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 .
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 1-23, and 27-28 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Specifically, representative Claim 1 recites:
A method of determining health of electrochemical energy supply elements in a set of electrochemical energy supply elements, the method comprising receiving sensor data representing sensed parameters of the electrochemical energy supply elements over time; fitting a model of a health parameter of respective electrochemical energy supply elements over the sensor data, the health parameter being dependent on operational parameters over time derivable from the sensor data and on health of the electrochemical energy supply element, the model modelling the distribution of the health parameter as a Gaussian Process having an overall kernel that combines a first kernel that models dependency of the health parameter on the operational parameters and a second kernel that is a non-stationary kernel that models degradation of the health parameter over time; and deriving health metrics over time for respective electrochemical energy supply elements comprising values of the health parameter that are predicted by the filled model in respect of a predetermined operating point of the operational parameters.
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements”.
Under the Step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (process).
Under the Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject matter Eligibility Guidance, it falls into the grouping of subject matter when recited as such in a claim limitation, that covers mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) and mental processes – concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion.
For example, steps of “fitting a model of a health parameter of respective electrochemical energy supply elements over the sensor data, the health parameter being dependent on operational parameters over time derivable from the sensor data and on health of the electrochemical energy supply element, the model modelling the distribution of the health parameter as a Gaussian Process having an overall kernel that combines a first kernel that models dependency of the health parameter on the operational parameters and a second kernel that is a non-stationary kernel that models degradation of the health parameter over time” is treated by the Examiner as belonging to mathematical concept grouping.
Next, under the Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application.
In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
The above claims comprise the following additional elements:
In Claim 27: a non-transitory computer-readable medium;
In Claim 28: system or apparatus, computing device, processor, a memory;
The additional element in the preamble of “a non-transitory computer-readable medium, system or apparatus, computing device, memory and processor (generic processor) are generally recited and are not qualified as particular machines.
Further, the limitations: “receiving sensor data representing sensed parameters of the electrochemical energy supply elements over time; deriving health metrics over time for respective electrochemical energy supply elements comprising values of the health parameter that are predicted by the filled model in respect of a predetermined operating point of the operational parameters,” are defined by MPEP 2106.05(g) as insignificant extra-solution activity, mere date gathering.
In conclusion, the above additional elements, considered individually and in combination with the other claim elements do not reflect an improvement to other technology or technical field, and, therefore, do not integrate the judicial exception into a practical application. Therefore, the claims are directed to a judicial exception and require further analysis under the Step 2B.
However, the above claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception (Step 2B analysis).
The claims, therefore, are not patent eligible.
With regards to the dependent claims, claims 2-23 and 27-28 provide additional features/steps which are part of an expanded algorithm, so these limitations should be considered part of an expanded abstract idea of the independent claims.
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 person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 3, 5, 8-9, 12 and 16-18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zhang, Yang et al. “Real-Time Capacity Estimation of Lithium-Ion Batteries Using a Novel Ensemble of Multi-Kernel Relevance Vector Machines.” 2019 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE). IEEE, 2019. 230–236. Web.
Regarding Claim 1, Zhang teach a method of determining health of electrochemical energy supply elements in a set of electrochemical energy supply elements (Abstract), the method comprising receiving sensor data representing sensed parameters of the electrochemical energy supply elements over time (pg. 231, section 2.1, F1-F5); fitting a model of a health parameter of respective electrochemical energy supply elements over the sensor data (section 2.1, last para; pg. 232, section 3, first para.), the health parameter being dependent on operational parameters over time derivable from the sensor data (pg. 232, section 3, first para.) and on health of the electrochemical energy supply element (pg. 232, section 3, first para., capacity (which is define on pg. 230, right column, second paragraph)), the model modelling the distribution of the health parameter as a Gaussian Process (pg. 232, section 3.1, first para.) having an overall kernel that combines a first kernel that models dependency of the health parameter on the operational parameters and a second kernel that is a non-stationary kernel that models degradation of the health parameter over time (pg. 233, left column, section 3.1, last para.); and deriving health metrics over time for respective electrochemical energy supply elements comprising values of the health parameter that are predicted by the filled model in respect of a predetermined operating point of the operational parameters (pg. 232, section 3, first para. And pg. 234-235, section 4.2).
Regarding Claim 3, Zhang further teaches the method according to claim 1 wherein the second kernel is a kernel representing any order of integration of the Wiener Process or a polynomial kernel of any order (pg. 233, left column, section 3.1, last para.).
Regarding Claim 5, Zhang further teaches the method according to claim 1, wherein the step of fitting the model of the health parameter outputs a posterior predictive distribution of the health parameter (pg. 231, left column, second para., last sentence).
Regarding Claim 8, Zhang further teaches The method according to claim 1, wherein the overall kernel that combines the first kernel and the second non-stationary kernel by addition or multiplication (pg. 233, eq. 13).
Regarding Claim 9, Zhang further teaches the method according to claim 1, wherein the values comprise absolute values of the health parameter (pg. 234, 4.2 Performance Metrics).
Regarding Claim 12, Zhang further teaches the method according to claim 1, wherein the sensed parameters comprise voltage, current and temperature (pg. 231, F1-F5).
Regarding Claim 16, Zhang further teaches the method according to claim 1, further comprising selecting segments of the sensor data that represent charging of the electrochemical energy supply element, the step of fitting the model of the health parameter comprising fitting the model of the health parameter to the selected segments of the sensor data (Section 2.1, first para.).
Regarding Claim 17, Zhang further teaches the method according to claim 1, wherein the electrochemical energy supply element is a battery element, for example a battery cell, a battery module, or an assembly of battery modules (Abstract).
Regarding Claim 18, Zhang further teaches the method according to claim 17, wherein the battery element is a lead-acid battery element or a lithium-ion battery element (Abstract).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 2 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Zheng in further view of Yuan et al. (CN106405427A, 2017-02-15) herein referred to as Yuan./
Regarding Claim 2, Zhang teaches all of the limitations of Claim 1. Zhang fails to teach wherein the first kernel is a stationary kernel, preferably a kernel in the Matern family of kernels, more preferably a squared exponential kernel. However, in a related field, Yuan teaches wherein the first kernel is a stationary kernel, preferably a kernel in the Matern family of kernels, more preferably a squared exponential kernel (pg. 4-5: Preferred Embodiment). Therefore, it would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Zhang to incorporate the teachings of Yuan by including: the limitation above in order to greatly improve accuracy and precision prediction.
Regarding Claim 11, Zhang teaches all of the limitations of Claim 1. Zhang fails to teach wherein the health parameter is internal resistance or capacity. However, in a related field, Yuan teaches wherein the health parameter is internal resistance or capacity (pg. 2). Therefore, it would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Zhang to incorporate the teachings of Yuan by including: the limitation above in order to determine the state of health of the battery.
Claims 4 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Zheng in further view of Schurch et al. (Recursive estimation for sparse Gaussian process regression, Automatica,Volume 120,2020,109127,ISSN 0005-1098,https://doi.org/10.1016/j.automatica.2020.109127.(https://www.sciencedirect.com/science/article/pii/S0005109820303253)
Regarding Claim 4, Zhang teaches all of the limitations of Claim 1. Zhang further teaches wherein fitting the model of the health parameter (section 2.1, last para; pg. 232, section 3, first para.), but fails to teach is performed using a recursive estimation framework. However, in a related field, Schurch teaches wherein the fitting of the model is performed using a recursive estimation framework (pg. 1-4).
Therefore, it would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Zhang to incorporate the teachings of Schurch by including: fitting of the model is performed using a recursive estimation framework in order to update parameters more frequently, speeding up optimization and improving overall performance.
Regarding Claim 10, Zhang teaches all of the limitations of Claim 1. Zhang fails to teach wherein the values comprise partial derivatives of the health parameter with respect to time (pg. 9-10, 4.1 Recursive Gradient Propagation (RGP). Therefore, it would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Zhang to incorporate the teachings of Schurch by including: the limitation above in order to update parameters more frequently, speeding up optimization and improving overall performance.
Claims 6 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang , in further view of Richardson, Robert R et al. “Gaussian Process Regression for In Situ Capacity Estimation of Lithium-Ion Batteries.” IEEE transactions on industrial informatics 15.1 (2019): 127–138. Web.
Regarding Claim 6, Zhang teaches all of the limitations of Claim 1. Zhang further teaches wherein fitting the model of the health parameter (section 2.1, last para; pg. 232, section 3, first para.), but fails to teach outputting a posterior estimate of hyperparameters of the overall kernel. However, in a related field, Richardson teaches outputting a posterior estimate of hyperparameters of the overall kernel (Conclusion and GP Regression, pg. 10-11). Therefore, it would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Zhang to incorporate the teachings of Richardson by including: outputting a posterior estimate of hyperparameters of the overall kernel in order to provide advantages such as greater flexibility, shorter diagnostic test requirements, and the provision of accurate estimates of uncertainty in its predictions.
Regarding Claim 7, Zhang teaches all of the limitations of Claim 5. Zhang fails to teach wherein either: the hyperparameters are common to all the electrochemical energy supply elements and are either fitted over the sensor data of all the electrochemical energy supply elements in the set; the hyperparameters are common to all the electrochemical energy supply elements and have predetermined values; or the hyperparameters are specific to each electrochemical energy supply element and fitted over the sensor data of respective electrochemical energy supply elements in the set.
However, in a related field, Richardson teaches wherein either: the hyperparameters are common to all the electrochemical energy supply elements and are either fitted over the sensor data of all the electrochemical energy supply elements in the set; the hyperparameters are common to all the electrochemical energy supply elements and have predetermined values; or the hyperparameters are specific to each electrochemical energy supply element and fitted over the sensor data of respective electrochemical energy supply elements in the set ((Conclusion and GP Regression, pg. 10-11). Therefore, it would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Zhang to incorporate the teachings of Richardson by including: the limitations above in order to provide advantages such as greater flexibility, shorter diagnostic test requirements, and the provision of accurate estimates of uncertainty in its predictions.
Claims 13 through 15 are rejected under 35 U.S.C. 103 as being unpatentable over Zheng in further in view of Sahinoglu, Gozde O et al. “Battery State-of-Charge Estimation Based on Regular/Recurrent Gaussian Process Regression.” IEEE transactions on industrial electronics (1982) 65.5 (2018): 4311–4321. Web.
Regarding Claim 13, Zhang teaches the method according to claim 1. Zhang fails to teach wherein the operational parameters comprise current, temperature and a state of charge. However, in a related field, Sahinoglu teaches wherein the operational parameters comprise current, temperature and a state of charge (Fig. 1; pg. 4312, Section I, subsection B, 1). Therefore, it would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Zhang to incorporate the teachings of Sahinoglu by including: the limitations above in order to accurately determine health parameter as these parameters are what determine battery health.
Regarding Claim 14, the combination teach the limitations of Claim 13. Zhang fails to specifically teach wherein the state of charge is a material property, for example a concentration of material in the electrochemical energy supply element. However, in a related field, Sahinoglu teaches wherein the state of charge is a material property, for example a concentration of material in the electrochemical energy supply element (pg. 4311, Section I). It is also implicit to one of ordinary skill in the art that state of charge represents a material property according to the chemical processes inside it. Therefore, it would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Zhang to incorporate the teachings of Sahinoglu by including: the limitations above in order to accurately determine health parameter as these parameters are what determine battery health.
Regarding Claim 15, the combination teach the limitations of Claim 13. Zhang fails to specifically teach deriving the state of charge from the sensed electrical parameters, optionally using Coulomb counting. However, in a related field, Sahinoglu teaches deriving the state of charge from the sensed electrical parameters, optionally using Coulomb counting (Fig. 1; pg. 4311, Section I). Therefore, it would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Zhang to incorporate the teachings of Sahinoglu by including: the limitations above in order to accurately determine health parameter as these parameters are what determine battery health.
Claims 27 and 28 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang, further in view of Weng et al. (CN116385956A, 2023-07-04), herein referred to as Weng.
Regarding Claim 27, Zhang teaches all of the limitations of Claim 1. Zhang fails to teach a non-transitory computer-readable medium embodying a program executable in at least one computing device, comprising code that upon execution performs a method according to claim 1. However, in a related field, Weng teaches a non-transitory computer-readable medium embodying a program executable in at least one computing device, comprising code that upon execution performs a method according to claim 1 (Claim 10). Therefore, it would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Zhang to incorporate the teachings of Weng by including: the limitations above in order to monitor for and alert when a fault is present.
Regarding Claim 28, Zhang teaches all of the limitations of Claim 1. Zhang fails to teach a system or apparatus, comprising: at least one computing device having a processor and a memory; and at least one application executable in the at least one computing device stored in the memory that, when executed by the processor, the application causes the at least one computing device to perform a method according to claim 1. However, in a related field, Weng teaches a system or apparatus, comprising: at least one computing device having a processor and a memory; and at least one application executable in the at least one computing device stored in the memory that, when executed by the processor, the application causes the at least one computing device to perform a method according to claim 1 (Claim 10). Therefore, it would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Zhang to incorporate the teachings of Weng by including: the limitations above in order to monitor for and alert when a fault is
present.
Allowable Subject Matter
Claims 19 through 23 would be allowable if written to overcome the 101 rejection set forth in this office action and rewritten in independent form to incorporate all the limitations of their base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding Claim 19, Zhang teaches all of the limitations of Claim 1. Zhang, along with all other references fail to teach the method of predicting faults of electrochemical energy supply elements in a test set, the method comprising: performing a method according to claim 1 in respect of the electrochemical energy supply elements in the test set, and classifying the electrochemical energy supply elements with a machine learning classifier that uses the derived the health metrics over time as input variables, into a plurality of classifications representing presence and absence of predicted faults.
It is for this reason, Claim 19 and all of its dependencies would be allowed.
Conclusion
The prior art made record and not relied upon is considered pertinent to applicant’s disclosure.
Wang et al. (SYSTEMS AND METHODS FOR DIAGNOSING HEALTH OF A BATTERY USING IN-VEHICLE IMPEDANCE ANALYSIS, 2023-07-13) teaches systems and methods for diagnosing health of a battery using in-vehicle impedance analysis. The method includes receiving a sensed current measurement for a cell of the battery; generating a current profile as a function of the sensed current measurement, including pulses to a peak current, the pulses having a pulse frequency (w); applying a current with the current profile to the cell; for each pulse in the current profile, receiving a voltage measurement for the cell that is responsive to the pulse, calculating an impedance for the cell responsive thereto, the impedance comprising a real component at the pulse frequency (RZw), and an imaginary component at the pulse frequency (IZw), identifying a battery health problem when either RZw exceeds a preprogrammed first threshold for the pulse frequency, or when IZw exceeds a preprogrammed second threshold for the pulse frequency, and storing the RZw and the IZw for the cell;
Lee (BATTERY DIAGNOSIS SYSTEM, POWER SYSTEM AND BATTERY DIAGNOSIS METHOD, 2022-08-25) teaches a battery diagnosis system for a battery pack including a battery group of a plurality of batteries connected in series, and a battery management system to transmit a notification signal indicating a battery parameter of each of the plurality of batteries. The battery diagnosis system includes a communication device to collect the notification signal via at least one of a wired network or a wireless network, a data preprocessing device to update big data indicating a change history of the battery parameter of each battery based on the notification signal, and a data analysis device to determine dispersion information of a data set including a plurality of characteristics values indicating the battery parameter of each of the plurality of batteries from the big data, and determine whether each battery is faulty based on the dispersion information and the plurality of characteristics values;
Yang et al. (Method For Estimating Performance Index Of Lithium Ion Battery, Storage Medium And Device, 2023-10-17) teaches a lithium ion battery performance index estimation method, storage medium and device, wherein the method comprises the following steps: obtaining the history work data of the lithium ion battery, pre-processing the history work data of the lithium ion battery; training the LSTM network with the pre-processed historical working data of the lithium ion battery as the characteristic variable; determining the initial threshold of the LSTM network hyperparameter by using the mayfly optimization algorithm; inputting the working data of the current lithium ion battery into the trained LSTM network, and estimating the performance index of the lithium ion battery. The application uses the mayfly algorithm to optimize the initial threshold value of the long-short time memory network, avoiding the algorithm to be partially optimal, improving the optimization search capability and convergence capability, effectively improving the prediction precision and efficiency of the model. at the same time, the battery performance index of the lithium ion battery can be directly estimated by using the same LSTM network model;
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL J SINGLETARY whose telephone number is (571)272-4593. The examiner can normally be reached Monday-Friday 8:00am-5:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Catherine Rastovski can be reached at (571) 270-0349. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHAEL J SINGLETARY/Examiner, Art Unit 2863