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 § 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.
Claim(s) 1, 3-8 and 10-11 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zhang et al. (U.S. 2012/0046776 hereinafter Zhang).
As Claim 1, Zhang teaches a method for reducing the time and cost of developing a cathode powder meeting custom performance parameters defining performance attributes for a battery, the method comprising:
receiving the customer performance parameters defining the performance attributes for the battery (Zhang (¶0029 line 1-5), “primary design target requirements are then specified for a given application. An example of target requirements includes battery discharge modes and rates, battery volume, battery weight, and battery capacity”);
converting the customer performance parameters to material parameters for the cathode of the battery (Zhang (¶0030 line 1-7), “the candidate materials are then pre-screened to reduce the size of the candidate set. Preferably, the pre-screen process identifies selected materials, and determines whether they are unsuitable. That is, the pre-screening process eliminates unsuitable materials with the consideration of the specified primary design target requirement”, Zhang (¶0037 line 3-8), “Lithium (Li) and Silicon (Si) are selected for the anode; Lithium-Polymer (L-P) and lithium phosphorus oxynitride (LIPON) are selected for the electrolyte; LiCoO2 (LCO) and LiV2O5 (LVO) are selected for the cathode; copper (Cu) and aluminum (Al) are selected for the current collector as 502 in FIG. 5.”);
providing the material parameters for the battery into a battery composition prediction model (Zhang (¶0031 line 1-4), “the selected materials for the respective solid-state electrochemical battery cell components are permuted to generate factorial combinations of the solid-state electrochemical battery cell.”);
receiving candidate compound formulations with synthesis processing parameters for the synthesis of the compound formulations from the battery composition prediction model (Zhang (¶0033 last 11 lines), “the computer simulations use the specified primary design target requirements, such as discharge rate and battery volume or capacity, as inputs for the physics model. The obtained simulation data is used to build a surrogate model. The generated surrogate model with high accuracy is provided to an optimization solver to identify the optimal design solution(s) of one or more second characteristics of solid-state electrochemical cell for a battery device. The optimization process using the surrogate model conducts a single or multi-objective optimization process which generates a single optimal solution or a set of optimal solutions”; Zhang (¶0037 last 3 lines), “Among the resulted 16 optimal designs as 505 in FIG. 5, the best design with the largest gravimetric energy density is the final cell design as 505 in FIG. 5.”).
As Claim 3, besides Claim 1, Zhang teaches further comprising:
selecting a subset of the candidate compound formulations with the processing parameters to be synthesized, whereby a number of potential candidate compound formulations for synthesis and experimentation are reduced (Zhang (¶0030 line 1-7), “the candidate materials are then pre-screened to reduce the size of the candidate set. Preferably, the pre-screen process identifies selected materials, and determines whether they are unsuitable. That is, the pre-screening process eliminates unsuitable materials with the consideration of the specified primary design target requirement”).
As Claim 4, besides Claim 3, Zhang teaches wherein the selected subset of the candidate compound formulations with the processing parameters comprises 50% of the candidate compound formulations with the processing parameters having the highest confidence value for meeting the material parameters of the cathode of the battery (Zhang (¶0030 line 1-7, fig. 5), “the candidate materials are then pre-screened to reduce the size of the candidate set. Preferably, the pre-screen process identifies selected materials, and determines whether they are unsuitable. That is, the pre-screening process eliminates unsuitable materials with the consideration of the specified primary design target requirement”; 50% is intended use).
As Claim 5, besides Claim 3, Zhang teaches wherein the selected subset of the candidate compound formulations with the processing parameters comprises 30% of the candidate compound formulations with the processing parameters having the highest confidence value for meeting the material parameters of the cathode of the battery (Zhang (¶0030 line 1-7, fig. 5), “the candidate materials are then pre-screened to reduce the size of the candidate set. Preferably, the pre-screen process identifies selected materials, and determines whether they are unsuitable. That is, the pre-screening process eliminates unsuitable materials with the consideration of the specified primary design target requirement”; 30% is intended use).
As Claim 6, besides Claim 3, Zhang teaches wherein the selected subset of the candidate compound formulations with the processing parameters comprises 20% of the candidate compound formulations with the processing parameters having the highest confidence value for meeting the material parameters of the cathode of the battery (Zhang (¶0030 line 1-7, fig. 5), “the candidate materials are then pre-screened to reduce the size of the candidate set. Preferably, the pre-screen process identifies selected materials, and determines whether they are unsuitable. That is, the pre-screening process eliminates unsuitable materials with the consideration of the specified primary design target requirement”; 20% is intended use).
As Claim 7, besides Claim 3, Zhang teaches further comprising:
generating respective cathode powders from the selected subset of the candidate compound formulations using the processing parameters from the battery composition prediction model (Zhang (¶0033 last 11 lines), “the computer simulations use the specified primary design target requirements, such as discharge rate and battery volume or capacity, as inputs for the physics model. The obtained simulation data is used to build a surrogate model. The generated surrogate model with high accuracy is provided to an optimization solver to identify the optimal design solution(s) of one or more second characteristics of solid-state electrochemical cell for a battery device. The optimization process using the surrogate model conducts a single or multi-objective optimization process which generates a single optimal solution or a set of optimal solutions”; Zhang (¶0037 last 3 lines), “Among the resulted 16 optimal designs as 505 in FIG. 5, the best design with the largest gravimetric energy density is the final cell design as 505 in FIG. 5.”); and
performing material characterizations on the candidate cathode powders (Zhang (¶0037 line 3-8), “Lithium (Li) and Silicon (Si) are selected for the anode; Lithium-Polymer (L-P) and lithium phosphorus oxynitride (LIPON) are selected for the electrolyte; LiCoO2 (LCO) and LiV2O5 (LVO) are selected for the cathode; copper (Cu) and aluminum (Al) are selected for the current collector as 502 in FIG. 5.”).
As Claim 8, besides Claim 7, Zhang teaches further comprising:
selecting a first subset of the respective candidate cathode powders based on the material characterizations for use in building respective batteries from the first subset of the respective candidate cathode powders (Zhang (¶0037 line 3-8), “Lithium (Li) and Silicon (Si) are selected for the anode; Lithium-Polymer (L-P) and lithium phosphorus oxynitride (LIPON) are selected for the electrolyte; LiCoO2 (LCO) and LiV2O5 (LVO) are selected for the cathode; copper (Cu) and aluminum (Al) are selected for the current collector as 502 in FIG. 5.”).
As Claim 10, besides Claim 7, Zhang teaches wherein the material characterizations of the cathode powders comprise one or more elemental compositions, phase purity, crystallinity, particle size, surface area, or tap density (Zhang (¶0037 line 3-8), “Lithium (Li) and Silicon (Si) are selected for the anode; Lithium-Polymer (L-P) and lithium phosphorus oxynitride (LIPON) are selected for the electrolyte; LiCoO2 (LCO) and LiV2O5 (LVO) are selected for the cathode; copper (Cu) and aluminum (Al) are selected for the current collector as 502 in FIG. 5.”).
As Claim 11, besides Claim 8, Zhang teaches further comprising:
determining, based on results from the material characterizations of the cathode powders, whether to build candidate coin cells from the first subset of the plurality of candidate cathode powder or determining, based on results from testing the candidate coin cells, whether to build candidate pouch cells from the first subset of the plurality of candidate cathode powders for an evaluation of the candidate powders under the plurality of environmental conditions (Zhang (¶0030 line 1-7, fig. 5), “the candidate materials are then pre-screened to reduce the size of the candidate set. Preferably, the pre-screen process identifies selected materials, and determines whether they are unsuitable. That is, the pre-screening process eliminates unsuitable materials with the consideration of the specified primary design target requirement”).
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) 2 and 12-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang in view of Senn et al. (U.S. 2022/0074994 hereinafter Senn).
As Claim 2, besides Claim 1, Zhang may not explicitly disclose further comprising:
performing Bayesian optimization based on the material parameters for the cathode to identify the candidate compound formulations and the processing parameters.
Senn teaches:
further comprising:
performing Bayesian optimization based on the material parameters for the cathode to identify the candidate compound formulations and the processing parameters (Senn (¶0068 line 4-6), “the most promising candidates can be identified with priority queues and Bayesian optimization”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify machine learning module of Zhang instead be a machine learning module taught by Senn, with a reasonable expectation of success. The motivation would be so that “The selector 62 can find the microstructure candidates that are near or within a predefined energy profile range” (Senn (¶0063 last 2 lines)).
As Claim 12, besides Claim 8, Zhang may not explicitly disclose further comprising:
monitoring performance attributes of the respective test batteries made from the first subset of the respective candidate cathode powders over a first interval;
providing data derived from the monitoring of the performance attributes for the respective test batteries made from the first subset of the respective candidate cathode powders over the first interval into a battery performance model; and
receiving predicted performance attributes for the test batteries over a second interval that is longer than the first interval.
Senn teaches:
further comprising:
monitoring performance attributes of the respective test batteries made from the first subset of the respective candidate cathode powders over a first interval (Senn (¶0027 last 8 lines), “for example, the neural network 20 may be trained using training data. Thus, the weights of the hidden layers can be considered as an encoding of meaningful patterns in the data. The weights of the connections between the nodes may be modified by training”);
providing data derived from the monitoring of the performance attributes for the respective test batteries made from the first subset of the respective candidate cathode powders over the first interval into a battery performance model (Senn (¶0027 last 8 lines), “The weights between the nodes may be determined, calculated, generated, assigned, learned, etc., during a training process for the neural network. For example, the neural network 20 may be trained using training data. Thus, the weights of the hidden layers can be considered as an encoding of meaningful patterns in the data. The weights of the connections between the nodes may be modified by training”); and
receiving predicted performance attributes for the test batteries over a second interval that is longer than the first interval (Senn (¶0062 last 3 lines), “the DNN 64 outputs a predicted energy and power profile for the selected micro-structures”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify machine learning module of Zhang instead be a machine learning module taught by Senn, with a reasonable expectation of success. The motivation would be so that “The selector 62 can find the microstructure candidates that are near or within a predefined energy profile range” (Senn (¶0063 last 2 lines)).
As Claim 13, besides Claim 12, Zhang in view of Senn teaches:
wherein the performance attributes of the battery comprise one or more of internal resistance, voltage, capacity, or cycle life (Zhang (¶0029 line 1-5), “primary design target requirements are then specified for a given application. An example of target requirements includes battery discharge modes and rates, battery volume, battery weight, and battery capacity”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify machine learning module of Zhang instead be a machine learning module taught by Senn, with a reasonable expectation of success. The motivation would be so that “The selector 62 can find the microstructure candidates that are near or within a predefined energy profile range” (Senn (¶0063 last 2 lines)).
As Claim 14, besides Claim 8, Zhang may not explicitly disclose further comprising:
providing the material characterizations on the respective candidate cathode powders as feedback to the battery composition prediction model, whereby the battery composition prediction model is refined based on the feedback.
Senn teaches:
further comprising:
providing the material characterizations on the respective candidate cathode powders as feedback to the battery composition prediction model, whereby the battery composition prediction model is refined based on the feedback (Senn (¶0062 last 3 lines), “This iterative improvement is continued until the uncertainty does not improve (decrease) significantly any more”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify machine learning module of Zhang instead be a machine learning module taught by Senn, with a reasonable expectation of success. The motivation would be so that “The selector 62 can find the microstructure candidates that are near or within a predefined energy profile range” (Senn (¶0063 last 2 lines)).
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang in view of Jahnke et al. (U.S. 2024/0222641 hereinafter Jahnke).
As Claim 9, besides Claim 7, Zhang may not explicitly disclose wherein the material characterizations of the cathode powders are performed by using one or more X-ray diffraction analyses (XRD), scanning electron microscopy (SEM), or Energy-dispersive X-ray spectroscopy (EDS).
Jahnke teaches:
wherein the material characterizations of the cathode powders are performed by using one or more X-ray diffraction analyses (XRD), scanning electron microscopy (SEM), or Energy-dispersive X-ray spectroscopy (EDS) (Jahnke (¶0017 line 3-7), “The simulation result data comprises at least of one of the following data: data on microscopic geometric features of the component, data on a conductivity of the component, data on a current collector, data on a binder phase, data on a diffusivity of the component and data on a charging ”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify machine learning module of Zhang instead be a machine learning module taught by Jahnke, with a reasonable expectation of success. The motivation would be to allow “a correlating microstructure of a real object can be obtained for example by using a Micro-CT scan and reconstruct the respective object” (Jahnke (¶0019 line 2-4)).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Miyamoto (U.S. 11,568,102) teaches a machine learning method for optimizing battery design.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NHAT HUY T NGUYEN whose telephone number is (571)270-7333. The examiner can normally be reached M-F: 12:00-8:00 EST.
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/NHAT HUY T NGUYEN/Primary Examiner, Art Unit 2147