field of electric vehicles is considered a field-of-use limitation, which does not integrate the exception. See 2106.05(h) Field of Use and Technological Environment Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05. This step determines whether the additional elements amount to "significantly more" than the exception by providing an unconventional technological solution. (see MPEP § 2106.05(g) and see MPEP § 2106.05(h)) The hardware invoked (controllers, communication devices, and battery current/voltage sensors) is well-understood, routine, and conventional in the automotive and telematics industries. (See 2106.05(d) Well-Understood, Routine, Conventional Activity) (See, e.g., Intellectual Ventures v. Symantec, 838 F.3d 1307, 1317; 120 USPQ2d 1353, 1359 (Fed. Cir. 2016)) Aggregating parameters from a fleet to find an average represents the "predictable use of prior art elements according to their established functions." (see MPEP § 2144) (The claims do not recite a non-conventional arrangement or a specific improvement to the hardware itself. (See MPEP § 2106.05(f)) (See also MPEP § 2106.07(b), item (2)) Conclusion : The additional elements do not amount to significantly more. Claim 10 recites “The method of claim 9, further comprising: (see claim 9) updating, by the electric vehicle, the parameter set of the energy consumption efficiency model by using the optimal parameter set.” (Mental Processes) (see MPEP § 2106.04(a)(2), subsection III) Claim 11 recites “The method of claim 9, (See claim 9) wherein the receiving of the parameter set of the energy consumption efficiency model includes: learning, by the electric vehicle, the energy consumption efficiency model by using the determined learning data. (Mental Processes) (see MPEP § 2106.04(a)(2), subsection III) Claim 12 recites "The method of claim 11, wherein the learning of the energy consumption efficiency model includes: obtaining, by the electric vehicle, an energy consumption prediction curve for a preset time by inputting driving data for the preset time to the energy consumption efficiency model; (mere data gathering) (See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015)) determining, by the electric vehicle, an energy consumption actual measurement curve for the preset time based on an output current and an output voltage of a battery; and determining, by the electric vehicle, the driving data for the preset time and the energy consumption actual measurement curve for the preset time as learning data when mean square error (MSE) values of the energy consumption prediction curve (Step 2A Prong 1 – mathematical concept MPEP 2106.04(a)(2)(I)(C), See Specification [0080]) and the energy consumption actual measurement curve exceed a threshold value...." (Mental/mathematical) Claim 13 recites The method of claim 12 (See claim 12), wherein the driving data Includes at least one of an accelerator pedal position (APS), a brake pedal position (BPS), a gear ratio, a vehicle speed, a front clutch state, a rear clutch state, a road gradient, a road curvature, a motor torque, a motor temperature, a battery state of charge (SOC), a temperature of the battery, an outside temperature, a time since departure, a vehicle weight, or a combination thereof. (mere data gathering) (See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015)) Claim 14 recites “The method of claim 11, wherein the learning of the energy consumption efficiency model includes: obtaining, by the electric vehicle, an energy consumption prediction curve of a first road section by imputing driving data of the first road section to the energy consumption efficiency model (mere data gathering) (See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015)) determining , by the vehicle, an energy consumption actual measurement curve of the first road section based on output current and an output voltage of a battery; , (Mental Processes) (see MPEP § 2106.04(a)(2), subsection III) (Additional Elements: Using physical battery sensors (measuring "output current and an output voltage") to generate an "actual measurement curve.") and determining, by the electric vehicle, the driving data of the first road section and the energy consumption actual measurement curve as learning data when mean square error (MSE) values of the energy consumption prediction curve and the energy consumption actual measurement curve exceed a threshold. (Mental Processes) (see MPEP § 2106.04(a)(2), subsection III) (Step 2A Prong 1 – mathematical concept MPEP 2106.04(a)(2)(I)(C), See Specification [0080]) Claim 15 recites “The method of claim 14, (see claim 14) wherein the driving data includes at least one of an accelerator pedal position (APS), a brake pedal position (BPS), a gear ratio, a vehicle speed, a front clutch state, a rear clutch state, a road gradient, a road curvature, a motor torque, a motor temperature, a battery state of charge (SOC), a temperature of the battery, an outside temperature, a time since departure, a vehicle weight, or a 15 combination thereof.” (mere data gathering) (See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015)) 07-30-03-h AIA Claim Interpretation 07-30-03 AIA The following is a quotation of 35 U.S.C. 112(f): (f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph: An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. 07-30-05 The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked. As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph: (A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function; (B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and (C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function. Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function. Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function. Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. 07-30-06 This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: "Communication device" and "Controller" in claim 1. Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. Three-Prong Analysis Prong 1: Use of a Generic Placeholder The limitation uses the term "device," which is explicitly identified in MPEP § 2181 as a non-structural generic placeholder (nonce term) that may invoke § 112(f). While "communication" specifies a field of use, the term "communication device" does not connote a specific physical structure to a person of ordinary skill in the art (POSITA) but serves as a functional "black box". (See Williamson, 792 F.3d at 1347, 115 USPQ2d at 1110.) Prong 2: Modified by Functional Language The placeholder is modified by the functional phrase: "configured to communicate with a plurality of electric vehicles". The phrase "configured to" is recognized as a linking phrase that associates the nonce term with a specific task. (See Signtech USA, 174 F.3d at 1356, 50 USPQ2d at 1374-75.) Prong 3: Lack of Sufficient Structure in the Claim "Communication Device": The specification identifies this as element 12 in the cloud server and element 23 in the electric vehicle [0049]. The corresponding structure is disclosed as including a "mobile communication module," a "wireless Internet module," or a "short-range communication module". Specific hardware implementations cited include modules for GSM, CDMA, LTE, Wi-Fi, Bluetooth, ZigBee, and NFC [0051]-[0051]. The claim itself does not recite any internal circuitry, specific hardware components (such as specific antenna arrays or modems), or protocols that would perform the "communicating" function. It merely names the goal of communication without providing the structural means to achieve it. (See Seal-Flex, 172 F.3d at 849, 50 USPQ2d at 1234 (Rader, J., concurring) Conclusion (112(f) Invocation): "Communication device" is interpreted as a means-plus-function limitation because "device" is a nonce term and the claim lacks sufficient structure to perform the recited function. Prong 1: Use of a Generic Placeholder Although "controller" can sometimes be structural, in the context of computer-implemented functions, it often acts as a nonce term when it does not denote a specific structural name to a POSITA for the recited operations. (See, e.g., Noah Systems Inc. v. Intuit Inc., 675 F.3d 1302, 1312, 102 USPQ2d 1410, 1417 (Fed. Cir. 2012); Aristocrat, 521 F.3d at 1333, 86 USPQ2d at 1239.) Here, "controller" serves as a substitute for "means for" performing complex data processing tasks. (See MPEP § 2181, subsection II.B.) Prong 2: Modified by Functional Language The controller is modified by three distinct functional requirements: (a) "receive a parameter set... from the plurality of electric vehicles"; (b) "determine an average of received parameter sets as an optimal parameter set"; and (c) "transmit the determined optimal parameter set to the plurality of electric vehicles". Prong 3: Lack of Sufficient Structure in the Claim "Controller": The specification identifies the controller as element 14 (server-side) [0046] and element 24 (vehicle-side) [0057]. These are described as central processing units (CPUs), processors, or semiconductor devices [0093]. Because these perform computer-implemented functions, the structure is the CPU as programmed by an algorithm. The specification discloses the corresponding algorithm in FIG. 7 and associated text: Step 701: Receiving parameter sets (e.g., weights) via the communication device. Step 702: Calculating the arithmetic average of received sets (illustrated by numerical sets A, B, and C) to determine the "optimal" set. Step 703: Transmitting the averaged result back to the fleet. The disclosure of this sequence of steps satisfies the structural requirement for a computer-implemented means-plus-function limitation. The claim does not recite the internal architecture of the controller or specific logic circuitry. In computer-implemented inventions, a processor only lends sufficient structure to "basic" functions like receiving or storing data; specialized functions—such as "determining an average... as an optimal parameter set"—require an algorithm to provide sufficient structure [Fig.7]. (See, e.g., Noah Systems Inc. v. Intuit Inc., 675 F.3d 1302, 1312, 102 USPQ2d 1410, 1417 (Fed. Cir. 2012); Aristocrat, 521 F.3d at 1333, 86 USPQ2d at 1239). Since no algorithm is recited within the claim itself, it lacks sufficient structure. (See Aristocrat, 521 F.3d at 1338, 86 USPQ2d at 1241.) Conclusion (112(f) Invocation): "Controller" is interpreted as a means-plus-function limitation because it is used as a functional placeholder for specialized data processing tasks without reciting the necessary structural algorithm within the claim. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 07-20-aia AIA 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. 07-103 AIA The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action. 07-23-aia AIA The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. 07-20-02-aia AIA This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention. 07-22-aia AIA Claim (s) 1-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2023/0196846 A1 By Braunstein et al . as applied to claim s above, and further in view of NPL: Parameterized Cloud-Connected Electro-Thermal Modelling of a Battery Electric Vehicle By Chakraborty et al. Regarding Claim 1 Braunstein et al. teaches (L1) A system for modeling energy consumption efficiency of an electric vehicle, the system comprising: (Braunstein et al. [Abstract] : “A control system is programmed for monitoring the health and charge of the batteries used to power a battery electric machine (BEM) or other heavy equipment.” The examiner interprets where modeling energy consumption efficiency is shown as monitoring the health and charge of the batteries and where electric vehicle is shown as battery electric machine.) (L2) a communication device 1 ; (Braunstein et al. [0028] : “In some variations, the system may be onboard the machine and completely independent from any exterior sources of information (other than onboard sensors), or, alternatively, automatically enabled via instructions received over a wireless communication system from a back office or other central server, or from one or more servers on the cloud.” The examiner interprets where the communication device is shown as a wireless communication system) configured to communicate with a plurality of electric vehicles; (Braunstein et al. [0028] : “In some variations, the system may be onboard the machine and completely independent from any exterior sources of information (other than onboard sensors), or, alternatively, automatically enabled via instructions received over a wireless communication system from a back office or other central server, or from one or more servers on the cloud.” The examiner interprets where communication is shown as instructions, and where electric vehicles is shown as machines. (L3) and a controller 2 configured to: (Braunstein et al. [Abstract]: “The control system is programmed to receive historical information mapping the performance and energy consumption of a BEM or other heavy equipment, such as battery state-of-charge, power usage, battery state- of-health ,and number of charge cycles for a battery supplying power to the BEM or other heavy equipment operating over a travel route segment, and instruct an operator to replace or perform maintenance on the batteries if the difference between present and historical performance exceeds a threshold level." (L4) determine an average of received parameter sets 3 as an optimal parameter set, (Braunstein et al. P.5 [0027]: “By calculating and then averaging the most efficient operational parameters over a given route, estimating and then measuring actual energy consumption and then employing a feedback loop to improve various assumptions made during the estimation process, the system can optimize energy usage within the machine's stated speed, desired tasks, and range, and adapt and change planned travel routes, tasks to be performed by the machine, or even issue commands for maintaining or repairing road surfaces over particular travel route segments when it is determined that the rolling resistance being encountered by the machine exceeds expected threshold amounts of rolling resistance.” and transmit the determined optimal parameter set to the plurality of electric vehicles. (Braunstein et al. [Fig.5]: PNG media_image1.png 1026 812 media_image1.png Greyscale The examiner interprets where the determined optimal parameter is shown as an updated machine model. Transmission is inherent as the update is for determining route selection and scheduled maintenance and enabled via instruction {see Braunstein et al. [0028]}) Braunstein et al. fails to teach teach a parameterized cloud-connected electro-thermal model for BEVs, emphasizing that the system receives and manages "parameterization and calibration of BEV component models" from different sources to improve consumption estimation. However, Chakraborty et al. teaches A system for modeling energy consumption efficiency of an electric vehicle, the system comprising: a communication device configured to communicate with a plurality of electric vehicles; and a controller configured to: receive a parameter set of an energy consumption efficiency model from the plurality of electric vehicles, determine an average of received parameter sets as an optimal parameter set, (Chakraborty et al. [equation 10]: PNG media_image2.png 58 494 media_image2.png Greyscale The examiner interprets where the determination of an average of received parameter sets as an optimal parameter set is shown as averaging model weights/parameters in equation 10 from a fleet to reach an optimal model) and transmit the determined optimal parameter set to the plurality of electric vehicles. (Chakraborty et al. [§IV¶1]: “ The simulation platform is connected to a proprietary cloud from which it receives real-time and predictive data about the route network, the traffic, the weather conditions and the charging stations.” The examiner interprets where the simulation platform (in-vehicle) is shown as the electronic vehicles, and where predictive data is shown as determined optimal parameter being sent from a cloud. It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the fleet management system of Braunstein to specifically receive, average, and redistribute the parameter sets of an energy efficiency model as taught by Chakraborty. The motivation for such a modification would be to utilize Braunstein's existing cloud-based averaging framework to refine the accuracy of Chakraborty's parameterized efficiency models. A POSITA would reasonably expect that averaging parameter sets from a wide variety of vehicles in a fleet would produce a more stable and "optimal" global model than any single vehicle could generate locally, thereby improving range prediction across the entire fleet. The result is the predictable use of prior art elements according to their established functions to achieve an optimized modeling system. Regarding Claim 2 Braunstein et al. teaches (L1) The system of claim 1, (See claim 1) (L2) wherein the electric vehicle is configured to update the parameter set of the energy consumption efficiency model by using the optimal parameter. (Braunstein et al. Fig.1: PNG media_image3.png 560 856 media_image3.png Greyscale The examiner interprets where the configuration is shown as the feedback loop. Braunstein et al. [0023]: “The predicted energy consumption that is determine by the power management logic may be used to command control of a machine, and implement changes , such as changes to the route that will be taken by one or more machines, changes to the tasks that will be performed by the one or more machines, changes to the operational parameters for the one or more machines, and changes to road repair and maintenance for the one or more routes or travel route segments that will be traversed by the one or more machines in the performance of its/their tasks.” The examiner interprets where the electric vehicle is shown as the power management logic (machine) and the update as shown in implementation of changes.) (Chakraborty et al. [Fig.5]: PNG media_image4.png 334 538 media_image4.png Greyscale The examiner interprets where the configuration is shown as adjustable model parameters by stakeholders. (L1): Braunstein discloses a system for modeling machine efficiency comprising a cloud-based controller that receives, averages, and transmits efficient operational parameters back to a plurality of machines to optimize usage. (L2): Braunstein further teaches that the system employs a feedback loop to "improve various assumptions made during the estimation process" and "optimize energy usage within the machine's stated... range". Braunstein explicitly states that the power management logic is used to "command control of a machine and implement changes" based on the received data. Chakraborty teaches that BEV efficiency models are parameterized and that these models are integrated into a cloud-connected platform to allow for "parameterization and calibration". Chakraborty further discloses that these parameters are adjustable to allow third-party users to refine the model logic. It would have been obvious to a POSITA at the time of the invention to configure the individual vehicles to update their local parameter sets using the optimal parameters transmitted by the server, as taught by Braunstein's feedback and implementation logic. The motivation for this modification would be to utilize Braunstein’s established fleet-wide optimization framework to calibrate the specific parameterized efficiency models described in Chakraborty. A POSITA would reasonably expect that by automatically updating local models with an averaged, globally optimal parameter set, individual vehicle range prediction would become more robust and accurate, which is the predictable use of prior art elements according to their established functions. Regarding Claim 3 Braunstein et al. teaches (L1) The system of claim 1, (See claim 1) (L2) wherein the electric vehicle is configured to: obtain an energy consumption prediction curve 4 for a preset time by inputting driving data for the preset time to the energy consumption efficiency model; ( See Braunstein et al. Fig.1 in claim 2: The examiner interprets where energy consumption prediction curve is shown as segment energy consumption estimate. Braunstein et al. [101]: “The method may still further include changing one or more of the new travel route segments for the presently operational machine, tasks to be performed by the presently operational machine, or repair or maintenance tasks to be performed on one or more of the new travel route segments for the presently operational machine based on a comparison of the predicted energy consumption for the presently operational machine with the actual historical energy consumption for the matched historical machine traveling over the historical travel route segment and based on a difference between the predicted energy requirement for the presently operational machine and the actual historical energy consumption for the matched historical machine traveling over the historical travel route segment exceeding a predetermined threshold value.” The examiner interprets where prediction curve is shown as predicted energy requirements and where the efficiency model is shown as model logic.) (Chakraborty et al. [§I]: “In [4], an electric passenger car has been simulated for estimating the energy consumption during driving and calculating charging energy requirement for mobile storage management.” (L3) determine an energy consumption actual measurement curve 4 (See Braunstein et al. Fig.1 in claim 2: The examiner interprets where an energy consumption actual measurement curve is shown segment energy actual requirements.) (Chakraborty et al. [§I]: “The simulation data was validated against published energy consumption values of BMW i3 model and around 2% to 6% error have been obtained between experimental and simulation data for the NEDC driving scenario.” The examiner interprets energy consumption actual measurement curve as shown in experiential data.) the preset time based on an output current and an output voltage of a battery; and ( Braunstein et al [0008]: “The control system may be programmed to determine the travel route segment where the BEM is operating, determine a terrain on which the BEM is operating, and generate signals indicative of data representing present performance information for the BEM including one or more of the present battery state-of-charge , battery state-of-health, and number of charge cycles for each of the batteries used to power the BEM, present physical and operational characteristics for the BEM, and present physical characteristics for the BEM, and present physical characteristics of each of the travel route segments over which the BEM is operating at a job site, using a sensing system .” The examiner interprets where an energy consumption actual measurement curve is shown as present performance information and where the output current and an output voltage of a battery is shown as battery state-of-charge.) (Chakraborty et al. [Fig.8 & Table II]: PNG media_image6.png 480 812 media_image6.png Greyscale PNG media_image7.png 740 444 media_image7.png Greyscale (L4) determine the driving data for the preset time and the energy consumption actual measurement curve for the preset time as learning data (See Braunstein et al. Fig.2 in claim 2: The examiner interprets where the determination of learning data as shown in the feedback loop to improve assumptions.) (See Chakraborty et al. [§I] in claim 3 (L3): The examiner interprets where the determination of learning data is shown in the identification of error between experimental and simulation data for NEDCC scenario to validate models.) when mean square error (MSE) 4 values of the energy consumption prediction curve and the energy consumption actual measurement curve exceed a threshold value. (See Braunstein et al. [Abstract] in claim 1 (L3)) (L1): Braunstein discloses a system for modeling machine efficiency using a cloud-connected controller. Braunstein discloses that the vehicle is configured to obtain a prediction of energy consumption (estimate) for segments of a route using a model and determine actual measurement signals (performance) based on battery sensors including current and voltage. Braunstein further teaches employing a feedback loop to improve assumptions when the difference between the predicted and actual performance exceeds a threshold level. Chakraborty teaches a parameterized energy model for BEVs and emphasizes validating simulated energy consumption data against experimental measurements to obtain an error value. Chakraborty further teaches that this process allows for the "parameterization and calibration" (learning) of component models. It would have been obvious to a POSITA at the time of the invention to implement the comparison and threshold trigger of Braunstein using mean square error (MSE) calculations applied to prediction and measurement curves. The motivation for this modification would be to utilize a standard, robust statistical method (MSE) to quantify the "difference" recited in Braunstein. A POSITA would recognize that energy usage over a route segment constitutes a data set or "curve" and that applying MSE to these curves is a predictable use of a known mathematical technique to yield a precise "learning" trigger. The result is an optimized modeling system that identifies high-error driving scenarios for calibration, as taught by the combination of Braunstein's feedback architecture and Chakraborty’s calibration goals. This represents a routine optimization of a known device to yield predictable results under MPEP 2143(A). Regarding Claim 4 Braunstein et al. in view of Chakraborty et al. teaches (L1) The system of claim 3, (See claim 3) (L2) wherein the electric vehicle is configured to learn the energy consumption efficiency model by using the determined learning data. (Braunstein et al. [0023]: “The power management logic may comprise software, hardware, or any combination of software or hardware. In some variations, the device and systems may include one or more processors (E.g., a microprocessor) that can perform the power management logic, and use machine learning and other artificial intelligence techniques to develop and improve virtual models..” The examiner interprets where configuration is shown as machine learning technique to continually improve models based on comparisons of historical and real-time data.) (Chakraborty et al. [§I¶2]: “Besides, parameterization and calibration of BEV component models from different developers and their integration into one entire virtual platform are also missing in the current studies [1]- [8]. To fulfil the above-mentioned research gap, this paper proposes a parametrized cloud-connected electro-thermal simulation platform for the BEVs.” The examiner interprets where learn efficiency model using learning data is shown as parameterizing and calibration of BEV component models. (L1): Braunstein discloses that an individual machine is configured to obtain predictions, determine actual measurement curves, and compare them. Braunstein further teaches employing a feedback loop to improve model assumptions when the difference between the actual and predicted performance exceeds a threshold level. L(2): Specifically, Braunstein discloses that the machine's logic may use "machine learning" or "other artificial intelligence techniques" to enable "continual improvements in predictive models of energy consumption". Chakraborty teaches a parameterized simulation platform for BEVs and emphasizes the need for "parameterization and calibration" (i.e., learning) of component models to improve the accuracy of energy consumption estimations. It would have been obvious to a POSITA at the time of the invention to configure the vehicle to learn or retrain the efficiency model using the driving data and actual measurements identified when the error (MSE) exceeds a threshold, as taught by Braunstein's machine-learning feedback loop. The motivation for this modification would be to utilize the high-error data points (where the model is least accurate) as a targeted "learning data" set to perform the "calibration" described by Chakraborty. A POSITA would recognize that retraining a model using its own identified failures is a routine machine learning technique (e.g., boosting or error-residual training) that yields the predictable result of increased predictive accuracy for the electric vehicle's range estimation. This represents the predictable use of prior art elements according to their established functions to improve a device ready for optimization. Regarding Claim 5 Braunstein et al. in view of Chakraborty et al. teaches (L3) The system of claim 3, (See claim 3) (L2) wherein the driving data includes at least one of: an accelerator pedal position (APS), a brake pedal position (BPS), a gear ratio, a vehicle speed, a front clutch state, a rear clutch state, a road gradient, a road curvature, a motor torque, a motor temperature a battery state of charge (SOC), a temperature of the battery, an outside temperature, a time since departure, a vehicle weight, or a combination thereof. ( Braunstein et al. [0070]: “For example, polling logic may coordinate continuous or periodic polling of Global Positioning System (GPS) information (e.g., giving information on the machine’s current terrain, machine’s destination, etc.), speedometer information (e.g ., machine’s current speed , motor speed), date and time information (e.g., the data and time may be used to determine personal driving habits and sun angle), gyroscope information (e.g., machine’s current orientation, pose, current slope/grade of road), wheel rotations per minute, accelerator and brake pedal position (e.g., pressure applied and/or current angle of the petals), the angle of the sun (e.g., sensor may detect latitude, longitude, time of date, date), weather (e.g., wind direction and velocity, rain, sun, snow, etc.), battery state (e.g ., state-of-charge , battery state-of-health, charging cycles, voltage, amp hour meter, etc.), tire pressure (e.g., may be used to calculate the drag force due to rolling resistance), headway control information (e.g., the distance from another machine or obstacle , the weight of the machine (e.g., weight of cargo, operator), airflow (e.g., the amount of air going to the engine), gas flow sensor (e.g., the amount of gas going to engine of a hybrid or ICE car), weight of operator (e.g., may be used to identify the operator and linked to personal driving habits). (Chakraborty et al. [Fig.1, 3, 7, 12, 13, Table I]: PNG media_image8.png 610 1108 media_image8.png Greyscale PNG media_image9.png 512 762 media_image9.png Greyscale PNG media_image10.png 284 504 media_image10.png Greyscale PNG media_image11.png 568 976 media_image11.png Greyscale PNG media_image12.png 538 820 media_image12.png Greyscale (L1): Braunstein discloses a system that obtains energy predictions, measures actual performance, and triggers a feedback loop when a threshold is exceeded. (L2): Braunstein explicitly discloses that the monitored signals and operational parameters include accelerator and brake pedal position (APS/BPS), gear ratio, vehicle speed, road gradient and curvature, motor torque, battery state of charge and battery temperature, outside temperature, and vehicle weight. Chakraborty further teaches that a parameterized simulation model for BEVs necessarily utilizes a powertrain thermal model (motor temperature), outside/ambient temperature [Fig 1], and time-based trip profiles [111, Fig 7] to optimize energy management strategies. It would have been obvious to a POSITA at the time of the invention to include the specific driving data inputs listed in Claim 5 when implementing the Modeling System of Braunstein. The motivation for this would be to utilize all available high-fidelity sensor data, as taught by Braunstein and Chakraborty, to ensure the "prediction curve" accurately reflects the complex interactions of drivetrain load and environmental resistance. A POSITA would recognize that these specific parameters are the art-recognized "result-effective variables" for energy consumption modeling. Therefore, selecting at least one of these known inputs to drive the model of Braunstein is a predictable use of prior art elements according to their established functions to yield an improved modeling result. Regarding Claim 6 Braunstein et al. teaches (L1) The system of claim 1, wherein the electric vehicle is configured to: (see claim 1) (L2) obtain an energy consumption prediction curve of a first road section by inputting driving data of the first road section to the energy consumption efficiency model; (See Braunstein et al. Fig.1 in claim 2: The examiner interprets where obtaining prediction curve of a first road section is shown as segment energy consumption estimate based on a travel route segment.). (See Chakraborty et al. Fig.1 in claim 5: The examiner interprets where obtaining prediction curve of a first road section is shown as the simulation to estimate energy consumption for specific mission profiles.) (L3) determine an energy consumption actual measurement curve of the first road section based on an output current and an output voltage of a battery; and (See Braunstein et al. Fig.1 in claim 2: The examiner interprets where determine actual measurement curve of read section via battery/voltage as shown in generating signals for segment energy actual requirements using a sensing system for route segments.) (See Chakraborty et al. [§I]: The examiner interprets where determine actual measurement curve of road via battery current/voltage is sown as experimental data [current/voltage] to validate simulates curve [see Fig.8 also]) (L4) determine the driving data of the first road section and the energy consumption actual measurement curve of the first road section as learning data when mean square error (MSE) values of the energy consumption prediction curve and the energy consumption actual measurement curve exceed a threshold value . (See claim 3 L4]) (L1): Braunstein discloses a cloud-connected system for modeling machine efficiency. (L2-L4) Braunstein discloses that the vehicle logic is programmed to obtain an energy consumption prediction (estimate) for a travel route segment (first road section) and determine an actual measurement (present performance) for that segment using battery sensors. Braunstein further teaches employing a feedback loop to improve assumptions when the difference between the actual and predicted performance exceeds a threshold level. Chakraborty teaches a parameterized simulation platform for BEVs and emphasizes the need for "parameterization and calibration" of component models based on identifying the "error" between experimental and simulation data. It would have been obvious to a POSITA at the time of the invention to implement the "difference" and "threshold" trigger of Braunstein using Mean Square Error (MSE) calculations applied to prediction and actual measurement curves for the road section. The motivation for this would be to utilize a standard, robust statistical method (MSE) to quantify the deviation recited in Braunstein. A POSITA would recognize that energy usage over a route segment constitutes a data set or "curve" and that applying MSE to these curves is a predictable use of a known mathematical technique to yield a precise "learning" trigger. The result is the predictable use of prior art elements according to their established functions to identify high-error driving scenarios for model calibration, as taught by the combination of Braunstein's segment-based feedback and Chakraborty’s calibration goals. This represents the application of a known technique to a known device ready for improvement to yield predictable results under MPEP 2143(D). Regarding Claim 7 Braunstein et al. teaches (L1) The system of claim 6, (see claim 6) (L2) wherein the electric vehicle is configured to learn the energy consumption efficiency model by using the determined learning data. (See Braunstein et al. claim 4 [L2]) See previous motivation/rationale. Regarding Claim 8 Braunstein et al teaches The system of Claim 6, (See Claim 6) wherein the driving data includes at least one of: an accelerator pedal position (APS), a brake pedal position (BPS), a gear ratio, a vehicle speed, a front clutch state, a rear clutch state, a road gradient, a road curvature, a motor torque, a motor temperature, a battery state of charge (SOC), a temperature of the battery, an outside temperature, a time since departure, a vehicle weight, or a combination thereof. (See claim 5) Regarding Claim 9 Braunstein et al. teaches A method of modeling energy consumption efficiency of an electric vehicle, the method comprising: (Method of claim 1. Similar rejection to claim 1) receiving, by a communication device, a parameter set of an energy consumption efficiency model from a plurality of electric vehicles; (Method of claim 1. Similar rejection to claim 1) determining, by a controller, an average of received parameter sets as an optimal parameter set; and (Method of claim 1. Similar rejection to claim 1) transmitting, by the controller, the determined optimal parameter set to the plurality of electric vehicles. (Method of claim 1. Similar rejection to claim 1) See previous motivation/rationale. Regarding Claim 10 Braunstein et al.. teaches The method of claim 9, (See claim 9) further comprising: updating, by the electric vehicle, the parameter set of the energy consumption efficiency model by using the optimal parameter set. (Method of claim 2. Similar rejection to claim 2 [L2]) See similar motivation/rationale. Regarding Claim 11 Braunstein et al. teaches The method of claim 9, (See claim 9) wherein the receiving of the parameter set of the energy consumption efficiency model includes: learning, by the electric vehicle, the energy consumption efficiency model by using the determined learning data. (Method of claim 3. Similar rejection to claim 2 [L2-L4]) See previous motivation and rationale. Regarding Claim 12 Braunstein et al. teaches (L1) The method of claim 11 (see claim 11) , wherein the learning of the energy consumption efficiency model includes: obtaining, by the electric vehicle, an energy consumption prediction curve for a preset time by inputting driving data for the preset time to the energy consumption efficiency model ; determining, by the electric vehicle, an energy consumption actual measurement curve for the preset time based on an output current and an output voltage of a battery; and determining, by the electric vehicle, the driving data for the preset time and the energy consumption actual measurement curve for the preset time as learning data when mean square error (MSE) values of the energy consumption prediction curve and the energy consumption actual measurement curve exceed a threshold value. (Method of claim 7. Similar rejection to claim 2 [L2]) See similar motivation and rationale. Regarding Claim 13 Braunstein et al. teaches The method of claim 12, (See claim 12) wherein the driving data includes at least one of: an accelerator pedal position (APS), a brake pedal position (BPS), a gear ratio, a vehicle speed, a front clutch state, a rear clutch state, a road gradient, a road curvature, a motor torque, a motor temperature, a battery state of charge (SOC), a temperature of the battery, an outside temperature, a time since departure, a vehicle weight, or a combination thereof ( Method of claim 5. Similar rejection to claim 2 [L2]) See similar motivation and rationale. Regarding Claim 14 Braunstein et al. teaches (L1) The method of claim 11, (See claim 11) (L2) wherein the learning of the energy consumption efficiency model includes: obtaining, by the electric vehicle, an energy consumption prediction curve of a first road section by inputting driving data of the first road section to the energy consumption efficiency model; determining, by the electric vehicle, the driving data of the first road section and the energy consumption actual measurement curve of the first road section as learning data when mean square error (MSE) values of the energy consumption prediction curve and the energy consumption actual measurement curve exceed a threshold value ( Method of claim 5. Similar rejection to claim 2 [L2]) See similar motivation and rationale. Regarding Claim 15 Braunstein et al. in view of Chakraborty et al. teaches (L1) The method of claim 14 , ( See claim 14) (L2) wherein the driving data includes at least one of: an accelerator pedal position (APS), a brake pedal position (BPS), a gear ratio, a vehicle speed, a front clutch state, a rear clutch state, a road gradient, a road curvature, a motor torque, a motor temperature, a battery state of charge (SOC), a temperature of the battery, an outside temperature, a time since departure, a vehicle weight, or a combination thereof. (See Braunstein et al. Fig.1 in claim 2: The examiner interprets where the road section includes an expressway/city road is shown as road conditions and location (Urban vs. highway) via GPS. Braunstein et al. [Fig.2]: PNG media_image13.png 712 786 media_image13.png Greyscale The examiner interprets where the road section includes: uphill/downhill road Is shown as segment elevation. Braunstein et al. [0005]: “ Adverse road conditions include soft underfoot conditions, steep grades, and potholes” The examiner interprets c steep grades. (See Chakraborty et al. [§I] in claim 3 (L3): The examiner interprets where the road section includes an expressway/city road is shown as simulation and testing for urban and rural (highway/city) driving scenarios. Chakraborty et al. [§IV ¶1]: “The driver model also adapts its speed profile to the route topology-induced constraints , in particular the vehicle speed limit due to route curvatures. The route curvature is reconstructed from the longitude and latitude coordinates along the chosen route to obtain the apparent route heading.” The examiner interprets where the road section includes uphill/downhill road Is shown as specific road profiles for uphill and downhill driving. (L1): Braunstein discloses a method for modeling machine efficiency that identifies deviations between predicted and actual energy requirements for route segments and triggers a feedback loop when a threshold is exceeded. (L2): Braunstein explicitly discloses that the "route segments" are analyzed based on signals indicative of location, elevation, and grade (uphill/downhill). Chakraborty further teaches that a parameterized simulation model for BEVs necessarily utilizes distinct mission profiles, specifically citing uphill, downhill, and urban (city road) scenarios to perform model calibration and resolve range estimation gaps. It would have been obvious to a POSITA at the time of the invention to include the specific road types listed in Claim 15 (expressway, city road, uphill, downhill) when defining the "road sections" for the modeling method of Braunstein. The motivation for this would be to utilize standard topographical and structural road categories, as taught by Braunstein and Chakraborty, to ensure the feedback and "learning" logic isolates segments where the model is technically deficient due to gravity or traffic-induced load changes. A POSITA would recognize that these specific road types are the art-recognized "result-effective variables" for energy consumption modeling. Therefore, applying these known categories to drive the feedback logic of the prior art is a predictable use of prior art elements according to their established functions to yield an improved modeling result. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to AARIC RAYJEE MARKS whose telephone number is (571)467-6372. The examiner can normally be reached Monday-Friday 8am-5pm. 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, Ryan Pitaro can be reached at (571) 272-4071. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /AARIC R MARKS/ /RYAN F PITARO/Supervisory Patent Examiner, Art Unit 2188 Application/Control Number: 18/083,865 Page 2 Art Unit: 2188 Application/Control Number: 18/083,865 Page 3 Art Unit: 2188 Application/Control Number: 18/083,865 Page 4 Art Unit: 2188 Application/Control Number: 18/083,865 Page 5 Art Unit: 2188 Application/Control Number: 18/083,865 Page 6 Art Unit: 2188 Application/Control Number: 18/083,865 Page 7 Art Unit: 2188 Application/Control Number: 18/083,865 Page 8 Art Unit: 2188 Application/Control Number: 18/083,865 Page 9 Art Unit: 2188 Application/Control Number: 18/083,865 Page 10 Art Unit: 2188 Application/Control Number: 18/083,865 Page 11 Art Unit: 2188 Application/Control Number: 18/083,865 Page 12 Art Unit: 2188 Application/Control Number: 18/083,865 Page 13 Art Unit: 2188 Application/Control Number: 18/083,865 Page 14 Art Unit: 2188 Application/Control Number: 18/083,865 Page 15 Art Unit: 2188 Application/Control Number: 18/083,865 Page 16 Art Unit: 2188 Application/Control Number: 18/083,865 Page 17 Art Unit: 2188 Application/Control Number: 18/083,865 Page 18 Art Unit: 2188 Application/Control Number: 18/083,865 Page 19 Art Unit: 2188 Application/Control Number: 18/083,865 Page 20 Art Unit: 2188 Application/Control Number: 18/083,865 Page 21 Art Unit: 2188 Application/Control Number: 18/083,865 Page 22 Art Unit: 2188 Application/Control Number: 18/083,865 Page 23 Art Unit: 2188 Application/Control Number: 18/083,865 Page 24 Art Unit: 2188 Application/Control Number: 18/083,865 Page 25 Art Unit: 2188 Application/Control Number: 18/083,865 Page 26 Art Unit: 2188 Application/Control Number: 18/083,865 Page 27 Art Unit: 2188 Application/Control Number: 18/083,865 Page 28 Art Unit: 2188 Application/Control Number: 18/083,865 Page 29 Art Unit: 2188 Application/Control Number: 18/083,865 Page 30 Art Unit: 2188 Application/Control Number: 18/083,865 Page 31 Art Unit: 2188 Application/Control Number: 18/083,865 Page 32 Art Unit: 2188 Application/Control Number: 18/083,865 Page 33 Art Unit: 2188 Application/Control Number: 18/083,865 Page 34 Art Unit: 2188 Application/Control Number: 18/083,865 Page 35 Art Unit: 2188 Application/Control Number: 18/083,865 Page 36 Art Unit: 2188 Application/Control Number: 18/083,865 Page 37 Art Unit: 2188 Application/Control Number: 18/083,865 Page 38 Art Unit: 2188 Application/Control Number: 18/083,865 Page 39 Art Unit: 2188 Application/Control Number: 18/083,865 Page 40 Art Unit: 2188 Application/Control Number: 18/083,865 Page 41 Art Unit: 2188 Application/Control Number: 18/083,865 Page 42 Art Unit: 2188 Application/Control Number: 18/083,865 Page 43 Art Unit: 2188 Application/Control Number: 18/083,865 Page 44 Art Unit: 2188 Application/Control Number: 18/083,865 Page 45 Art Unit: 2188 Application/Control Number: 18/083,865 Page 46 Art Unit: 2188 1 Spec [0065] “The communication device 23, which is a module that provides a communication interface to the communication device 12 provided in the cloud server 100, may include at least one of a mobile communication module, a wireless Internet module. or a short-range communication module.” 2 Spec [0069] “ The controller24 may perform over all control such that each component performs its function. The controller 24 may be implemented in the form of hardware or software or may be implemented in a combination of hardware and software. In an embodiment, the controller 24 may be implemented as a microprocessor but is not limited thereto. For example, the controller 24 may be implemented with a Vehicle control unit (VCU)” 3 Spec [0039] For example, when the parameter set received from a first electric vehicle is A1, B1, and C1, the parameter set received from a second electric vehicle is A2, B2, and C2, and the parameter set received from a third electric vehicle is A3, B3, and C3, the average of the parameter sets is (A1+A2+A3)/3, (B1+B2+B3)/3 and (Cl+C2+С3)/3. 4 Spec [Equation 1]: PNG media_image5.png 74 742 media_image5.png Greyscale
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