DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This action is responsive to pending claims 1-20 filed 11/13/2025.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: communications interface (claim 8), storage, input interface, output interface (claim 9), communications interface (claim 10).
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 101
The 101 rejections are withdrawn based on the Applicant’s amendments.
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, 9-20 are rejected under 35 U.S.C. 103 as being unpatentable over Che ("Lifetime and aging degradation prognostics for lithium-ion battery packs based on a cell to pack method", published 1/9/2022) in view of Negoita (US 20230194614 A1) in view of Ugaji (US 8102152 B2).
For claim 1, Che discloses: acquire an output of a battery lifespan model associated with the battery data (§3.3, fig.5 gives overview of the process, with battery health indicator (HI) degradation predicted using a LSTM, the degraded health indicators then being fed into a pack-specific gaussian process regression (GPR) model for battery lifetime prediction; hence, a capacity output is obtained from LSTM / GPR model);
predict a lifespan value of the battery based on the output of the battery lifespan model (ibid: lifespan values such as future capacity, health indicators are calculated via the above model; see also §4.2 ¶2 contemplates remaining useful life (RUL) estimates based on the above model); and
wherein the battery lifespan model comprises:
a cell lifespan model associated with a basic lifespan model trained using first battery cell data collected in a first operating environment (ibid: cell capacity LSTM models are generated both for single battery cells (SBCs) for a first environment to arrive at deep learning degradation mode, see §3.2 last ¶, fig.5; see also §3.1 last ¶: training a SBC / CBC GPR model), wherein the cell lifespan model is retrained using second battery cell data collected in a second operating environment (fig.4, §3.2 ¶1, last ¶: the pre-trained LSTM network is tuned for particular battery packs via transfer learning in the destination domain via early information in the destination domain; see also §3.1 last ¶: tuning a combined cell-lifespan GPR pack model on early discharge cycles); and
a pack lifespan model trained using battery pack data collected in the first operating environment (§3.3 gives overview of acquiring a pack lifespan model comprising a GPR model trained based on CBC HI’s and initial pack capacity readings (§3.1 last ¶) and the output of a LSTM tuned with the CBC HI’s (§3.2), hence, the pack lifespan model trained using battery pack data collected in the first environment (the LSTM initial training, §3.2) and an output of the retrained cell lifespan model (the collective HI’s from , the output of each LSTM of the CBC’s being iteratively tuned to generate the pack LSTM, §3.2) in order to generate the pack prognostics) and an output of the retrained cell lifespan model (§3.3 discloses using the output of a trained GPR pack model (§3.1 last §¶), trained on early cycles, in order to combine with the tuned LTSM model in order to generate battery lifetime model including capacity degradation model and lifespan; hence, the output of a retrained combined cell lifespan GPR pack model is used in order to iteratively generate the pack lifespan capacity curves and predictions, hence, the pack lifespan model being learned or trained based on output of cell lifespan model)
Che does not disclose: a vehicle comprising: a display; a battery; a battery sensor configured to acquire battery data of the battery; a charging circuit configured to charge the battery, and a processor configured to perform the above steps; display an indication associated with the lifespan value of the battery on the display;
wherein the first battery cell data collected in the first operating environment and the second battery cell data collected in the second operating environment are collected from different vehicles;
where in the pack lifespan model is trained via an output of the retrained cell lifespan model (Che directly uses the tuned HI model to generate pack HI (§3.2 last ¶) which is fed into a tuned pack GPR model (§3.1 last ¶), hence, the pack lifespan model simply accepts the output of the retrained cell lifespan model but is not trained via that output));
wherein the processor is further configured to control a charge current for charging the battery so that the charge current is limited based on the lifespan value of the battery.
Negoita discloses: a vehicle comprising: a display; a battery; a battery sensor configured to acquire battery data of the battery; a charging circuit configured to charge the battery, and a processor configured to perform the above steps (0046, 0060: use of BMS in vehicles; with fig.5 contemplating computer implementation with display, processor; 0126: display of SOH-related metrics; fig.1A-B gives battery overview; fig.4:402 disclosing sensor readings; 0128 contemplates collecting data during charge cycles, hence, charge circuits);
wherein the first battery cell data collected in the first operating environment and the second battery cell data collected in the second operating environment are collected from different vehicles (0056 contemplates acquiring metrics via field and driving data such as from numerous electric vehicles for use in a vehicle’s BMS, hence, different operating environments; see also 0046, 0122 contemplates using the BMS in different vehicles, hence, combination with collecting further data in a target environment for tuning of Che fig.5 yielding a technique where second data may be collected in a different deployed vehicle);
where in the pack lifespan model is trained via an output of the retrained cell lifespan model (fig.3a contemplates an output of the cell model 302 being fed into a pack model in order to generate an output, hence, the training incorporating the output of the cell model, see also 0051-52, 0055-56, fig.3B, 0072);
wherein the processor is further configured to control a current so that the charge current is limited based on the lifespan value of the battery (0060 contemplates using model SOH output in order to assess and control battery operations including load distribution based on metrics including current (see 0123, 0136)).
It would have been obvious before the effective filing date to a person of ordinary skill in the art to modify the system of Che by incorporating hierarchical BMS technique of Negoita. Both concern the art of battery life prediction in EVs, and the incorporation would have, according to Negoita, better model underlying physical and spatial interactions (0051), improve user experience, e.g., anxiety (0126).
Che modified by Negoita does not disclose: wherein the current is charge current for charging the battery.
Ugaji discloses: wherein the current is charge current for charging the battery (fig.3:7, c.6 last ¶ to c.7 ¶2: controlling charging current based on deterioration level including controlling current to stop or start at an earlier time).
It would have been obvious before the effective filing date to a person of ordinary skill in the art to modify the system of Che modified by Negoita by incorporating charging control of Ugaji. Both concern the art battery management, and the incorporation would have, according to Ugaji, decrease battery deterioration (c.6 last ¶ to c.7 ¶2).
For claim 2, Che modified by Negoita modified by Ugaji discloses the vehicle of claim 1, as described above. Che modified by Negoita modified by Ugaji further discloses: a communication interface to receive the first battery cell data (0056-57: communication interface from sensors to BMS / neural networks for training),
wherein each of the first battery cell data and the battery pack data is associated with at least one battery of at least one second vehicle (Che fig.5: first battery cell data and the second cell battery data ae associated with the second battery in measuring and being associated with the same parameters, e.g., HI, SOH, etc.), and
wherein the second battery cell data is associated with the battery data of the battery (Che fig.5: second cell battery data is associated with battery data as it is in the current, target domain)
For claim 3, Che modified by Negoita modified by Ugaji discloses the vehicle of claim 1, as described above. Che modified by Negoita modified by Ugaji further discloses: wherein the lifespan value of the battery comprises a maximum output voltage of the battery predicted after a charge and discharge cycle (Negoita 0123, 0136, with 0053, 0090, etc. disclosing charge discharge cycles).
For claim 4, Che modified by Negoita modified by Ugaji discloses the vehicle of claim 1, as described above. Che modified by Negoita modified by Ugaji further discloses: wherein the cell lifespan model comprises a long short-term memory (LSTM) model and a fully connected (FC) layer (Che §3.2, fig.4, Negoita fig.3D).
For claim 5, Che modified by Negoita modified by Ugaji discloses the vehicle of claim 4, as described above. Che further discloses: wherein the LSTM model and the FC layer are trained using the first battery cell data collected in the first operating environment (Che §3.2 fig.4 last ¶; Negoita fig.3D).
For claim 6, Che modified by Negoita modified by Ugaji discloses the vehicle of claim 5, as described above. Che modified by Negoita modified by Ugaji further discloses: wherein the trained FC layer is further trained using the second battery cell data collected in the second operating environment (Che §3.2 fig.4 last ¶).
For claim 9, Che discloses: train, using the first battery data, a battery lifespan model for predicting a lifespan of a battery of the vehicle (§3.3, fig.5: a battery lifespan model is trained using the source domain data source);
acquire, based on the second battery data, an output of the trained battery lifespan model (ibid: using early indicators in the destination domain data, an output is acquired of predicted degraded health indicators (HI)); and
predict, based on the output of the trained battery lifespan model, a lifespan value of the battery (ibid: predicted HIs are fed into the gaussian process regression (GPR) model for determining battery lifespan); and
wherein the battery lifespan model comprises:
a basic lifespan model trained using with first battery cell data collected in a first operating environment (ibid: cell capacity LSTM models are generated both for single battery cells (SBCs) and connected battery cells (CBCs), the model being trained via a the source domain data comprising training data, hence, first operating environment, see also §2.1 contemplating various environments for the training data);
a cell lifespan model retrained with second battery cell data collected in a second operating environment (fig.4, §3.2, particularly ¶1, last ¶: the pre-trained LSTM network is tuned for particular battery packs via transfer learning in the destination domain via early information in the destination domain); and
a pack lifespan model associated with an output of the retrained cell lifespan model and battery pack data collected in the first operating environment (§3.3, fig.5: a pack lifespan model is generated via the pre-trained model in the first environment, the pack lifespan model drawing on the output of the tuned cell lifespan model, the pack lifespan model taking as an output of the retrained or tuned LSTM model for predictions).
Che does not disclose: an apparatus comprising: a data storage configured to store first battery data; an input interface configured to acquire second battery data from a vehicle; a processor configured to perform the above steps; an output interface configured to transmit the predicted lifespan value of the battery to the vehicle;
wherein the first battery cell data collected in the first operating environment and the second battery cell data collected in the second operating environment are collected from different vehicles; and
wherein the processor is further configured to control a charge current for charging the battery so that the charge current is limited based on the lifespan value of the battery.
Negoita discloses: an apparatus comprising: a data storage configured to store first battery data; an input interface configured to acquire second battery data from the vehicle; a processor configured to perform the above steps; an output interface configured to transmit the predicted lifespan value of the battery to the vehicle (0046, 0060: use of BMS in vehicles; with fig.5 contemplating computer implementation with display, processor, memory for storing data; fig.3a-b shows input interface for acquiring raw sensor data; 0126: display of SOH-related metrics, hence, communication output interfaced configured to transmit lifespan values to the vehicle; fig.1A-B gives battery overview; fig.4:402 disclosing sensor readings; 0128 contemplates collecting data during charge cycles, hence, charge circuits);
wherein the first battery cell data collected in the first operating environment and the second battery cell data collected in the second operating environment are collected from different vehicles (0056 contemplates acquiring metrics via field and driving data such as from numerous electric vehicles for use in a vehicle’s BMS, hence, different operating environments; see also 0046, 0122 contemplates using the BMS in different vehicles, hence, combination with collecting further data in a target environment for tuning of Che fig.5 yielding a technique where second data may be collected in a different deployed vehicle);
wherein the processor is further configured to control a current so that the current is limited based on the lifespan value of the battery (0060 contemplates using model SOH output in order to assess and control battery operations including load distribution based on metrics including current (see 0123, 0136)).
It would have been obvious before the effective filing date to a person of ordinary skill in the art to modify the system of Che by incorporating hierarchical BMS technique of Negoita. Both concern the art of battery life prediction in EVs, and the incorporation would have, according to Negoita, better model underlying physical and spatial interactions (0051), improve user experience, e.g., anxiety (0126).
Che modified by Negoita does not disclose: wherein the current is charge current for charging the battery.
Ugaji discloses: wherein the current is charge current for charging the battery (fig.3:7, c.6 last ¶ to c.7 ¶2: controlling charging current based on deterioration level including controlling current to stop or start at an earlier time).
It would have been obvious before the effective filing date to a person of ordinary skill in the art to modify the system of Che modified by Negoita by incorporating charging control of Ugaji. Both concern the art battery management, and the incorporation would have, according to Ugaji, decrease battery deterioration (c.6 last ¶ to c.7 ¶2).
For claim 10, Che modified by Negoita modified by Ugaji discloses the vehicle of claim 1, as described above. Che modified by Negoita modified by Ugaji further discloses: a communication interface to receive, from at least one second vehicle, the first battery data (0056-57: communication interface from vehicle sensors to BMS / neural networks for training),
wherein each of the first battery cell data and the battery pack data is associated with at least one battery of the at least one second vehicle (Che fig.5: first battery cell data and the second cell battery data ae associated with the second battery in measuring and being associated with the same parameters, e.g., HI, SOH, etc.), and
wherein the second battery cell data is associated with the battery of the vehicle (Che fig.5: second cell battery data is associated with battery data as it is in the current, target domain, furthermore, second battery cell data and the second cell battery data ae associated with the second battery in measuring and being associated with the same parameters, e.g., HI, SOH, etc.)
For claim 15, Che discloses: a method comprising:
training, using the first battery data, a battery lifespan model to predict a lifespan of a battery of the vehicle (§3.3, fig.5: a battery lifespan model is trained using the source domain data source);
acquiring, based on the second battery data, an output of the trained battery lifespan model (ibid: using early indicators in the destination domain data, an output is acquired of predicted degraded health indicators (HI));
determining, based on the output of the trained battery lifespan model, a predicted lifespan value of the battery (ibid: predicted HIs are fed into the gaussian process regression (GPR) model for determining battery lifespan);
wherein the training the battery lifespan model comprises:
training, using first battery cell data collected in a first operating environment, a basic lifespan model (ibid: cell capacity LSTM models are generated both for single battery cells (SBCs) and connected battery cells (CBCs), the model being trained via a the source domain data comprising training data, hence, first operating environment, see also §2.1 contemplating various environments for the training data);
perform transfer learning from the basic lifespan model to a cell lifespan model using second battery cell data collected in a second operating environment (fig.4, §3.2, particularly ¶1, last ¶: the pre-trained LSTM network is tuned for particular battery packs via transfer learning in the destination domain via early information in the destination domain); and
training, using battery pack data collected in the first operating environment, a pack lifespan model (§3.3, fig.5: a pack lifespan model is generated via the pre-trained model in the first environment, the pack lifespan model drawing on the output of the tuned cell lifespan model).
Che does not disclose: storing, by an apparatus, first battery data;
acquiring second battery data from a vehicle; transmitting, to the vehicle, the predicted lifespan value of the battery.
wherein the training uses an output of the cell lifespan model;
charging the battery by controlling a charge current being limited based on the predicted lifespan value of the battery;
wherein the first battery cell data collected in the first operating environment and the second battery cell data collected in the second operating environment are collected from different vehicles.
Negoita discloses: a storing, by an apparatus, first battery data;
acquiring second battery data from a vehicle; transmitting, to the vehicle, the predicted lifespan value of the battery (0046, 0060: use of BMS in vehicles; with fig.5 contemplating computer implementation with display, processor and storage for storing battery data for propagation through BMS neural networks; 0126: display of SOH-related metrics; fig.1A-B gives battery overview; fig.4:402 disclosing sensor readings; 0128 contemplates collecting data during charge cycles, hence, charge circuits);
wherein the training uses an output of the cell lifespan model (fig.3a contemplates an output of the cell model 302 being fed into a pack model in order to generate an output, hence, the training incorporating the output of the cell model, see also 0051-52, 0055-56, fig.3B, 0072);
wherein the first battery cell data collected in the first operating environment and the second battery cell data collected in the second operating environment are collected from different vehicles (0056 contemplates acquiring metrics via field and driving data such as from numerous electric vehicles for use in a vehicle’s BMS, hence, different operating environments; see also 0046, 0122 contemplates using the BMS in different vehicles, hence, combination with collecting further data in a target environment for tuning of Che fig.5 yielding a technique where second data may be collected in a different deployed vehicle);
control the battery by controlling a current being limited based on the predicted lifespan value of the battery (0060 contemplates using model SOH output in order to assess and control battery operations including load distribution based on metrics including current (see 0123, 0136), hence, limiting current).
It would have been obvious before the effective filing date to a person of ordinary skill in the art to modify the system of Che by incorporating hierarchical BMS technique of Negoita. Both concern the art of battery life prediction in EVs, and the incorporation would have, according to Negoita, better model underlying physical and spatial interactions (0051), improve user experience, e.g., anxiety (0126).
Che modified by Negoita does not disclose: wherein control comprises charging the battery; wherein the current is charge current for charging the battery.
Ugaji discloses: wherein control comprises charging the battery; wherein the current is charge current for charging the battery(fig.3:7, c.6 last ¶ to c.7 ¶2: controlling charging current based on deterioration level including controlling current to stop or start at an earlier time).
It would have been obvious before the effective filing date to a person of ordinary skill in the art to modify the system of Che modified by Negoita by incorporating charging control of Ugaji. Both concern the art battery management, and the incorporation would have, according to Ugaji, decrease battery deterioration (c.6 last ¶ to c.7 ¶2).
For claim 18, Che modified by Negoita modified by Ugaji discloses the vehicle of claim 15, as described above. Che further discloses: wherein the cell lifespan model comprises a long short-term memory (LSTM) model and a fully connected (FC) layer (§3.2, fig.4).
For claim 19, Che modified by Negoita modified by Ugaji discloses the vehicle of claim 18, as described above. Che further discloses: training the LSTM and the FC layer using the first battery cell data collected in the first operating environment (§3.2 last ¶, fig.4).
For claim 20, Che modified by Negoita modified by Ugaji discloses the vehicle of claim 19, as described above. Che modified by Negoita modified by Ugaji further discloses: wherein the training the battery lifespan model further comprises performing transfer learning of the trained FC layer using the second battery cell data collected in the second operating environment (§3.2 last ¶, fig.4).
Claims 11-14, 16-17 recite apparatuses and methods corresponding to claims 3-6, 10-11 above and are hence rejected for the same reasons.
Claim(s) 8 are rejected under 35 U.S.C. 103 as being unpatentable over Che ("Lifetime and aging degradation prognostics for lithium-ion battery packs based on a cell to pack method", published 1/9/2022) in view of Negoita (US 20230194614 A1) in view of Ugaji (US 8102152 B2) in view of Budan (US 11527786 B1).
For claim 8, Che modified by Negoita modified by Ugaji discloses the vehicle of claim 1, as described above. Che modified by Negoita modified by Ugaji does not disclose the limitations of claim 8.
Budan discloses: wherein the vehicle further comprises a communication interface configured to communicate with an external device (fig.1 shows communication over a network, with fig.21 contemplating hardware implementations), and
wherein the processor is further configured to transmit the battery data to the external device and receive an output of the battery lifespan model from the external device (figs. 7-8 discloses interaction between on-board components (see fig.7, c.17 ¶4-c.18 ¶2 disclosing cloud-components and BMS local components), with fig.8 contemplating various data transfers between cloud and edge models (see fig.7:600), hence, battery data is transferred to the external cloud device (e.g., state of health parameters, usage profile, see fig.8, c.11 ¶5) and the output, e.g., RUL model parameters, is returned from the cloud, see c.18 ¶4-5, for estimating RUL).
It would have been obvious before the effective filing date to a person of ordinary skill in the art to modify the system of Che modified by Negoita modified by Ugaji by incorporating the communication interface of Budan. Both concern the art vehicle battery management, and the incorporation would have, according to Budan, allow cloud based estimation and management of models, hence, improving computing efficiency (c.8 ¶2, c.18 ¶2-3).
Claim(s) 21 are rejected under 35 U.S.C. 103 as being unpatentable over Che ("Lifetime and aging degradation prognostics for lithium-ion battery packs based on a cell to pack method", published 1/9/2022) in view of Negoita (US 20230194614 A1) in view of Ugaji (US 8102152 B2) in view of Zhang (US 20220250505 A1).
For claim 21, Che modified by Negoita modified by Ugaji discloses the vehicle of claim 1, as described above. Che modified by Negoita modified by Ugaji further discloses: wherein the first battery cell data collected in the first operating environment is data collected from first vehicles, wherein the second battery cell data collected in the second operating environment is data collected from second vehicles (Negoita 0056 contemplates acquiring metrics via field and driving data such as from numerous electric vehicles for use in a vehicle’s BMS, hence, different operating environments; see also 0046, 0122 contemplates using the BMS in different vehicles, hence, combination with collecting further data in a target environment for tuning of Che fig.5 yielding a technique where second data may be collected in a different deployed vehicle).
Che modified by Negoita modified by Ugaji does not disclose: wherein the second vehicles are newer vehicles than the first vehicles.
Zhang discloses: wherein the second vehicles are newer vehicles (0066 contemplates using transfer learning to transfer algorithms to new vehicles, hence, combination with Negoita’s disclosure of developing baseline BMS ML algorithms from an EV population yielding the second vehicles being newer than the first vehicles).
It would have been obvious before the effective filing date to a person of ordinary skill in the art to modify the system of Che modified by Negoita modified by Ugaji by incorporating new vehicle BMS technique of Zhang. Both concern the art or vehicle battery management, and the incorporation would have, according to Zhang, improve learning performance, network performance, and situational adaptability (0066).
Response to Arguments
In the remarks, Applicant argues:
1. The recited elements recite structural elements.
Examiner respectfully disagrees, as the named elements, as interpreted by one of ordinary skill in the art under BRI, could recite software, hardware, or a combination.
2. The claims are not directed to abstract ideas without significantly more.
Examiner agrees and the 101 rejections are withdrawn.
3. The art of record does not disclose the newly amended limitations directed to a retrained battery model via data collected in first and second environments from different vehicles.
Applicant’s arguments are moot in view of newly cited art.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Tanaka (US 9153845 B2) discloses a BMS for limiting charge or discharge based on SOH estimates.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LIANG LI whose telephone number is (303)297-4263. The examiner can normally be reached Mon-Fri 9-12p, 3-11p MT (11-2p, 5-1a ET).
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/LIANG LI/
Primary examiner AU 2143