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 § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 2 and 3 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “actual” in claim 2 and 3 is a relative term which renders the claim indefinite. The term “actual” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Examiner suggest removing the term or clearly defining actual battery performance..
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 1-9 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 for predicting the battery performance based on a combination of material parameters of a battery pulping process, comprising: obtaining a target prediction model, wherein the target prediction model comprises a plurality of sub-models, and the plurality of the sub-models correspond to different combinations of model parameters, respectively; and obtaining a combination of material parameters to be predicted corresponding to the battery pulping process, and inputting the combination of the material parameters to be predicted into the target prediction model to obtain a target battery performance level corresponding to the combination of the material parameters to be predicted, wherein the target battery performance level is determined according to battery performance levels output by the sub-models based on the combination of the material parameters to be predicted, respectively.
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements”.
Under 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 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 “inputting the combination of the material parameters to be predicted into the target prediction model to obtain a target battery performance level corresponding to the combination of the material parameters to be predicted, wherein the target battery performance level is determined according to battery performance levels output by the sub-models based on the combination of the material parameters to be predicted, respectively” are treated by the Examiner as belonging to both mathematical concept grouping and mental process grouping.
Similar limitations comprise the abstract ideas of Claims 8 and 9.
Next, under 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 8: apparatus, prediction module.
In Claim 9: terminal, memory and one or more processors.
The additional element of an apparatus, prediction module, a terminal a memory and a processor (generic processor) are generally recited and are not qualified as particular machines.
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 2-7 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 § 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 1, 8 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Nishi et al. (CN101512365A, 2009-08-19), herein referred to as Nishi, in view of Qin et al. (CN114047452A, 2022-02-15) herein referred to as Qin.
Regarding Claim 1, Nishi teaches a method for predicting the battery performance (Claim 1) comprising: obtaining a target prediction model (pg. 7, last paragraph); and obtaining a combination of material parameters to be predicted corresponding to the battery pulping process, and inputting the combination of the material parameters to be predicted into the target prediction model to obtain a target battery performance level corresponding to the combination of the material parameters to be predicted (pg. 7).
Nishi fails to teach a combination of material parameters of a battery pulping process, wherein the target prediction model comprises a plurality of sub-models, and the plurality of the sub-models correspond to different combinations of model parameters, and wherein the target battery performance level is determined according to battery performance levels output by the sub-models based on the combination of the material parameters to be predicted, respectively.
However, in a related field, Qin teaches combination of material parameters of a battery pulping process, wherein the target prediction model comprises a plurality of sub-models, and the plurality of the sub-models correspond to different combinations of model parameters, and wherein the target battery performance level is determined according to battery performance levels output by the sub-models based on the combination of the material parameters to be predicted, respectively (pg. 3-5 (para. 20-28)). 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 Nishi to incorporate the teachings of Qin by including: the limitations above in order to improve the predicting accuracy of the model.
Regarding Claim 8, Nishi teaches an apparatus for predicting the battery performance (Claim 1) comprising: an obtaining module configured to obtaining a target prediction model (pg. 7, last paragraph); a prediction module configured to obtaining a combination of material parameters to be predicted corresponding to the battery pulping process, and inputting the combination of the material parameters to be predicted into the target prediction model to obtain a target battery performance level corresponding to the combination of the material parameters to be predicted (pg. 7).
Nishi fails to teach a combination of material parameters of a battery pulping process, wherein the target prediction model comprises a plurality of sub-models, and the plurality of the sub-models correspond to different combinations of model parameters, and wherein the target battery performance level is determined according to battery performance levels output by the sub-models based on the combination of the material parameters to be predicted, respectively.
However, in a related field, Qin teaches combination of material parameters of a battery pulping process, wherein the target prediction model comprises a plurality of sub-models, and the plurality of the sub-models correspond to different combinations of model parameters, and wherein the target battery performance level is determined according to battery performance levels output by the sub-models based on the combination of the material parameters to be predicted, respectively (pg. 3-5 (para. 20-28)). 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 Nishi to incorporate the teachings of Qin by including: the limitations above in order to improve the predicting accuracy of the model.
Regarding Claim 9, Nishi teaches a terminal (ECU (50)) comprising a memory (501-503 (CPU, ROM, RAM)) and one or more processors, wherein the memory stores one or more programs, and the programs are adapted to be loaded and executed by the processor (pg. 12-13) to:
obtaining module configured to obtaining a target prediction model (pg. 7, last paragraph); a prediction module configured to obtaining a combination of material parameters to be predicted corresponding to the battery pulping process, and inputting the combination of the material parameters to be predicted into the target prediction model to obtain a target battery performance level corresponding to the combination of the material parameters to be predicted (pg. 7).
Nishi fails to teach a combination of material parameters of a battery pulping process, wherein the target prediction model comprises a plurality of sub-models, and the plurality of the sub-models correspond to different combinations of model parameters, and wherein the target battery performance level is determined according to battery performance levels output by the sub-models based on the combination of the material parameters to be predicted, respectively.
However, in a related field, Qin teaches combination of material parameters of a battery pulping process, wherein the target prediction model comprises a plurality of sub-models, and the plurality of the sub-models correspond to different combinations of model parameters, and wherein the target battery performance level is determined according to battery performance levels output by the sub-models based on the combination of the material parameters to be predicted, respectively (pg. 3-5 (para. 20-28)). 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 Nishi to incorporate the teachings of Qin by including: the limitations above in order to improve the predicting accuracy of the model.
Allowable Subject Matter
Claims 2-7 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 2, the combination of Nishi and Qin teach the method for predicting the battery performance based on a combination of material parameters of a battery pulping process according to claim 1. The combination further teaches a training process comprises: obtaining a plurality of combinations of material parameters corresponding to the battery pulping process and actual battery performance levels respectively corresponding to the plurality of the combinations of the material parameters (Qin: pg. 3-5 (para. 20-28)); inputting one of the combinations of the material parameters into the target prediction model (Qin: pg. 7).
Although Wang (CN113592019A) teaches calculating loss value, Wang, Nishi and Qin, along with all other references fail to teach wherein the target prediction model is trained in advance; determining a loss value corresponding to each of the sub-models based on the first predicted battery performance level, the second predicted battery performance levels, and the actual battery performance level corresponding to the combination of the material parameters: determining whether the loss value corresponding to each of the sub-models converges to a target value; and if not, correcting a combination of initial model parameters of the sub-model according to the loss value of the sub-model not converging to the target value, and continuing to perform the step of inputting one of the combinations of the material parameters into the target prediction model until the loss value corresponding to each of the sub-models converges to the target value to obtain the trained target prediction model. It is for this reason, Claim 2 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. (Fault Detection Method Based On Multi-model Fusion, Device, Device And Medium, 2021-11-02) teaches a fault detection method based on multi-model fusion, comprising: standardizing format of original system data set; obtaining initial system data set; extracting time domain characteristic and frequency domain characteristic of initial system data set; summarizing to obtain system characteristic data set; using system characteristic data set respectively a plurality of sub-models; to obtain the fault predicted value output by multiple sub-models; calculating the loss value between the fault predicted value and the real data set; adjusting the parameter in the fusion formula according to the loss value and outputting the standard fusion model; inputting the monitoring data of the system to be detected to the standard fusion model to obtain the standard fusion value; when the standard fusion value is greater than or equal to the abnormal threshold value, determining that the system to be detected has a detection fault. The invention further claims a fault detection device based on multi-model fusion, an electronic device and a computer readable storage medium. The invention can solve the problem of low efficiency of fault detection.
Wu et al. (A Simulation Method For Predicting Device Safety Performance Of Lithium Ion Battery Material And, 2017-08-18) teaches a method for predicting device safety performance of lithium ion battery material and simulation method, comprising the following steps: step 1, step 2, establishing the physical model of the obtained characteristic parameter, step 3, establishing the mathematical model, step 4, simulation calculation, the final prediction performance of battery materials and devices. predicting device safety performance of lithium ion battery material and simulation method of the invention has low testing cost and high data accuracy, strong security and real-time property is good.
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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/MICHAEL J SINGLETARY/Examiner, Art Unit 2857
/ALEXANDER SATANOVSKY/Primary Examiner, Art Unit 2857
