DETAILED ACTION
1. This is the first action on the merits relating to U.S. Application Serial No. 18/457,573 filed on August 29, 2023. Currently claims 1-20 remain in the examination.
Notice of Pre-AIA or AIA Status
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 112
3. The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
4. Claims 2 and 16 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 2 recites the limitation "the input battery data values” in claim 1. There is insufficient antecedent basis for this limitation in the claim.
Claim 16 recites the limitation "the input battery data values” in claim 15. There is insufficient antecedent basis for this limitation in the claim.
Appropriate correction is required.
Double Patenting
5. 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.
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6. Claims 1, 2, 7-15, and 20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 2, 7-15, and 20 of copending Application No. 18/457,512 (published as US 2025/0076386 A1, hereinafter “386 application”) as the following claims comparison would show.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Instant Application
386 application
Claim 1
A method comprising:
receiving via a communication interface a plurality of observed battery system values for one or more battery systems associated with a respective one or more devices, the plurality of observed battery system values characterizing operation of the one or more battery systems over a plurality of time intervals;
determining via a processor a plurality of prospective control profiles for the one or more battery systems, each of the plurality of prospective control profiles corresponding to a respective time-varying pattern of battery system charge and/or discharge for a respective prospective course of action of a plurality of prospective courses of action for the one or more devices over a period of time;
determining a plurality of predicted battery system values for the one or more battery systems by applying a trained temporal convolutional neural network to the plurality of prospective control profiles and the plurality of observed battery system values;
selecting a designated control profile of the plurality of prospective control profiles based on the plurality of predicted battery system values; and
transmitting an instruction via the communication interface to a designated device of the one or more devices to execute a course of action corresponding with the designated control profile.
Claim 1
A method comprising:
receiving a plurality of observed battery system values for a battery system associated with a device, the plurality of observed battery system values characterizing one or more states associated with the battery system over a plurality of time intervals;
determining a prospective control profile identifying a time-varying pattern of battery system charge and/or discharge for the designated device over a period of time;
determining a plurality of prospective perturbed control profiles, each of the plurality of prospective perturbed control profiles introducing variation over time into the prospective control profile;
determining a plurality of predicted battery state estimate values and a corresponding plurality of predicted battery state variance values for the
designated battery system by applying a trained temporal convolutional neural network to the plurality of prospective perturbed control profiles and the plurality of observed battery system values, each of the predicted battery state variance values indicating a respective degree of statistical uncertainty for the predicted battery state estimate values;
selecting a designated perturbed control profile of the plurality of prospective control profiles based on the plurality of predicted battery state variance values; and
transmitting an instruction to the device to execute a course of action corresponding with the designated perturbed control profile.
Claim 2
The method recited in claim 1, wherein the input battery data values include observed data values encoding information corresponding to one or more sensors associated with the one or more battery systems.
Claim 2
The method recited in claim 1, wherein the observed battery system values encode information corresponding to one or more sensors associated with the battery system.
Claim 7
The method recited in claim 1, wherein the plurality of predicted battery system values includes a hidden value not observable via one or more sensors associated with the one or more battery systems, the hidden value selected from the group consisting of: a hidden resistance value, a hidden capacitance value, a hidden electrolyte connectivity value, and a hidden a cathode conductivity value.
Claim 7
The method recited in claim 1, wherein the plurality of predicted battery system values includes a hidden value not observable via one or more sensors associated with the one or more battery systems, the hidden value selected from the group consisting of: a hidden resistance value, a hidden capacitance value, a hidden electrolyte connectivity value, and a hidden a cathode conductivity value.
Claim 8
The method recited in claim 1, wherein the plurality of predicted battery system values includes a designated value selected from the group consisting of: a voltage value, an open-circuit voltage value, an internal resistance value, an external resistance value, a battery system temperature value, a state- of-charge value, and a state of health value.
Claim 8
The method recited in claim 1, wherein the plurality of predicted battery system values includes a designated value selected from the group consisting of: a voltage value, an open-circuit voltage value, an internal resistance value, an external resistance value, a battery system temperature value, a state- of-charge value, and a state of health value.
Claim 9
The method recited in claim 1, wherein a designated control profile of the plurality of control profiles includes a designated current profile defining an amount of current over a designated period of time.
Claim 9
The method recited in claim 1, wherein a designated control profile of the plurality of control profiles includes a designated current profile defining an amount of current over a designated period of time.
Claim 10
The method recited in claim 1, wherein a designated control profile of the plurality of control profiles includes a designated power profile defining an amount of power over a designated period of time.
Claim 10
The method recited in claim 1, wherein a designated control profile of the plurality of control profiles includes a designated power profile defining an amount of power over a designated period of time.
Claim 11
The method recited in claim 1, wherein the temporal convolutional neural network includes an output layer comprising a plurality of output neurons, the output neurons corresponding to the plurality of predicted battery system values.
Claim 11
The method recited in claim 1, wherein the temporal convolutional neural network includes an output layer comprising a plurality of output neurons, the output neurons corresponding to the plurality of predicted battery system values.
Claim 12
The method recited in claim 1, wherein the temporal convolutional neural network includes an input layer comprising a plurality of input neurons, a subset of the input neurons corresponding to the plurality of observed battery system values.
Claim 12
The method recited in claim 1, wherein the temporal convolutional neural network includes an input layer comprising a plurality of input neurons, a subset of the input neurons corresponding to the plurality of observed battery system values.
Claim 13
The method recited in claim 1, wherein the temporal convolutional neural network includes one or more hidden layers each comprising a respective plurality of hidden layer neurons, a designated hidden layer neuron receiving as input data values corresponding to a respective two or more different time periods, the designated hidden layer neuron including an activation function configured to transmit an output signal to a recipient neuron based on the input data values.
Claim 13
The method recited in claim 1, wherein the temporal convolutional neural network includes one or more hidden layers each comprising a respective plurality of hidden layer neurons, a designated hidden layer neuron receiving as input data values corresponding to a respective two or more different time periods, the designated hidden layer neuron including an activation function configured to transmit an output signal to a recipient neuron based on the input data values.
Claim 14
The method recited in claim 13, wherein the hidden layers collectively perform time-dilation on a plurality of input values spread over a period of time to predict an output value corresponding to a single period of time.
Claim 14
The method recited in claim 13, wherein the hidden layers collectively perform time-dilation on a plurality of input values spread over a period of time to predict an output value corresponding to a single period of time.
Claim 15
A system comprising:
a communication interface configured to receive a plurality of observed battery system values for one or more battery systems associated with a respective one or more devices, the plurality of observed battery system values characterizing operation of the one or more battery systems over a plurality of time intervals;
and a processor system configured to:
determine a plurality of prospective control profiles for the one or more battery systems, each of the plurality of prospective control profiles corresponding to a respective time-varying pattern of battery system charge and/or discharge for a respective prospective course of action of a plurality of prospective courses of action for the one or more devices over a period of time,
determine a plurality of predicted battery system values for the one or more battery systems by applying a trained temporal convolutional neural network to the plurality of prospective control profiles and the plurality of observed battery system values, and
select a designated control profile of the plurality of prospective control profiles based on the plurality of predicted battery system value, wherein the communication interface is configured to transmit to a designated device of the one or more devices to execute a course of action corresponding with the designated control profile.
Claim 15
A system comprising:
a storage system operable to receive a plurality of observed battery system values for a battery system associated with a device, the plurality of observed battery system values characterizing one or more states associated with the battery system over a plurality of time intervals;
a processor operable to:
determine a prospective control profile identifying a time-varying pattern of battery system charge and/or discharge for the designated device over a period of time,
determine a plurality of prospective perturbed control profiles, each of the plurality of prospective perturbed control profiles introducing variation over time into the prospective control profile,
determine a plurality of predicted battery state estimate values and a corresponding plurality of predicted battery state variance values for the designated battery system by applying a trained temporal convolutional neural network to the plurality of prospective perturbed control profiles and the plurality of observed battery system values, each of the predicted battery state variance values indicating a respective degree of statistical uncertainty for the predicted battery state estimate values; and
select a designated perturbed control profile of the plurality of prospective control profiles based on the plurality of predicted battery state variance values; and a communication interface operable to transmit an instruction to the device to execute a course of action corresponding with the designated perturbed control profile.
Claim 20
One or more non-transitory computer readable media having instructions thereon for performing a method, the method comprising:
receiving via a communication interface a plurality of observed battery system values for one or more battery systems associated with a respective one or more devices, the plurality of observed battery system values characterizing operation of the one or more battery systems over a plurality of time intervals;
determining via a processor a plurality of prospective control profiles for the one or more battery systems, each of the plurality of prospective control profiles corresponding to a respective time-varying pattern of battery system charge and/or discharge for a respective prospective course of action of a plurality of prospective courses of action for the one or more devices over a period of time;
determining a plurality of predicted battery system values for the one or more battery systems by applying a trained temporal convolutional neural network to the plurality of prospective control profiles and the plurality of observed battery system values;
selecting a designated control profile of the plurality of prospective control profiles based on the plurality of predicted battery system values; and
transmitting an instruction via the communication interface to a designated device of the one or more devices to execute a course of action corresponding with the designated control profile.
Claim 20
One or more non-transitory computer readable media having instructions thereon for performing a method, the method comprising:
receiving a plurality of observed battery system values for a battery system associated with a device, the plurality of observed battery system values characterizing one or more states associated with the battery system over a plurality of time intervals;
determining a prospective control profile identifying a time-varying pattern of battery system charge and/or discharge for the designated device over a period of time;
determining a plurality of prospective perturbed control profiles, each of the plurality of prospective perturbed control profiles introducing variation over time into the prospective control profile;
determining a plurality of predicted battery state estimate values and a corresponding plurality of predicted battery state variance values for the designated battery system by applying a trained temporal convolutional neural network to the plurality of prospective perturbed control profiles and the plurality of observed battery system values, each of the predicted battery state variance values indicating a respective degree of statistical uncertainty for the predicted battery state estimate values;
selecting a designated perturbed control profile of the plurality of prospective control profiles based on the plurality of predicted battery state variance values; and
transmitting an instruction to the device to execute a course of action corresponding with the designated perturbed control profile.
As shown in the claim comparison above, the underlined section of claim 1 of 386 application corresponds to each element listed in claim 1 of the instant application. In comparing claim 1 of the instant application, and claim 1 of 389 application, the step of “determining via a processor a plurality of prospective control profiles for the one or more battery systems, each of the plurality of prospective control profiles corresponding to a respective time-varying pattern of battery system charge and/or discharge for a respective prospective course of action of a plurality of prospective courses of action for the one or more devices over a period of time” is broken into two separate steps in claim 1 of 398 application, however, they appear to be limited to the same subject matter. For example, in the instant application, some prospective control profile may require a course of action, and in 398 application, it simply states a prospective perturbed control profiles. They may not be the same in verbatim manner, however, they both require some course of action in time.
Claims 15 is limited to a system and claim 20 to a computer program product stored in a storage device. The corresponding claims 15 and 20 of 389 application show the same analogous pattern shown in claim 1s. Some dependent claims of the instant application are identical to the corresponding claims of 389 application. In view of the above, the above listed claims of the instant application and the corresponding claims of 389 application are limited to the same subject matter.
Allowable Subject Matter
7. Claims 3-6 and 17-19 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
8. The following is a statement of reasons for the indication of allowable subject matter: The limitations in the above listed dependent claims are neither disclosed nor suggested by the cited references, and therefore allowable.
Conclusion
The pertinent prior arts made of record but not relied are listed in the attached form PTO-892. These are considered pertinent to Applicant's disclosure. Applicant is respectfully suggested to carefully review these references.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ahshik Kim whose telephone number is (571)272-2393. The examiner can normally be reached between the hours of 8:00 AM to 5:00 PM Monday thru Friday. Examiner’s fax phone number is (571)273-2393.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Thomas Pham, can be reached on (571)272-3699. The fax phone number for this Group is (571)273-8300.
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/AHSHIK KIM/Primary Examiner, Art Unit 2876
June 4, 2026
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