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 .
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.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 02/04/2026 has been entered.
Response to Amendment
This non-final office action is a response to the RCE filed 05/06/2026.
Claim(s) 1, 4-20 is/are pending.
Claim(s) 1 is/are amended.
Claim(s) 4-20 is/are previously presented.
Claim(s) 2-3 is/are cancelled.
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.
Claim 1 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 3 of U.S. Patent No. 12548808 in view of US 20220126723 A1 Ferguson; Kenneth Ramon (hereinafter Ferguson). Claim 3 (a dependent claim of claim 1) of U.S. Patent No. 12548808 discloses all limitations of claim 1 as laid out in the table below, except for the newly added limitation of “a baseline reading of the one or more batteries originating from a battery sensor contained in the EV and coupled to the one or more batteries”.
Claim #
Instant Application: 17/980,944
Claim #
Corresponding application: U.S. Patent No. 12548808
1
A computer-implemented method of monitoring one or more batteries of an electric vehicle (EV),
1
A server for monitoring one or more batteries of an electric vehicle (EV), the server comprising:
1
carried out by one or more processors, the method comprising:
1
a processor interfacing with the transceiver and the memory, and configured to execute the computer-executable instructions to cause the processor to:
1
receiving, by the one or more processors and from an electronic device associated with the EV, telematics data generated by one or more sensors associated with the electronic device that is indicative of operation of the EV;
1
receive, from the electronic device, telematics data generated by one or more sensors associated with the electronic device that is indicative of operation of the EV;
1
determining, by the one or more processors, a battery status of the one or more batteries based upon the telematics data,
1
determine a battery status of the one or more batteries based upon the telematics data,
1
wherein the determining if further based upon a battery charging mode associated with the one or more batteries; and
3
The server of claim 1, wherein the processor is configured to execute the computer-executable instructions to cause the processor to determine the battery status based upon a battery charging mode associated with the one or more batteries.
1
mapping, by the one or more processors, the battery status of the one or more batteries to a digital record corresponding to the EV in a database.
1
map the battery status of the one or more batteries to a digital record corresponding to the EV in a database, wherein the digital record is a first digital record including the first battery status
Ferguson discloses the consideration of historical battery data in determining battery status (see Ferguson at least [0030] The power system 160 may include battery sensors for determining a current charge level of the battery 150 or other on-board battery measurements and [0013] the impedance spectroscopy measurements can be compared to a historical data set to determine parameters describing current battery capacity and estimated remaining useful life of the battery).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the electric vehicle battery monitoring method disclosed by Gross to include the consideration of battery charging mode tendencies in the determination of battery state of Ferguson. One of ordinary skill in the art would have been motivated to make this modification because battery state-determining models use information on the ways in which different batteries charge in order to determine battery characteristics (i.e., states) and because the information about the performance of a battery in its earlier life can inform future decisions regarding the battery, such as imposing charging limits, as suggested by Ferguson (see Ferguson at least [0061] a neural network may take temperature data, terrain data, and/or charging data as input features and predict a battery characteristic based on part on these features and [0069] The battery health system 430 collects historical data over the course of these batteries lifetimes to determine one or more conditions under which applying a particular charge bound provides a benefit to the battery health).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 4-12, and 15-20 are rejected under 35 U.S.C. 101 because the claimed inventions are directed to a judicial exception without significantly more, as determined by the Subject Matter Eligibility Test detailed below.
Step 1
Step 1 of the Subject Matter Eligibility Test entails considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: process, machine, manufacture, or composition of matter.
Independent claim 1 is directed towards a method. Therefore, independent claim 1 and the corresponding dependent claims 4-20 are directed to a statutory category of invention under step 1.
Step 2A, Prong 1
If the claim recites a statutory category of invention, the claim requires further analysis in Step
2A. Step 2A of the Subject Matter Eligibility Test is a two-prong inquiry. In Prong 1, examiners evaluate whether the claim recites a judicial exception.
Regarding Prong 1, 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 1 recites abstract limitations, including those shown in bold below.
A computer-implemented method of monitoring one or more batteries of an electric vehicle (EV), carried out by one or more processors, the method comprising:
receiving, by the one or more processors and from an electronic device associated with the EV, telematics data generated by one or more sensors associated with the electronic device that is indicative of operation of the EV;
determining, by the one or more processors, a battery status of the one or more batteries based upon the telematics data and a baseline reading of the one or more batteries originating from a battery sensor contained in the EV and coupled to the one or more batteries,
wherein the determining is further based upon a battery charging mode associated with the one or more batteries; and
mapping, by the one or more processors, the battery status of the one or more batteries to a digital record corresponding to the EV in a database.
These limitations, as drafted, describe a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind, or by a human using pen and paper, and therefore recites mental processes. For example, “determining… a battery status of the one or more batteries based upon the telematics data and a baseline reading of the one or more batteries originating from a battery sensor contained in the EV and coupled to the one or more batteries” may be interpreted as a mental determination made according to observable data, such as determining that the vehicle battery is running low, due to observations that the performance of the vehicle is sluggish compared to historical performance of the vehicle and previous knowledge of the battery’s level. The recitation of “wherein the determining is further based upon a battery charging mode associated with the one or more batteries” serves to further limit the recited mental process of determining. This further limitation under its broadest reasonable interpretation may be interpreted as further considerations taken into account by a person mentally observing both perhaps the sluggishness of a vehicle and acknowledging that the electric vehicle’s battery has usually been charged with a slow mode of charging, for example, and use both of these pieces of information to create a mental estimate of the vehicle’s battery. Thus, the claim recites an abstract idea.
Step 2A, Prong 2
If the claim recites a judicial exception in Step 2A, Prong 1, the claim requires further analysis in Step 2A, Prong 2. In Step 2A, Prong 2, examiners evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
Regarding Prong 2, 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 MPEP § 2106.04(d), 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 the use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application”.
Claim 1 recites additional elements including those underlined below.
A computer-implemented method of monitoring one or more batteries of an electric vehicle (EV), carried out by one or more processors, the method comprising:
receiving, by the one or more processors and from an electronic device associated with the EV, telematics data generated by one or more sensors associated with the electronic device that is indicative of operation of the EV;
determining, by the one or more processors, a battery status of the one or more batteries based upon the telematics data and a baseline reading of the one or more batteries originating from a battery sensor contained in the EV and coupled to the one or more batteries,
wherein the determining is further based upon a battery charging mode associated with the one or more batteries; and
mapping, by the one or more processors, the battery status of the one or more batteries to a digital record corresponding to the EV in a database.
The recitation of receiving, by the one or more processors and from an electronic device associated with the EV, telematics data generated by one or more sensors associated with the electronic device that is indicative of operation of the EV amounts to mere data receiving, which is a form of insignificant extra-solution activity. Furthermore, the recitation of mapping… the battery status of the one or more batteries to a digital record corresponding to the EV in a database amounts to sending or displaying information, which is a form of insignificant extra-solution activity. The recitation of by the one or more processors is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component. The recitation of monitoring one or more batteries of an electric vehicle (EV) in the preamble amounts to merely indicating a field of use or technological environment in which to apply a judicial exception and cannot integrate the judicial exception into a practical application (see MPEP 2106.05(h)).
Accordingly, in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B
If the additional elements do not integrate the exception into a practical application in step 2A Prong 2, then the claim is directed to the recited judicial exception, and requires further analysis under Step 2B to determine whether it provides an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself).
As discussed above, the additional elements of by the one or more processors amount to mere instructions to apply the exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea does not provide significantly more. 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) (computer server and telephone unit).
As discussed above, receiving, by the one or more processors and from an electronic device associated with the EV, telematics data generated by one or more sensors associated with the electronic device that is indicative of operation of the EV amounts to insignificant extra-solution activity. 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 (as it is here).
As discussed above, mapping, by the one or more processors, the battery status of the one or more batteries to a digital record corresponding to the EV in a database amounts to insignificant extra-solution activity. MPEP 2106.05(d)(II), and the cases cited therein, including in Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93, for example, indicated that the storing and retrieving of information in memory is a well understood, routine, and conventional function.
The additional elements add well understood, routine, and conventional functions in addition to the recited judicial exception, and don’t function to incorporate the judicial exception into practical application. Thus, even when viewed as an ordered combination, nothing in the claims adds significantly more (i.e., an inventive concept) to the abstract idea.
Dependent claims 4 and 5 further characterize the receiving of data.
Dependent claim 6 further characterizes the determination step by generally reciting a computer performing the judicial exception.
Dependent claim 7 recites further additional elements of accessing and sending data.
Dependent claim 8 recited an additional determination step which under its broadest reasonable interpretation may be interpreted as a mental process carried out by a person and as such recites a judicial exception, in addition to extra-solution activities of transmitting data.
Dependent claims 9 and 10 further characterize the storing of information in the database.
Dependent claim 11 further characterizes receiving data and abstract determinations.
Dependent claim 12 further characterizes receiving data and abstract determinations.
Dependent claim 15 further characterizes the storing of information in the database.
Dependent claim 16 recites an additional extra-solution activity of displaying.
Dependent claim 17 further characterizes the data receiving step.
Dependent claims 18-20 further characterize the technical field.
Dependent claims 4-12, and 15-20 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the various limitations of dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine, and conventional additional elements that do not integrate the judicial exception into a practical application (i.e., further characterizing the data receiving step and the sending and displaying of information). Therefore, dependent claims 4-12, and 15-20 are not patent eligible under the same rationale as provided for in the rejection of independent claim 1.
Dependent claim 13 recites “in response to the detecting… (1) rotating the one or more batteries of the second EV from the second EV to the first EV or (2) rotating out the first EV with the second EV”. This step provides a practical application of the battery status determination step by actively reciting control changes of the EV based on the results of the determination step. As such, dependent claim 13 is not rejected under 35 USC § 101. Additionally, dependent claim 14 is not rejected under 35 USC § 101 due to its dependency on claim 13.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 6, 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Michel, in view of US 20220126723 A1 Ferguson; Kenneth Ramon (hereinafter Ferguson).
Regarding claim 1, Michel teaches: A computer-implemented method of monitoring one or more batteries of an electric vehicle (EV) (see Michel at least [0001] methods for managing batteries in vehicles and fleets of vehicles), carried out by one or more processors (see Michel at least [0025] the onboard computer), the method comprising:
receiving, by the one or more processors and from an electronic device associated with the EV, telematics data generated by one or more sensors associated with the electronic device that is indicative of operation of the EV (see Michel at least [0025] one or more sensors in the sensor suite 102 are coupled to the batteries 106, and capture information regarding a state of charge of the batteries 106 and/or a state of health of the batteries 106 and [0029] the onboard computer 104 receives indications of the state of charge and/or state of health of the batteries 106);
determining, by the one or more processors, a battery status of the one or more batteries based upon the telematics data (see Michel at least [0029] the onboard computer 104 receives indications of the state of charge and/or state of health of the batteries 106 and determines operations to be performed by the autonomous vehicle 110 based on the state of the batteries 106); and
mapping, by the one or more processors, the battery status of the one or more batteries to a digital record corresponding to the EV in a database (see Michel at least [0067] The central computing system 702 may include one or more battery data databases to store state of charge for each vehicle 710a-710c).
Michel does not teach: determining, by the one or more processors, a battery status based upon a baseline reading of the one or more batteries originating from a battery sensor contained in the EV and coupled to the one or more batteries, wherein the determining is further based upon a battery charging mode associated with the one or more batteries.
However, Ferguson teaches: determining, by the one or more processors, a battery status based upon a baseline reading of the one or more batteries originating from a battery sensor contained in the EV and coupled to the one or more batteries (see Ferguson at least [0030] The power system 160 may include battery sensors for determining a current charge level of the battery 150 or other on-board battery measurements and [0013] the impedance spectroscopy measurements can be compared to a historical data set to determine parameters describing current battery capacity and estimated remaining useful life of the battery),
wherein the determining is further based upon a battery charging mode associated with the one or more batteries (see Ferguson at least [0061] a battery that routinely receives a DC fast charge may degrade differently from a battery that usually receives a slower DC or AC charge and the machine-learning module 540 may develop different models or sets of models that apply to batteries charged by types of charging stations and [0062] the battery health system 430 uses the battery model 550 to compute a battery characteristic based on data received from a battery assessment station and/or other sources).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the electric vehicle battery monitoring method disclosed by Michel to include the consideration of battery charging mode tendencies in the determination of battery state of Ferguson. One of ordinary skill in the art would have been motivated to make this modification because battery state-determining models use information on the ways in which different batteries charge in order to determine battery characteristics (i.e., states) and because the information about the performance of a battery in its earlier life can inform future decisions regarding the battery, such as imposing charging limits, as suggested by Ferguson (see Ferguson at least [0061] a neural network may take temperature data, terrain data, and/or charging data as input features and predict a battery characteristic based on part on these features and [0069] The battery health system 430 collects historical data over the course of these batteries lifetimes to determine one or more conditions under which applying a particular charge bound provides a benefit to the battery health).
Regarding claim 6, Michel and Ferguson teach: The computer-implemented method of claim 1, wherein the determining is further based upon a machine learning algorithm trained to predict the battery status using training data that associates different types of telematics data with the battery status (see Ferguson at least [0013] The historical data set can be obtained using a fleet of EVs and can be used to train a machine-learned model that predicts battery capacity, remaining useful life, and other parameters or conditions based on impedance spectroscopy measurements).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the electric vehicle battery monitoring method disclosed by Michel and Ferguson to include the machine-learned model of Ferguson. One of ordinary skill in the art would have been motivated to make this modification because a machine-learned model is able to determine relationships between different types of data and their effects on the battery status, as suggested by Ferguson (see Ferguson at least [0060] The machine-learning module 540 further determines a relationship between the set of features (e.g., the impedance measurements obtained in response to applying a stimulus signal at the identified set of frequencies) and the battery characteristic (e.g., the predicted SoH)).
Regarding claim 10, Michel and Ferguson teach: The computer-implemented method of claim 1, wherein:
the EV is a first EV, the battery status is a first battery status, the digital record is a first digital record including the first battery status (see Michel at least Fig. 2 Fig. 2 depicts the elements of a battery monitoring system within a first vehicle), the method further comprising:
mapping, by the one or more processors, a second battery status of one or more batteries of a second EV to a second digital record corresponding to the second EV in the database (see Michel at least [0067] The central computing system 702 may include one or more battery data databases to store state of charge for each vehicle 710a-710c).
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Michel, in view of Ferguson, and further in view of US 20200212510 A1 Holden; Mason et al. (hereinafter Holden).
Regarding claim 4, Michel and Ferguson teach: The computer-implemented method of claim 1.
Michel and Ferguson do not teach: wherein the telematics data comprises data indicating an accident associated with the EV or a predicted accident associated with the EV.
However, Holden teaches: wherein the telematics data comprises data indicating an accident associated with the EV or a predicted accident associated with the EV (see Holden at least [0074] In some examples, data from a battery sensor 668, such as an accelerometer sensor, may be utilized to determine if the rechargeable battery 604 has been subjected to a damaging event, such as a drop, kick, crash etc.).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the electric vehicle battery monitoring method disclosed by Michel and Ferguson to include the safety features regarding possible battery damage due to vehicle crashes of Holden. One of ordinary skill in the art would have been motivated to make this modification because involvement in an accident may result in damage to a vehicle’s battery which would render its continued use dangerous and thus the battery may be disconnected for safety purposes, as suggested by Holden (see Holden at least [0074] If the rechargeable battery has been subjected to such an event, one or more switches 668 and/or one or more fusible links 680 may be activated such that the terminals 676 are disconnected from the battery cells 608).
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Michel, in view of Ferguson, and further in view of US 20200324653 A1 Breen; Eric (hereinafter Breen).
Regarding claim 5, Michel and Ferguson teach: The computer-implemented method of claim 1.
Michel and Ferguson do not teach: wherein the telematics data comprises data indicating flat towing of the EV, pushing of the EV, bi-directional jump charging of the EV with another EV, or whether the EV is coupled to another vehicle.
However, Breen teaches: wherein the telematics data comprises data indicating flat towing of the EV, pushing of the EV, bi-directional jump charging of the EV with another EV, or whether the EV is coupled to another vehicle (see Breen at least [0076] while the EV 104 is being towed, the battery management system 743 may monitor the battery pack 742 and may determine that the battery pack 742 is charging faster or slower than desired).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the electric vehicle battery monitoring method disclosed by Michel and Ferguson to include the data regarding towing of the electric vehicle of Breen. One of ordinary skill in the art would have been motivated to make this modification because the towing status of the electrical vehicle may affect the charging rate of the electric vehicle’s battery, and thus the system parameters may require adjustment so the electric vehicle’s battery may be charged as desired during towing, as suggested by Breen (see Breen at least [0076] The battery management system 743 may provide the measured charge rate to the controller 740 so that the charge rate of the battery pack 742 may be suitably adjusted (e.g., increased or decreased) to provide a fully charged battery pack 742 by the time a towing destination is reached).
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Michel, in view of Ferguson, and further in view of WO 2020036984 A1 SASTINSKY MICHAL (hereinafter Sastinsky).
Regarding claim 7, Michel and Ferguson teach: The computer-implemented method of claim 1.
Michel and Ferguson do not teach: further comprising: accessing, by the one or more processors, the database to retrieve the battery status of the one or more batteries and corresponding recommendation data that includes instructions for improving the battery status; and transmitting, by the one or more processors and to the electronic device or a user device, at least one of the battery status and the recommendation data.
However, Sastinsky teaches: further comprising: accessing, by the one or more processors, the database to retrieve the battery status of the one or more batteries and corresponding recommendation data that includes instructions for improving the battery status (see Sastinsky at least [0023] the power cell tracking and optimization system described herein can operate as a direct communication service for monitoring battery conditions, performance, capacity, etc., and provide battery-powered device servicers or technicians with usage recommendations (e.g., to replace a battery, to adjust charging technique, or suggest recycling) along with contextual information regarding the history of the battery (e.g., from a full report of the battery, which is stored in the distributed ledger) and [0024] The power cell tracking and optimization system can store this updated information on the distributed ledger, compare the updated information with previous data corresponding to the battery-powered device, determine one or more recommendations for an owner, operator, or technician of the battery-powered device); and
transmitting, by the one or more processors and to the electronic device or a user device, at least one of the battery status and the recommendation data (see Sastinsky at least [0024] provide the recommendation(s) accordingly (e.g., through an application program notification on a computing device of the owner, operator, or technician)).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the electric vehicle battery monitoring method disclosed by Michel and Ferguson to include the providing of battery-related recommendations to a user device of Sastinsky. One of ordinary skill in the art would have been motivated to make this modification because recommendations presented to a user’s device may inform the user of actions they can take to increase the life of their vehicle’s battery and performance, as suggested by Sastinsky (see Sastinsky at least [0024] As provided herein, these recommendations can be provided to decrease a degradation rate of the battery, and optimize the power output, operating conditions, performance, and ultimately the ABEL of the batteries that run the battery-powered device).
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Michel, in view of Ferguson, and further in view of US 20140232356 A1 KYOUNG; Jin-Soo (hereinafter Kyoung).
Regarding claim 8, Michel and Ferguson teach: The computer-implemented method of claim 1.
Michel and Ferguson do not teach: further comprising: determining, by the one or more processors, an insurance premium or discount associated with the EV based upon the battery status; and transmitting, by the one or more processors and to the electronic device or a user device, the insurance premium or the discount.
However, Kyoung teaches: further comprising: determining, by the one or more processors, an insurance premium or discount associated with the EV based upon the battery status (see Kyoung at least [0077] SOC management apparatus 10 may determine an incentive (or penalty) to be given an EV user, based on the SOC management evaluation information); and
transmitting, by the one or more processors and to the electronic device or a user device, the insurance premium or the discount (see Kyoung at least [0077] transmit the incentive information (or penalty information) to at least one of business operator system 14 and user equipment 40).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the electric vehicle battery monitoring method disclosed by Michel and Ferguson to include the insurance incentive or penalty program relating to battery status of Kyoung. One of ordinary skill in the art would have been motivated to make this modification because incentives may encourage electric vehicle users to conform to battery charging protocols to increase the health of the vehicle batteries, as suggested by Kyoung (see Kyoung at least [0023] The method may further include determining at least one of an incentive and a penalty for a corresponding user according to the conformity degree and [0021] The determining the conformity degree may include determining the conformity degree based on at least one of (i) whether the SOC history information is within an SOC range corresponding to the predetermined SOC management condition, (ii) a proximity degree between charge start SOCs, and (iii) a proximity degree between charge end SOCs).
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Michel, in view of Ferguson, and further in view of US 10783500 B1 Bromwich; David Keith et al. (hereinafter Bromwich).
Regarding claim 9, Michel and Ferguson teach: The computer-implemented method of claim 1.
Michel and Ferguson do not teach: further comprising: updating, by the one or more processors and in the database, the digital record to designate that the one or more batteries are to be, based upon the battery status in relation to a predetermined threshold, at least one of (1) replaced, (2) transferred to another EV, (3) recycled, (4) used as an emergency power source, or (5) recharged.
However, Bromwich teaches: further comprising: updating, by the one or more processors and in the database, the digital record to designate that the one or more batteries are to be, based upon the battery status in relation to a predetermined threshold, at least one of (1) replaced, (2) transferred to another EV, (3) recycled, (4) used as an emergency power source, or (5) recharged (see Bromwich at least [col. 5, lines 41-48] a trip minutes for the last trip for the electrically-assisted personal mobility vehicle may be calculated and used to update the stored estimated remaining battery life value in the database. The stored estimated battery life value may be compared to the battery threshold level to determine whether an electrically-assisted personal mobility vehicle should be blocked for a battery swap).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the electric vehicle battery monitoring method disclosed by Michel and Ferguson to include the threshold-dependent battery swap determination and logging method of Bromwich. One of ordinary skill in the art would have been motivated to make this modification because logging vehicles whose batteries are to be replaced based on thresholds allows nearby vehicles to be assessed similarly and more efficient battery replacement to occur, as suggested by Bromwich (see Bromwich at least [col. 5, lines 25-29] using a dynamic battery threshold level, once one bike is blocked at a station the battery threshold level may be modified to block additional bikes at the station based on a technician being at the station to perform battery swapping).
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Michel, in view of Ferguson, and further in view of US 20120126754 A1 Akahane; Fumihiro et al. (hereinafter Akahane).
Regarding claim 11, Michel and Ferguson teach: The computer-implemented method of claim 10.
Michel and Ferguson do not teach: detecting, by the one or more processors, that the first battery status does not satisfy a predetermined threshold and the second battery status satisfies the predetermined threshold; and in response to the detecting, determining, by the one or more processors, to replace the one or more batteries of the first EV with the one or more batteries of the second EV.
However, Akahane teaches: detecting, by the one or more processors, that the first battery status does not satisfy a predetermined threshold (see Akahane at least [0044] the forklift 3 is engaged in work high in load and the lithium ion battery C is greatly drained as compared with other lithium ion batteries) and the second battery status satisfies the predetermined threshold (see Akahane at least [0044] lithium ion battery A which has been previously charged); and
in response to the detecting, determining, by the one or more processors, to replace the one or more batteries of the first EV with the one or more batteries of the second EV (see Akahane at least [0044] the forklift 3 is made to return to the base station 10, and the battery thereof is replaced with the lithium ion battery A and [0047] lithium ion batteries A to E respectively installed in the plurality of forklifts 1 to 4).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the electric vehicle battery monitoring method disclosed by Michel and Ferguson to include the swapping of batteries based on relative battery levels of Akahane. One of ordinary skill in the art would have been motivated to make this modification because prioritizing replacing the battery of a vehicle whose battery levels is low makes for more efficient management of battery levels of a group of vehicles, allowing them to do their work, as suggested by Akahane (see Akahane at least [0014] the management of the work vehicles and the priority order for replacing the secondary batteries makes more efficient a schedule for using, replacing, and charging in rotation the secondary batteries installed in the work vehicle).
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Michel, in view of Ferguson, further in view of CN 108973755 A WANLI LUO et al. (hereinafter Wanli), and further in view of US 20230009016 A1 Slepchenkov; Mikhail et al. (hereinafter Slepchenkov).
Regarding claim 12, Michel and Ferguson teach: The computer-implemented method of claim 10.
Michel and Ferguson do not teach: detecting, by the one or more processors, that the first battery status satisfies a predetermined threshold and that the first EV is damaged; and in response to the detecting, determining, by the one or more processors, to transfer the one or more batteries of the first EV to the second EV.
However, Wanli teaches: detecting, by the one or more processors, that the first battery status satisfies a predetermined threshold and that the first EV is damaged (see Wanli at least [0019] For the new energy vehicle after the collision, the controller determines the battery power of the new energy vehicle according to the data transmitted back from the communication line in the charging gun. If the battery level of the new energy vehicle is higher than the safety threshold, the energy absorption module is activated).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the electric vehicle battery monitoring method disclosed by Michel and Ferguson to include the collision damage detection and battery level detection and comparison of Wanli. One of ordinary skill in the art would have been motivated to make this modification because vehicle collisions may damage any part of the vehicle, and it is important to check the health of the vehicle battery to mitigate any risks of batteries catching fire, as suggested by Wanli (see Wanli at least [0025] After the battery power drops below the safety threshold, the controller 1 disconnects the electronically controlled switch 31 to unload the resistor 32, thereby preventing the battery of the new energy vehicle from short-circuiting and catching fire during the towing process).
Michel, Ferguson, and Wanli do not teach: in response to the detecting, determining, by the one or more processors, to transfer the one or more batteries of the first EV to the second EV.
However, Slepchenkov teaches: in response to the detecting, determining, by the one or more processors, to transfer the one or more batteries of the first EV to the second EV (see Slepchenkov at least [0268] An example of a first life application for batteries 206 is within an energy storage system for an EV. Then, at the end of that life (e.g., after 100,000 miles of driving, or after degradation of the batteries within that battery pack by a threshold amount), the batteries 206 can be removed from the battery pack, optionally subjected to refurbishing and testing, and then implemented in a second life application that can be, e.g., use within a stationary energy storage system (e.g., residential, commercial, or industrial energy buffering, EV charging station energy buffering, renewable source (e.g., wind, solar, hydroelectric), energy buffering, and the like) or another mobile energy storage system (e.g., battery pack for an electric car, bus, train, or truck)).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the electric vehicle battery monitoring method disclosed by Michel, Ferguson, and Wanli to include the step of removing a battery from a setting in which it may no longer be used and repurposing the battery of Slepchenkov. One of ordinary skill in the art would have been motivated to make this modification because modular batteries may be used interchangeably or nearly interchangeably between different vehicles or in different applications, as suggested by Slepchenkov (see Slepchenkov at least [0265] The structure and/or topology of modules 108 also allows for second life applications of modules 108 and/or their sources 206 without major changes to modules 108 and also allows for accurate measurement and valuation of residual life of sources 206 at the end of life).
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Michel, in view of CN 112061127 A JIN, Xian-xie (hereinafter Jin).
Regarding claim 13, Michel and Ferguson teach: The computer-implemented method of claim 10, wherein the first EV and the second EV belong to a fleet of vehicles (see Michel at least [0057] the vehicles 710a-710c in the fleet).
Michel and Ferguson do not teach: detecting, by the one or more processors, that the first battery status indicates that the one or more batteries of the first EV has less charge than the one or more batteries of the second EV indicated by the second battery status; and in response to the detecting, maintaining, by the one or more processors, an overall battery status of the fleet by (1) rotating the one or more batteries of the second EV from the second EV to the first EV or (2) rotating out the first EV with the second EV.
However, Jin teaches: detecting, by the one or more processors, that the first battery status indicates that the one or more batteries of the first EV has less charge than the one or more batteries of the second EV indicated by the second battery status (see Jin at least [pg. 12, para. 3, beginning with “Thus, a partial embodiment”] the vehicle with the highest battery SoC can be located at the front position of the fleet (the position will increase the consumption rate of the battery SoC of the collar vehicle); then the SoC of the vehicle with the highest battery SoC in the following vehicle can be compared with the SoC of the leader vehicle); and
in response to the detecting, maintaining, by the one or more processors, an overall battery status of the fleet (see Jin at least [pg. 12, para. 3, beginning with “Thus, a partial embodiment”] a strategic method of balancing the battery SoC between vehicles) by (1) rotating the one or more batteries of the second EV from the second EV to the first EV or (2) rotating out the first EV with the second EV (see Jin at least [pg. 12, para. 3, beginning with “Thus, a partial embodiment”] when the difference between them is equal to or greater than a specific value, the corresponding vehicle can move to the front position of the fleet).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the electric vehicle battery monitoring method disclosed by Michel and Ferguson to include the step of replacing a vehicle in a fleet with a different vehicle that has a higher battery status of Jin. One of ordinary skill in the art would have been motivated to make this modification because the possible driving distance of the entire fleet can be increased if the lead vehicle is replaced regularly, depending on relative vehicle battery levels, as suggested by Jin (see Jin at least [pg. 6, para. 8, beginning with “That is to say”] in order to make the driving distance is the largest, the first method, the second method and the third method is a method for exchanging the position of the vehicle and one of the following vehicles).
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Michel, in view of Ferguson, further in view of Jin, and further in view of US 20230196846 A1 Braunstein; Michael Dennis et al. (hereinafter Braunstein).
Regarding claim 14, Michel, Ferguson, and Jin teach: The computer-implemented method of claim 13.
Michel, Ferguson, and Jin do not teach: wherein the maintaining is further based upon a machine learning algorithm trained to predict the overall battery status of the fleet using training data that associates individual battery status of each vehicle in the fleet with the overall battery status of the fleet.
However, Braunstein teaches: wherein the maintaining is further based upon a machine learning algorithm trained to predict the overall battery status of the fleet using training data that associates individual battery status of each vehicle in the fleet with the overall battery status of the fleet (see Braunstein at least [0023] The devices and systems for managing the energy consumption of the machine may include power management logic that can calculate an estimated energy requirement for the machine batteries based on information provided from the external environment of the machine, the operational status of the machine, the rolling resistance encountered by the machine over a particular segment of the travel path, one or more command inputs from an operator, and one or more operational parameters of the machine… machine learning and other artificial intelligence techniques to develop and improve virtual models that may be used in predicting the energy consumption for a particular machine traveling over a particular travel route segment, and/or the energy consumption for one or more machines, or even an entire fleet of machines traveling over many different travel route segments).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the electric vehicle battery monitoring method disclosed by Michel, Ferguson, and Jin to include the machine learning technique for determining fleet battery statistics of Braunstein. One of ordinary skill in the art would have been motivated to make this modification because machine learning is a helpful tool in processing large amounts of data gathered by complex systems such as fleets of electric vehicles, as suggested by Braunstein (see Braunstein at least [0088] Machine learning algorithms and artificial intelligence may be particularly useful in processing the large amounts of data acquired over time from operating many different types of machines on many different terrains under many different conditions).
Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Michel, in view of US 20230064434 A1 LI; Zhanliang et al. (hereinafter Li).
Regarding claim 15, Michel and Ferguson teach: The computer-implemented method of claim 1.
Michel and Ferguson do not teach: wherein the mapping of the battery status of the one or more batteries comprises a battery location for at least one of the one or more batteries, wherein the battery location indicates a physical location onboard the EV where the battery is installed.
However, Li teaches: wherein the mapping of the battery status of the one or more batteries comprises a battery location for at least one of the one or more batteries, wherein the battery location indicates a physical location onboard the EV where the battery is installed (see Li at least [0078] For S230, the actual physical location information of the battery may be calculated according to the actual physical location information of the vehicle and the relative location of the battery on the vehicle).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the electric vehicle battery monitoring method disclosed by Michel and Ferguson to include the battery location within vehicle determination and tracking of Li. One of ordinary skill in the art would have been motivated to make this modification because replacing a vehicle’s battery is made more convenient by having information regarding the battery location in advance, as suggested by Li (see Li at least [0082] even if the user parks the vehicle randomly in the battery swapping area, the location of the vehicle's battery in the battery swapping area can still be determined, and the battery swapping module is moved to this location for battery swapping. Thus the convenience of the battery swapping process is improved).
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Michel, in view of Ferguson, further in view of Li, and further in view of US 20240170976 A1 Alobaidi; Mohammed et al. (hereinafter Alobaidi).
Regarding claim 16, Michel, Ferguson, and Li teach: The computer-implemented method of claim 15.
Michel, Ferguson, and Li do not teach: further comprising: rendering the mapping of the battery location on a graphical user interface (GUI) indicating where the EV and the battery location are depicted.
However, Alobaidi teaches: further comprising: rendering the mapping of the battery location on a graphical user interface (GUI) indicating where the EV and the battery location are depicted (see Alobaidi at least [0008] The present disclosure also includes disclosure of the system, wherein the swappable battery charging system is used in... battery powered vehicles and [0028] The present disclosure also includes disclosure of the system, further comprising at least one of a display, a monitor, or another visual indicator to provide real time information on a charge level of each of the plurality of battery packs, a location of particular battery packs, and other battery pack health or status information).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the electric vehicle battery monitoring method disclosed by Michel, Ferguson, and Li to include the battery location communication of Alobaidi. One of ordinary skill in the art would have been motivated to make this modification because knowing the location of the battery pack and whether it is in its expected location can act as a theft prevention technique, as suggested by Alobaidi (see Alobaidi at least [0068] The BLE capability may communicate battery pack's 200 location within the workplace. The GPS may transmit the battery pack's 200 location and/or act as an anti-theft solution).
Claim(s) 17, 18, 19, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Michel, in view of Ferguson, and further in view of US 20120262104 A1 Kirsch; David M. (hereinafter Kirsch).
Regarding claim 17, Michel and Ferguson teach: The computer-implemented method of claim 1.
Michel and Ferguson do not teach: wherein the electronic device is one of a mobile electronic device or a vehicle telematics system onboard the EV.
However, Kirsch teaches: wherein the electronic device is one of a mobile electronic device or a vehicle telematics system onboard the EV (see Kirsch at least [0027] The operation center 20, at ignition off cycle, can communicate information contained within the vehicle processing means 26 and navigation unit 22 via telematics control unit 24 to a remote location having a computer including programming capable of calculating charge conditions.... The communicated information can be state of charge).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the electric vehicle battery monitoring method disclosed by Michel and Ferguson to include the telematics system of Kirsch. One of ordinary skill in the art would have been motivated to make this modification because the telematics system supports communication of vehicle charging information between different sources, as suggested by Kirsch (see Kirsch at least [0023] an integrated network of communication is provided which allows a remote computer or hand held device to access vehicle data for storage and analysis of vehicle conditions, including charging status).
Regarding claim 18, Michel and Ferguson teach: The computer-implemented method of claim 1.
Michel and Ferguson do not teach: wherein the EV is a solar electric vehicle (EV).
However, Kirsch teaches: wherein the EV is a solar electric vehicle (EV) (see Kirsch at least [0005] Solar vehicles are used herein to refer to electric and hybrid vehicles which have one or more solar panels on the body to provide part of the electricity for the electric motor and/or for charging the batteries).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the electric vehicle battery monitoring method disclosed by Michel and Ferguson to include the solar electric vehicle of Kirsch. One of ordinary skill in the art would have been motivated to make this modification because solar modifications allow for more vehicle charging opportunities when the vehicle is exposed to sunlight, so the electric vehicle does not need to rely solely on plug-in charging, as suggested by Kirsch (see Kirsch at least [0005] A typical car belonging to an individual is parked 90% of the time. Therefore, solar charging can provide a significant portion of the energy used. In the case of an electric vehicle, the solar vehicle would likely also be a plug-in, so if sunlight is unavailable for any reason (weather, parked underground etc.) the battery can be charged from grid power. In the case of a hybrid vehicle, the battery of the solar hybrid can be charged by the solar panels and by the engine and perhaps also as a plug-in).
Regarding claim 19, Michel, Ferguson, and Kirsch teach: The computer-implemented method of claim 18, wherein at least one of the one or more batteries is a solar battery (see Kirsch at least [0005] Solar vehicles are used herein to refer to electric and hybrid vehicles which have one or more solar panels on the body to provide part of the electricity for the electric motor and/or for charging the batteries).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the electric vehicle battery monitoring method disclosed by Michel, Ferguson, and Kirsch to include the solar battery of Kirsch. One of ordinary skill in the art would have been motivated to make this modification because solar modifications allow for more battery charging opportunities when the vehicle is exposed to sunlight, so the electric vehicle does not need to rely solely on plug-in charging, as suggested by Kirsch (see Kirsch at least [0005] A typical car belonging to an individual is parked 90% of the time. Therefore, solar charging can provide a significant portion of the energy used. In the case of an electric vehicle, the solar vehicle would likely also be a plug-in, so if sunlight is unavailable for any reason (weather, parked underground etc.) the battery can be charged from grid power. In the case of a hybrid vehicle, the battery of the solar hybrid can be charged by the solar panels and by the engine and perhaps also as a plug-in).
Regarding claim 20, Michel, Ferguson, and Kirsch teach: The computer-implemented method of claim 18, wherein the solar EV is configured to operate in either of: (a) a solar mode that allows the solar EV to use solar energy as a power source, or (b) a power mode that allows the solar EV to use EV battery power (see Kirsch at least [0005] In the case of a hybrid vehicle, the battery of the solar hybrid can be charged by the solar panels and by the engine and perhaps also as a plug-in).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the electric vehicle battery monitoring method disclosed by Michel to include the solar or plug-in charging options of Kirsch. One of ordinary skill in the art would have been motivated to make this modification because ------relying on solar power alone would be limiting due to variations in weather and overhead cover, so vehicles will have more reliable access to power by distributing power source options, as suggested by Kirsch (see Kirsch at least [0005] if sunlight is unavailable for any reason (weather, parked underground etc.) the battery can be charged from grid power).
Response to Arguments
Applicant's arguments filed 02/04/2026 have been fully considered.
Regarding the arguments provided for the 35 U.S.C. §103 rejections of claim(s) 1 and 4-20, the applicant's arguments have been considered but are not persuasive.
(A) Applicant argues: “Michel teaches that the onboard computer receives indications of the state of charge and/or state of health of the batteries and determines operations to be performed by the autonomous vehicle based on the state of the batteries. See Michel, para. [0029]. However, Michel does not teach determining battery status based upon the combination of telematics data indicative of EV operation and a baseline reading from a battery sensor, as now recited by amended claim 1.” (Remarks pgs. 11-12)
As to point (A), Examiner partially agrees that Michel does not disclose determining battery status based on baseline battery data. However, Michel, when viewed in combination with Ferguson’s disclosure – which will be discussed below – renders obvious the amended claims.
(B) Applicant argues: “Ferguson teaches that the machine-learning module develops different models based upon battery type and usage, and that "a battery that routinely receives a DC fast charge may degrade differently from a battery that usually receives a slower DC or AC charge and the machine-learning module 540 may develop different models or sets of models that apply to batteries charged by types of charging stations." Ferguson, paragraph [0061]. Ferguson uses historical charging patterns to train different models, not to incorporate battery charging mode as a direct input to battery status determination in conjunction with telematics data and a baseline battery sensor reading.
Ferguson paragraph [0062] states that "the battery model may be used to predict a battery characteristic, such as a state-of-health or remaining useful life of the battery." Ferguson, para. [0062]. However, this disclosure relates to using a machine-learned model which was trained based on historical data including charging patterns-to predict battery characteristics. Ferguson does not teach or suggest determining battery status based upon the combination of (1) telematics data indicative of EV operation, (2) a baseline reading of the batteries originating from a battery sensor contained in the EV and coupled to the batteries, and (3) a battery charging mode associated with the batteries, as now recited by amended claim 1.” (remarks pg. 12)
As to point (B), Examiner respectfully disagrees. Ferguson discloses using a battery model determine a battery state of health (see Ferguson at least [0063] a parameter indicating the SoH of a battery 150 calculated using the battery model 550). The battery model used by Ferguson in determining the battery status is developed using baseline data, telematics data, and battery charging mode. As such, the determination of the battery status is based on each of these elements. Regarding the baseline data, Ferguson discloses using historical (i.e., baseline) data to develop a battery model (see Ferguson at least [0060] The machine-learning module 540 process the historical data 510 to develop one or more machine-learning models, such as the battery model 550). Ferguson describes that this historical data set includes on-board data (see Ferguson at least [0057] the historical data 510 includes impedance data 515, acoustic data 520, on-board data 525), which is collected from battery sensors on the vehicle (see Ferguson at least [0058] The on-board data 525 is obtained from on-board battery sensors, such as the on-board sensors 370). Additionally, Ferguson discloses that the historical data used to develop the battery model is based upon EV usage data (i.e., telematics data) (see Ferguson at least [0057] the historical data 510 includes impedance data 515, acoustic data 520, on-board data 525, Coulomb counting data 530, and EV usage data 535), wherein the EV usage data includes vehicle operation information such as the vehicle’s traveling speed and environmental conditions that may impact the battery status (see Ferguson at least [0059] In addition, the battery health system 430 collects EV usage data 535 describing usage of the batteries, such… speed of travel (e.g., average speed, ratio of highway driving to city driving), etc. The EV usage data 535 may also include environmental factors that can have an impact on battery health, such as temperature, humidity, type of terrain traveled by the EV (e.g., mountainous or flat), etc. and [0027] The EV 110 includes a sensor suite 140… the sensor suite 140 may include photodetectors, cameras, radar, sonar, lidar, GPS, wheel speed sensors). Note that the instant application’s specification enumerates myriad examples of telematics data, including vehicle speed information (see instant application specification [0026] the vehicle telematics system in each of EVs 12-1 through 12-N may include sensors and/or subsystems configured to collect any one or more types of telematics data, such as velocity information, acceleration information, braking information, speed information). Finally, Ferguson discloses using the battery charging mode to inform the battery model used to determine the battery status, as mentioned in previous correspondence and restated herein. Ferguson [0061] recites batteries being charged with “DC fast charge” and/or “slower DC or AC charge”, which a person having ordinary skill in the art of electric vehicles would understand to be encompassed by the broadest reasonable interpretation of the instant application’s language of “battery charging mode”. While the word “mode” is not explicitly recited in Ferguson’s specification, the word’s plain meaning is understood to mean a way of doing something. As interpreted with regards to Ferguson [0061], “DC fast charge” describes one way to charge an electric vehicle battery (i.e., one mode), while “slower DC or AC charge” describes alternative ways to charge an electric vehicle battery (i.e., a second and third mode). Additionally, the instant application specification [0035] describes different battery charging modes, differentiating between fast charging modes and slow charging modes, similarly to the description of modes found in Ferguson. Ferguson [0061] describes that batteries which routinely receive charging of different modes may degrade differently from one another, and as such, different models may be developed to reflect the types of charging of different batteries. As recited in Ferguson [0062], the battery model – which is informed by the charging mode of the battery – is used to determine the battery characteristic (i.e., the battery state [0061]). As such, Ferguson teaches each element of the limitations “determining, by the one or more processors, a battery status of the one or more batteries based upon the telematics data and a baseline reading of the one or more batteries originating from a battery sensor contained in the EV and coupled to the one or more batteries, wherein the determining is further based upon a battery charging mode associated with the one or more batteries” and, when viewed in combination with Michel, renders obvious the amended claim 1.
(C) Applicant argues:” Specifically, Ferguson's approach involves training different machine-learned models based upon historical charging data, and then using those trained models to predict battery characteristics. This is fundamentally different from the claimed invention, which requires determining battery status based upon the direct combination of telematics data, a baseline reading from a battery sensor physically coupled to the batteries, and battery charging mode. Ferguson does not disclose using telematics data indicative of EV operation as an input to battery status determination. Ferguson also does not disclose obtaining a baseline reading from a battery sensor contained in the EV and coupled to the batteries. Rather, Ferguson relies on impedance spectroscopy measurements obtained at battery assessment stations. See Ferguson, para. [0012].
Ferguson's use of historical charging patterns to train predictive models does not somehow disclose or teach the claimed direct use of battery charging mode as an input to battery status determination in conjunction with telematics data and a baseline battery sensor reading. Amended claim 1 requires that the determining step be "based upon" the combination of these three specific data inputs-telematics data, a baseline reading from a battery sensor coupled to the batteries, and battery charging mode. Ferguson's disclosure of using historical charging data to train machine-learned models does not teach this claimed combination.” (remarks pgs. 12-13)
As to point (C), Examiner respectfully disagrees. The claimed invention as amended does not preclude the incorporation of various vehicle data in the manner disclosed by Ferguson. Ferguson describes – as is detailed above – the incorporation of speed data, AC vs. DC charging mode data, and historical on-board sensor data being used as a basis for the models which are used to determine the vehicle battery status, and as such Ferguson (in combination with Michel), reads on the claimed invention as amended. Additionally, see (B) above.
(D) Applicant argues: “Neither Michel nor Ferguson teaches or suggests determining battery status based upon the specific combination of: (1) telematics data indicative of EV operation, (2) a baseline reading of the batteries originating from a battery sensor contained in the EV and coupled to the batteries, and (3) a battery charging mode associated with the batteries, as now recited by amended claim 1. To support a prima facie case of obviousness under 35 U.S.C. § 103, an examiner must establish "a finding that the prior art included each element claimed, although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference." MPEP §2143(A) (citing KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007)). The cited references fail to teach or suggest this specific combination of elements.”
As to point (D), see (B) above.
Regarding the arguments provided for the 35 U.S.C. §101 rejection of claim(s) 1, 4-12, 15-20, the applicant's arguments have been considered but are not persuasive.
(E) applicant argues, "The Office Action alleges… Applicant respectfully submits… Amended claim 1 now recites… As described in the specification… The specification further describes…." (from remarks pages 6-7)
As to point (E), Examiner respectfully disagrees. While the cited section of the specification does describe in general terms the calculation of the battery status using telematics data and baseline battery readings, the specific method of implementation is presented with a level of generality that effectively describes the battery status calculation as some function of its inputs. As generally recited, a technological improvement is not necessarily found, since these calculations could reasonably be construed as a mental process and/or as a mathematical operation.
(F) applicant argues, "Applicant respectfully submits… to determine battery status." (from remarks page 8)
As to point (F), Examiner respectfully disagrees. Examiner acknowledges that a human person could not mentally measure a baseline reading of a battery. However, the measurement of a baseline reading of a battery (which is not currently claimed) likely would be considered an insignificant data-gathering step so long as the baseline reading is ultimately used in a determination step considered to be an abstract idea and/or a database storage step considered further insignificant extra-solution activity. Given the data points of telematics data, baseline battery reading, and battery charging mode, a human could mentally estimate a battery status.
(G) applicant argues, "The specification also describes… and processor cycles." (from remarks page 8)
As to point (G), Examiner respectfully disagrees. The prior art teaches electric vehicle battery status determination using a variety of parameters for calculations. The claimed invention recites broad parameters including “telematics data” involved in the determination of a battery status. According to paragraph [0006] of the instant specification: “The telematics data may pertain to driving events (e.g., acceleration, braking, cornering, direction, and speed) and their frequency and/or duration. The telematics data may also pertain to route length and road infrastructure features, weather conditions (e.g., snow, rain, fog, etc.), traffic characteristics (e.g., traffic density, traffic direction, traffic flow, primary EV types in traffic, etc.), and so on. In some cases, the telematics data may pertain to levels of distraction of the driver while driving the EV. Considering that distracted driving often leads to accidents, the telematics data may include data indicating an accident associated with the EV or a predicted accident associated with the EV.” The wide breadth of information considered to be part of telematics data, in combination with baseline battery readings and a battery charging mode, yields an amount of data that is not necessarily a lighter computational load than those seen in the prior art. As such, a technical improvement to the functioning of a computer is not seen in the claimed invention.
(H) applicant argues, "The Office Action’s characterization… coupled to the batteries." (from remarks page 8)
As to point (H), see point F.
(I) applicant argues, “Amended claim 1 specifically… alleged abstract idea.” (from remarks pages 8-9)
As to point (I), see points E, F, and G.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20160349330 A1 Barfield, JR.; James Ronald et al. discloses using vehicle telematics data and historical data to determine electric vehicle battery status
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ELLE ROSE KNUDSON whose telephone number is (703) 756-1742. The examiner can normally be reached 1000-1700 ET M-F.
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, Hitesh Patel can be reached at (571) 270-5442. 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.
/ELLE ROSE KNUDSON/Examiner, Art Unit 3667
/Hitesh Patel/Supervisory Patent Examiner, Art Unit 3667
9/4/26
Prosecution Insights