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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 5/15/2026 was in compliance with the provisions of 37 CFR 1.97. Accordingly, the IDS is being considered by the examiner.
Response to Amendment
Claims 1-18 are amended. Claims 1-18 are pending.
Response to Arguments
Applicant's arguments filed 3/20/2026 have been fully considered.
Regarding the interpretation of “first reception unit” in claims 1 and 18, “estimation model generation unit” in claims, 1, 3-4, 6, 8-10, and 18, “second reception unit” in claim 13, “estimation unit” in claim 13, and notification information generation unit” in claims 13 and 15, as noted by Applicant on page 6 of the response, sufficient structure characterizing the underlying functions has been added by amendment. Therefore, the interpretations under 112(f) are withdrawn.
Regarding the objection to claim 1, and as noted by Applicant on page 6 of the response, the amendment to claim 1 overcomes the objection, which is withdrawn.
Regarding the rejections of claims 7-8 and 12 under 112(b), Examiner agrees, particularly in view of the explanation provided on page 7 of the response, that the apparent dash symbol in fact indicates a subtraction/mathematical difference, such that the rejections of claims 7-8 and 12 under 112(b) are withdrawn. Furthermore, due to the corrected interpretation of claims 7-8 and 12 being applied with new grounds for rejecting claims 7 and 12 under 103, this Action is made Non-Final.
Regarding the rejection of claim 18 under 101 as directed to non-statutory subject matter, as noted by Applicant on page 8 of the response, the amendment to claim 18 overcomes the rejection, which is withdrawn.
Regarding the rejections of claims 1-18 under 101 as directed to the abstract idea judicial exception without significantly more, Examiner respectfully disagrees with Applicant’s arguments for the following reasons.
On page 7 of the response and regarding claim 1, Applicant contends that the recited structural features entail hardware for performing the specialized task of receiving and processing charge/discharge data from multiple battery management systems and determining a battery degradation state based on specific electrochemical data points, such as OCV differentials involves complex technical analysis rooted in battery science that cannot be performed in the human mind.
Examiner submits that the recited computing/networking infrastructure appears to represent ordinary distributed computing for collecting, distributing, and processing battery related information and does not appear in any way uniquely configured to implement the battery health (degradation) processing implemented by the degradation estimation step. Regarding potential complexity of the degradation estimation step itself, Examiner notes that the type of input data used broadly encompasses any “charge/discharge” data (e.g., data indicating a state of charge) and the nature of the estimation (degradation indicator) is similar sufficiently broad such that it appears readily evident that such degradation indicators could be ascertained via mental processes and/or via mathematical concepts per claims 3-8.
On pages 7-8 of the response, Applicant contends that the claims integrate any arguable mental processes into a practical application in terms of improvement to an existing technology. The argument appearing primarily directed to claims 7-8 and 12, Applicant contends on page 8 that the use of the specific differential between charge and discharge OCV (OCV after charge – OCV after discharge), the system generates a more accurate degradation indicator, such that it represents non-generic modeling that constitutes a specialized technical tool that improves the functioning of battery management systems and therefore represents a clear improvement to the technical field of battery life-cycle management.
As explained in the grounds for rejecting claims 7-8 and 12 under 101, using OCV voltages relating to charge and discharge battery characteristics is well-known including “OCV after charge – OCV after discharge” values. Therefore, the utility in terms of modification to a technical field is confined to the step falling within the judicial exception (model and use of regression (mathematical concept)) itself, with no contribution in terms of the additional elements combined with the judicial exception.
Regarding Step 2B of the analysis, Applicant contends on page 8 that claim 1 recites an inventive concept by combining specific hardware (communication interface and server) with a specialized algorithm for battery health estimation. Examiner notes that per the grounds for rejecting claim 1 under 103 the claim does not appear to represent an inventive concept. Furthermore, regarding claims 7-8 and 12, the combination of communication interface and server infrastructure does not appear to have any particularized functional significance to the element of using OCV after charge – OCV after discharge metrics such that the additional elements fail to result in the claim as a whole amounting to significantly more than the judicial exception.
Regarding the rejection of claim 1 under 102, Applicant contends on page 9 of the response that “Ukumori fails to disclose or suggest both the reception and utilization of discharge data as a basis for the estimation model as required by claim 1,” citing claim 1 as reciting that the estimation model is generated based on charge/discharge data. In support, Applicant asserts that Ukumori’s model is driven by the delta between measured and predicted environmental values and does not rely on the actual discharge data of the battery.
Examiner submits that as set forth in claim 1, a broadest reasonable interpretation of “charge/discharge” may simply be various levels of charge (e.g., state of charge) that reflects relative charge/discharge of a battery as indicated in the grounds for rejecting claim 1. If Applicant intends “charge/discharge data” to be interpreted with respect to discharge activity, such meaning must be clearly conveyed by the claim language.
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 1-18 are rejected under 35 U.S.C. 101 because the claimed invention in each of these claims is directed to the abstract idea judicial exception without significantly more.
Claim 1, substantially representative also of independent claims 17-18, recites:
“[a] server system comprising:
one or more computer systems, the one or more computer systems comprising:
a communication interface circuit in communication with a communication network through which communication with a plurality of secondary battery management systems is performed;
a first reception unit configured to receive, from the plurality of secondary battery management systems, charge/discharge data of one or more secondary batteries managed by the secondary battery management systems; and
an estimation model generation unit which generates an estimation model for estimating a degradation indicator indicating a state of degradation of the one or more secondary batteries based on the charge/discharge data.”
The claim limitations considered to fall within in the abstract idea are highlighted in bold font above and the remaining features are “additional elements.”
Step 1 of the subject matter eligibility analysis entails determining whether the claimed subject matter falls within one of the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: process, machine, manufacture, or composition of matter. Claim 1 recites a device, claim 17 recites a method, and claim 18 recites an article of manufacture and each therefore falls within a statutory category.
Step 2A, Prong One of the analysis entails determining whether the claim recites a judicial exception such as an abstract idea. Under a broadest reasonable interpretation, the highlighted portions of claim 1 fall within the abstract idea judicial exception. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, the highlighted subject matter falls within the mental processes category (including an observation, evaluation, judgment, opinion). MPEP § 2106.04(a)(2).
The recited function “estimating a degradation indicator indicating a state of degradation of the one or more secondary batteries based on the charge/discharge data,” may be performed via mental processes (e.g., evaluation of charge/discharge data and judgement to estimate a degradation indicator).
Step 2A, Prong Two of the analysis entails determining whether the claim includes additional elements that integrate the recited judicial exception into a practical application. “A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception” (MPEP § 2106.04(d)).
MPEP § 2106.04(d) sets forth considerations to be applied in Step 2A, Prong Two for determining whether or not a claim integrates a judicial exception into a practical application. Based on the individual and collective limitations of claim 1 and applying a broadest reasonable interpretation, the most applicable of such considerations appear to include: improvements to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a)); applying the judicial exception with, or by use of, a particular machine (MPEP 2106.05(b)); and effecting a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)).
Regarding improvements to the functioning of a computer or other technology, none of the “additional elements” including a “server system comprising: one or more computer systems, the one or more computer systems comprising: a communication interface circuit in communication with a communication network through which communication with a plurality of secondary battery management systems is performed,” “a first reception unit configured to receive, from the plurality of secondary battery management systems, charge/discharge data of one or more secondary batteries managed by the secondary battery management systems,” and “an estimation model generation unit which generates an estimation model,” in any combination appear to integrate the abstract idea type judicial exception in a manner that technologically improves any aspect of a device or system that may be used to implement the highlighted step or a device for implementing the highlighted step such as a signal processing device or a generic computer. Instead, “server system comprising: one or more computer systems, the one or more computer systems comprising: a communication interface circuit in communication with a communication network through which communication with a plurality of secondary battery management systems is performed,” merely represents known data processing system and networking functionality for collecting and distributing data (“server” and “network” features from the standard battery management facilities (“battery management systems”) and further represents processing function (server) implementing the step falling within the judicial exception and therefore constitutes insignificant extra solution activity that fails to integrate the judicial exception into a practical application. Similarly, “a first reception unit configured to receive, from the plurality of secondary battery management systems, charge/discharge data of one or more secondary batteries managed by the secondary battery management systems” represents a data interface for receiving data which entails routine, conventional data processing activity for collecting and processing data and therefore represents high-level data collection constituting extra solution activity that fails to integrate the judicial exception into a practical application. The additional element “an estimation model generation unit which generates an estimation model” represents routine, conventional data processing activity for implementing program instructions that implement the step falling within the judicial exception, and therefore constitutes extra solution activity that fails to integrate the judicial exception into a practical application.
Regarding application of the judicial exception with, or by use of, a particular machine, the additional elements are configured and implemented in a conventional rather than a particularized manner of implementing health monitoring of batteries.
Regarding a transformation or reduction of a particular article to a different state or thing, claim 1 does not include any such transformation or reduction. Instead, claim 1 as a whole entails receiving input information (charge/discharge data of secondary batteries from ordinary battery information sources), applying standard processing techniques (data modeling) to the information to determine battery degradation information with the additional elements failing to provide a meaningful integration of the abstract idea in an application that transforms an article to a different state. Instead, the additional elements, individually and in combination, represent extra-solution activity that does not integrate the judicial exception into a practical application. In view of the various considerations encompassed by the Step 2A, Prong Two analysis, claim 1 does not include additional elements that integrate the recited abstract idea into a practical application.
Therefore, claim 1 is directed to a judicial exception and requires further analysis under Step 2B.
Regarding Step 2B, and as explained in the Step 2A Prong Two analysis, the additional elements in claim 1 constitute extra solution activity. Therefore, the additional elements, individually and in combination, fail to result in the claim as a whole amounting to significantly more than the judicial exception. Furthermore, the additional elements appear to be generic and well understood as evidenced by the disclosures of Ukumori (US 2021/0048482 A1) and Rahimian (US 2020/0235441 A1), each of which teach a substantially similar data processing environment for collecting and processing battery monitoring data.
As explained in the grounds for rejecting claim 1 under 102, Ukumori teaches “server system comprising: one or more computer systems, the one or more computer systems comprising: a communication interface circuit in communication with a communication network through which communication with a plurality of secondary battery management systems is performed,” “a first reception unit configured to receive, from the plurality of secondary battery management systems, charge/discharge data of one or more secondary batteries managed by the secondary battery management systems,” and “an estimation model generation unit which generates an estimation model,” as does Rahimian (see FIG. 1 depicting battery management system 102 of vehicle 118 coupled via network to central battery management system 128 (effectively a server); [0023], [0027] may be multiple vehicles/BMSs; FIG. 2 central battery management system 126 including battery health component; [0019] data fitting models for fitting battery data; [0033] secondary batteries monitored).
Therefore, the additional elements are insufficient to amount to significantly more than the judicial exception.
Claim 1 is therefore not patent eligible under 101.
Independent claims 17 and 18 each include substantially the same elements falling within the judicial exception as claim 1 and include no further significant additional elements that either integrate the judicial exception into a practical application or result in the claim as a whole amounting to significantly more than the judicial exception.
Independent claims 17 and 18 are therefore also not patent eligible under 101.
Claims 2-16, depending from claim 1, provide additional features/steps which are part of an expanded algorithm that includes the abstract idea of claim 1 (Step 2A, Prong One). None of dependent claims 2-16 recite additional elements that integrate the abstract idea into practical application (Step 2A, Prong Two), and all fail the “significantly more” test under the step 2B for similar reasons as discussed with regards to claim 1.
For example, claim 2, substantially representative also of claim 16, further characterizes the secondary battery as not containing an active material in a negative electrode, which does not further limit the structure/function of the recited “information processing device” and therefore constitutes extra solution activity that neither integrates the judicial exception into a practical application nor results in the claim as a whole amounting to significantly more than the judicial exception.
Claim 3 further characterizes the function of the estimation model generation unit as “generat[ing] the estimation model as a regression formula model by executing a regression analysis based on the charge/discharge data” which falls within the mathematical relations sub-category of the mathematical concepts judicial exception because regression analysis is fundamentally characterized by mathematical relations/calculations.
Claims 4-8 further characterize the nature/source of the data/information that is processed and/or generated in accordance with the elements falling within the judicial exception (mathematical concepts) and therefore themselves fall within the same judicial exception. More specifically regarding claims 4-6, the uses of OCV after discharge may be broadly interpreted to encompass any OCV value obtained at any point following discharge of a battery as set forth in the grounds for rejecting claims 4-6 under 103. Use of such relatively ordinary battery metrics itself in a regression analysis (mathematical concept) appears to be confined to the judicial exception itself. Regarding claims 7-8, Examiner submits that the additional characterization of the charge related OCV data as being “OCV after charge – OCV after discharge” also represents substantially ordinary data collection/processing, which may entail collection of battery OCV hysteresis data, which is itself is commonly collected/determined and therefore relatively high-level battery health data as evidenced by Choi (US 2015/0377974 A1) (FIG. 3 block 220 calculate hysteresis/diffusion factor, [0064]; [0015]-[0016] hysteresis coefficient used in regression modeling; [0029 hysteresis reflects difference in OCV for a given SOC between after charge state and after discharge state), and Verbrugge (US 2018/0306865 A1) ([0035]-[0037] explaining the significance of battery charge/discharge hysteresis in determining battery condition. The use of OCV after charge – OCV after discharge as a regression parameter therefore does not entail a form of additional element that integrates the judicial exception into a practical application.
Claim 9 further recites that the estimation model generation unit generates the estimation model as a machine-learning model by executing machine learning based on the charge/discharge data, which represents high-level provisioning/preparing of computer program instructions via execution of instructions, and therefore constitutes extra solution activity that neither integrates the judicial exception into a practical application nor results in the claim as a whole amounting to significantly more than the judicial exception.
Claims 10-12 further characterize the nature/source of the data/information that is processed and/or generated in accordance with the elements falling within the judicial exception and therefore themselves fall within the same judicial exception (similar relevance of OCV after charge – OCV after discharge in claim 12 as explained above with reference to claims 7-8).
Claim 13 recites “a second reception unit which receives from one of the secondary battery management systems, charge/discharge data of a secondary battery managed by the one secondary battery management system,” which represents high-level data collection (computer interface) and therefore constitutes extra solution activity that neither integrates the judicial exception into a practical application nor results in the claim as a whole amounting to significantly more than the judicial exception.
Claim 13 further recites “an estimation unit which inputs the charge/discharge data acquired from the one secondary battery management system to the estimation model,” which represents conventional data processing structure (interface) and function for applying data to a program construct (model). The feature “to acquire an estimated value of a degradation indicator of the secondary battery managed by the one secondary battery management system” effectively conveys a determination (the model determines the “estimated value” that falls within the mental processes judicial exception because it can be performed via mental processes (e.g., evaluation and judgement).
Claim 13 further recites “a notification information generation unit which generates notification information based on the acquired estimated value,” and “a transmission unit which transmits the notification information to the one secondary battery management system,” which individually or in combination represent routine, conventional data processing activity (outputting results relevant to processing) at a high level and therefore constitute extra solution activity that neither integrates the judicial exception into a practical application nor results in the claim as a whole amounting to significantly more than the judicial exception.
Claim 14 further characterizes the nature of the notification information in a manner having no significant functional relation to the elements falling within the judicial exception and therefore constitutes extra solution activity.
Claim 15 recites that the notification information generation unit determines a predetermined notification condition based on the estimated value, which falls within the mental processes judicial exception because it can be performed via mental processes (e.g., evaluation and judgement). Claim 15 further recites that the notification information generation unit generates the notification information when the notification condition is determined to be satisfied, which represents routine, conventional data processing activity (outputting results relevant to processing) at a high level and therefore constitutes extra solution activity that neither integrates the judicial exception into a practical application nor results in the claim as a whole amounting to significantly more than the judicial exception.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 3-4, 9-10, and 17-18 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Ukumori (US 2021/0048482 A1).
As to claim 1, Ukumori teaches “[a] server system (FIGS. 1 and 2 remote monitoring system 100 configured with server apparatus 2 such that system 100 is a server system; FIG. 4 server apparatus 2) comprising:
one or more computer systems (FIGS. 1 and 2 server apparatus 2 and client apparatuses/computers 3), the one or more computer systems comprising:
a communication interface circuit in communication with a communication network (FIGS. 1 and 2 server apparatus 2 and client apparatuses configured for communication with each other and with power generation systems that contain/manage target apparatuses P, U, D, M that include a power conditioner P, an uninterruptible power supply U, a rectifier D, and a management apparatus M (therefore includes communication interfaces) via network N) through which communication with a plurality of secondary battery management systems is performed ([0095] target apparatuses comprise battery management systems; FIG. 3 each target apparatus including respective battery management apparatus);
a first reception unit (FIGS. 2 and 4 server apparatus 2) configured to receive from the plurality of secondary battery management systems (FIG. 2 target apparatuses configured to provide battery monitoring data via network N to server apparatus 2; FIG. 3 each target apparatus including respective battery management apparatus; [0096]; [0093] batteries are rechargeable/secondary batteries; [0108]-[0109]), charge/discharge data of one or more secondary batteries managed by the secondary battery management systems ([0096] state of charge (indicates charge/discharge) included in information monitored and collected; [0113]-[0114]); and
an estimation model generation unit (FIG. 4 processing unit 23 within server apparatus 2) which generates an estimation model (FIG. 4 processing unit 23 configured to generate/process learning data via learning data generation unit 25 to generate (train) learning model 26; [0115]) for estimating a degradation indicator indicating a state of degradation of the one or more secondary batteries based on the charge/discharge data ([0027] and [0044] learning model determines storage cell degradation based on measured and predicted time series data; [0096] system 100 (includes server 2 and model) detects degradation of storage cell; [0113]-[0115]; FIG. 5 model 26 configured to determine degradation of storage device based on measure time series data that per [0117] includes SOC-OCV data).”
As to claim 3, Ukumori teaches “[t]he server system according to claim 1, wherein the one or more estimation model generation unit generates the estimation model as a regression formula model by executing a regression analysis based on the charge/discharge data ([0218] model 26 may be a regression neural network (the modeling would consequently entail executing a regression analysis based on the input data that includes charge/discharge data)).”
As to claim 4, Ukumori teaches “[t]he server system according to claim 3, wherein the estimation model generation unit includes a parameter based on a post-discharge open circuit voltage (OCV) included in the charge/discharge data in an explanatory variable of the estimation model in the regression analysis ([0117] and [0207] input data into model (explanatory/independent data) may include SOC-OCV curve (i.e., variations in OCV at different charge/discharge levels, which would inherently include some level of discharge for all points below fully-charged)).”
As to claim 9, Ukumori teaches “[t]he server system according to claim 1, wherein the estimation model generation unit generates the estimation model as a machine-learned model ([0044] model is a machine learning model; FIG. 4 processing unit 23 configured to generate learning model 26 using learning data generation unit 25) by executing machine learning based on the charge/discharge data ([0114]-[0115] machine learning model generated by executing a learning mode in which the measured data including voltage; [0137] learning data includes the time series data (includes the charge/discharge data)).”
As to claim 10, Ukumori teaches “[t]he server system according to claim 9, wherein the estimation model generation unit includes a parameter based on a post-discharge open circuit voltage (OCV) included in the charge/discharge data in the input of the estimation model in the machine learning ([0117] and [0207] input data into model (explanatory/independent data) may include SOC-OCV curve (i.e., variations in OCV at different charge/discharge levels, which would inherently include some level of discharge for all points below fully-charged).”
As to claim 17, Ukumori teaches “[a] method for generating an estimation model (method implemented by remote monitoring system 100 in FIGS. 1 and 2; FIG. 4 processing unit 23 configured to generate/process learning data via learning data generation unit 25 to generate (train) learning model 26), the method comprising:
receiving, by a computer system (FIGS. 1, 2, and 4 server apparatus 2), from a plurality of secondary battery management systems (FIG. 2 target apparatuses configured to provide battery monitoring data via network to server apparatus 2; FIG. 3 each target apparatus including respective battery management apparatus; [0096]; [0093] batteries are rechargeable/secondary batteries; [0108]-[0109]), charge/discharge data of one or more secondary batteries managed by the secondary battery management systems ([0096] state of charge (indicates charge/discharge) included in information monitored and collected; [0113]-[0114]); and
generating, by the computer system, an estimation model (FIG. 4 processing unit 23 configured to generate/process learning data via learning data generation unit 25 to generate (train) learning model 26; [0115]) for estimating a degradation indicator indicating a state of degradation of the one or more secondary batteries based on the charge/discharge data ([0027] and [0044] learning model determines storage cell degradation based on measured and predicted time series data; [0096] system 100 (includes server 2 and model) detects degradation of storage cell; [0113]-[0115]; FIG. 5 model 26 configured to determine degradation of storage device based on measure time series data that per [0117] includes SOC-OCV data).
As to claim 18, Ukumori teaches “[a] non-transitory processor-readable medium on which is stored a program (FIG. 4 server apparatus 2 includes storage unit 22 and processing unit 23 (server is a computing device that inherently includes storage for storing program instructions for execution by processor)) for allowing a computer to function as:
a first reception unit (FIGS. 2 and 4 server apparatus 2) which receives from a plurality of secondary battery management systems (FIG. 2 target apparatuses configured to provide battery monitoring data via network to server apparatus 2; FIG. 3 each target apparatus including respective battery management apparatus; [0096]; [0093] batteries are rechargeable/secondary batteries; [0108]-[0109]), charge/discharge data of one or more secondary batteries managed by the secondary battery management systems ([0096] state of charge (indicates charge/discharge) included in information monitored and collected; [0113]-[0114]); and
an estimation model generation unit (FIG. 4 processing unit 23 within server apparatus 2) which generates an estimation model (FIG. 4 processing unit 23 configured to generate/process learning data via learning data generation unit 25 to generate (train) learning model 26; [0115]) for estimating a degradation indicator indicating a state of degradation of the one or more secondary batteries based on the charge/discharge data ([0027] and [0044] learning model determines storage cell degradation based on measured and predicted time series data; [0096] system 100 (includes server 2 and model) detects degradation of storage cell; [0113]-[0115]; FIG. 5 model 26 configured to determine degradation of storage device based on measure time series data that per [0117] includes SOC-OCV data).”
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.
Claims 2 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Ukumori in view of Li (US 2021/0263108 A1).
As to claim 2, Ukumori teaches “[t]he server system according to claim 1,” and further suggests that the method is broadly applicable to different types of energy storage units ([0093]) but does not specifically characterize the one or more secondary batteries as not containing an active material in a negative electrode.
Li discloses a system/method for monitoring battery performance including monitoring anode-free battery condition by monitoring discharge capacity in a processing that includes curve fitting for an anode-free battery ([0031] monitoring discharge capacity for anode-free battery; [0082]. Examiner notes that anode-free is understood in the art as lack of active material in a negative electrode).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Li’s teaching of anode-free battery being subject to charge/discharge related health monitoring to the system taught by Ukumori in which the monitoring is applicable to a variety of battery types, such that in combination the system is configured to monitor one or more secondary batteries that do not contain an active material in a negative electrode.
Such a combination would amount to selecting a known battery type as a secondary battery to be monitored to achieve predictable results (the Examiner notes that while Applicant’s specification relates arguably unexpectedly linear results when an anode-free battery is used as the monitoring target, the general result of obtaining useful information relating to battery condition by applying the known monitoring method to the known battery type would still be achieved absent Applicant’s disclosure).
As to claim 16, Ukumori teaches “[t]he server system according to claim 1,” and further suggests that the method is broadly applicable to different types of energy storage units ([0093]) but does not specifically characterize the secondary batteries managed by the respective battery management systems and subject to the monitoring as not containing an active material in a negative electrode.
Li discloses a system/method for monitoring battery performance including monitoring anode-free battery condition by monitoring discharge capacity in a processing that includes curve fitting for an anode-free battery ([0031] monitoring discharge capacity for anode-free battery; [0082]. Examiner notes that anode-free is understood in the art as lack of active material in a negative electrode).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Li’s teaching of anode-free battery being subject to charge/discharge related health monitoring to the system taught by Ukumori in which the monitoring is applicable to a variety of battery types that are respectively managed by a battery monitoring system, such that in combination the system is configured to monitor at least one secondary battery each having a respective battery management system and each not containing an active material in a negative electrode.
Such a combination would amount to selecting a known battery type as the secondary batteries having respective battery management systems and being monitored to achieve predictable results (the Examiner notes that while Applicant’s specification relates arguably unexpectedly linear results when an anode-free battery is used as the monitoring target, the general result of obtaining useful information relating to battery condition by applying the known monitoring method to the known battery type would still be achieved absent Applicant’s disclosure).
Claims 5-6 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Ukumori in view of Rahimian (US 2020/0235441 A1).
As to claim 5, Ukumori teaches “[t]he server system according to claim 4,” and further at least suggests “wherein the parameter is an OCV after discharge ([0117] and [0207] input data into model (explanatory/independent data) may include SOC-OCV curve (i.e., variations in OCV at different charge/discharge levels, which would inherently include some level of discharge for all points below fully-charged).”
Rahimian discloses a system/method for monitoring battery cell degradation (Abstract) that uses regression data fitting to determine battery health/degradation based on OCV (Abstract, [0015], [0019]) including using after discharge OCV as the regression data fitting parameter ([0043]-[0047] measured OCV at different charge/discharge levels used to determine battery health metrics; [0049]-[0050]).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Rahimian’s teaching of using post-discharge OCV as a regression modeling parameter to determine battery health/condition to the system taught by Ukumori that uses various charge/discharge parameters including SOC-OCV curve data for such modeling, such that in combination the system is configured to use a model input parameter (explanatory parameter) that is an OCV after discharge.
Such a combination would amount to selecting a known design option for regression modeling inputs for determining battery health/degradation to achieve predictable results.
As to claim 6, the combination of Ukumori and Rahimian teaches “[t]he server system according to claim 5,” and Rahimian further teaches “the estimation model generation unit sets only the OCV after discharge as an explanatory variable of the estimation model in the regression analysis ([0046] OCV voltages as the sole inputs to the data fitting (regression) analysis).”
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Rahimian’s teaching of using post-discharge OCV as the only regression modeling parameter to the fitting/regression model for determining battery health/condition to the system taught by Ukumori that uses various charge/discharge parameters including SOC-OCV curve data for such modeling, such that in combination the system is configured to include an additional and/or alternative degradation model that uses only an OCV after discharge as a model input parameter (explanatory parameter).
Such a combination would amount to selecting a known design option for regression modeling inputs for determining battery health/degradation to achieve predictable results.
As to claim 11, Ukumori teaches “[t]he server system according to claim 10,” and further at least suggests “wherein the parameter is an OCV after discharge ([0117] and [0207] input data into model (explanatory/independent data) may include SOC-OCV curve (i.e., variations in OCV at different charge/discharge levels, which would inherently include some level of discharge for all points below fully-charged).”
Rahimian discloses a system/method for monitoring battery cell degradation (Abstract) that uses regression data fitting to determine battery health/degradation based on OCV (Abstract, [0015], [0019]) including using after discharge OCV as the regression data fitting parameter ([0043]-[0047] measured OCV at different charge/discharge levels used to determine battery health metrics; [0049]-[0050]).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Rahimian’s teaching of using post-discharge OCV as a regression modeling parameter to determine battery health/condition to the system taught by Ukumori that uses various charge/discharge parameters including SOC-OCV curve data for such modeling, such that in combination the system is configured to use a model input parameter (explanatory parameter) that is an OCV after discharge.
Such a combination would amount to selecting a known design option for regression modeling inputs for determining battery health/degradation to achieve predictable results.
Claims 7 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Ukumori in view of Choi (US 2015/0377974 A1).
As to claim 7, Ukumori teaches “[t]he server system according to claim 4,” and Ukumori at least suggests “wherein the parameter is OCV ([0117] and [0207] input data into model (explanatory/independent data) may include SOC-OCV curve (i.e., variations in OCV at different charge/discharge levels),” but does not appear to teach using OCV hysteresis (OCV after charge – OCVE after discharge) as an independent (explanatory variable) for regression analysis.
Choi discloses a system/method for determining battery SOH (Abstract; FIG. 3 blocks 250 and 260) using regression analysis (FIG. 3 block 230 (ARX (autoregressive) model computing coefficient factors (calculated using recursive least squares) and battery parameters; and blocks 250 and 260 implemented by dual extended Kalman filter; [0014]) that includes input parameters (independent variables) that includes and/or are derived from OCV hysteresis (difference between after charge and after discharge OCVs) (FIG. 3 block 220 calculate hysteresis/diffusion factor, [0064]; [0015]-[0016] hysteresis coefficient used in regression modeling; [0029 hysteresis reflects difference in OCV for a given SOC between after charge state and after discharge state).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Choi’s teaching of determining battery health using regression analysis/modeling in which OCV hysteresis (OCV after charge – OCV after discharge) is an explanatory/independent variable to the system taught by Ukumori such that in combination “the parameter is OCV after charge – OCV after discharge.
Such a combination would have amounted to implementing a known technique (regression analysis processing OCV hysteresis) for determining battery health to achieve predictable results.
As to claim 12, Ukumori teaches “[t]he server system according to claim 10,” and Ukumori at least suggests “wherein the parameter is OCV ([0117] and [0207] input data into model (explanatory/independent data) may include SOC-OCV curve (i.e., variations in OCV at different charge/discharge levels),” but does not appear to teach using OCV hysteresis (OCV after charge – OCVE after discharge) as an independent (explanatory variable) for regression analysis.
Choi discloses a system/method for determining battery SOH (Abstract; FIG. 3 blocks 250 and 260) using regression analysis (FIG. 3 block 230 (ARX (autoregressive) model computing coefficient factors (calculated using recursive least squares) and battery parameters; and blocks 250 and 260 implemented by dual extended Kalman filter; [0014]) that includes input parameters (independent variables) that includes and/or are derived from OCV hysteresis (difference between after charge and after discharge OCVs) (FIG. 3 block 220 calculate hysteresis/diffusion factor, [0064]; [0015]-[0016] hysteresis coefficient used in regression modeling; [0029 hysteresis reflects difference in OCV for a given SOC between after charge state and after discharge state).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Choi’s teaching of determining battery health using regression analysis/modeling in which OCV hysteresis (OCV after charge – OCV after discharge) is an explanatory/independent variable to the system taught by Ukumori such that in combination “the parameter is OCV after charge – OCV after discharge.
Such a combination would have amounted to implementing a known technique (regression analysis processing OCV hysteresis) for determining battery health to achieve predictable results.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Ukumori in view of Choi, and in further view of Rahimian (US 2020/0235441 A1).
As to claim 8, the combination of Ukumori and Choi teaches “[t]he server system according to claim 7,” and as set forth in the grounds for rejecting claim 7, Choi teaches using OCV after charge – OCV after discharge as an independent regression parameter but does not appear to teaching using only OCV after charge – OCV after discharge as an independent regression parameter. Rahimian discloses a system/method for monitoring battery cell degradation (Abstract) that uses regression data fitting to determine battery health/degradation based on OCV (Abstract, [0015], [0019]) including using after discharge OCV as the regression data fitting parameter and further teaches “wherein the estimation model generation unit sets only the OCV” “as an explanatory variable of the estimation model in the regression analysis ([0046] OCV voltages as the sole inputs to the data fitting (regression) analysis).”
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Rahimian’s teaching of using post-charge and/or post-discharge OCV as the only regression modeling parameter to the fitting/regression model for determining battery health/condition to the system taught by Ukumori as combined with Choi that uses OCV after charge – OCV after discharge for regression modeling, such that in combination the system is configured to include an additional and/or alternative degradation model that uses only an OCV after charge – OCV after discharge as a model input parameter (explanatory parameter).
The motivation for using only OCV after charge – OCV after discharge as the regression explanatory/independent variable would have been provide a clear characterization of the OCV after charge – OCV after discharge to provide a more clear/isolated view of the manner in which a particular type of OCV data may vary such as over different states of charge as suggested by Rahimian. Such a combination would amount to selecting a known design option for regression modeling inputs (OCV after charge – OCV after discharge) in known ways (isolating a particular type of OCV data for isolated regression analysis) to achieve predictable results.
Claims 13-15 are rejected under 35 U.S.C. 103 as being unpatentable over Ukumori (US 2021/0048482 A1).
As to claim 13, Ukumori teaches “[t]he server system according to claim 1, further including:
a second reception unit (FIGS. 2 and 4 server apparatus 2 (broadest reasonable interpretation of “second” reception unit in view of Applicant’s specification encompasses same reception unit structure in a different application instance)) which receives from one of the secondary battery management systems (FIG. 2 one or more target apparatuses configured to provide battery monitoring data via network to server apparatus 2; FIG. 3 each target apparatus including respective battery management apparatus; [0096]; [0093] batteries are rechargeable/secondary batteries; [0108]-[0109]), charge/discharge data of a secondary battery managed by the one secondary battery management system ([0096] state of charge (indicates charge/discharge) included in information monitored and collected; [0113]-[0114]);
an estimation unit (FIG. 4 processing unit 23 within server apparatus 2) which inputs the charge/discharge data acquired from the one secondary battery management system to the estimation model (FIG. 4 processing unit 23 configured to input charge/discharge data received via communication unit 21 ([0112]) into learning model 26; FIG. 19 blocks S21, S23, [0170]-[0171]) to acquire an estimated value ([0044] and [0047] model determines/estimates normal or degraded as a “value”; [0119] output may be a probability of degradation or normal) of a degradation indicator (normality of battery) of the secondary battery managed by the one secondary battery management system (FIG. 19 block S24, [0171]).”
In a second embodiment depicted in part in FIG. 20, Ukumori further teaches,
“a notification information generation unit (FIG. 20 processing unit 23 including operation support information providing unit 234) which generates notification information based on the acquired estimated value ([0204] information providing unit 234 provides support information based on a determination of abnormality (in relation constitutes an effective notification); and
a transmission unit (FIG. 20 communication unit 21 in combination with information providing unit 234 within server apparatus 2) which transmits the notification information ([0112] communication unit 21 transmits data from server apparatus via network (i.e., transmits to other networked devices that per FIGS. 2 and 3 includes BMS); [0204] information providing unit 234 provides (sends) the support information that is specific to a battery condition).”
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Ukumori second embodiment teaching of generating and transmitting a notification based on the abnormality determination to the Ukumori first embodiment, which also performs an abnormality determination, such that in combination the system would be configured to generate notification information based on the acquired estimated value and transmit the notification information in a manner to effectuate remediation of the abnormality.
The motivation would have been to leverage the abnormality information to remediate the battery abnormality as suggested by Ukumori. Furthermore, in such a configuration, and in accordance with Ukumori’s disclosure in [0204] that the support information relates to the particular system/battery that is determined to be abnormal including remedial information such as load reduction and that the information providing unit is communicatively coupled with the BMS, it would have been obvious to one of ordinary skill in the art before the effective filing date, to transmit the notification information “to the one secondary battery management system” for implementation of such remedial action.
The motivation would have been to provide the information such as load reduction directly to a controller having load reduction and other battery operations control capability to most efficiently address the detected battery abnormality.
As to claim 14, Ukumori teaches “[t]he server system according to claim 13, wherein the notification information includes at least either of information indicating the estimated value ([0204] support information includes load reduction/replacement information, which is indicative of abnormal state) and a message related to the secondary battery based on the estimated value ([0204] support information includes load reduction/replacement information, which is effectively a message related to the battery based on the estimated value (normal or degraded or probability of degraded)).”
As to claim 15, Ukumori teaches “[t]he server system according to claim 13, wherein the notification information generation unit determines a predetermined notification condition based on the estimated value (FIG. 20 information providing unit 20 operates as part of processing unit 23 (is a programmed entity) and is therefore processes the abnormality result ([0204]) in a predetermined (programmed) manner), and generates the notification information when the notification condition is determined to be satisfied ([0204] information providing unit 20 processes the abnormality result ([0204]) to generate the notification result in a predetermined/programmed based on the result (e.g., if abnormal generate/provide corresponding support information)).
Conclusion
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/MATTHEW W. BACA/Examiner, Art Unit 2857
/ANDREW SCHECHTER/Supervisory Patent Examiner, Art Unit 2857
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